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Autonomous, Collaborative, and Confined Infrastructure Assessment with Purpose-Built Mega-Joey Robots
Model-Based Predictive Control for Position and Orientation Tracking in a Multilayer Architecture for a Three-Wheeled Omnidirectional Mobile Robot

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 (Control and Optimization)
Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 21.8 days after submission; acceptance to publication is undertaken in 2.9 days (median values for papers published in this journal in the first half of 2025).
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: 3.3 (2024); 5-Year Impact Factor: 3.8 (2024)

Imprint Information Journal Flyer Open Access ISSN: 2218-6581

Latest Articles

16 pages, 10372 KiB

Open AccessArticle

PRONOBIS: A Robotic System for Automated Ultrasound-Based Prostate Reconstruction and Biopsy Planning

by Matija Markulin, Luka Matijević, Janko Jurdana, Luka Šiktar, Branimir Ćaran, Toni Zekulić, Filip Šuligoj, Bojan Šekoranja, Tvrtko Hudolin, Tomislav Kuliš, Bojan Jerbić and Marko Švaco

Robotics 2025, 14(8), 100; https://doi.org/10.3390/robotics14080100 - 22 Jul 2025

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 [...] Read more.

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. Full article

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

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13 pages, 2559 KiB

Open AccessArticle

An AI Approach to Markerless Augmented Reality in Surgical Robots

by Abhishek Shankar, Luay Jawad and Abhilash Pandya

Robotics 2025, 14(7), 99; https://doi.org/10.3390/robotics14070099 - 19 Jul 2025

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. [...] Read more.

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. Full article

(This article belongs to the Section Medical Robotics and Service Robotics)

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15 pages, 1795 KiB

Open AccessArticle

Minimum-Energy Trajectory Planning for an Underactuated Serial Planar Manipulator

by Domenico Dona’, Jason Bettega, Iacopo Tamellin, Paolo Boscariol and Roberto Caracciolo

Robotics 2025, 14(7), 98; https://doi.org/10.3390/robotics14070098 - 18 Jul 2025

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 [...] Read more.

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. Full article

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

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21 pages, 6802 KiB

Open AccessArticle

Digital Twin Driven Four-Dimensional Path Planning of Collaborative Robots for Assembly Tasks in Industry 5.0

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

Robotics 2025, 14(7), 97; https://doi.org/10.3390/robotics14070097 - 15 Jul 2025

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Robot Teleoperation Integrating with Augmented Reality)

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22 pages, 4827 KiB

Open AccessArticle

Development of a Multifunctional Mobile Manipulation Robot Based on Hierarchical Motion Planning Strategy and Hybrid Grasping

by Yuning Cao, Xianli Wang, Zehao Wu and Qingsong Xu

Robotics 2025, 14(7), 96; https://doi.org/10.3390/robotics14070096 - 15 Jul 2025

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 [...] Read more.

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. Full article

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

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27 pages, 6183 KiB

Open AccessArticle

A Cartesian Parallel Mechanism for Initial Sonography Training

by Mykhailo Riabtsev, Jean-Michel Guilhem, Victor Petuya, Mónica Urizar and Med Amine Laribi

Robotics 2025, 14(7), 95; https://doi.org/10.3390/robotics14070095 - 10 Jul 2025

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 [...] Read more.

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. Full article

(This article belongs to the Section Medical Robotics and Service Robotics)

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27 pages, 2922 KiB

Open AccessArticle

Methodology for Modeling Coupled Rigid Multibody Systems Using Unitary Quaternions: The Case of Planar RRR and Spatial PRRS Parallel Robots

by Francisco Cuenca Jiménez, Eusebio Jiménez López, Mario Acosta Flores, F. Peñuñuri, Ricardo Javier Peón Escalante and Juan José Delfín Vázquez

Robotics 2025, 14(7), 94; https://doi.org/10.3390/robotics14070094 - 3 Jul 2025

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 [...] Read more.

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. Full article

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

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40 pages, 5657 KiB

Open AccessReview

Optimizing Coalition Formation Strategies for Scalable Multi-Robot Task Allocation: A Comprehensive Survey of Methods and Mechanisms

by Krishna Arjun, David Parlevliet, Hai Wang and Amirmehdi Yazdani

Robotics 2025, 14(7), 93; https://doi.org/10.3390/robotics14070093 - 2 Jul 2025

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). [...] Read more.

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. Full article

(This article belongs to the Section AI in Robotics)

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14 pages, 2424 KiB

Open AccessArticle

Grasping Task in Teleoperation: Impact of Virtual Dashboard on Task Quality and Effectiveness

by Antonio Di Tecco, Daniele Leonardis, Antonio Frisoli and Claudio Loconsole

Robotics 2025, 14(7), 92; https://doi.org/10.3390/robotics14070092 - 30 Jun 2025

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Extended Reality and AI Empowered Robots)

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22 pages, 7125 KiB

Open AccessArticle

Planar Inverse Statics and Path Planning for a Tendon-Driven Discrete Continuum Robot

by Yeoun-Jae Kim and Daehan Wi

Robotics 2025, 14(7), 91; https://doi.org/10.3390/robotics14070091 - 30 Jun 2025

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 [...] Read more.

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 (

T_{l}

) in the range of 100–1000 g while distal tension (

T_{s}

) was continuously increased to induce bending. During bending, the curves were interpolated using third-order to fifth-order polynomials at discrete

T_{l}

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. Full article

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

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18 pages, 8647 KiB

Open AccessArticle

An Improved DHA Star and ADA-DWA Fusion Algorithm for Robot Path Planning

by Yizhe Jia, Yong Cai, Jun Zhou, Hui Hu, Xuesheng Ouyang, Jinlong Mo and Hao Dai

Robotics 2025, 14(7), 90; https://doi.org/10.3390/robotics14070090 - 29 Jun 2025

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 [...] Read more.

