Robotics doi: 10.3390/robotics14080108

Authors: Promit Panja Madan Mohan Rayguru Sabur Baidya

Ensuring the safe operation of Unmanned Aerial Vehicles (UAVs) is crucial for both mission-critical and safety-critical tasks. In scenarios where UAVs must track airborne targets, they need to follow the target’s path while maintaining a safe distance, even in the presence of unmodeled dynamics and environmental disturbances. This paper presents a novel collision avoidance strategy for dynamic quadrotor UAVs during target-tracking missions. We propose a safety controller that combines a learning-based Control Barrier Function (CBF) with standard sliding mode feedback. Our approach employs a neural network that learns the true CBF constraint, accounting for wind disturbances, while the sliding mode controller addresses unmodeled dynamics. This unified control law ensures safe leader-following behavior and precise trajectory tracking. By leveraging a learned CBF, the controller offers improved adaptability to complex and unpredictable environments, enhancing both the safety and robustness of the system. The effectiveness of our proposed method is demonstrated through the AirSim platform using the PX4 flight controller.

Robotics doi: 10.3390/robotics14080107

Authors: Worathris Chungsangsatiporn Chaiwuth Sithiwichankit Ratchatin Chancharoen Ronnapee Chaichaowarat Nopdanai Ajavakom Gridsada Phanomchoeng

This study presents the design, fabrication, and clinical validation of a lightweight, body-powered prosthetic index finger actuated via metacarpophalangeal (MCP) joint motion. The proposed system incorporates an underactuated, cable-driven mechanism combining rigid and compliant elements to achieve passive adaptability and embodied intelligence, supporting intuitive user interaction. Results indicate that the prosthesis successfully mimics natural finger flexion and adapts effectively to a variety of grasping tasks with minimal effort. This study was conducted in accordance with ethical standards and approved by the Institutional Review Board (IRB), Project No. 670161, titled “Biologically-Inspired Synthetic Finger: Design, Fabrication, and Application.” The findings suggest that the device offers a viable and practical solution for individuals with partial hand loss, particularly in settings where electrically powered systems are unsuitable or inaccessible.

Robotics doi: 10.3390/robotics14080105

Authors: Mohamed Sorour Barbara Webb

Ants use their mandibles—effectively a two-finger gripper—for a wide range of grasping activities. Here, we investigate whether mimicking the internal hairs found on ant mandibles can improve performance of a two-finger parallel plate robot gripper. With bin-picking applications in mind, the gripper fingers are long and slim, with interchangeable soft gripping pads that can be hairy or hairless. A total of 2400 video-documented experiments have been conducted, comparing hairless to hairy pads with different hair patterns. Simply by adding hairs, the grasp success rate was increased by at least 29%, and the number of objects that remain securely gripped during manipulation more than doubled. This result not only advances the state of the art in grasping technology, but also provides novel insight into the mechanical role of mandible hairs in ant biology.

Robotics doi: 10.3390/robotics14080106

Authors: Raúl Calderón-Sesmero Adrián Lozano-Hernández Fernando Frontela-Encinas Guillermo Cabezas-López Mireya De-Diego-Moro

Disassembly is a crucial process in industrial operations, especially in tasks requiring high precision and strict safety standards when handling components with collaborative robots. However, traditional methods often rely on rigid and sequential task planning, which makes it difficult to adapt to unforeseen changes or dynamic environments. This rigidity not only limits flexibility but also leads to prolonged execution times, as operators must follow predefined steps that do not allow for real-time adjustments. Although techniques like teleoperation have attempted to address these limitations, they often hinder direct human–robot collaboration within the same workspace, reducing effectiveness in dynamic environments. In response to these challenges, this research introduces an advanced human–robot interaction (HRI) system leveraging a mixed-reality (MR) interface embedded in a head-mounted device (HMD). The system enables operators to issue real-time control commands using multimodal inputs, including voice, gestures, and gaze tracking. These inputs are synchronized and processed via the Robot Operating System (ROS2), enabling dynamic and flexible task execution. Additionally, the integration of deep learning algorithms ensures precise detection and validation of disassembly components, enhancing accuracy. Experimental evaluations demonstrate significant improvements, including reduced task completion times, enhanced operator experience, and compliance with strict adherence to safety standards. This scalable solution offers broad applicability for general-purpose disassembly tasks, making it well-suited for complex industrial scenarios.

Robotics doi: 10.3390/robotics14080104

Authors: Mingzhi Chen Yuan Liu Daqi Zhu Wen Pang Jianmin Zhu

Underwater navigation remains constrained by technological limitations, driving the exploration of alternative approaches such as polarized light-based systems. This review systematically examines advances in polarized navigation from three perspectives. First, the principles of atmospheric polarization navigation are analyzed, with their operational mechanisms, advantages, and inherent constraints dissected. Second, innovations in bionic polarization multi-sensor fusion positioning are consolidated, highlighting progress beyond conventional heading-direction extraction. Third, emerging underwater polarization navigation techniques are critically evaluated, revealing that current methods predominantly adapt atmospheric frameworks enhanced by advanced filtering to mitigate underwater interference. A comprehensive synthesis of underwater polarization modeling methodologies is provided, categorizing physical, data-driven, and hybrid approaches. Through rigorous analysis of studies, three persistent barriers are identified: (1) inadequate polarization pattern modeling under dynamic cross-media conditions; (2) insufficient robustness against turbidity-induced noise; (3) immature integration of polarization vision with sonar/IMU (Inertial Measurement Unit) sensing. Targeted research directions are proposed, including adaptive deep learning models, multi-spectral polarization sensing, and bio-inspired sensor fusion architectures. These insights establish a roadmap for developing reliable underwater navigation systems that transcend current technological boundaries.

Robotics doi: 10.3390/robotics14080103

Authors: Yongping Shi Tianbing Ma Hao Wang Tao Zhang Xin Zhang Huapeng Wu Ming Li

With the application and rapid development of light industrial robots, it is vital to accelerate the prototype design to fulfill the demands of shortening the robot’s production cycle, owing to rapid update iterations. Since the traditional design method cannot intuitively and efficiently check the deficiencies in the design preparation, the secondary design iterations will result in higher equipment costs, longer design cycles, and lower development efficiency. The MBD (model-based design), a full 3D (three-dimensional) design and manufacturing method, is proposed to swiftly finish the prototype design for solving the above problems. Firstly, the robot design preparation is completed with the design requirements to generate a robot 3D model. Secondly, several design methods are used: (i) the rapid prototyping, which includes the joint component verification and selection to further optimize the 3D model; (ii) the robot kinematics algorithm, which provides a theoretical foundation for the 3D model design; (iii) the robot kinematics simulation, which verifies the correctness of the kinematics algorithm. Finally, the feasibility of the MBD is verified by the robot prototype and the motion control system test. Taking the MBD to design a 5-DoF (five-degrees-of-freedom) robot as an example, the joint verification and selection are finished quickly and accurately to build the robot prototype without the need for secondary design processing, and the kinematic algorithm verified by the co-simulation platform can be used directly in the actual motion control of the robot prototype, which accelerates the development of the robot motion control system.

Robotics doi: 10.3390/robotics14080102

Authors: Angus Fung Aaron Hao Tan Haitong Wang Bensiyon Benhabib Goldie Nejat

Robotic search of people in human-centered environments, including healthcare settings, is challenging, as autonomous robots need to locate people without complete or any prior knowledge of their schedules, plans, or locations. Furthermore, robots need to be able to adapt to real-time events that can influence a person’s plan in an environment. In this paper, we present MLLM-Search, a novel zero-shot person search architecture that leverages multimodal large language models (MLLM) to address the mobile robot problem of searching for a person under event-driven scenarios with varying user schedules. Our approach introduces a novel visual prompting method to provide robots with spatial understanding of the environment by generating a spatially grounded waypoint map, representing navigable waypoints using a topological graph and regions by semantic labels. This is incorporated into an MLLM with a region planner that selects the next search region based on the semantic relevance to the search scenario and a waypoint planner that generates a search path by considering the semantically relevant objects and the local spatial context through our unique spatial chain-of-thought prompting approach. Extensive 3D photorealistic experiments were conducted to validate the performance of MLLM-Search in searching for a person with a changing schedule in different environments. An ablation study was also conducted to validate the main design choices of MLLM-Search. Furthermore, a comparison study with state-of-the-art search methods demonstrated that MLLM-Search outperforms existing methods with respect to search efficiency. Real-world experiments with a mobile robot in a multi-room floor of a building showed that MLLM-Search was able to generalize to new and unseen environments.

Robotics doi: 10.3390/robotics14080101

Authors: Huaqiang Zhang Norzalilah Mohamad Nor

Two-wheeled self-balancing robots (TWSBRs) are underactuated, inherently nonlinear systems that exhibit unstable dynamics. Due to their structural simplicity and rich control challenges, TWSBRs have become a standard platform for validating and benchmarking various control algorithms. This paper presents a comprehensive and structured review of control strategies applied to TWSBRs, encompassing classical linear approaches such as PID and LQR, modern nonlinear methods including sliding mode control (SMC), model predictive control (MPC), and intelligent techniques such as fuzzy logic, neural networks, and reinforcement learning. Additionally, supporting techniques such as state estimation, observer design, and filtering are discussed in the context of their importance to control implementation. The evolution of control theory is analyzed, and a detailed taxonomy is proposed to classify existing works. Notably, a comparative analysis section is included, offering practical guidelines for selecting suitable control strategies based on system complexity, computational resources, and robustness requirements. This review aims to support both academic research and real-world applications by summarizing key methodologies, identifying open challenges, and highlighting promising directions for future development.

Robotics doi: 10.3390/robotics14080100

Authors: Matija Markulin Luka Matijevi? Janko Jurdana Luka ?iktar Branimir ?aran Toni Zekuli? Filip ?uligoj Bojan ?ekoranja Tvrtko Hudolin Tomislav Kuli? Bojan Jerbi? Marko ?vaco

This paper presents the PRONOBIS project, an ultrasound-only, robotically assisted, deep learning-based system for prostate scanning and biopsy treatment planning. The proposed system addresses the challenges of precise prostate segmentation, reconstruction and inter-operator variability by performing fully automated prostate scanning, real-time CNN-transformer-based image processing, 3D prostate reconstruction, and biopsy needle position planning. Fully automated prostate scanning is achieved by using a robotic arm equipped with an ultrasound system. Real-time ultrasound image processing utilizes state-of-the-art deep learning algorithms with intelligent post-processing techniques for precise prostate segmentation. To create a high-quality prostate segmentation dataset, this paper proposes a deep learning-based medical annotation platform, MedAP. For precise segmentation of the entire prostate sweep, DAF3D and MicroSegNet models are evaluated, and additional image post-processing methods are proposed. Three-dimensional visualization and prostate reconstruction are performed by utilizing the segmentation results and robotic positional data, enabling robust, user-friendly biopsy treatment planning. The real-time sweep scanning and segmentation operate at 30 Hz, which enable complete scan in 15 to 20 s, depending on the size of the prostate. The system is evaluated on prostate phantoms by reconstructing the sweep and by performing dimensional analysis, which indicates 92% and 98% volumetric accuracy on the tested phantoms. Three-dimansional prostate reconstruction takes approximately 3 s and enables fast and detailed insight for precise biopsy needle position planning.

Robotics doi: 10.3390/robotics14070099

Authors: Abhishek Shankar Luay Jawad Abhilash Pandya

This paper examines the integration of markerless augmented reality (AR) within the da Vinci Surgical Robot, utilizing artificial intelligence (AI) for improved precision. The main challenge in creating AR for these systems is the small size (5 mm diameter) of the cameras used. Traditional camera-calibration approaches produce significant errors when used for miniature cameras. Further, the use of external markers can be obstructive and inaccurate in dynamic surgical environments. The study focuses on overcoming these limitations of traditional AR methods by employing advanced neural networks for camera calibration and real-time image processing. We demonstrate the use of a dense neural network to reduce the total projection error by directly learning the mapping of a 3D point to a 2D image plane. The results show a median error of 7 pixels (1.4 mm) when using a neural network, as compared to an error of 50 pixels (10 mm) when using a more traditional approach involving camera calibration and robot kinematics. This approach not only enhances the accuracy of AR for surgical procedures but also offers a more seamless integration with existing robotic platforms. These research findings underscore the potential of AI in revolutionizing AR applications in medical robotics and other teleoperated systems, promising efficient and safer interventions.

Robotics doi: 10.3390/robotics14070098

Authors: Domenico Dona’ Jason Bettega Iacopo Tamellin Paolo Boscariol Roberto Caracciolo

Underactuated robotic systems are appealing for industrial use due to their reduced actuator number, which lowers energy consumption and system complexity. Underactuated systems are, however, often affected by residual vibrations. This paper addresses the challenge of generating energy-optimal trajectories while imposing theoretical null residual (and yet practical low) vibration in underactuated systems. The trajectory planning problem is cast as a constrained optimal control problem (OCP) for a two-degree-of-freedom revolute–revolute planar manipulator. The proposed method produces energy-efficient motion while limiting residual vibrations under motor torque limitations. Experiments compare the proposed trajectories to input shaping techniques (ZV, ZVD, NZV, NZVD). Results show energy savings that range from 12% to 69% with comparable and negligible residual oscillations.

Robotics doi: 10.3390/robotics14070097

Authors: Ilias Chouridis Gabriel Mansour Asterios Chouridis Vasileios Papageorgiou Michel Theodor Mansour Apostolos Tsagaris

Collaborative robots are vital in Industry 5.0 operations. They are utilized to perform tasks in collaboration with humans or other robots to increase overall production efficiency and execute complex tasks. Aiming at a comprehensive approach to assembly processes and highlighting new applications of collaborative robots, this paper presents the development of a digital twin (DT) for the design, monitoring, optimization and simulation of robots’ deployment in assembly cells. The DT integrates information from both the physical and virtual worlds to design the trajectory of collaborative robots. The physical information about the industrial environment is replicated within the DT in a computationally efficient way that aligns with the requirements of the path planning algorithm and the DT’s objectives. An enhanced artificial fish swarm algorithm (AFSA) is utilized for the 4D path planning optimization, taking into account dynamic and static obstacles. Finally, the proposed framework is utilized for the examination of a case in which four industrial robotic arms are collaborating for the assembly of an industrial component.

