Robotics

23 pages, 2392 KB

Open AccessArticle

Performance Evaluation of Cable-Driven Wrench Applicators: Geometric and Experimental Analysis

by Federico Guerra, Edoardo Ida’, Marco Carricato and Sunil Agrawal

Robotics 2026, 15(2), 36; https://doi.org/10.3390/robotics15020036 - 2 Feb 2026

Cable-driven wrench applicators (CDWAs) are parallel robotic systems that apply controlled wrenches to the robot end-effector through cable actuation. The presented study introduces a framework for the performance evaluation of CDWAs based on dedicated metrics. It focuses on the geometric analysis of n [...] Read more.

Cable-driven wrench applicators (CDWAs) are parallel robotic systems that apply controlled wrenches to the robot end-effector through cable actuation. The presented study introduces a framework for the performance evaluation of CDWAs based on dedicated metrics. It focuses on the geometric analysis of n-cable CDWAs controlling

n - 2

wrench components and on the experimental comparison of a 4-cable architecture with an 8-cable CDWA. The geometric analysis reveals intrinsic properties of the 4-cable system’s tension distribution and inherent limits in achieving specific control objectives. Both simulations and experimental validation demonstrate that the 4-cable CDWA attains comparable performance in wrench control while requiring higher tensions, yet offers greater ease of use and mechanical simplicity. Full article

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

45 pages, 5418 KB

Open AccessReview

Visual and Visual–Inertial SLAM for UGV Navigation in Unstructured Natural Environments: A Survey of Challenges and Deep Learning Advances

by Tiago Pereira, Carlos Viegas, Salviano Soares and Nuno Ferreira

Robotics 2026, 15(2), 35; https://doi.org/10.3390/robotics15020035 - 2 Feb 2026

Abstract

Localization and mapping remain critical challenges for Unmanned Ground Vehicles (UGVs) operating in unstructured natural environments, such as forests and agricultural fields. While Visual SLAM (VSLAM) and Visual–Inertial SLAM (VI-SLAM) have matured significantly in structured and urban scenarios, their extension to outdoor natural [...] Read more.

Localization and mapping remain critical challenges for Unmanned Ground Vehicles (UGVs) operating in unstructured natural environments, such as forests and agricultural fields. While Visual SLAM (VSLAM) and Visual–Inertial SLAM (VI-SLAM) have matured significantly in structured and urban scenarios, their extension to outdoor natural domains introduces severe challenges, including dynamic vegetation, illumination variations, a lack of distinctive features, and degraded GNSS availability. Recent advances in Deep Learning have brought promising developments to VSLAM- and VI-SLAM-based pipelines, ranging from learned feature extraction and matching to self-supervised monocular depth prediction and differentiable end-to-end SLAM frameworks. Furthermore, emerging methods for adaptive sensor fusion, leveraging attention mechanisms and reinforcement learning, open new opportunities to improve robustness by dynamically weighting the contributions of camera and IMU measurements. This review provides a comprehensive overview of Visual and Visual–Inertial SLAM for UGVs in unstructured environments, highlighting the challenges posed by natural contexts and the limitations of current pipelines. Classic VI-SLAM frameworks and recent Deep-Learning-based approaches were systematically reviewed. Special attention is given to field robotics applications in agriculture and forestry, where low-cost sensors and robustness against environmental variability are essential. Finally, open research directions are discussed, including self-supervised representation learning, adaptive sensor confidence models, and scalable low-cost alternatives. By identifying key gaps and opportunities, this work aims to guide future research toward resilient, adaptive, and economically viable VSLAM and VI-SLAM pipelines, tailored for UGV navigation in unstructured natural environments. Full article

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

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25 pages, 4689 KB

Open AccessArticle

Extended Operational Space Kinematics, Dynamics, and Control of Redundant Non-Serial Compound Robotic Manipulators

by Edward J. Haug and Vincent De Sapio

Robotics 2026, 15(2), 34; https://doi.org/10.3390/robotics15020034 - 2 Feb 2026

Abstract

An extended operational space kinematics and dynamics formulation is presented for the control of redundant non-serial compound robotic manipulators. A broad spectrum of high-load-capacity non-serial manipulators used in earth moving, material handling, and construction applications is addressed. Departing from conventional approaches that rely [...] Read more.

