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Non-Holonomic Mobile Manipulator Obstacle Avoidance with Adaptive Prioritization

Journal Description

Robotics

Robotics is an international, peer-reviewed, open access journal on robotics published monthly online by MDPI. The IFToMM is affiliated with Robotics and its members receive a discount on the article processing charges.

Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
High Visibility: indexed within Scopus, ESCI (Web of Science), dblp, Inspec, and other databases.
Journal Rank: JCR - Q2 (Robotics) / CiteScore - Q1 (Mechanical Engineering)
Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 21 days after submission; acceptance to publication is undertaken in 2.6 days (median values for papers published in this journal in the second half of 2024).
Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.

Impact Factor: 2.9 (2023); 5-Year Impact Factor: 3.1 (2023)

Imprint Information Journal Flyer Open Access ISSN: 2218-6581

Latest Articles

16 pages, 1696 KiB

Open AccessArticle

A Motion Propagation Force Analysis of Multi-DoF Systems Using the Partial Lagrangian Method

by Hironori Gunji, Takashi Kusaka and Takayuki Tanaka

Robotics 2025, 14(5), 54; https://doi.org/10.3390/robotics14050054 - 24 Apr 2025

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 [...] Read more.

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. Full article

(This article belongs to the Section Intelligent Robots and Mechatronics)

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27 pages, 32676 KiB

Open AccessArticle

Action Recognition via Multi-View Perception Feature Tracking for Human–Robot Interaction

by Chaitanya Bandi and Ulrike Thomas

Robotics 2025, 14(4), 53; https://doi.org/10.3390/robotics14040053 - 19 Apr 2025

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, [...] Read more.

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. Full article

(This article belongs to the Special Issue Human–AI–Robot Teaming (HART))

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26 pages, 9486 KiB

Open AccessArticle

Non-Holonomic Mobile Manipulator Obstacle Avoidance with Adaptive Prioritization

by Federico Neri, Giacomo Palmieri and Massimo Callegari

Robotics 2025, 14(4), 52; https://doi.org/10.3390/robotics14040052 - 18 Apr 2025

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Localization and 3D Mapping of Intelligent Robotics)

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23 pages, 5106 KiB

Open AccessArticle

Upper-Limb Robotic Rehabilitation: Online Sliding Mode Controller Gain Tuning Using Particle Swarm Optimization

by Deira Sosa Méndez, David Bedolla-Martínez, Maarouf Saad, Yassine Kali, Cecilia E. García Cena and Ángel L. Álvarez

Robotics 2025, 14(4), 51; https://doi.org/10.3390/robotics14040051 - 17 Apr 2025

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) [...] Read more.

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. Full article

(This article belongs to the Special Issue Adaptive and Nonlinear Control of Robotics)

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22 pages, 6991 KiB

Open AccessArticle

Robotic Rehabilitation Through Multilateral Shared Control Architecture

by Srikar Annamraju, Harris Nisar, Anne Christine Horowitz and Dušan Stipanović

Robotics 2025, 14(4), 50; https://doi.org/10.3390/robotics14040050 - 16 Apr 2025

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Development of Biomedical Robotics)

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24 pages, 802 KiB

Open AccessArticle

A New Proposal for Intelligent Continuous Controller of Robotic Finger Prostheses Using Deep Deterministic Policy Gradient Algorithm Through Simulated Assessments

by Guilherme de Paula Rúbio, Matheus Carvalho Barbosa Costa and Claysson Bruno Santos Vimieiro

Robotics 2025, 14(4), 49; https://doi.org/10.3390/robotics14040049 - 14 Apr 2025

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 [...] Read more.

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 \pm 2.899

mm for the sinusoidal trajectory and

3.220 \pm 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. Full article

(This article belongs to the Section Neurorobotics)

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23 pages, 5838 KiB

Open AccessArticle

Four-Dimensional Path Planning Methodology for Collaborative Robots Application in Industry 5.0

by Ilias Chouridis, Gabriel Mansour, Vasileios Papageorgiou, Michel Theodor Mansour and Apostolos Tsagaris

Robotics 2025, 14(4), 48; https://doi.org/10.3390/robotics14040048 - 11 Apr 2025

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Collaborative Robotics: Safety, Applications and Trends)

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25 pages, 1931 KiB

Open AccessArticle

Geometric Path Planning and Synchronization for Multiple Vehicles

by Hongjun Yu and Lanyong Zhang

Robotics 2025, 14(4), 47; https://doi.org/10.3390/robotics14040047 - 11 Apr 2025

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 [...] Read more.