Robotics doi: 10.3390/robotics14070096

Authors: Yuning Cao Xianli Wang Zehao Wu Qingsong Xu

A mobile manipulation robot combines the navigation capability of unmanned ground vehicles and manipulation advantage of robotic arms. However, the development of a mobile manipulation robot is challenging due to the integration requirement of numerous heterogeneous subsystems. In this paper, we propose a multifunctional mobile manipulation robot by integrating perception, mapping, navigation, object detection, and grasping functions into a seamless workflow to conduct search-and-fetch tasks. To realize navigation and collision avoidance in complex environments, a new hierarchical motion planning strategy is proposed by fusing global and local planners. Control Lyapunov Function (CLF) and Control Barrier Function (CBF) are employed to realize path tracking and to guarantee safety during navigation. The convolutional neural network and the gripper’s kinematic constraints are adopted to construct a learning-optimization hybrid grasping algorithm to generate precise grasping poses. The efficiency of the developed mobile manipulation robot is demonstrated by performing indoor fetching experiments, showcasing its promising capabilities in real-world applications.

Robotics doi: 10.3390/robotics14070095

Authors: Mykhailo Riabtsev Jean-Michel Guilhem Victor Petuya Mónica Urizar Med Amine Laribi

This paper presents the development and analysis of a novel 6-DOF Cartesian parallel mechanism intended for use as a haptic device for initial sonography training. The system integrates a manipulator designed for delivering force feedback in five degrees of freedom; however, in the current stage, only mechanical architecture and kinematic validation have been conducted. Future enhancements will focus on implementing and evaluating closed-loop force control to enable complete haptic feedback. To assess the kinematic performance of the mechanism, a detailed kinematic model was developed, and both the Kinematic Conditioning Index (KCI) and Global Conditioning Index (GCI) were computed to evaluate the system’s dexterity. A trajectory simulation was conducted to validate the mechanism’s movement, using motion patterns typical in sonography procedures. Quasi-static analysis was performed to study the transmission of force and torque for generating realistic haptic feedback, critical for simulating real-life sonography. The simulation results showed consistent performance, with dexterity and torque distribution confirming the suitability of the mechanism for haptic applications in sonography training. Additionally, structural analysis verified the robustness of key components under expected loads. In order to validate the proposed design, the prototype was constructed using a combination of aluminum components and 3D-printed ABS parts, with Igus® linear guides for precise motion. The outcomes of this study provide a foundation for the further development of a low-cost, effective sonography training system.

Robotics doi: 10.3390/robotics14070094

Authors: Francisco Cuenca Jiménez Eusebio Jiménez López Mario Acosta Flores F. Pe?u?uri Ricardo Javier Peón Escalante Juan José Delfín Vázquez

Quaternions are used in various applications, especially in those where it is necessary to model and represent rotational movements, both in the plane and in space, such as in the modeling of the movements of robots and mechanisms. In this article, a methodology to model the rigid rotations of coupled bodies by means of unit quaternions is presented. Two parallel robots were modeled: a planar RRR robot and a spatial motion PRRS robot using the proposed methodology. Inverse kinematic problems were formulated for both models. The planar RRR robot model generated a system of 21 nonlinear equations and 18 unknowns and a system of 36 nonlinear equations and 33 unknowns for the case of space robot PRRS; both systems of equations were of the polynomial algebraic type. The systems of equations were solved using the Broyden–Fletcher–Goldfarb–Shanno nonlinear programming algorithm and Mathematica V12 symbolic computation software. The modeling methodology and the algebra of unitary quaternions allowed the systematic study of the movements of both robots and the generation of mathematical models clearly and functionally.

Robotics doi: 10.3390/robotics14070093

Authors: Krishna Arjun David Parlevliet Hai Wang Amirmehdi Yazdani

In practical applications, the utilization of multi-robot systems (MRS) is extensive and spans various domains such as search and rescue operations, mining operations, agricultural tasks, and warehouse management. The surge in demand for MRS has prompted extensive exploration of Multi-Robot Task Allocation (MRTA). Researchers have devised a range of methodologies to tackle MRTA problems, aiming to achieve optimal solutions, yet there remains room for further enhancements in this field. Among the complex challenges in MRTA, the identification of an optimal coalition formation (CF) solution stands out as one of the (Nondeterministic Polynomial) NP-hard problems. CF pertains to the effective coordination and grouping of agents or robots for efficient task execution, achieved through optimal task allocation. In this context, this paper delivers a succinct overview of dynamic task allocation and CF strategies. It conducts a comprehensive examination of diverse strategies employed for MRTA. The analysis encompasses the advantages, disadvantages, and comparative assessments of these strategies with a focus on CF. Furthermore, this study introduces a novel classification system for prominent task allocation methods and compares these methods with simulation analysis. The fidelity and effectiveness of the proposed CF approach are substantiated through comparative assessments and simulation studies.

Robotics doi: 10.3390/robotics14070092

Authors: Antonio Di Tecco Daniele Leonardis Antonio Frisoli Claudio Loconsole

This research study investigates the impact of a virtual dashboard on the quality of task execution in robotic teleoperation. More specifically, this study investigates how a virtual dashboard improves user awareness and grasp precision in a teleoperated pick-and-place task by providing users with critical information in real-time. An experiment was conducted with 30 participants in a robotic teleoperated task to measure their task performance in two different experimental conditions: a control group used conventional interfaces, and an experimental group utilized the virtual dashboard with additional information. Research findings indicate that integrating a virtual dashboard improves grasping accuracy, reduces user fatigue, and speeds up task completion, thereby improving task effectiveness and the quality of the experience.

Robotics doi: 10.3390/robotics14070091

Authors: Yeoun-Jae Kim Daehan Wi

This study addresses the clinical requirements of a transoral surgery-assisting continuum robot. This application requires both high bendability and stiffness in order to ensure precise positioning and stable fixation of surgical tools. To meet these needs, we developed a tendon-driven discrete continuum robot unit featuring a ball–socket joint and superelastic Nitinol rods. One to three serially connected robot units were tested by applying proximal tendon tension (Tl) in the range of 100–1000 g while distal tension (Ts) was continuously increased to induce bending. During bending, the curves were interpolated using third-order to fifth-order polynomials at discrete Tl levels. The interpolated inverse statics were validated experimentally and compared with finite element simulations using ANSYS. Furthermore, we propose a planar path planning algorithm and numerically evaluate it for a three-unit robot following an arc-shaped trajectory. The inverse statics successfully captured the nonlinear bending behavior of the tendon-driven robot. Validation experiments showed average angular errors of 2.7%, 6.6%, and 5.3% for one, two, and three connected units, respectively. The proposed path planning method achieved an average positional deviation from the reference trajectory ranging from 0.95 mm to 19.77 mm. This work presents a practical and generalizable experimental mapping framework for the inverse statics of tendon-driven discrete continuum robots, avoiding the need for complex analytical models.

Robotics doi: 10.3390/robotics14070090

Authors: Yizhe Jia Yong Cai Jun Zhou Hui Hu Xuesheng Ouyang Jinlong Mo Hao Dai

The advancement of mobile robot technology has made path planning a necessary condition for autonomous navigation, but traditional algorithms have issues with efficiency and reliability in dynamic and unstructured environments. This study proposes a Dynamic Hybrid A* (DHA*)–Adaptive Dynamic Window Approach (ADA-DWA) fusion algorithm for efficient and reliable path planning in dynamic unstructured environments. This paper improves the A* algorithm by introducing a dynamic hybrid heuristic function, optimizing the selection of key nodes, and enhancing the neighborhood search strategy, and collaboratively optimizes the search efficiency and path smoothness through curvature optimization. On this basis, the local planning layer introduces a self-adjusting weight-adaptive system in the DWA framework to dynamically optimize the speed, sampling distribution, and trajectory evaluation metrics, achieving a balance between obstacle avoidance and environmental adaptability. The proposed fusion algorithm’s comprehensive advantages over traditional methods in key operational indicators, including path optimality, computational efficiency, and obstacle avoidance capability, have been widely verified through numerical simulations and physical platforms. This method successfully resolves the inherent trade-off between efficiency and reliability in complex robot navigation scenarios, providing enhanced operational robustness for practical applications ranging from industrial logistics to field robots.

Robotics doi: 10.3390/robotics14070089

Authors: Diego Tiozzo Fasiolo Lorenzo Scalera Eleonora Maset Alessandro Gasparetto

This paper presents a mobile robotic system designed for autonomous navigation and forest and tree trait estimation, with a focus on the location of individual trees and the diameter of the trunks. The system integrates light detection and ranging data and images using a framework based on simultaneous localization and mapping (SLAM) and a deep learning model for trunk segmentation and tree keypoint detection. Field experiments conducted in a wooded area in Udine, Italy, using a skid-steered mobile robot, demonstrate the effectiveness of the system in navigating, while avoiding obstacles (even in cases where the Global Navigation Satellite System signal is not reliable). The results highlight that the proposed robotic system is capable of autonomously generating maps of forests as point clouds with minimal drift thanks to the loop closure strategy integrated in the SLAM algorithm, and estimating tree traits automatically.

Robotics doi: 10.3390/robotics14070088

Authors: Dinesh Elayaperumal Sachin Sakthi Kuppusami Sakthivel Sathishkumar Moorthy Sathiyamoorthi Arthanari Young Hoon Joo Jae Hoon Jeong

This study aims to explore the tracking control challenge in a swarm of multiple nonholonomic wheeled mobile robots (NWMRs) by utilizing a distributed leader–follower strategy grounded in the cascade system theory. Firstly, the kinematic control law is developed for the leader by constructing a sliding surface based on the error tracking model with a virtual reference trajectory. Secondly, a communication topology with the desired formation pattern is modeled for the multiple robots by using the graph theory. Further, in the leader–follower NWMR system, each follower lacks direct access to the leader’s information. Therefore, a novel distributed-based controller by PD-based controller for the follower is developed, enabling each follower to obtain the leader’s information. Thirdly, for each case, we give a further analysis of the closed-loop system to guarantee uniform global asymptotic stability with the conditions based on the cascade system theory. Finally, the trajectory tracking performance of the proposed controllers for the NWMR system is illustrated through simulation results. The leader robot achieved a low RMSE of 1.6572 (Robot 1), indicating accurate trajectory tracking. Follower robots showed RMSEs of 2.6425 (Robot 2), 3.0132 (Robot 3), and 4.2132 (Robot 3), reflecting minor variations due to the distributed control strategy and local disturbances.

Robotics doi: 10.3390/robotics14070087

Authors: Mahboobe Habibi Giuseppe Sutera Dario Calogero Guastella Giovanni Muscato

This study presents the design, fabrication, and experimental validation of a two-finger robotic gripper featuring a 135° V-shaped fingertip profile tailored for lightweight waste collection in laboratory-scale environmental robotics. The gripper was developed with a strong emphasis on cost-effectiveness and manufacturability, utilizing a desktop 3D printer and off-the-shelf servomotors. A four-bar linkage mechanism enables parallel jaw motion and ensures stable surface contact during grasping, achieving a maximum opening range of 71.5 mm to accommodate common cylindrical objects. To validate structural integrity, finite element analysis (FEA) was conducted under a 0.6 kg load, yielding a safety factor of 3.5 and a peak von Mises stress of 12.75 MPa—well below the material yield limit of PLA. Experimental testing demonstrated grasp success rates of up to 80 percent for typical waste items, including bottles, disposable cups, and plastic bags. While the gripper performs reliably with rigid and semi-rigid objects, further improvements are needed for handling highly deformable materials such as thin films or soft bags. The proposed design offers significant advantages in terms of rapid prototyping (a print time of approximately 10 h), modularity, and low manufacturing cost (with an estimated in-house material cost of USD 20 to 40). It provides a practical and accessible solution for small-scale robotic waste-collection tasks and serves as a foundation for future developments in affordable, application-specific grippers.

Robotics doi: 10.3390/robotics14070086

Authors: Lucía Güitta-López Vincenzo Suriani Jaime Boal álvaro J. López-López Daniele Nardi

Deep Reinforcement Learning (DRL) is a powerful framework for solving complex sequential decision-making problems, particularly in robotic control. However, its practical deployment is often hindered by the substantial amount of experience required for learning, which results in high computational and time costs. In this work, we propose a novel integration of DRL with semantic knowledge in the form of Knowledge Graph Embeddings (KGEs), aiming to enhance learning efficiency by providing contextual information to the agent. Our architecture combines KGEs with visual observations, enabling the agent to exploit environmental knowledge during training. Experimental validation with robotic manipulators in environments featuring both fixed and randomized target attributes demonstrates that our method achieves up to 60% reduction in learning time and improves task accuracy by approximately 15 percentage points, without increasing training time or computational complexity. These results highlight the potential of semantic knowledge to reduce sample complexity and improve the effectiveness of DRL in robotic applications.

Robotics doi: 10.3390/robotics14070085

Authors: Yuanji Huang Pavithra Sripathanallur Murali Gustavo Vejarano

This paper contributes a two-step approach to monitor clusters of thermal targets on the ground using unmanned aerial vehicles (UAVs) and Gaussian mixture models (GMMs) in a distributed manner. The approach is tailored to networks of UAVs that establish a flying ad hoc network (FANET) and operate without central command. The first step is a monitoring algorithm that determines if the GMM corresponds to the current spatial distribution of clusters of thermal targets on the ground. UAVs make this determination using local data and a sequence of data exchanges with UAVs that are one-hop neighbors in the FANET. The second step is the calculation of a new GMM when the current GMM is found to be unfit, i.e., the GMM no longer corresponds to the new distribution of clusters on the ground due to the movement of thermal targets. A distributed expectation-maximization algorithm is developed for this purpose, and it operates on local data and data exchanged with one-hop neighbors only. Simulation results evaluate the performance of both algorithms in terms of the number of communication exchanges. This evaluation is completed for an increasing number of clusters of thermal targets and an increasing number of UAVs. The performance is compared with well-known solutions to the monitoring and GMM calculation problems, demonstrating convergence with a lower number of communication exchanges.