An extended operational space kinematics and dynamics formulation is presented for the control of redundant non-serial compound robotic manipulators. A broad spectrum of high-load-capacity non-serial manipulators used in earth moving, material handling, and construction applications is addressed. Departing from conventional approaches that rely on Jacobian pseudoinverses and local null-space projections, a globally valid, differential-geometry-based, multi-valued inverse kinematic mapping is defined at the configuration level, with the explicit self-motion parameterization of manipulator redundancy. The formulation yields coupled second-order ordinary differential equations of manipulator dynamics on the product space of task variables and self-motion coordinates. This enables the direct integration of system dynamics with control strategies, such as model predictive control or feedback design, while maintaining task constraint compliance. The methods presented are validated through the simulation and control of a non-serial compound material loader manipulator with multiple degrees of redundancy, demonstrating advantages in generality, numerical accuracy, and trajectory smoothness. Full article

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

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26 pages, 12305 KB

Open AccessArticle

Development and Experimental Evaluation of the Athena Parallel Robot for Minimally Invasive Pancreatic Surgery

by Alexandru Pusca, Razvan Ciocan, Bogdan Gherman, Andra Ciocan, Andrei Caprariu, Nadim Al Hajjar, Calin Vaida, Adrian Pisla, Corina Radu, Andrei Cailean, Paul Tucan, Damien Chablat and Doina Pisla

Robotics 2026, 15(2), 33; https://doi.org/10.3390/robotics15020033 - 1 Feb 2026

Abstract

This paper presents the development and experimental evaluation of the Athena parallel robot, a novel system designed for robot-assisted pancreatic surgery. The development of the experimental model based on the kinematic scheme, including the command and control system (hardware and software), the calibration [...] Read more.

This paper presents the development and experimental evaluation of the Athena parallel robot, a novel system designed for robot-assisted pancreatic surgery. The development of the experimental model based on the kinematic scheme, including the command and control system (hardware and software), the calibration procedure, and the performance measurements of the experimental model based on finite element analyses of the 3D model, are also detailed in this paper. Based on these finite element analyses, a region of the robot that introduces clearance during the operation of the experimental model is found. The paper also presents the methodology used for mapping the robot’s workspace with an optical system, which enabled improvements to ensure coverage of the entire pancreas area. The results obtained before and after the mechanical improvements are presented, demonstrating a reduction in clearance by up to 4.1 times following part replacement, as well as a workspace extension that enables the active instrument to reach the entire pancreatic region. Full article

(This article belongs to the Special Issue Selected Papers from MESROB2025: 9th IFToMM International Workshop on New Trends in Medical and Service Robotics)

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25 pages, 519 KB

Open AccessArticle

Optimizing Multi-Robot Task Allocation with Dynamic Crisis Response: A Genetic Algorithm Approach with Task Resumption and Island Model Enhancement

by Ameur Touir, Mohsen Denguir, Achraf Gazdar and Safwan Qasem

Robotics 2026, 15(2), 32; https://doi.org/10.3390/robotics15020032 - 29 Jan 2026

Abstract

This paper presents an optimization framework for Multi-Robot Task Allocation (MRTA) for a heterogeneous robot fleet operating in dynamic, failure-prone environments. In contrast to traditional MRTA approaches that handle only the initial allocation, our system extends functionality by integrating real-time crisis response and [...] Read more.

This paper presents an optimization framework for Multi-Robot Task Allocation (MRTA) for a heterogeneous robot fleet operating in dynamic, failure-prone environments. In contrast to traditional MRTA approaches that handle only the initial allocation, our system extends functionality by integrating real-time crisis response and intelligent task recovery from failure points. The framework combines island model genetic algorithm-based initial optimization with an event-driven architecture for handling robot failures during mission execution. Our key contribution is the integration of crisis-aware capabilities with the island model paradigm, enabling task resumption from failure points and dynamic reoptimization, while preserving the diversity benefits of multi-population evolution. When a robot fails, the system intelligently substitutes replacement robots and resumes interrupted tasks from their exact failure point, rather than restarting from the beginning. This significantly improves mission efficiency and resilience. We introduce a temporal scheduling mechanism that tracks actual task execution states and calculates remaining work upon failure, enabling true task continuation. Experimental validation across 57 diverse scenarios with 2340 independent runs demonstrates that the island model achieves higher fitness scores, maintains greater population diversity, exhibits more consistent performance, and recovers faster from crisis events compared to the standard single-population genetic algorithm. Full article