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. Full article

(This article belongs to the Topic New Trends in Robotics: Automation and Autonomous Systems)

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18 pages, 2858 KiB

Open AccessArticle

Analysis of Distributed Dynamic Loads Induced by the Own Mass of Manipulator Links and Their Visualization on Interactive 3D Computer Models

by Muratulla Utenov, Tarek Sobh, Yerbol Temirbekov, Saltanat Zhilkibayeva, Sarosh Patel, Dauren Baltabay and Zhadyra Zhumasheva

Robotics 2025, 14(4), 46; https://doi.org/10.3390/robotics14040046 - 7 Apr 2025

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Robotics and Parallel Kinematic Machines)

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23 pages, 844 KiB

Open AccessArticle

Optimal Trajectory Tracking for Underactuated Systems via the Takagi–Sugeno Framework: An Autonomous Underwater Vehicle Mission Case Study

by Georgios P. Kladis, Lefteris Doitsidis and Nikos C. Tsourveloudis

Robotics 2025, 14(4), 45; https://doi.org/10.3390/robotics14040045 - 1 Apr 2025

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Autonomous Robotics for Exploration)

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16 pages, 33317 KiB

Open AccessArticle

Exploiting a Variable-Sized Map and Vicinity-Based Memory for Dynamic Real-Time Planning of Autonomous Robots

by Aristeidis Geladaris, Lampis Papakostas, Athanasios Mastrogeorgiou and Panagiotis Polygerinos

Robotics 2025, 14(4), 44; https://doi.org/10.3390/robotics14040044 - 31 Mar 2025

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 [...] Read more.

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. Full article

(This article belongs to the Section Aerospace Robotics and Autonomous Systems)

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19 pages, 1940 KiB

Open AccessArticle

Adaptive Robot Navigation Using Randomized Goal Selection with Twin Delayed Deep Deterministic Policy Gradient

by Romisaa Ali, Sedat Dogru, Lino Marques and Marcello Chiaberge

Robotics 2025, 14(4), 43; https://doi.org/10.3390/robotics14040043 - 31 Mar 2025

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 [...] Read more.

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. Full article

(This article belongs to the Section Sensors and Control in Robotics)

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19 pages, 5285 KiB

Open AccessArticle

Enhancing Positional Accuracy of Mechanically Modified Industrial Robots Using Laser Trackers

by Mojtaba A. Khanesar, Aslihan Karaca, Minrui Yan, Mohammed Isa, Samanta Piano and David Branson

Robotics 2025, 14(4), 42; https://doi.org/10.3390/robotics14040042 - 31 Mar 2025

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 [...] Read more.

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. Full article

(This article belongs to the Section Industrial Robots and Automation)

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38 pages, 5906 KiB

Open AccessReview

Perception and Computation for Speed and Separation Monitoring Architectures

by Odysseus Adamides, Karthik Subramanian, Sarthak Arora and Ferat Sahin

Robotics 2025, 14(4), 41; https://doi.org/10.3390/robotics14040041 - 31 Mar 2025

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Embodied Intelligence: Physical Human–Robot Interaction)

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16 pages, 6589 KiB

Open AccessArticle

A System for Surgeon Fatigue Monitoring in Robotic Surgery

by Reenu Arikkat Paul and Abhilash Pandya

Robotics 2025, 14(4), 40; https://doi.org/10.3390/robotics14040040 - 31 Mar 2025

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Development of Biomedical Robotics)

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19 pages, 5707 KiB

Open AccessArticle

Optimizing Path Planning for Automated Guided Vehicles in Constrained Warehouse Environments: Addressing the Challenges of Non-Rotary Platforms and Irregular Layouts

by Pavlo Pikulin, Vitalii Lishunov and Konrad Kułakowski

Robotics 2025, 14(4), 39; https://doi.org/10.3390/robotics14040039 - 29 Mar 2025

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 [...] Read more.

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. Full article

(This article belongs to the Topic Advances in Mobile Robotics Navigation, 2nd Volume)

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24 pages, 4540 KiB

Open AccessArticle

Robotic Motion Intelligence Using Vector Symbolic Architectures and Blockchain-Based Smart Contracts

by Daswin De Silva, Sudheera Withanage, Vidura Sumanasena, Lakshitha Gunasekara, Harsha Moraliyage, Nishan Mills and Milos Manic

Robotics 2025, 14(4), 38; https://doi.org/10.3390/robotics14040038 - 28 Mar 2025

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 [...] Read more.

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. Full article

(This article belongs to the Section AI in Robotics)

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4 pages, 141 KiB

Open AccessEditorial

Special Issue on Social Robots for Human Well-Being

by Martin Cooney and Mariacarla Staffa

Robotics 2025, 14(4), 37; https://doi.org/10.3390/robotics14040037 - 24 Mar 2025

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

(This article belongs to the Special Issue Social Robots for the Human Well-Being)

26 pages, 10785 KiB

Open AccessArticle

Real Time MEMS-Based Joint Friction Identification for Enhanced Dynamic Performance in Robotic Applications

by Paolo Righettini, Giovanni Legnani, Filippo Cortinovis, Federico Tabaldi and Jasmine Santinelli

Robotics 2025, 14(4), 36; https://doi.org/10.3390/robotics14040036 - 21 Mar 2025

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 [...] Read more.

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. Full article

(This article belongs to the Section Intelligent Robots and Mechatronics)

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16 pages, 13516 KiB

Open AccessArticle

DUnE: A Versatile Dynamic Unstructured Environment for Off-Road Navigation

by Jack M. Vice and Gita Sukthankar

Robotics 2025, 14(4), 35; https://doi.org/10.3390/robotics14040035 - 21 Mar 2025

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 [...] Read more.

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. Full article

(This article belongs to the Section AI in Robotics)

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