Robotics doi: 10.3390/robotics14060084

Authors: Belkacem Bekhiti Jamshed Iqbal Kamel Hariche George F. Fragulis

This paper introduces a robust neural adaptive MIMO control strategy to improve the stability and adaptability of bipedal locomotion amid uncertainties and external disturbances. The control combines nonlinear dynamic inversion, finite-time convergence, and radial basis function (RBF) neural networks for fast, accurate trajectory tracking. The main novelty of the presented control strategy lies in unifying instantaneous feedback, real-time learning, and dynamic adaptation within a multivariable feedback framework, delivering superior robustness, precision, and real-time performance under extreme conditions. The control scheme is implemented on a 5-DOF underactuated RABBIT robot using a dSPACEDS1103 platform with a sampling rate of ∆t=1.5 ms (667 Hz). The experimental results show excellent performance with the following: The robot achieved stable cyclic gaits while keeping the tracking error within e=±0.04 rad under nominal conditions. Under severe uncertainties of trunk mass variations ∆mtrunk=+100%, limb inertia changes ∆Ilimb=±30%, and actuator torque saturation at τ=±150 Nm, the robot maintains stable limit cycles with smooth control. The performance of the proposed controller is compared with classical nonlinear decoupling, non-adaptive finite-time, neural-fuzzy learning, and deep learning controls. The results demonstrate that the proposed method outperforms the four benchmark strategies, achieving the lowest errors and fastest convergence with the following: IAE=1.36, ITAE=2.43, ISE=0.68, tss=1.24 s, and Mp=2.21%. These results demonstrate evidence of high stability, rapid convergence, and robustness to disturbances and foot-slip.

Robotics doi: 10.3390/robotics14060083

Authors: Hongquan Le Marc in het Panhuis Geoffrey M. Spinks Gursel Alici

Gesture recognition based on conventional machine learning is the main control approach for advanced prosthetic hand systems. Its primary limitation is the need for feature extraction, which must meet real-time control requirements. On the other hand, deep learning models could potentially overfit when trained on small datasets. For these reasons, we propose a hybrid Linear Discriminant Analysis–convolutional neural network (LDA-CNN) framework to improve the gesture recognition performance of sEMG-based prosthetic hand control systems. Within this framework, 1D-CNN filters are trained to generate latent representation that closely approximates Fisher’s (LDA’s) discriminant subspace, constructed from handcrafted features. Under the train-one-test-all evaluation scheme, our proposed hybrid framework consistently outperformed the 1D-CNN trained with cross-entropy loss only, showing improvements from 4% to 11% across two public datasets featuring hand gestures recorded under various limb positions and arm muscle contraction levels. Furthermore, our framework exhibited advantages in terms of induced spectral regularization, which led to a state-of-the-art recognition error of 22.79% with the extended 23 feature set when tested on the multi-limb position dataset. The main novelty of our hybrid framework is that it decouples feature extraction in regard to the inference time, enabling the future incorporation of a more extensive set of features, while keeping the inference computation time minimal.

Robotics doi: 10.3390/robotics14060082

Authors: Diego Marussi Michele Cipriano Nicola Scianca Leonardo Lanari Giuseppe Oriolo

We address the problem of humanoid locomotion in 3D environments consisting of planar regions with arbitrary inclination and elevation, such as staircases, ramps, and multi-floor layouts. The proposed framework combines an offline randomized footstep planner with an online control pipeline that includes a model predictive controller for gait generation and a whole-body controller for computing robot torque commands. The planner efficiently explores the environment and returns the highest-quality plan it can find within a user-specified time budget, while the control layer ensures dynamic balance and adequate ground friction. The complete framework was evaluated via dynamic simulation in MuJoCo, placing the JVRC1 humanoid in four scenarios of varying complexity.

Robotics doi: 10.3390/robotics14060081

Authors: Yang Wang Xu Han Baiye Xin Ping Zhao

With the continuous increase in the global aging population, stroke has become one of the major diseases affecting the health of the elderly, and the upper limb motor dysfunction it causes often requires long-term rehabilitation. To improve rehabilitation outcomes for hemiplegic patients and alleviate the shortage of rehabilitation physicians, upper limb rehabilitation robots have shown great potential in enhancing motor function and improving stroke patients’ rehabilitation outcomes in clinical research. This paper first classifies rehabilitation robots based on their driving mechanisms and interaction modes, describing the application of their structural features in various scenarios. It then analyzes the optimization methods used in the trajectory planning process of rehabilitation robots at different stages. Finally, based on existing shortcomings, the paper summarizes the future development directions of upper limb rehabilitation robots, providing prospects for the development of upper limb rehabilitation robots in the areas of artificial intelligence and compliant control, multi-sensory feedback and interactive training, ergonomics and new driving technologies, modular and customizable designs, and multi-modal brain stimulation techniques.

Robotics doi: 10.3390/robotics14060080

Authors: Hitesh Bhardwaj Nabil Shaukat Andrew Barber Andy Blight George Jackson-Mills Andrew Pickering Manman Yang Muhammad Azam Mohd Sharif Linyan Han Songyan Xin Robert Richardson

The inspection of sewer pipes in the UK is costly, and if not inspected regularly, they are costly and disruptive to repair. This paper presents the Mega-Joey, a novel miniature, tether-less robot platform that is capable of autonomously navigating and assessing confined spaces, such as small-diameter underground pipelines. This paper also discusses a novel decentralized event-based-broadcasting autonomous exploration algorithm designed for exploring such pipe networks collaboratively. The designed robot is able to operate in pipes with an inclination of up to 20 degrees in dry and up to 10 degrees in wet conditions. A team of Mega-Joeys was used to explore a test network using the proposed algorithm. The experimental results show that the team of robots was able to explore a 3850 mm long test network within a faster period (36% faster) and in a more energy-efficient manner (approximately 54% more efficient) than a single robot could achieve.

Robotics doi: 10.3390/robotics14060079

Authors: Ernesto Christian Orozco-Magdaleno Eduardo Castillo-Casta?eda Omar Rodríguez-Abreo Giuseppe Carbone

Most of the developed and studied service robots for vertical locomotion, as visual inspection, are made up by a rigid body with legs, wheels, or both. Thus, the robot can only displace over regular and/or flat surfaces since it is not able to adapt to the irregularities and projections of the wall. Therefore, this paper presents the design and analysis of an adaptable robot for vertical locomotion service tasks, which has a body made up of four wheeled legs that can easily adapt to the different irregularities and projections of building facades. The robot uses an Electric Ducted Fan (EDF) as the vortex adhesion system. Each leg has a rubber cover, which allows a higher mechanical adaptability of the robot over different irregularities of the wall. Theoretical backgrounds and open issues are addressed by considering some challenging problems such as mechanical adaptability modeling as well as kinematic and static analysis. Laser sensors are mounted over the robot to measure the adaptability of the robot, between the legs and body, at each time of the experimental tests for vertical locomotion.

Robotics doi: 10.3390/robotics14060078

Authors: Bing Li Hafiz Muhammad Muzzammil Junwu Zhu Lipeng Yuan

To achieve obstacle-avoiding puncture in breast interventional surgery, a robotics system based on three-fingered breast target-point manipulation is proposed and designed. Firstly, based on the minimum number of control points required for three-dimensional breast deformation control and the bionic structure of the human hand, the structure and control scheme of the robotics system based on breast target-point manipulation are proposed. Additionally, the workspace of the robotics system is analyzed. Then, an optimal control point selection method based on the minimum resultant force principle is proposed to achieve precise manipulation of the breast target point. Concurrently, a breast soft tissue manipulation framework incorporating a Model Reference Adaptive Control (MRAC) system is developed to enhance operational accuracy. A dynamic model of breast soft tissue is developed by using the manipulative force–displacement data obtained during the process of manipulating breast soft tissue with mechanical fingers to realize the manipulative force control of breast tissue. Finally, through simulation and experiments on breast target-point manipulation tasks, the results show that this robotic system can achieve spatial control of breast positioning at arbitrary points. Meanwhile, the robotic system proposed in this study demonstrates high-precision control with an accuracy of approximately 1.158 mm (standard deviation: 0.119 mm), fulfilling the requirements for clinical interventional surgery in target point manipulation.

Robotics doi: 10.3390/robotics14060077

Authors: Laurence Roberts-Elliott Gautham P. Das Grzegorz Cielniak

One of the most commonly performed environmental explorations is soil sampling to identify soil properties of agricultural fields, which can inform the farmer about the variable rate treatment of fertilisers in precision agriculture. However, traditional manual methods are slow, costly, and yield low spatial resolution. Deploying multiple robots with proximal sensors can address this challenge by parallelising the sampling process. Yet, multi-robot soil sampling is under-explored in the literature. This paper proposes an auction-based multi-robot task allocation that efficiently coordinates the sampling, coupled with a dynamic sampling strategy informed by Kriging variance from interpolation. This strategy aims to reduce the number of samples needed for accurate mapping by exploring and sampling areas that maximise information gained per sample. The key innovative contributions include (1) a novel Distance Over Variance (DOV) bid calculation for auction-based multi-robot task allocation, which incentivises sampling in high-uncertainty, nearby areas; (2) integration of the DOV bid calculation into the cheapest insertion heuristic for task queuing; and (3) thresholding of newly created tasks at locations with low Kriging variance to drop those unlikely to offer significant information gain. The proposed methods were evaluated through comparative simulated experiments using historical soil compaction data. Evaluation trials demonstrate the suitability of the DOV bid calculation combined with task dropping, resulting in substantial improvements in key performance metrics, including mapping accuracy. While the experiments were conducted in simulation, the system is compatible with ROS and the ‘move_base’ action client to allow real-world deployment. The results from these simulations indicate that the Kriging-variance-informed approach can be applied to the exploration and mapping of other soil properties (e.g., pH, soil organic carbon, etc.) and environmental data.

Robotics doi: 10.3390/robotics14060076

Authors: Carlos Salda?a Enderica José Ramon Llata Carlos Torre-Ferrero

This study proposes a robust methodology for vibration suppression and trajectory tracking in rotary flexible-link systems by leveraging guided reinforcement learning (GRL). The approach integrates the twin delayed deep deterministic policy gradient (TD3) algorithm with a linear quadratic regulator (LQR) acting as a guiding controller during training. Flexible-link mechanisms common in advanced robotics and aerospace systems exhibit oscillatory behavior that complicates precise control. To address this, the system is first identified using experimental input-output data from a Quanser® virtual plant, generating an accurate state-space representation suitable for simulation-based policy learning. The hybrid control strategy enhances sample efficiency and accelerates convergence by incorporating LQR-generated trajectories during TD3 training. Internally, the TD3 agent benefits from architectural features such as twin critics, delayed policy updates, and target action smoothing, which collectively improve learning stability and reduce overestimation bias. Comparative results show that the guided TD3 controller achieves superior performance in terms of vibration damping, transient response, and robustness, when compared to conventional LQR, fuzzy logic, neural networks, and GA-LQR approaches. Although the controller was validated using a high-fidelity digital twin, it has not yet been deployed on the physical plant. Future work will focus on real-time implementation and structural robustness testing under parameter uncertainty. Overall, this research demonstrates that guided reinforcement learning can yield stable and interpretable policies that comply with classical control criteria, offering a scalable and generalizable framework for intelligent control of flexible mechanical systems.

Robotics doi: 10.3390/robotics14060075

Authors: Catarina Rema Pedro Costa Manuel Silva Eduardo J. Solteiro Pires

The advent of Industry 4.0, driven by automation and real-time data analysis, offers significant opportunities to revolutionize manufacturing, with mobile robots playing a central role in boosting productivity. In smart job shops, scheduling tasks involves not only assigning work to machines but also managing robot allocation and travel times, thus extending traditional problems like the Job Shop Scheduling Problem (JSSP) and Traveling Salesman Problem (TSP). Common solution methods include heuristics, metaheuristics, and hybrid methods. However, due to the complexity of these problems, existing models often struggle to provide efficient optimal solutions. Machine learning, particularly reinforcement learning (RL), presents a promising approach by learning from environmental interactions, offering effective solutions for task scheduling. This systematic literature review analyzes 71 papers published between 2014 and 2024, critically evaluating the current state of the art of task scheduling with mobile robots. The review identifies the increasing use of machine learning techniques and hybrid approaches to address more complex scenarios, thanks to their adaptability. Despite these advancements, challenges remain, including the integration of path planning and obstacle avoidance in the task scheduling problem, which is crucial for making these solutions stable and reliable for real-world applications and scaling for larger fleets of robots.

Robotics doi: 10.3390/robotics14060074

Authors: Jose Manuel Alcayaga Oswaldo Anibal Menéndez Miguel Attilio Torres-Torriti Juan Pablo Vásconez Tito Arévalo-Ramirez Alvaro Javier Prado Romo

Autonomous navigation in mining environments is challenged by complex wheel–terrain interaction, traction losses caused by slip dynamics, and sensor limitations. This paper investigates the effectiveness of Deep Reinforcement Learning (DRL) techniques for the trajectory tracking control of skid-steer mobile robots operating under terra-mechanical constraints. Four state-of-the-art DRL algorithms, i.e., Proximal Policy Optimization (PPO), Deep Deterministic Policy Gradient (DDPG), Twin Delayed DDPG (TD3), and Soft Actor–Critic (SAC), are selected to evaluate their ability to generate stable and adaptive control policies under varying environmental conditions. To address the inherent partial observability in real-world navigation, this study presents an original approach that integrates Long Short-Term Memory (LSTM) networks into DRL-based controllers. This allows control agents to retain and leverage temporal dependencies to infer unobservable system states. The developed agents were trained and tested in simulations and then assessed in field experiments under uneven terrain and dynamic model parameter changes that lead to traction losses in mining environments, targeting various trajectory tracking tasks, including lemniscate and squared-type reference trajectories. This contribution strengthens the robustness and adaptability of DRL agents by enabling better generalization of learned policies compared with their baseline counterparts, while also significantly improving trajectory tracking performance. In particular, LSTM-based controllers achieved reductions in tracking errors of 10%, 74%, 21%, and 37% for DDPG-LSTM, PPO-LSTM, TD3-LSTM, and SAC-LSTM, respectively, compared with their non-recurrent counterparts. Furthermore, DDPG-LSTM and TD3-LSTM reduced their control effort through the total variation in control input by 15% and 20% compared with their respective baseline controllers, respectively. Findings from this work provide valuable insights into the role of memory-augmented reinforcement learning for robust motion control in unstructured and high-uncertainty environments.