(This article belongs to the Section AI in Robotics)

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21 pages, 1967 KB

Open AccessArticle

Unified Promptable Panoptic Mapping with Dynamic Labeling Using Foundation Models

by Mohamad Al Mdfaa, Raghad Salameh, Geesara Kulathunga, Sergey Zagoruyko and Gonzalo Ferrer

Robotics 2026, 15(2), 31; https://doi.org/10.3390/robotics15020031 - 27 Jan 2026

Abstract

Panoptic maps enable robots to reason about both geometry and semantics. However, open-vocabulary models repeatedly produce closely related labels that split panoptic entities and degrade volumetric consistency. The proposed UPPM advances open-world scene understanding by leveraging foundation models to introduce a panoptic Dynamic [...] Read more.

Panoptic maps enable robots to reason about both geometry and semantics. However, open-vocabulary models repeatedly produce closely related labels that split panoptic entities and degrade volumetric consistency. The proposed UPPM advances open-world scene understanding by leveraging foundation models to introduce a panoptic Dynamic Descriptor that reconciles open-vocabulary labels with unified category structure and geometric size priors. The fusion for such dynamic descriptors is performed within a multi-resolution multi-TSDF map using language-guided open-vocabulary panoptic segmentation and semantic retrieval, resulting in a persistent and promptable panoptic map without additional model training. Based on our evaluation experiments, UPPM shows the best overall performance in terms of the map reconstruction accuracy and the panoptic segmentation quality. The ablation study investigates the contribution for each component of UPPM (custom NMS, blurry-frame filtering, and unified semantics) to the overall system performance. Consequently, UPPM preserves open-vocabulary interpretability while delivering strong geometric and panoptic accuracy. Full article

(This article belongs to the Section AI in Robotics)

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78 pages, 920 KB

Open AccessSystematic Review

Autonomous Forklifts for Warehouse Automation: A Comprehensive Review

by Aditya Dilip Patil and Siavash Farzan

Robotics 2026, 15(2), 30; https://doi.org/10.3390/robotics15020030 - 26 Jan 2026

Abstract

Despite decades of research, autonomous forklifts remain deployed at a small scale (2–50 vehicles), while industrial warehouses require coordinating hundreds of vehicles in environments shared with human workers. This systematic review analyzes forklift-specific autonomous technologies published between 2010 and 2025 across major robotics [...] Read more.

Despite decades of research, autonomous forklifts remain deployed at a small scale (2–50 vehicles), while industrial warehouses require coordinating hundreds of vehicles in environments shared with human workers. This systematic review analyzes forklift-specific autonomous technologies published between 2010 and 2025 across major robotics databases (including IEEE Xplore, ACM, Elsevier, and related venues) to identify deployment barriers. Following the PRISMA guidelines, we systematically selected 122 peer-reviewed papers addressing forklift-specific challenges across eight subsystems: vehicle modeling, localization, planning, control, vision-based manipulation, multi-vehicle coordination, and safety. We synthesized 80 methods through 8 standardized comparison tables with quality assessment based on validation rigor. State-of-the-art approaches demonstrate strong laboratory performance: localization achieving ±1.4 mm accuracy, control enabling sub-centimeter manipulation, planning reducing mission times by 2–55%, vision reaching 98%+ recognition, and safety frameworks cutting rollover risk by 53–59%. However, validation predominantly occurs at laboratory scale, revealing a critical deployment gap. These achievements do not scale to industrial environments due to fleet coordination complexity, payload variability, and unpredictable human behavior. Our contributions include the following: (1) performance rankings with technology selection guidance, (2) systematic gap characterization, and (3) research priorities addressing mixed-fleet coordination, learning-enhanced control, and human-aware safety. This review was not prospectively registered. Full article