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. Full article

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

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21 pages, 13446 KiB

Open AccessArticle

Field Evaluation of an Autonomous Mobile Robot for Navigation and Mapping in Forest

by Diego Tiozzo Fasiolo, Lorenzo Scalera, Eleonora Maset and Alessandro Gasparetto

Robotics 2025, 14(7), 89; https://doi.org/10.3390/robotics14070089 - 27 Jun 2025

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 [...] Read more.

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. Full article

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

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34 pages, 2660 KiB

Open AccessArticle

Cascade-Based Distributed Estimator Tracking Control for Swarm of Multiple Nonholonomic Wheeled Mobile Robots via Leader–Follower Approach

by Dinesh Elayaperumal, Sachin Sakthi Kuppusami Sakthivel, Sathishkumar Moorthy, Sathiyamoorthi Arthanari, Young Hoon Joo and Jae Hoon Jeong

Robotics 2025, 14(7), 88; https://doi.org/10.3390/robotics14070088 - 26 Jun 2025

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 [...] Read more.

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. Full article

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

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18 pages, 24410 KiB

Open AccessArticle

Design and Experimental Validation of a 3D-Printed Two-Finger Gripper with a V-Shaped Profile for Lightweight Waste Collection

by Mahboobe Habibi, Giuseppe Sutera, Dario Calogero Guastella and Giovanni Muscato

Robotics 2025, 14(7), 87; https://doi.org/10.3390/robotics14070087 - 25 Jun 2025

This study presents the design, fabrication, and experimental validation of a two-finger robotic gripper featuring a 135

^{\circ}

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 [...] Read more.

This study presents the design, fabrication, and experimental validation of a two-finger robotic gripper featuring a 135

^{\circ}

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. Full article

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

18 pages, 14780 KiB

Open AccessArticle

Boosting Deep Reinforcement Learning with Semantic Knowledge for Robotic Manipulators

by Lucía Güitta-López, Vincenzo Suriani, Jaime Boal, Álvaro J. López-López and Daniele Nardi

Robotics 2025, 14(7), 86; https://doi.org/10.3390/robotics14070086 - 24 Jun 2025

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Applications of Neural Networks in Robot Control)

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25 pages, 3468 KiB

Open AccessArticle

Distributed Monitoring of Moving Thermal Targets Using Unmanned Aerial Vehicles and Gaussian Mixture Models

by Yuanji Huang, Pavithra Sripathanallur Murali and Gustavo Vejarano

Robotics 2025, 14(7), 85; https://doi.org/10.3390/robotics14070085 - 22 Jun 2025

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Multi-Robot Systems for Environmental Monitoring and Intervention)

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43 pages, 10203 KiB

Open AccessArticle

Neural Adaptive Nonlinear MIMO Control for Bipedal Walking Robot Locomotion in Hazardous and Complex Task Applications

by Belkacem Bekhiti, Jamshed Iqbal, Kamel Hariche and George F. Fragulis

Robotics 2025, 14(6), 84; https://doi.org/10.3390/robotics14060084 - 17 Jun 2025

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 [...] Read more.

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

R A B B I T

robot using a

d S P A C E D S 1103

platform with a sampling rate of

∆ t = 1.5 m s (667 H z)

. The experimental results show excellent performance with the following: The robot achieved stable cyclic gaits while keeping the tracking error within

e = \pm 0.04 r a d

under nominal conditions. Under severe uncertainties of trunk mass variations

{∆ m}_{t r u n k} = + 100 %

, limb inertia changes

{∆ I}_{l i m b} = \pm 30 %

, and actuator torque saturation at

τ = \pm 150 N m

, 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:

I A E = 1.36

,

I T A E = 2.43

,

I S E = 0.68

,

t_{s s} = 1.24 s

, and

M_{p} = 2.21 %

. These results demonstrate evidence of high stability, rapid convergence, and robustness to disturbances and foot-slip. Full article

(This article belongs to the Section Humanoid and Human Robotics)

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23 pages, 4949 KiB

Open AccessArticle

Hybrid LDA-CNN Framework for Robust End-to-End Myoelectric Hand Gesture Recognition Under Dynamic Conditions

by Hongquan Le, Marc in het Panhuis, Geoffrey M. Spinks and Gursel Alici

Robotics 2025, 14(6), 83; https://doi.org/10.3390/robotics14060083 - 17 Jun 2025

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue AI for Robotic Exoskeletons and Prostheses)

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20 pages, 8680 KiB

Open AccessArticle

Humanoid Motion Generation in Complex 3D Environments

by Diego Marussi, Michele Cipriano, Nicola Scianca, Leonardo Lanari and Giuseppe Oriolo

Robotics 2025, 14(6), 82; https://doi.org/10.3390/robotics14060082 - 16 Jun 2025

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 [...] Read more.

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. Full article

(This article belongs to the Section Humanoid and Human Robotics)

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27 pages, 3094 KiB

Open AccessReview

Innovations in Upper Limb Rehabilitation Robots: A Review of Mechanisms, Optimization, and Clinical Applications

by Yang Wang, Xu Han, Baiye Xin and Ping Zhao

Robotics 2025, 14(6), 81; https://doi.org/10.3390/robotics14060081 - 11 Jun 2025

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 [...] Read more.

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. Full article

(This article belongs to the Section Medical Robotics and Service Robotics)

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