Robotics doi: 10.3390/robotics14060073

Authors: Jennifer Brade Sarah Mandl Franziska Klimant Anja Strobel Philipp Klimant Martin Dix

Remote support in general is a method that saves time and resources. A relatively new and promising technology for remote support that combines video conferencing and physical mobility is that of telepresence systems. The remote assistant, that is, the user of said technology, gains both presence and maneuverability in the distant location. As telepresence systems vary greatly in their design, the question arises as to whether the design influences the perception of the remote assistant. Unlike pure design studies, the present work focuses not only on the design and evaluation of the telepresence system itself, but especially on its perception during a collaborative task involving a human partner visible through the telepresence system. This paper presents two studies in which participants performed an assembly task under the guidance of a remote assistant. The remote assistant was visible through differently designed telepresence systems that were evaluated in terms of social perception and trustworthiness. Four telepresence systems were evaluated in study 1 (N = 32) and five different systems in study 2 (N = 34). The results indicated that similarly designed systems showed only marginal differences, but a system that was designed to transport additional loads and was therefore less agile and rather bulky was rated significantly less positively regarding competence than the other systems. It is particularly noteworthy that it was not the height of the communication medium that was decisive for the rating, but above all, the agility and mobility of the system. These results provide evidence that the design of a telepresence system can influence the social perception of the remote assistant and therefore has implications for the acceptance and use of telepresence systems.

Robotics doi: 10.3390/robotics14060072

Authors: Elena Villalba-Aguilera Joaquim Blesa Pere Ponsa

This paper presents the design and implementation of a Model-based Predictive Control (MPC) strategy integrated within a modular multilayer architecture for a three-wheeled omnidirectional mobile robot, the Robotino 4 from Festo. The implemented architecture is organized into three hierarchical layers to support modularity and system scalability. The upper layer is responsible for trajectory planning. This planned trajectory is forwarded to the intermediate layer, where the MPC computes the optimal velocity commands to follow the reference path, taking into account the kinematic model and actuator constraints of the robot. Finally, these velocity commands are processed by the lower layer, which uses three independent PID controllers to regulate the individual wheel speeds. To evaluate the proposed control scheme, it was implemented in MATLAB R2024a using a lemniscate trajectory as the reference. The MPC problem was formulated as a quadratic optimization problem that considered the three states: the global position coordinates and orientation angle. The simulation included state estimation errors and motor dynamics, which were experimentally identified to closely match real-world behavior. The simulation and experimental results demonstrate the capability of the MPC to track the lemniscate trajectory efficiently. Notably, the close agreement between the simulated and experimental results validated the fidelity of the simulation model. In a real-world scenario, the MPC controller enabled simultaneous regulation of both the position and orientation, which offered a greater performance compared with approaches that assume a constant orientation.

Robotics doi: 10.3390/robotics14060071

Authors: Alessandro Minervini Adrian Carrio Giorgio Guglieri

Visual–Inertial Odometry (VIO) algorithms are widely adopted for autonomous drone navigation in GNSS-denied environments. However, conventional monocular and stereo VIO setups often lack robustness under challenging environmental conditions or during aggressive maneuvers, due to the sensitivity of visual information to lighting, texture, and motion blur. In this work, we enhance an existing open-source VIO algorithm to improve both the robustness and accuracy of the pose estimation. First, we integrate an IMU-based motion prediction module to improve feature tracking across frames, particularly during high-speed movements. Second, we extend the algorithm to support a multi-camera setup, which significantly improves tracking performance in low-texture environments. Finally, to reduce the computational complexity, we introduce an adaptive feature selection strategy that dynamically adjusts the detection thresholds according to the number of detected features. Experimental results validate the proposed approaches, demonstrating notable improvements in both accuracy and robustness across a range of challenging scenarios.

Robotics doi: 10.3390/robotics14060070

Authors: Bryan Van Scoy Peter Jamieson Veena Chidurala

Robotics has widespread applications throughout industrial automation, autonomous vehicles, agriculture, and more. For these reasons, undergraduate education has begun to focus on preparing engineering students to directly contribute to the design and use of such systems. However, robotics is inherently multi-disciplinary and requires knowledge of controls and automation, embedded systems, sensors, signal processing, algorithms, and artificial intelligence. This makes training the future robotics workforce a challenge. In this paper, we evaluate our experiences with project-based learning approaches to teaching robotics at the undergraduate level at Miami University. Specifically, we analyze three consecutive years of capstone design projects on increasingly complex robotics design problems for multi-robot systems. We also evaluate the laboratories taught in our course “ECE 314: Elements of Robotics”. We have chosen these four experiences since they focus on the use of “cheap” first-principled robots, meaning that these robots sit on the fringe of embedded system design in that much of the student time is spent on working with a micro-controller interfacing with simple and cheap actuators and sensors. To contextualize our results, we propose the Robotic System Levels (RSL) model as a structured way to understand the levels of abstraction in robotic systems. Our main conclusion from these case studies is that, in each experience, students are exposed primarily to a subset of levels in the RSL model. Therefore, the curriculum should be designed to emphasize levels that align with educational objectives and the skills required by local industries.

Robotics doi: 10.3390/robotics14060069

Authors: Byungseo Kwak Seungbum Lim Jungwook Suh

The construction of high-rise buildings necessitates efficient and reliable material transport systems to improve productivity and reduce labor-intensive tasks. Traditional methods such as cranes and elevators are widely used but are often constrained by high costs and spatial limitations. Manipulator-based robotic systems have been explored as alternatives; however, they require complex control algorithms and struggle with confined construction environments. To address these challenges, we propose a lifting robot designed for repetitive inter-floor material transport in construction sites. The proposed system integrates a gear-connected double parallelogram linkage with a crank-rocker mechanism, enabling one-degree of freedom (1-DOF) operation for simplified control and precise positioning. Additionally, a spring-cable-based gravity compensation mechanism is implemented to reduce actuator torque, enhancing energy efficiency and structural stability. A prototype was fabricated, and experimental validation was conducted to evaluate torque reduction, positioning accuracy, and structural performance. Results demonstrate that the proposed system effectively minimizes driving torque, improves load-handling stability, and enhances overall operational efficiency. This study provides a foundation for developing automated lifting solutions in construction, contributing to reduced worker strain and increased productivity.

Robotics doi: 10.3390/robotics14060068

Authors: Dimitrios Loukatos Ioannis Glykos Konstantinos G. Arvanitis

The integration of new technologies in Industry 4.0 has modernised agriculture, fostering the concept of smart agriculture (Agriculture 4.0). Higher education institutions are incorporating digital technologies into agricultural curricula, equipping students in agriculture, agronomy, and engineering with essential skills. The implementation of targeted STEM activities has the potential to enhance the teaching of Agriculture 4.0 through the utilisation of practical applications that stimulate student interest, thereby facilitating more accessible and effective teaching. In this context, this study presents a system comprising retrofitted real-scale components that facilitate the understanding of digital technologies and automations in agriculture. The specific system utilises a typical centrifugal electric pump and a water tank and adds logic to it, so that its flow follows various user-defined setpoints, given and monitored via a smartphone application, despite the in-purpose disturbances invoked via intermediating valves. This setup aims for students to gain familiarity with concepts such as closed-loop systems and PID controllers. Going further, fertile ground is provided for experimentation on the efficiency of the PID controller via testing different algorithmic variants incorporating non-linear methods as well. Feedback collected from the participating students via a corresponding survey highlights the importance of integrating similar hands-on interdisciplinary activities into university curricula to foster engineering education.

Robotics doi: 10.3390/robotics14050067

Authors: Rocco De Marco Francesco Di Nardo Alessandro Rongoni Laura Screpanti David Scaradozzi

The escalating conflict between cetaceans and fisheries underscores the need for efficient mitigation strategies that balance conservation priorities with economic viability. This study presents a TinyML-driven approach deploying an optimized Convolutional Neural Network (CNN) on a Raspberry Pi Zero 2 W for real-time detection of bottlenose dolphin whistles, leveraging spectrogram analysis to address acoustic monitoring challenges. Specifically, a CNN model previously developed for classifying dolphins’ vocalizations and originally implemented with TensorFlow was converted to TensorFlow Lite (TFLite) with architectural optimizations, reducing the model size by 76%. Both TensorFlow and TFLite models were trained on 22 h of underwater recordings taken in controlled environments and processed into 0.8 s spectrogram segments (300 × 150 pixels). Despite reducing model size, TFLite models maintained the same accuracy as the original TensorFlow model (87.8% vs. 87.0%). Throughput and latency were evaluated by varying the thread allocation (1–8 threads), revealing the best performance at 4 threads (quad-core alignment), achieving an inference latency of 120 ms and sustained throughput of 8 spectrograms/second. The system demonstrated robustness in 120 h of continuous stress tests without failure, underscoring its reliability in marine environments. This work achieved a critical balance between computational efficiency and detection fidelity (F1-score: 86.9%) by leveraging quantized, multithreaded inference. These advancements enable low-cost devices for real-time cetacean presence detection, offering transformative potential for bycatch reduction and adaptive deterrence systems. This study bridges artificial intelligence innovation with ecological stewardship, providing a scalable framework for deploying machine learning in resource-constrained settings while addressing urgent conservation challenges.

Robotics doi: 10.3390/robotics14050066

Authors: Adrian-Paul Botezatu Andrei-Iulian Iancu Adrian Burlacu

This work proposes a hybrid deep learning-based framework for visual feedback control in an eye-in-hand robotic system. The framework uses an early fusion approach in which real and synthetic images define the training data. The first layer of a ResNet-18 backbone is augmented to fuse interest-point maps with RGB channels, enabling the network to capture scene geometry better. A manipulator robot with an eye-in-hand configuration provides a reference image, while subsequent poses and images are generated synthetically, removing the need for extensive real data collection. The experimental results reveal that this enriched input representation significantly improves convergence accuracy and velocity smoothness compared to a baseline that processes real images alone. Specifically, including feature point maps allows the network to discriminate crucial elements in the scene, resulting in more precise velocity commands and stable end-effector trajectories. Thus, integrating additional, synthetically generated map data into convolutional architectures can enhance the robustness and performance of the visual servoing system, particularly when real-world data gathering is challenging. Unlike existing visual servoing methods, our early fusion strategy integrates feature maps directly into the network’s initial convolutional layer, allowing the model to learn critical geometric details from the very first stage of training. This approach yields superior velocity predictions and smoother servoing compared to conventional frameworks.

Robotics doi: 10.3390/robotics14050065

Authors: Yuki Kawawaki Kenichi Murakami Yuji Yamakawa

In recent years, the realization of a society in which humans and robots coexist has become highly anticipated. As a result, robots are expected to exhibit versatility regardless of their operating environments, along with high responsiveness, to ensure safety and enable dynamic task execution. To meet these demands, we design a comprehensive system composed of two primary components: high-speed skeleton tracking and path planning. For tracking, we implement a high-speed skeleton tracking method that combines deep learning-based detection with optical flow-based motion extraction. In addition, we introduce a dynamic search area adjustment technique that focuses on the target joint to extract the desired motion more accurately. For path planning, we propose a high-speed, geometry-informed potential field model that addresses four key challenges: (P1) avoiding local minima, (P2) suppressing oscillations, (P3) ensuring adaptability to dynamic environments, and (P4) handling obstacles with arbitrary 3D shapes. We validated the effectiveness of our high-frequency feedback control and the proposed system through a series of simulations and real-world collision-free path planning experiments. Our high-speed skeleton tracking operates at 250 Hz, which is eight times faster than conventional deep learning-based methods, and our path planning method runs at over 10,000 Hz. The proposed system offers both versatility across different working environments and low latencies. Therefore, we hope that it will contribute to a foundational motion generation framework for human–robot collaboration (HRC), applicable to a wide range of downstream tasks while ensuring safety in dynamic environments.

Robotics doi: 10.3390/robotics14050064

Authors: Ahmad Aldaher Sergei Savin

In this paper, we study H∞ control for systems with explicit mechanical constraints and a lack of state information, such as walking robots. This paper proposes an H∞ control design scheme based on solving an optimization problem with linear matrix inequality constraints. Our method is based on the orthogonal decomposition of the state variables and the use of two linear controllers and a Luenberger observer, tuned to achieve the desired properties of the closed-loop system. The method takes into account static linear additive disturbance, which appears due to the uncertainties associated with the mechanical constraints. We propose a dynamics linearization procedure for systems with mechanical constraints, taking into account the inevitable lack of information about the environment; this procedure allows a nonlinear system to be transformed into a form suitable for the application of the proposed control design method. The method is tested on a constrained underactuated three-link robot and a flat quadruped robot, showing the desired behavior in both cases.

Robotics doi: 10.3390/robotics14050063

Authors: Muhammad Liman Gambo Abubakar Danasabe Basem Almadani Farouq Aliyu Abdulrahman Aliyu Esam Al-Nahari

The increasing demand for automation has led to the complexity of the design and operation of robotic systems. This paper presents a systematic literature review (SLR) focused on the applications and challenges of Data Distribution Service (DDS)-based middleware in robotics from 2006 to 2024. We explore the pivotal role of DDS in facilitating efficient communication across heterogeneous robotic systems, enabling seamless integration of actuators, sensors, and computational elements. Our review identifies key applications of DDS in various robotic domains, including multi-robot coordination, real-time data processing, and cloud–edge–end fusion architectures, which collectively enhance the performance and scalability of robotic operations. Furthermore, we identify several challenges associated with implementing DDS in robotic systems, such as security vulnerabilities, performance and scalability requirements, and the complexities of real-time data transmission. By analyzing recent advancements and case studies, we provide insights into the potential of DDS to overcome these challenges while ensuring robust and reliable communication in dynamic environments. This paper aims to contribute to the transformative impact of DDS-based middleware in robotics, offering a comprehensive overview of its benefits, applications, and security implications. Our findings underscore the necessity for continued research and development in this area, paving the way for more resilient and intelligent robotic systems that operate effectively in real-world scenarios. This review not only fills existing gaps in the literature but also serves as a foundational resource for researchers and practitioners seeking to leverage DDS in the design and implementation of next-generation robotic solutions.