(This article belongs to the Topic Advanced Technologies and Applications for Unmanned Systems)

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17 pages, 6269 KB

Open AccessArticle

Robust Graph-Based Spatial Coupling of Dynamic Movement Primitives for Multi-Robot Manipulation

by Zhenxi Cui, Jiacong Chen, Xin Xu and Henry K. Chu

Robotics 2026, 15(1), 29; https://doi.org/10.3390/robotics15010029 - 22 Jan 2026

Abstract

Dynamic Movement Primitives (DMPs) provide a flexible framework for robotic trajectory generation, offering adaptability, robustness to disturbances, and modulation of predefined motions. Yet achieving reliable spatial coupling among multiple DMPs in cooperative manipulation tasks remains a challenge. This paper introduces a graph-based trajectory [...] Read more.

Dynamic Movement Primitives (DMPs) provide a flexible framework for robotic trajectory generation, offering adaptability, robustness to disturbances, and modulation of predefined motions. Yet achieving reliable spatial coupling among multiple DMPs in cooperative manipulation tasks remains a challenge. This paper introduces a graph-based trajectory planning framework that designs dynamic controllers to couple multiple DMPs while preserving formation. The proposed method is validated in both simulation and real-world experiments on a dual-arm UR5 robot performing tasks such as soft cloth folding and object transportation. Results show faster convergence and improved noise resilience compared to conventional approaches. These findings demonstrate the potential of the proposed framework for rapid deployment and effective trajectory planning in multi-robot manipulation. Full article

(This article belongs to the Special Issue Visual Servoing-Based Robotic Manipulation)

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25 pages, 1674 KB

Open AccessArticle

Relaxed Monotonic QMIX (R-QMIX): A Regularized Value Factorization Approach to Decentralized Multi-Agent Reinforcement Learning

by Liam O’Brien and Hao Xu

Robotics 2026, 15(1), 28; https://doi.org/10.3390/robotics15010028 - 21 Jan 2026

Abstract

Value factorization methods have become a standard tool for cooperative multi-agent reinforcement learning (MARL) in the centralized-training, decentralized-execution (CTDE) setting. QMIX (a monotonic mixing network for value factorization), in particular, constrains the joint action–value function to be a monotonic mixing of per-agent utilities, [...] Read more.

Value factorization methods have become a standard tool for cooperative multi-agent reinforcement learning (MARL) in the centralized-training, decentralized-execution (CTDE) setting. QMIX (a monotonic mixing network for value factorization), in particular, constrains the joint action–value function to be a monotonic mixing of per-agent utilities, which guarantees consistency with individual greedy policies but can severely limit expressiveness on tasks with non-monotonic agent interactions. This work revisits this design choice and proposes Relaxed Monotonic QMIX (R-QMIX), a simple regularized variant of QMIX that encourages but does not strictly enforce the monotonicity constraint. R-QMIX removes the sign constraints on the mixing network weights and introduces a differentiable penalty on negative partial derivatives of the joint value with respect to each agent’s utility. This preserves the computational benefits of value factorization while allowing the joint value to deviate from strict monotonicity when beneficial. R-QMIX is implemented in a standard PyMARL (an open-source MARL codebase) and evaluated on the StarCraft Multi-Agent Challenge (SMAC). On a simple map (3m), R-QMIX matches the asymptotic performance of QMIX while learning substantially faster. On more challenging maps (MMM2, 6h vs. 8z, and 27m vs. 30m), R-QMIX significantly improves both sample efficiency and final win rate (WR), for example increasing the final-quarter mean win rate from

42.3 %

to

97.1 %

on MMM2, from

0.0 %

to

57.5 %

on 6h vs. 8z, and from

58.0 %

to

96.6 %

on 27m vs. 30m. These results suggest that soft monotonicity regularization is a practical way to bridge the gap between strictly monotonic value factorization and fully unconstrained joint value functions. A further comparison against QTRAN (Q-value transformation), a more expressive value factorization method, shows that R-QMIX achieves higher and more reliably convergent win rates on the challenging SMAC maps considered. Full article

(This article belongs to the Special Issue AI-Powered Robotic Systems: Learning, Perception and Decision-Making)