Robotics doi: 10.3390/robotics14050062

Authors: Yasir Mehmood Ferdinando Cannella Silvio Cocuzza

A comprehensive literature review on the kinematics and dynamics modeling and virtual prototyping (V.P) of the Cartesian robots with a flexible configuration is presented in this paper. Different modeling approaches of the main components of the Cartesian robot, which includes linear belt drives and structural components, are presented and discussed in this paper. Furthermore, the vibrations modeling, trajectory planning, and control strategies of the Cartesian robot are also presented. The performance optimization of the Cartesian robot is discussed here, which is affected by the highly flexible configuration of the robot incurred due to high-mix, low-volume production. The importance of virtual prototyping techniques, like finite element analysis and multi-body dynamics, for modeling Cartesian robots or its components is presented. Design and performance optimization methods for robots with a flexible configuration are discussed, although their application to Cartesian robots is rare in the literature and it presents an exciting opportunity for future research in this area. This review paper focuses on the importance of further research on the virtual prototyping tools for flexibly configured robots and their integration with experimental validation. The findings offer useful insights to industries looking to maximize their production processes while keeping the customization, reliability, and efficiency.

Robotics doi: 10.3390/robotics14050061

Authors: Zoltán Forgó Ferenc Tolvaly-Ro?ca Attila Csobán

The number of applications of parallel topology robots in industry is growing, and the interest of academics in finding new solutions and applications to implement such mechanisms is present all over the world. Industrywide, the most commonly used motion types need four- and six-degrees-of-freedom (DoF) robots. While there are commercial variants from different robot vendors, this study offers new alternatives to these. Based on Lie algebra synthesis, symmetrical parallel structures are identified, according to certain rules. Implementing 2-DoF actuation modules, the number of robot limbs is reduced compared to existing commercial robot structures. In terms of the applicability of a parallel mechanism (also concerning the control algorithm), it is important to determine singular configurations. Therefore, in addition to the kinematic schematics of the newly proposed mechanisms, their singular configurations are also discussed. Based on some dimensional simplifications (without a loss of generality), the conditions for the singular configurations are enumerated for the presented parallel topology robots with symmetrical kinematic chains. Finally, a comparison of the proposed mechanism is presented, considering its singular configurations.

Robotics doi: 10.3390/robotics14050060

Authors: Craig Carignan Giacomo Marani

Task prioritization for inverse kinematics can be a powerful tool for realizing objectives in robot manipulation. This is particularly true for robots with redundant degrees of freedom, but it can also help address a debilitating singularity in six-axis robots. A roll-pitch-roll wrist is especially problematic for any six-axis robot because it produces a “gimbal-lock” singularity in the middle of the wrist workspace when the roll axes align. A task priority methodology can be used to realize only the achievable components of the commanded motion in the reduced operational space of a manipulator near singularities while phasing out the uncontrollable direction. In addition, this approach allows the operator to prioritize translation and rotation in the region of singularities. This methodology overcomes a significant drawback to the damped least-squares method, which can produce tool motion that deviates significantly from the desired path even in directions that are controllable. The approach used here reduces the operational space near the wrist singularity while maintaining full command authority over tool translation. The methodology is demonstrated in simulations conducted on a six degree-of-freedom Motoman MH250 manipulator.

Robotics doi: 10.3390/robotics14050059

Authors: Muhammad Shahab Ali Nasir Nezar M. Alyazidi

This research investigates the impact of actuator faults on the formation control of multiple-wheeled mobile robots—a critical aspect in coordinating multi-robot systems for applications such as surveillance, exploration, and transportation. When a fault occurs in any of the robots, it can disrupt the formation and adversely affect the system’s performance, thereby compromising system efficiency and reliability. While numerous studies have focused on fault-tolerant control strategies to maintain formation integrity, there is a notable gap in the literature regarding the relationship between controller gains and settling time under varying degrees of actuator loss. In this paper, we develop a kinematic model of wheeled mobile robots and implement a leader–follower-based formation control strategy. Actuator faults are systematically introduced with varying levels of effectiveness (e.g., 80%, 60%, and 40% of full capacity) to observe their effects on formation maintenance. We generate data correlating controller gains with settling time under different actuator loss conditions and fit a polynomial curve to derive an equation describing this relationship. Comprehensive MATLAB simulations are conducted to evaluate the proposed methodology. The results demonstrate the influence of actuator faults on the formation control system and provide valuable insights into optimizing controller gains for improved fault tolerance. These findings contribute to the development of more robust multi-robot systems capable of maintaining formation and performance despite the presence of actuator failures.

Robotics doi: 10.3390/robotics14050058

Authors: Roni Azriel Oded Degani Avital Bechar

This paper presents an improved methodology for characterizing task-oriented optimal manipulator configuration, tested on a case study of selective spraying in vineyards. It compares the current approach for optimizing manipulator configurations, which relies on simulation and optimization algorithms, with an improved methodology that integrates machine learning models to enhance the optimization process. The simulation tool was developed using the Gazebo simulator and ROS software to evaluate potential robotic configurations within a simulated vineyard. Particle Swarm Optimization (PSO) was employed as the optimization algorithm in a finite solution space, with the performance measure based on maximizing the Manipulability Index of manipulator configurations reaching all targets. In the proposed methodology, XGBoost models were used to replace the simulation stage in the process and predict the manipulator’s ability to reach the target positions in the spraying task. This prediction served as decision support in selecting which configurations should be tested in the simulation, thereby reducing computational time. The integration of machine learning models in the proposed methodology resulted in an average runtime reduction of 59% while maintaining an average manipulability index score in comparison to the original approach, which did not include the XGBoost model. This methodology demonstrates significant enhancements in optimizing robot configuration for a specific task and shows strong potential for broader applications across various industries.

Robotics doi: 10.3390/robotics14050057

Authors: Qimeng Li Franco Cicirelli Andrea Vinci Antonio Guerrieri Wen Qi Giancarlo Fortino

Quadruped robots have emerged as a prominent field of research due to their exceptional mobility and adaptability in complex terrains. This paper presents an overview of quadruped robots, encompassing their design principles, control mechanisms, perception systems, and applications across various industries. We review the historical evolution and technological milestones that have shaped quadruped robotics. To understand their impact on performance and functionality, key aspects of mechanical design are analyzed, including leg configurations, actuation systems, and material selection. Control strategies for locomotion, balance, and navigation are all examined, highlighting the integration of artificial intelligence and machine learning to enhance adaptability and autonomy. This review also explores perception and sensing technologies that enable environmental interaction and decision-making capabilities. Furthermore, we systematically examine the diverse applications of quadruped robots in sectors including the military, search and rescue, industrial inspection, agriculture, and entertainment. Finally, we address challenges and limitations, including technical hurdles, ethical considerations, and regulatory issues, and propose future research directions to advance the field. By structuring this review as a systematic study, we ensure clarity and a comprehensive understanding of the domain, making it a valuable resource for researchers and engineers in quadruped robotics.

Robotics doi: 10.3390/robotics14050056

Authors: Aitor Ibarguren Sotiris Aivaliotis Javier González Huarte Arkaitz Urquiza Panagiotis Baris Apostolis Papavasileiou George Michalos Sotiris Makris

The automotive industry is one of the most automatized industries, employing more than one million robots worldwide. Although several steps in car production are completely automated, many steps are still carried out by operators, especially in tasks requiring high dexterity. Additionally, customization and deployability are still pending issues in this industry, where a real collaboration between robots and operators would increase the reconfigurability of the assembly lines. This paper presents an innovative robotic cell focused on the motor and gearbox assembly, including collaborative industrial robots and autonomous mobile manipulators along the different assembly stations. The design also incorporates a human-centered approach, with an enhanced human interface to facilitate the interaction with operators with the complete robotic cell. The proposed approach has been deployed and validated on a real automotive industrial scenario, obtaining promising metrics and results.

Robotics doi: 10.3390/robotics14050055

Authors: Samuel Romero Jorge Valero Andrea Valentina García Carlos F. Rodríguez Ana Maria Montes Cesar Marín Ruben Bola?os David álvarez-Martínez

Recent industrial production paradigms have seen the promotion of the outsourcing of low-value-added operations to robotic cells as a service, particularly end-of-line packaging. As a result, various types of research have emerged, offering different approaches to the trajectory design optimization of robotic manipulators and their applications. Over time, numerous improvements and updates have been made to the proposed methodologies, addressing the limitations and restrictions of earlier work. This survey-type article compiles research articles published in recent years that focus on the main algorithms proposed for addressing placement and minimum-time path planning for a manipulator responsible for performing pick-and-place tasks. Specifically, the research examines the construction of an automated robotic cell for the palletizing of regular heterogeneous boxes on a collision-free mixed pallet. By reviewing and synthesizing the most recent research, this article sheds light on the state-of-the-art manipulator planning algorithms for pick-and-place tasks in palletizing applications.

Robotics doi: 10.3390/robotics14050054

Authors: Hironori Gunji Takashi Kusaka Takayuki Tanaka

A partial Lagrangian method is proposed as an inverse dynamics analysis method for multi-link systems. This method, combined with automatic differentiation, allows for the derivation of equations of motion and analytical extraction of motion-induced torque components. We introduce the concept of motion propagation force to describe joint torque components generated by the motion of other joints. This concept aligns with existing notions such as interaction torque, while providing a novel analytical perspective. The effectiveness of the proposed method is confirmed through simulations using a three-DoF arm model, where motion propagation torques are visualized and validated. This method is useful for motion analysis and impedance control in complex robotic systems.

Robotics doi: 10.3390/robotics14040053

Authors: Chaitanya Bandi Ulrike Thomas

Human–Robot Interaction (HRI) depends on robust perception systems that enable intuitive and seamless interaction between humans and robots. This work introduces a multi-view perception framework designed for HRI, incorporating object detection and tracking, human body and hand pose estimation, unified hand–object pose estimation, and action recognition. We use the state-of-the-art object detection architecture to understand the scene for object detection and segmentation, ensuring high accuracy and real-time performance. In interaction environments, 3D whole-body pose estimation is necessary, and we integrate an existing work with high inference speed. We propose a novel architecture for 3D unified hand–object pose estimation and tracking, capturing real-time spatial relationships between hands and objects. Furthermore, we incorporate action recognition by leveraging whole-body pose, unified hand–object pose estimation, and object tracking to determine the handover interaction state. The proposed architecture is evaluated on large-scale, open-source datasets, demonstrating competitive accuracy and faster inference times, making it well-suited for real-time HRI applications.

Robotics doi: 10.3390/robotics14040052

Authors: Federico Neri Giacomo Palmieri Massimo Callegari

This paper presents an obstacle avoidance strategy for mobile manipulators consisting of a robotic arm and a base with a non-holonomic differential wheel system. The algorithm makes it possible to avoid obstacles in a dynamic environment, without planning the path a priori. A series of examples are proposed in simulation using Matlab and analyzed to show how the algorithm works if the obstacle interferes with the manipulator or the base. In addition, the possibility of prioritizing the movement of certain parts of the system using the weighted pseudo-inverse matrix is introduced. In this way, it is possible to give movement priority to the base if it is necessary to move the robot over long distances while keeping the manipulator as still as possible. The use of null space to keep the end-effector stationary while it avoids obstacles is also explored, exploiting the system’s redundancy and allowing the rest of the kinematic chain and the mobile base to move accordingly. Finally, current standards are analyzed and a solution is shown that allows the robot to vary its behavior to avoid obstacles depending on the distance to the target point.

Robotics doi: 10.3390/robotics14040051

Authors: Deira Sosa Méndez David Bedolla-Martínez Maarouf Saad Yassine Kali Cecilia E. García Cena ángel L. álvarez

Two primary challenges in controlling robotic rehabilitation devices are the uncertainties in dynamic models and, more importantly, the need for controllers capable of adapting to external disturbances due to human–robot interaction. To address these issues, this paper proposes the particle swarm optimization (PSO) algorithm for the real-time gain tuning in the sliding mode controller (SMC) based on the exponential reaching law (ERL). The proposed approach was designed for a seven-degrees-of-freedom (DOF) robotic exoskeleton used in upper-limb physical rehabilitation. The optimization algorithm aims to minimize tracking errors in rehabilitation exercises through the robust ERL controller applied to nonlinear systems with external disturbances. The proposed method was validated through experimental tests conducted on two healthy subjects, and the outcomes indicated a reduction of over 20% in tracking errors compared to heuristically tuned gains. Mathematical analyses of dynamic modeling and algorithm convergence are shown.

Robotics doi: 10.3390/robotics14040050

Authors: Srikar Annamraju Harris Nisar Anne Christine Horowitz Du?an Stipanovi?

The shortage of therapists required for the rehabilitation of stroke patients, together with the patients’ lack of motivation in regular therapy, creates the need for a robotic rehabilitation platform. While studies on shared control architectures are present in the literature as a means of training, state-of-the-art training systems involve a complex architecture and, moreover, have notable performance limitations. In this paper, a simplified training architecture is proposed that is particularly targeted for rehabilitation and also adds missing features, such as complete force feedback, enhanced learning rate, and dynamic monitoring of the patient’s performance. In addition to the novel architecture, the design of controllers to ensure system stability is presented. These controllers are analytically shown to meet the performance objectives and maintain the system’s passivity. An experimental setup is built to test the architecture and the controllers. A comparison with the state-of-the-art methods is also performed to demonstrate the superiority of the proposed method. It is further demonstrated that the proposed architecture facilitates correcting the inaccurate frequencies at which the patient might operate. This was achieved by defining attribute-wise individual recovery factors for the patient.

Robotics doi: 10.3390/robotics14040049

Authors: Guilherme de Paula Rúbio Matheus Carvalho Barbosa Costa Claysson Bruno Santos Vimieiro

To improve the adaptability of the hand prosthesis, we propose a new smart control for a physiological finger prosthesis using the advantages of the deep deterministic policy gradient (DDPG) algorithm. A rigid body model was developed to represent the finger as a training environment. The geometric characteristics and physiological physical properties of the finger available in the literature were assumed, but the joint’s stiffness and damping were neglected. The standard DDPG algorithm was modified to train an artificial neural network (ANN) to perform two predetermined trajectories: linear and sinusoidal. The ANN was evaluated through the use of a computational model that simulated the functionality of the finger prosthesis. The model demonstrated the capacity to successfully execute both sinusoidal and linear trajectories, exhibiting a mean error of 3.984±2.899 mm for the sinusoidal trajectory and 3.220±1.419 mm for the linear trajectory. Observing the torques, it was found that the ANN used different strategies to control the movement in order to adapt to the different trajectories. Allowing the ANN to use a combination of both trajectories, our model was able to perform trajectories that differed from purely linear and sinusoidal, showing its ability to adapt to the movement of the physiological finger. The results showed that it was possible to develop a controller for multiple trajectories, which is essential to provide more integrated and personalized prostheses.

Robotics doi: 10.3390/robotics14040048

Authors: Ilias Chouridis Gabriel Mansour Vasileios Papageorgiou Michel Theodor Mansour Apostolos Tsagaris

Industry 5.0 is a developing phase in the evolution of industrialization that aims to reshape the production process by enhancing human creativity through the utilization of automation technologies and machine intelligence. Its central pillar is the collaboration between robots and humans. Path planning is a major challenge in robotics. An offline 4D path planning algorithm is proposed to find the optimal path in an environment with static and dynamic obstacles. The time variable was embodied in an enhanced artificial fish swarm algorithm (AFSA). The proposed methodology considers changes in robot speeds as well as the times at which they occur. This is in order to realistically simulate the conditions that prevail during cooperation between robots and humans in the Industry 5.0 environment. A method for calculating time, including changes in robot speed during path formation, is presented. The safety value of dynamic obstacles, the coefficients of the importance of the terms of the agent’s distance to the ending point, and the safety value of dynamic obstacles were introduced in the objective function. The coefficients of obstacle variation and speed variation are also proposed. The proposed methodology is applied to simulated real-world challenges in Industry 5.0 using an industrial robotic arm.

Robotics doi: 10.3390/robotics14040047

Authors: Hongjun Yu Lanyong Zhang

In known environments, vehicles plan paths to the target and take precautions to minimize risks. Due to limited dynamics, bounded turning radii, and unfavorable initial conditions, they may be momentarily exposed to threats. In this study, we propose multi-objective real-time optimization based on Dubins paths for multiple vehicles. They synchronize target arrival by reasonably changing speeds and selecting paths of similar lengths. The closer the threats are to the robots and the target, the more path options are available. Risk is reduced in path planning by minimizing the duration of exposure to threats. Vehicles strike a balance between exposure to threats and travel time to targets. We use a probability-based approach to reduce the computation burden and select satisfactory paths such that vehicles synchronize target arrival reasonably far away from threats. The performances of the proposed methods are verified in several simulation examples.

Robotics doi: 10.3390/robotics14040046

Authors: Muratulla Utenov Tarek Sobh Yerbol Temirbekov Saltanat Zhilkibayeva Sarosh Patel Dauren Baltabay Zhadyra Zhumasheva

This study proposes an approach to 3D modeling of spatial manipulators in the Maple 2023 software environment. Algorithms and program codes have been developed to create computer 3D models of manipulators controlled by generalized coordinates. The implementation of these algorithms and program codes has enabled the creation of three-dimensional computer models of manipulators with clear visual representations of links, their cross-sections, kinematic pairs, grippers, and loads, differing in structure and degrees of freedom while ensuring a comprehensive view from all spatial perspectives. During the motion of the manipulator, complex distributed dynamic loads arise in its links due to their intrinsic masses. These dynamic loads create several challenges: for instance, excessive dynamic loads or significant deformation of the links may lead to failure of the manipulator or a loss of precision in the positioning of the gripper. Such loads significantly impact the design, operation, and reliability of manipulators. The study and understanding of dynamic loads in manipulators are crucial areas in mechanics and robotics, enabling the development of more reliable and efficient systems. The Denavit–Hartenberg method was applied to control the motion of the created computer 3D models of manipulators using generalized coordinates. Using the recursive Newton–Euler equations, the necessary kinematic characteristics of the manipulator’s links were determined for calculating the distributed dynamic loads arising from the intrinsic masses of the links at each cross-section, relative to the local coordinate systems rigidly attached to the links. Algorithms and program codes were developed for controlling the motion of 3D models of manipulators, as well as for constructing visual diagrams of distributed dynamic loads in mutually perpendicular planes, formed by the principal axes of the link cross-sections and the axes passing along the longitudinal axes of the links. The implementation of these algorithms and program codes enabled the generation of distribution diagrams of all dynamic loads in all links of the moving manipulator. These diagrams visually illustrate the changes in direction and magnitude of the distributed dynamic loads in all cross-sections of the links throughout the full cycle of the manipulator’s operation. This allows for the consideration of the identified dynamic loads in the strength and stiffness calculations of the manipulator links, which is essential for the design of new innovative manipulators.

Robotics doi: 10.3390/robotics14040045

Authors: Georgios P. Kladis Lefteris Doitsidis Nikos C. Tsourveloudis

Autonomy of underwater vehicles has become an imperative feature due to increasingly challenging deep sea mission scenarios. In particular, for trajectory-tracking problems of Autonomous Underwater Vehicles (AUVs), the use of Lyapunov theory tools in state-of-the-art methods is common practice. These often require special assumptions, according to the application considered, and ‘intuition’ for the choice of a control law, which often leads to conservative results. This article suggests a systematic analysis for the horizontal motion of an AUV which ensures global asymptotic stability for the closed loop system. A nonlinear underactuated AUV system is considered with linear and angular velocity constraints. The Takagi–Sugeno (TS) framework design is adopted for the representation of the original nonlinear system. The control law is synthesised using the standard parallel distributed compensation (PDC) control law structure and stability is guaranteed for the closed loop system. The design criteria are posed as linear matrix inequalities (LMIs) where sufficient conditions for the design of the control law are shown. The proposed approach can be easily adopted for different types of autonomous vehicles with minor modifications.

Robotics doi: 10.3390/robotics14040044

Authors: Aristeidis Geladaris Lampis Papakostas Athanasios Mastrogeorgiou Panagiotis Polygerinos

This paper presents a complete system for autonomous navigation in GPS-denied environments using a minimal sensor suite that operates onboard a robotic vehicle. Our system utilizes a single camera and, given a target destination without prior knowledge of the environment, replans in real time to generate a collision-free trajectory that avoids static and dynamic obstacles. To achieve this, we introduce, for the first time, a local Euclidean Signed Distance Field (ESDF) map with variable size and resolution, which scales as a function of the vehicle’s velocity. The map is updated at a high rate, requiring minimal computational power. Additionally, a short-term vicinity-based memory is maintained for previously observed areas to facilitate smooth trajectory generation, addressing the limited field-of-view provided by the RGB-D camera. System validation is carried out by deploying our algorithm on a differential drive vehicle in both simulation and real-world experiments involving static and dynamic obstacles. We benchmark our robotic system against state-of-the-art autonomous navigation frameworks, successfully navigating to designated target locations while avoiding obstacles in both static and dynamic scenarios, all without introducing additional computational overhead. Our approach consistently achieves the target goals even in complex settings where current state-of-the-art methods may fall short.

Robotics doi: 10.3390/robotics14040043

Authors: Romisaa Ali Sedat Dogru Lino Marques Marcello Chiaberge

The primary challenge in robotic navigation lies in enabling robots to adapt effectively to new, unseen environments. Addressing this gap, this paper enhances the Twin Delayed Deep Deterministic Policy Gradient (TD3) model’s adaptability by introducing randomized start and goal points. This approach aims to overcome the limitations of fixed goal points used in prior research, allowing the robot to navigate more effectively through unpredictable scenarios. This proposed extension was evaluated in unseen environments to validate the enhanced adaptability and performance of the TD3 model. The experimental results highlight improved flexibility and robustness in the robot’s navigation capabilities, demonstrating the ability of the model to generalize effectively to unseen environments. Additionally, this paper provides a concise overview of TD3, focusing on its core mechanisms and key components to clarify its implementation.

Robotics doi: 10.3390/robotics14040042

Authors: Mojtaba A. Khanesar Aslihan Karaca Minrui Yan Mohammed Isa Samanta Piano David Branson

Highly accurate positioning of industrial robots is crucial to performing industrial operations with high quality. This paper presents a mechanical modification to an industrial robot aiming at enhancing the system actuation resolution, thereby enhancing its positional accuracy. The industrial robot under consideration is a six-degrees of freedom (DoF) robot with revolute joints. By integrating a linear stage, a prismatic joint is introduced to the robot’s end effector, reconfiguring it into a 7 DoF system with more precise step size capabilities. To improve the positional accuracy of the overall system, a closed-loop control structure is chosen. Positional feedback is provided using an industrial laser tracker. Initially, a multi-layer perceptron neural network (MLPNN) is used to identify the forward kinematics (FK) of the overall 6RP robotic system. The FK of the industrial robot using the pretrained MLPNN is then used online to compute the real-time sensitivity of positional error to changes in the joint angle values of the industrial robot and displacements of the prismatic joint. Different trajectories are used to test the accuracy of the proposed positioning algorithm. From the implementation results obtained using the proposed control structure, it is observed that the accuracy of the industrial robot improves significantly. Statistical results for five different points selected from the ISO 9283 trajectory over 30 times of measurements show an 82% improvement for the measurements using the proposed approach as compared to the original industrial robot controller.

Robotics doi: 10.3390/robotics14040040

Authors: Reenu Arikkat Paul Abhilash Pandya

Surgeon fatigue during robotic surgery is an often-overlooked factor contributing to patient harm. This study presents the design, development, and testing of a real-time fatigue monitoring system aimed at enhancing safety in robotic surgery using the da Vinci surgical system. The system monitors critical fatigue indicators, including instrument collisions, blink rate, and workspace utilization, delivering immediate feedback to surgeons to mitigate fatigue-induced errors. The system was verified with simulated fatigue scenarios, such as reduced blink rates, abrupt tool movements, and inefficient utilization of the surgical workspace. The verification testing showed that the system detected fatigue-related indicators and provided timely alerts. This research underscores the potential of integrating advanced real-time monitoring technologies into robotic-assisted surgical practice to improve safety and efficiency. By identifying early signs of fatigue, the system facilitates immediate interventions, potentially preventing surgical errors. Additionally, the data collected can inform proactive future scheduling strategies to address surgeon fatigue. While the system demonstrated promising performance in simulated environments, further validation through subject studies and clinical trials is essential to establish its efficacy in real-world surgical settings.

Robotics doi: 10.3390/robotics14040041

Authors: Odysseus Adamides Karthik Subramanian Sarthak Arora Ferat Sahin

Human–Robot Collaboration (HRC) has been a significant research topic within the Industry 4.0 movement over the past decade. The interest in HRC research has continued on with the dawn of Industry 5.0 focusing on worker experience. Within the study of HRC, the collaboration approach of Speed and Separation Monitoring (SSM) has been implemented through various architectures. The different configuration strategies involve different perception-sensing modalities, mounting strategies, data filtration, computational platforms, and calibration methods. This paper explores the evolution of the perception architectures used to perform SSM, and highlights innovations in sensing and processing technologies that can open up the door to significant advancements in this sector of HRC research.

Robotics doi: 10.3390/robotics14040039

Authors: Pavlo Pikulin Vitalii Lishunov Konrad Ku?akowski

Efficient path planning for Automated Guided Vehicles (AGVs) in warehouse automation is crucial yet challenging, particularly in environments with irregular structures and constrained spaces. This study addresses these challenges by focusing on AGVs without rotary platforms, which require the rotation of the entire robot-rack assembly for directional changes, demanding additional space and complex path planning. We have developed dedicated algorithms that integrate robotics and optimization principles to tackle these issues. Our methods take into account the spatial requirements for rack rotation, navigating through limited inter-rack clearance, and adapting to irregular warehouse layouts. Extensive simulations and real-world applications validate the proposed solutions, demonstrating significant improvements in traversal efficiency and collision risk mitigation. The results indicate that our algorithms effectively enhance the operational efficiency and reliability of AGV systems in complex warehouse environments. This research adapts AGV path planning by providing robust strategies to optimize navigation in challenging settings, ultimately improving warehouse productivity.

Robotics doi: 10.3390/robotics14040038

Authors: Daswin De Silva Sudheera Withanage Vidura Sumanasena Lakshitha Gunasekara Harsha Moraliyage Nishan Mills Milos Manic

The rapid adoption of artificial intelligence (AI) systems, such as predictive AI, generative AI, and explainable AI, is in contrast to the slower development and uptake of robotic AI systems. Dynamic environments, sensory processing, mechanical movements, power management, and safety are inherent complexities of robotic intelligence capabilities that can be addressed using novel AI approaches. The current AI landscape is dominated by machine learning techniques, specifically deep learning algorithms, that have been effective in addressing some of these challenges. However, these algorithms are subject to computationally complex processing and operational needs such as high data dependency. In this paper, we propose a computation-efficient and data-efficient framework for robotic motion intelligence (RMI) based on vector symbolic architectures (VSAs) and blockchain-based smart contracts. The capabilities of VSAs are leveraged for computationally efficient learning and noise suppression during perception, motion, movement, and decision-making tasks. As a distributed ledger technology, smart contracts address data dependency through a decentralized, distributed, and secure transactions ledger that satisfies contractual conditions. An empirical evaluation of the framework confirms its value and contribution towards addressing the practical challenges of robotic motion intelligence by significantly reducing the learnable parameters by 10 times while preserving sufficient accuracy compared to existing deep learning solutions.

Robotics doi: 10.3390/robotics14040037

Authors: Martin Cooney Mariacarla Staffa

Social robots are rapidly emerging as a transformative technology aimed at enhancing human well-being [...]

Robotics doi: 10.3390/robotics14040036

Authors: Paolo Righettini Giovanni Legnani Filippo Cortinovis Federico Tabaldi Jasmine Santinelli

The mechatronic design approach to robotics deploys, inter alia, widely available mechanical design engineering tools that, together with standard production techniques, allow the accurate quantification of the system’s mass properties. While this enables the synthesis of model-based centralized controllers, friction still limits the achievable dynamic performances, as its prediction at the design stage is hampered by complex dependencies on loads, temperature, wear, and lubrication. Further uncertainties affecting mechatronic devices stem from the actuation systems, whose parameters are specified by the manufacturer with relatively loose accuracy. These challenges are addressed here through a method based on MEMS IMUs for the real-time estimation of both friction effects and uncertain actuator parameters. The resulting model, inclusive of the frictionless dynamics, is applied in a closed loop to improve the control performance. An experimental comparison with decentralized and non-adaptive regulators highlights severalfold reductions in tracking errors; the ability to track temperature-dependent friction variations is also shown. From this work, it may be concluded that the use of MEMS sensors, together with identification and adaptive control algorithms, sensibly increases the dynamic performance of robotic systems. The real-time properties of the method also enable future investigations into topics such as MEMS-based diagnostics and predictive maintenance.