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14 pages, 1255 KB

Open AccessArticle

Real-Time Control of Six-DOF Serial Manipulators via Learned Spherical Kinematics

by Meher Madhu Dharmana and Pramod Sreedharan

Robotics 2026, 15(1), 27; https://doi.org/10.3390/robotics15010027 - 21 Jan 2026

Abstract

Achieving reliable and real-time inverse kinematics (IK) for 6-degree-of-freedom (6-DoF) spherical-wrist manipulators remains a significant challenge. Analytical formulations often struggle with complex geometries and modeling errors, and standard numerical solvers (e.g., Levenberg–Marquardt) can stall near singularities or converge slowly. Purely data-driven approaches may [...] Read more.

Achieving reliable and real-time inverse kinematics (IK) for 6-degree-of-freedom (6-DoF) spherical-wrist manipulators remains a significant challenge. Analytical formulations often struggle with complex geometries and modeling errors, and standard numerical solvers (e.g., Levenberg–Marquardt) can stall near singularities or converge slowly. Purely data-driven approaches may require large networks and struggle with extrapolation. In this paper, we propose a low-latency, polynomial-based IK solution for spherical-wrist robots. The method leverages spherical coordinates and low-degree polynomial fits for the first three (positional) joints, coupled with a closed-form analytical solver for the final three (wrist) joints. An iterative partial-derivative refinement adjusts the polynomial-based angle estimates using spherical-coordinate errors, ensuring near-zero final error without requiring a full Jacobian matrix. The method systematically enumerates up to eight valid IK solutions per target pose. Our experiments against Levenberg–Marquardt, damped least-squares, and an fmincon baseline show an approximate 8.1× speedup over fmincon while retaining higher accuracy and multi-branch coverage. Future extensions include enhancing robustness through uncertainty propagation, adapting the approach to non-spherical wrists, and developing criteria-based automatic solution-branch selection. Full article

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

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4 pages, 139 KB

Open AccessEditorial

UAV Systems and Swarm Robotics

by Gerardo Flores, Héctor M. Becerra, Juan Pablo Ramirez-Paredes and Alexandre Santos Brandão

Robotics 2026, 15(1), 26; https://doi.org/10.3390/robotics15010026 - 20 Jan 2026

Abstract

A possible classification for organization purposes: [...] Full article

(This article belongs to the Special Issue UAV Systems and Swarm Robotics)

20 pages, 1970 KB

Open AccessReview

Synergistic Advancement of Physical and Information Interaction in Exoskeleton Rehabilitation Robotics: A Review

by Cuizhi Fei, Qiaoling Meng, Hongliu Yu and Xuhua Lu

Robotics 2026, 15(1), 25; https://doi.org/10.3390/robotics15010025 - 19 Jan 2026

Abstract

The exoskeleton rehabilitation robot is a structural robot that uses the actuator to control, so as to construct a human–robot collaborative rehabilitation training system to realize the perception and decoding of patients and promotes the recovery of limb function and neural remodeling. This [...] Read more.

The exoskeleton rehabilitation robot is a structural robot that uses the actuator to control, so as to construct a human–robot collaborative rehabilitation training system to realize the perception and decoding of patients and promotes the recovery of limb function and neural remodeling. This review focused on the synergistic advancement of physical and information interaction in exoskeleton rehabilitation robotics. This review systematically retrieved literature related to the synergistic advancement of physical and information interaction in exoskeleton rehabilitation robotics. Publications from 2011 to 2025 were searched for across the EI, IEEE Xplore, PubMed, and Web of Science databases. The included studies mainly covered the period from 2018 to 2025, reflecting recent technological progress. This article summarizes the collaborative progress of physical and informational interaction in exoskeleton rehabilitation robots. The physical and information interaction is manifested in the bionic structure, physiological information detection and information processing technology to identify human movement intention. The bionic structural design is fundamental to realize natural coordination between human and robot to improve the following of movements. The active participation and movement intention recognition accuracy are enhanced based on multimodal physiological signal detection and information processing technology, which provides a clear direction for the development of intelligent rehabilitation technology. Full article

(This article belongs to the Section Neurorobotics)