Robotics doi: 10.3390/robotics14040035

Authors: Jack M. Vice Gita Sukthankar

Navigating uneven, unstructured terrain with dynamic obstacles remains a challenge for autonomous mobile robots. This article introduces Dynamic Unstructured Environment (DUnE) for evaluating the performance of off-road navigation systems in simulation. DUnE is a versatile software framework that implements the Gymnasium reinforcement learning (RL) interface for ROS 2, incorporating unstructured Gazebo simulation environments and dynamic obstacle integration to advance off-road navigation research. The testbed automates key performance metric logging and provides semi-automated trajectory generation for dynamic obstacles including simulated human actors. It supports multiple robot platforms and five distinct unstructured environments, ranging from forests to rocky terrains. A baseline reinforcement learning agent demonstrates the framework’s effectiveness by performing pointgoal navigation with obstacle avoidance across various terrains. By providing an RL interface, dynamic obstacle integration, specialized navigation tasks, and comprehensive metric tracking, DUnE addresses significant gaps in existing simulation tools.

Robotics doi: 10.3390/robotics14030034

Authors: Tomonari Tanioka Hikaru Nagano Yuichi Tazaki Yasuyoshi Yokokohji

This study investigated the role of haptic feedback in precision peg insertion tasks conducted via teleoperation under varying visual resolution and communication latency conditions. Experiment 1 examined the combined effects of haptic feedback and the visual resolution, revealing that haptic feedback significantly reduces the maximum normal force and mental workload, while enhancing subjective operability, particularly in low-visual-resolution conditions. Experiment 2 evaluated the impact of communication latency, showing that the maximum normal force, operability, and mental workload are affected by increased latency. Notably, the maximum normal force is sensitive even to minimal latency (100 ms), whereas the mental workload and operability remain acceptable under lower-latency conditions. These findings underscore the importance of multi-metric evaluations, as different aspects of performance respond differently to latency. Overall, the results demonstrate the critical role of haptic feedback in enhancing task performance and the user experience in teleoperated precision tasks, offering valuable insights for the design and development of more effective and user-friendly teleoperation systems.

Robotics doi: 10.3390/robotics14030033

Authors: Michele Nasser Giuseppe Fulvio Gaglio Valeria Seidita Antonio Chella

This study presents an approach for developing digital avatars replicating individuals’ physical characteristics and communicative style, contributing to research on virtual interactions in the metaverse. The proposed method integrates large language models (LLMs) with 3D avatar creation techniques, using what we call the Tree of Style (ToS) methodology to generate stylistically consistent and contextually appropriate responses. Linguistic analysis and personalized voice synthesis enhance conversational and auditory realism. The results suggest that ToS offers a practical alternative to fine-tuning for creating stylistically accurate responses while maintaining efficiency. This study outlines potential applications and acknowledges the need for further work on adaptability and ethical considerations.

Robotics doi: 10.3390/robotics14030032

Authors: Ilias Chouridis Gabriel Mansour Vasileios Papageorgiou Michel Theodor Mansour Apostolos Tsagaris

Path planning is a vital challenge in robot navigation. In the real world, robots operate in 3D environments with various obstacles and restrictions. An improved artificial fish swarm algorithm (AFSA) is proposed to solve 3D path planning problems in environments with obstacles. The improved AFSA incorporates a 3D model of 24 possible movement points to more realistically simulate real-world robot movement capabilities. Several improvements are adopted, such as methods of simple and advanced 3D elimination. The 3D implementation of an agent’s navigation model, called an “obstacle heatmap”, is also presented. The use of a safety value factor and a total movement point factor in the AFSA’s objective function are introduced. The combination of improved AFSA and a ray-casting algorithm is also presented. Finally, a method called “multiple laser activation” is introduced to overcome both the main disadvantage of the application of AFSAs in path planning and the limitation of the finite number of possible movement points that appear when bio-inspired algorithms are applied to generate the optimal path in a grid environment. The resulting path is applied to real-world challenges with drones, coordinate measuring machines, and industrial robotic arms.

Robotics doi: 10.3390/robotics14030031

Authors: Takashi Kusaka Takayuki Tanaka

The Euler–Lagrange and Newton–Euler methods are typically used to derive equations of motion for serial-link manipulators. We previously proposed a partial Lagrangian method, which is similar to the Lagrangian method, for handling the equations of motion analytically. Moreover, the proposed method can efficiently handle multi-link analyses, similar to the Newton–Euler method. The partial Lagrangian method organizes the Lagrangian, which is obtained from the link structure, and torque, which is obtained by differential operations, into a table that can be easily handled by both manual calculations and computer analysis. Furthermore, by representing it using a computational graph, it is possible to perform dynamic analysis while maintaining the structure of a system. By observing the intermediate nodes of this computational graph, it is possible to observe how the torque generated at a particular link affects the joint. Organizing the structure with graphs allows us to consider complex systems as a collection of subgraphs, making this method highly compatible with our proposed partial Lagrangian approach. This study shows that the partial torque tensor can be used as an analog of the partial torque table for serial-link systems by interpreting the meaning of the table, i.e., the partial torque, as the interaction between the links in order to simplify the treatment of branching link systems. Due to the use of the partial torque tensor, the dimensions of the tensor correspond one-to-one with the number of branches, allowing the description of any branching system. Furthermore, by using the proposed building block representation, even complex branching systems can be easily designed and analyzed.

Robotics doi: 10.3390/robotics14030030

Authors: Damir Filko Emmanuel Karlo Nyarko

Chronic wounds require accurate and objective assessment to monitor healing progress and optimize treatment. Traditional contact-based methods for wound measurement are often uncomfortable for patients, impractical for clinicians, and prone to inaccuracies due to the complex shapes of wounds. Advances in computational power and data analysis have enabled non-contact techniques, particularly digital imaging, to play a greater role in wound assessment. However, challenges persist, as chronic wounds can vary greatly in size, shape, and surface geometry, making accurate 3D modeling difficult. Dynamic changes in wound dimensions during treatment and the potential for occluded areas further complicate assessment. Handheld 3D cameras and sensors, while promising, are limited by user experience and the potential for incomplete reconstructions. To address these challenges, this paper introduces a fully automated system for analyzing chronic wounds. The system consists of a robotic arm, an industrial-grade 3D scanner, and advanced algorithms for extracting and analyzing wound features. This complete pipeline improves the robustness and functionality of the system and enables precise 3D wound modeling and comprehensive data extraction. This paper discusses the operational system, highlights its advancements, and evaluates its potential for enhancing wound monitoring and healing outcomes.

Robotics doi: 10.3390/robotics14030029

Authors: Moritz Schappler

Parallel-kinematic machines or parallel robots have only been established in a few applications where their advantage over serial kinematics due to their high payload capacity, stiffness, or dynamics with their limited workspace-to-installation-space ratio pays out. However, some applications still have not yet been sufficiently or satisfactorily automated in which parallel robots could be advantageous. As their performance is much more dependent on their complex dimensioning, an automated design tool—not existing yet—is required to optimize the parameterization of parallel robots for applications. Combined structural and dimensional synthesis considers all principally possible kinematic structures and performs a separate dimensioning for each to obtain the best task-specific structure. However, this makes the method computationally demanding. The proposed computationally efficient approach for dimensional synthesis extends multi-objective particle swarm optimization with hierarchical constraints. A cascaded (bilevel) optimization includes the design optimization of components and the redundancy resolution for tasks with rotational symmetry, like milling. Two case studies for different end-effector degrees of freedom demonstrate the broad applicability of the combined structural and dimensional synthesis for symmetric parallel robots with rigid links and serial-kinematic leg chains. The framework produces many possible task-optimal structures despite numerous constraints and can be applied to other problems as an open-source Matlab toolbox.

Robotics doi: 10.3390/robotics14030028

Authors: Chathura Semasinghe Drake Taylor Siavash Rezazadeh

While the concept of humanoid robots stems from the goal of replicating human movement, these systems have yet to match the elegance and efficiency of human locomotion. A key reason for this gap is that current humanoid robots differ from humans in their kinematics, dynamics, and actuator properties. This work seeks to close that gap by designing an optimized humanoid robot with characteristics closely resembling those of an average human. For this purpose, we built a detailed framework for the in-depth electromechanical modeling of actuator components. This model was used in the comprehensive optimization of the robot’s actuator system, which was designed as a multi-objective scheme based on the objectives introduced in our previous work. This process helped both in achieving efficient and high-performance actuators and in streamlining the design of the structural parts to have mass and inertia distributions similar to those of humans. The proposed design process was utilized in the design of our humanoid robot, Mithra. Initial test showed that Mithra achieved its design goals in terms of human-like kinematics and dynamics characteristics, together with sufficient actuator strength for tasks such as stair navigation, squatting, and running.

Robotics doi: 10.3390/robotics14030027

Authors: Samarathunga S. M. B. P. B. Marcello Valori Giovanni Legnani Irene Fassi

As collaborative robots (cobots) increasingly share workspaces with humans, ensuring safe physical human–robot interaction (pHRI) has become paramount. This systematic review addresses safety assessment in pHRI, focussing on the industrial field, with the objective of collecting approaches and practices developed so far for modelling, simulating, and verifying possible collisions in human–robot collaboration (HRC). To this aim, advances in human–robot collision modelling and test-based safety evaluation over the last fifteen years were examined, identifying six main categories: human body modelling, robot modelling, collision modelling, determining safe limits, approaches for evaluating human–robot contact, and biofidelic sensor development. Despite the reported advancements, several persistent challenges were identified, including the over-reliance on simplified quasi-static models, insufficient exploration of transient contact dynamics, and a lack of inclusivity in demographic data for establishing safety thresholds. This analysis also underscores the limitations of the biofidelic sensors currently used and the need for standardised validation protocols for the impact scenarios identified through risk assessment. By providing a comprehensive overview of the topic, this review aims to inspire researchers to address underexplored areas and foster innovation in developing advanced, but suitable, models to simulate human–robot contact and technologies and methodologies for reliable and user-friendly safety validation approaches. Further deepening those topics, even combined with each other, will bring about the twofold effect of easing the implementation while increasing the safety of robotic applications characterised by pHRI.

Robotics doi: 10.3390/robotics14030026

Authors: Yuning Cao? Zehao Wu? I-Ming Chen? Qingsong Xu?

In the post-COVID era, international business and tourism are quickly recovering from the global lockdown, with people and products traveling faster at higher frequency. This boosts the economy while facilitating the spread of pathogens, causing waves of COVID aftershock with new variants like Omicron XBB. Hence, continuous disinfection of our living environments becomes our first priority. Autonomous disinfection robots provide an efficient solution to this issue. Compared to human cleaners, disinfection robots are able to operate tirelessly in harsh environments without increasing the risk of cross-infection. In this paper, we propose the design of a new generation of the Smart Cleaner disinfection robot, which is equipped with both an Ultraviolet-C (UVC) light tower and a hydrogen peroxide (HP) aerosol dispenser. The safety of an autonomous disinfection robot has been a persistent problem, especially when they work in complex environments. To tackle this problem, Hamilton–Jacobi (HJ) reachability is adopted to construct a safety filter for motion control, which guarantees that the disinfection path taken by the robot is collision-free without severely compromising the optimality of control actions. The effectiveness of the developed robot has been demonstrated by conducting extensive experimental studies.

Robotics doi: 10.3390/robotics14030025

Authors: Joslin Numbi Nadjet Zioui Mohamed Tadjine

We describe a quantum teleportation protocol for exchanging data between a mobile robot and its control station. Because of the high cost of quantum network systems, we use MATLAB software to simulate the teleportation of data. Our simulation models the dynamic motion of a car-like mobile robot (CLMR), considering its mass and inertia and the environmental viscosity. Our remote control method accurately reproduces a mathematical model of the CLMR’s real-world motion. The CLMR’s trajectory is represented by differential equations, with the velocity calculated using the Jacobian matrix. The velocity inputs are teleported from the control station to the CLMR, enabling it to move. Nevertheless, physical constraints cause the deviation of the robot’s trajectory from the predicted trajectory. To correct this deviation, the CLMR’s current position is teleported to the control station. Before implementing this protocol, we calculate the quantum teleportation circuit, and we use quantum gates in matrix form to simulate the data teleportation process. The protocol’s accuracy is assessed by comparing the original data and teleported data, and a good match is obtained. This study demonstrates the feasibility of quantum teleportation for remotely controlling real-time robotic systems over long distances and in environments that interfere with classical wireless communication.

Robotics doi: 10.3390/robotics14030024

Authors: Jesus Moncada-Ramirez Jose-Luis Matez-Bandera Javier Gonzalez-Jimenez Jose-Raul Ruiz-Sarmiento

Large Language Models (LLMs) provide cognitive capabilities that enable robots to interpret and reason about their workspace, especially when paired with semantically rich representations like semantic maps. However, these models are prone to generating inaccurate or invented responses, known as hallucinations, that can produce an erratic robotic operation. This can be addressed by employing agentic workflows, structured processes that guide and refine the model’s output to improve response quality. This work formally defines and qualitatively analyzes the impact of three agentic workflows (LLM Ensemble, Self-Reflection, and Multi-Agent Reflection) on enhancing the reasoning capabilities of an LLM guiding a robotic system to perform object-centered planning. In this context, the LLM is provided with a pre-built semantic map of the environment and a query, to which it must respond by determining the most relevant objects for the query. This response can be used in a multitude of downstream tasks. Extensive experiments were carried out employing state-of-the-art LLMs and semantic maps generated from the widely-used datasets ScanNet and SceneNN. The results show that agentic workflows significantly enhance object retrieval performance, especially in scenarios requiring complex reasoning, with improvements averaging up to 10% over the baseline.

Robotics doi: 10.3390/robotics14030023

Authors: Marius Boshoff Bernd Kuhlenk?tter Paul Koslowski

The optimal viewpoint for monitoring robotic production processes is crucial for maintenance, inspection, and error handling, especially in large-scale production facilities, as it maximizes visual information. This paper presents a method for dynamic camera planning using an Unmanned Aerial Vehicle (UAV), enabling collision-free operation and measurable, high perspective coverage for a user-defined Region of Interest (ROI). Therefore, optimal viewpoints are searched with a greedy search algorithm and a decision for the optimal viewpoint is derived. The method is implemented within a simulation framework in Unity and evaluated in a robotic palletizing application. Results show that the use of a UAV as dynamic camera achieves up to twice the perspective coverage during continuous flight compared to the current capabilities of static cameras.

Robotics doi: 10.3390/robotics14030022

Authors: Bart Engelen Sander Teck Jef R. Peeters Karel Kellens

The transition towards a circular economy, as outlined in the European Union’s Green Deal, requires the development of industries dedicated to recycling and material recovery. Within this context, the recycling of plastic and packaging waste is critical in reducing greenhouse gas emissions. Traditional pick-and-place systems encounter significant challenges when applied to heterogeneous waste streams due to the variability in shape, weight, and material properties of the processed materials. To address these challenges, this research proposes a heuristic to optimize the use of multiple gripper systems within a multirobot multigripper sorting setup, with the goal of both maximizing sorting efficiency and recovery rates in PPW recycling. Therefore, the performance of grippers on specific PPW objects, materials and shapes is quantitatively assessed by measuring the grasp efficiency. This grasp efficiency is incorporated into the proposed scheduling heuristic and used to assign the PPW objects to the different available robots, taking into account the position of the object with respect to the robot and the gripper installed on the robot. This heuristic is then evaluated and benchmarked through simulations considering the sorting system design and the waste stream composition based on a real-world portable robotic material recycling facility. The findings demonstrate substantial improvements in picking efficiency of up to 3.6% and pick rates up to 37.5%, underscoring the potential of advanced heuristic algorithms in robotic waste sorting systems. Future work will focus on refining gripper designs and exploring predictive algorithms to further enhance grasp success rates.

Robotics doi: 10.3390/robotics14020021

Authors: Abeer Daoud Habibur Rehman Lotfi Romdhane Shayok Mukhopadhyay

The main contribution of this paper is the integration of a battery management system (BMS) to ensure safe battery operation and automated battery swapping for an electric scooter (e-scooter). The BMS constantly monitors the battery state of charge (SOC) and temperature, and initiates battery swapping under predefined conditions. This is crucial because the conventional BMS sometimes fails to detect early signs of potential issues, leading to safety hazards if not addressed promptly. Battery swapping stations are an effective solution, offering an alternative to traditional charging stations by addressing the issue of lengthy charging time. Also, this paper addresses the problem of frequent battery recharging, which limits e-scooters’ operational range. The proposed solution employs a robotic arm to execute battery swaps without human intervention. A computer vision system is utilized to detect an e-scooter’s battery, compensating for any tilt in a parked e-scooter to ensure accurate alignment, thereby enabling the robotic arm to efficiently plan and execute the battery swap. The proposed system requires minimal modifications to the existing e-scooter design by incorporating a specifically designed battery compartment thus offering significant improvements over manual swapping methods.

Robotics doi: 10.3390/robotics14020020

Authors: Takamaru Saito Ming Jiang Marco Ceccarelli Yukio Takeda

There is an urgent need for a gait assisting device that simultaneously compensates for the loss of gait function reducing ankle plantarflexion torque and limited dorsiflexion range of motion (ROM). In this work, a walking assistance is proposed by using a device with an independent link attached on the ankle joint. We investigated the effect of the independent link on ankle joint torque and the assisted motion range via analysis. The results of the analysis revealed that the proposed device could assist patients with limited ankle dorsiflexion ROM and low plantarflexion strength simultaneously. For patients with mild dorsiflexion ROM limitation of the ankle joint (7° or more), it was found that walking within the limited ROM is possible and that a large assisting torque is possible by front side link assistance. The ankle torque load can be reduced by 60% under the conditions of this work. For patients with severe ankle dorsiflexion ROM limitation (2° or more, 6° or less), it was found that walking within the ROM was possible and that torque assistance is possible by back side link assistance. The ankle joint torque load can be reduced by 30% under the proposed conditions in this work.

Robotics doi: 10.3390/robotics14020019

Authors: Valeria Sarno Elisa Stefanini Giorgio Grioli Lucia Pallottino

The growing use of mobile robots in unconventional environments demands new programming approaches to make them accessible to non-expert users. Indeed, traditional programming methods require specialized expertise in robotics and programming, limiting robots’ accessibility to a broader audience. End-user robot programming has emerged to overcome these limitations, aiming to simplify robot programming through intuitive methods. In this work, we propose a code-free approach for programming mobile robots to autonomously execute navigation tasks, i.e., reach a desired goal location from an arbitrary initial position. Our method relies on instructing the robot on new paths through demonstrations while creating and continuously updating a topometric map of the environment. Moreover, by leveraging the information gathered during the instruction phase, the robot can perceive slight environmental changes and autonomously make the best decision in response to unexpected situations (e.g., adjusting its path, stopping, or requesting user intervention). Experiments conducted in both simulated and real-world environments support the validity of our approach, as they show that the robot can successfully reach its assigned goal location in the vast majority of cases.

Robotics doi: 10.3390/robotics14020018

Authors: Jun-ya Nagase Takuya Kawase Syunya Ueno

Passive dynamic locomotion, which relies solely on the interaction between the body and the environment, is being explored as an energy-efficient method of movement. The authors’ laboratory investigates passive hopping mechanisms that do not require actuators or sensors. In previous studies, it was demonstrated that an asymptotically stable limit cycle exists in the leg dynamics of a passive hopping model with constrained torso posture. In this study, a monopedal passive hopping robot with constrained torso posture was constructed to validate the existence of the limit cycle. The leg dynamics were evaluated by comparing the trajectories of the model and robot. The results revealed that the leg dynamics of the simulation model represent those of the physical robot. Furthermore, robustness to step disturbances confirmed the validity of leg dynamics.

Robotics doi: 10.3390/robotics14020017

Authors: Jennifer David Rafael Valencia

This paper addresses the challenge of coordinating task allocation and generating collision-free trajectories for a fleet of mobile robots in dynamic environments. Our approach introduces an integrated framework comprising a centralized task allocation system and a distributed trajectory planner. The centralized task allocation system, employing a heuristic approach, aims to minimize the maximum spatial cost among the slowest robots. Tasks and trajectories are continuously refined using a distributed version of CHOMP (Covariant Hamiltonian Optimization for Motion Planning), tailored for multiple-wheeled mobile robots where the spatial costs are derived from a high-level global path planner. By employing this combined methodology, we are able to achieve near-optimal solutions and collision-free trajectories with computational performance for up to 50 robots within seconds.

Robotics doi: 10.3390/robotics14020016

Authors: Florentin Buium Ioan Doroftei Stelian Alaci

This paper examines the influence of the eight assembling modes of the 3-RRR planar manipulator on its workspace. The workspace is analyzed considering both first-type and second-type singularities. Understanding these issues is crucial in the process of designing such manipulators to avoid unfavorable cases. Additionally, a modular platform concept, suitable for experimental testing and informed by the numerical results presented here, is proposed. The outcomes of the experimental tests will be addressed in future work.

Robotics doi: 10.3390/robotics14020015

Authors: Hikaru Nagano Tomoki Nishino Yuichi Tazaki Yasuyoshi Yokokohji

Teleoperation technology enables remote control of machines, but often requires complex manoeuvres that pose significant challenges for operators. To mitigate these challenges, assistive systems have been developed to support teleoperation. This study presents a teleoperation guidance system that provides assistive force feedback to help operators align more accurately with desired trajectories. Two key issues remain: (1) the lack of a flexible, real-time approach to defining desired trajectories and calculating assistive forces, and (2) uncertainty about the effects of forward motion assistance within the assistive forces. To address these issues, we propose a novel approach that captures the posture trajectory of the local control interface, statistically generates a reference trajectory, and incorporates forward motion as an adjustable parameter. In Experiment 1, which involved simulating an object transfer task, the proposed method significantly reduced the operator’s workload compared to conventional techniques, especially in dynamic target scenarios. Experiment 2, which involved more complex paths, showed that assistive forces with forward assistance significantly improved manoeuvring performance.

Robotics doi: 10.3390/robotics14020014

Authors: Mehdi Fazilat Nadjet Zioui

Maintaining precise and robust control in robotic systems, particularly those with nonlinear dynamics and external disturbances, is a significant challenge in robotics. Sliding-mode control (SMC) is a widely used technique to tackle these issues; however, it is plagued by chattering and computational complexity, which limit its effectiveness in high-precision environments. This study aims to develop and assess a quantum-inspired sliding-mode control (QSMC) strategy to enhance the SMC’s robustness, precision, and computational efficiency, specifically in controlling a six-jointed articulated robotic arm. The methodology involves creating a comprehensive kinematic and dynamic model of the robot, followed by implementing both classic SMC and the proposed Q-SMC in a comparative way. The simulation results confirm that the Q-SMC method outperforms the classic SMC, particularly in reducing chattering, improving tracking accuracy, and decreasing energy consumption by approximately 3.79%. These findings suggest that the Q-SMC technique provides a promising alternative to classical control methods, with potential applications in tasks requiring high precision and efficient robotic manipulations.

Robotics doi: 10.3390/robotics14020013

Authors: António Ferreira José Almeida Aníbal Matos Eduardo Silva

Due to space and energy restrictions, lightweight autonomous underwater vehicles (AUVs) are usually fitted with low-power processing units, which limits the ability to run demanding applications in real time during the mission. However, several robotic perception tasks reveal a parallel nature, where the same processing routine is applied for multiple independent inputs. In such cases, leveraging parallel execution by offloading tasks to a GPU can greatly enhance processing speed. This article presents a collection of generic matrix manipulation kernels, which can be combined to develop parallelized perception applications. Taking advantage of those building blocks, we report a parallel implementation for the 3DupIC algorithm—a probabilistic scan matching method for sonar scan registration. Tests demonstrate the algorithm’s real-time performance, enabling 3D sonar scan matching to be executed in real time onboard the EVA AUV.

Robotics doi: 10.3390/robotics14020012

Authors: Xiao Sun Koji Makino Daichi Kurita Hiromi Kaneko Kazuyoshi Ishida Hidetsugu Terada

This article describes an apparatus developed by the authors as a substitution for physical therapists regarding mechanical movements in the rehabilitation of frozen shoulder. In particular, the performance of this apparatus is improved in comparison with existing methods in terms of the following two major points: (1) realization of individual rehabilitation for the patient’s scapula by an innovative parallel–serial hybrid linkage design and supporting parts that can fix and move the patient’s scapula; and (2) the addition of a “teaching” and “playback” mode to enable the apparatus to record the motion of rehabilitation, allowing it to be customized for each patient by physical therapists and reproduce the recorded motion accurately, thus freeing physical therapists from repetitive rehabilitation routines. With the introduction of the whole system, experimental results are shown and discussed to verify and evaluate the performance of the developed apparatus.

Robotics doi: 10.3390/robotics14020011

Authors: Rogério R. Lima Guilherme A. S. Pereira

This paper presents a path-planning approach for tethered robots. The proposed planner finds paths that minimize the tether tension due to tether–obstacle and tether–floor interaction. The method assumes that the tether is managed externally by a tether management system and pulled by the robot. The planner is initially formulated for ground robots in a 2D environment and then extended for 3D scenarios, where it can be applied to tethered aerial and underwater vehicles. The proposed approach assumes a taut tether between two consecutive contact points and knowledge of the coefficient of friction of the obstacles present in the environment. The method first computes the visibility graph of the environment, in which each node represents a vertex of an obstacle. Then, a second graph, named the tension-aware graph, is built so that the tether–environment interaction, formulated in terms of tension, is computed and used as the cost of the edges. A graph search algorithm (e.g., Dijkstra) is then used to compute a path with minimum tension, which can help the tethered robot reach longer distances by minimizing the tension required to drag the tether along the way. This paper presents simulations and a real-world experiment that illustrate the characteristics of the method.

Robotics doi: 10.3390/robotics14020010

Authors: Renat Kermenov Alessandro Di Biase Ilaria Pellicani Sauro Longhi Andrea Bonci

Enabling robots to work safely close to humans requires both adherence to safety standards and the development of appropriate strategies to plan and control robot movements in accordance with human movements. Collaboration between humans and robots in a shared environment is a joint activity aimed at completing specific tasks, requiring coordination, synchronisation, and sometimes physical contact, in which each party contributes its own skills and resources. Among the most challenging tasks of human–robot cooperation is the co-transport of deformable materials such as fabrics. This paper proposes a method for generating the trajectory of a collaborative manipulator. The method is designed for the co-transport of materials such as fabrics. It combines a near time-optimal control strategy that ensures responsiveness in following human actions while simultaneously guaranteeing compliance with the safety limits imposed by current regulations. The combination of these two elements results in a viable co-transport solution which preserves the safety of human operators. This is achieved by constraining the path of the robot trajectory with prescribed velocities and accelerations while simultaneously ensuring a near time-optimal control strategy. In short, the robot movement is generated in such a way as to ensure both the tracking of humans in the co-transportation task and compliance with safety limits. As a first attempt to adopt the proposed approach to integrate time-optimal strategies into human–robot interaction, the simulations and preliminary experimental result obtained are promising.

Robotics doi: 10.3390/robotics14020009

Authors: Maria Spagnuolo Giuseppe Todde Maria Caria Nicola Furnitto Giampaolo Schillaci Sabina Failla

The adoption of agricultural robots is revolutionizing the agricultural sector, offering innovative solutions to optimize production and reduce environmental impact. This review examines the main functions and applications of agricultural robots, with a focus on the crops handled and the technologies employed. The study analyzes the current state of the art regarding the market trend of agricultural robots used in field and greenhouse operations. Several solutions are emerging, some already implemented and others still in the prototype or project stage. These solutions are beginning to spread, though they may still seem far from widespread field application, particularly given the peculiarities and heterogeneity of the global agricultural landscape. In the face of the many benefits associated with the use of agricultural robots, even today some technical bottlenecks and costs limit their widespread use by farmers. The review provides a fairly comprehensive and up-to-date overview of current trends in agricultural automation, suggesting new areas of research to improve the efficiency and adaptability of robotic systems to different types of crops and environments.