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27 pages, 4407 KB

Open AccessSystematic Review

Artificial Intelligence in Agri-Robotics: A Systematic Review of Trends and Emerging Directions Leveraging Bibliometric Tools

by Simona Casini, Pietro Ducange, Francesco Marcelloni and Lorenzo Pollini

Robotics 2026, 15(1), 24; https://doi.org/10.3390/robotics15010024 - 15 Jan 2026

Abstract

Agricultural robotics and artificial intelligence (AI) are becoming essential to building more sustainable, efficient, and resilient food systems. As climate change, food security pressures, and labour shortages intensify, the integration of intelligent technologies in agriculture has gained strategic importance. This systematic review provides [...] Read more.

Agricultural robotics and artificial intelligence (AI) are becoming essential to building more sustainable, efficient, and resilient food systems. As climate change, food security pressures, and labour shortages intensify, the integration of intelligent technologies in agriculture has gained strategic importance. This systematic review provides a consolidated assessment of AI and robotics research in agriculture from 2000 to 2025, identifying major trends, methodological trajectories, and underexplored domains. A structured search was conducted in the Scopus database—which was selected for its broad coverage of engineering, computer science, and agricultural technology—and records were screened using predefined inclusion and exclusion criteria across title, abstract, keywords, and eligibility levels. The final dataset was analysed through descriptive statistics and science-mapping techniques (VOSviewer, SciMAT). Out of 4894 retrieved records, 3673 studies met the eligibility criteria and were included. As with all bibliometric reviews, the synthesis reflects the scope of indexed publications and available metadata, and potential selection bias was mitigated through a multi-stage screening workflow. The analysis revealed four dominant research themes: deep-learning-based perception, UAV-enabled remote sensing, data-driven decision systems, and precision agriculture. Several strategically relevant but underdeveloped areas also emerged, including soft manipulation, multimodal sensing, sim-to-real transfer, and adaptive autonomy. Geographical patterns highlight a strong concentration of research in China and India, reflecting agricultural scale and investment dynamics. Overall, the field appears technologically mature in perception and aerial sensing but remains limited in physical interaction, uncertainty-aware control, and long-term autonomous operation. These gaps indicate concrete opportunities for advancing next-generation AI-driven robotic systems in agriculture. Funding sources are reported in the full manuscript. Full article

(This article belongs to the Special Issue Smart Agriculture with AI and Robotics)

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60 pages, 3790 KB

Open AccessReview

Autonomous Mobile Robot Path Planning Techniques—A Review: Metaheuristic and Cognitive Techniques

by Mubarak Badamasi Aremu, Gamil Ahmed, Sami Elferik and Abdul-Wahid A. Saif

Robotics 2026, 15(1), 23; https://doi.org/10.3390/robotics15010023 - 14 Jan 2026

Abstract

Autonomous mobile robots (AMRs) require robust, efficient path planning to operate safely in complex, often dynamic environments (e.g., logistics, transportation, and healthcare). This systematic review focuses on advanced metaheuristic and learning- and reasoning-based (cognitive) techniques for AMR path planning. Drawing on approximately 230 [...] Read more.

Autonomous mobile robots (AMRs) require robust, efficient path planning to operate safely in complex, often dynamic environments (e.g., logistics, transportation, and healthcare). This systematic review focuses on advanced metaheuristic and learning- and reasoning-based (cognitive) techniques for AMR path planning. Drawing on approximately 230 articles published between 2018 and 2025, we organize the literature into two prominent families, metaheuristic optimization and AI-based navigation, and introduce and apply a unified taxonomy (planning scope, output type, and constraint awareness) to guide the comparative analysis and practitioner-oriented synthesis. We synthesize representative approaches, including swarm- and evolutionary-based planners (e.g., PSO, GA, ACO, GWO), fuzzy and neuro-fuzzy systems, neural methods, and RL/DRL-based navigation, highlighting their operating principles, recent enhancements, strengths, and limitations, and typical deployment roles within hierarchical navigation stacks. Comparative tables and a compact trade-off synthesis summarize capabilities across static/dynamic settings, real-world validation, and hybridization trends. Persistent gaps remain in parameter tuning, safety, and interpretability of learning-enabled navigation; sim-to-real transfer; scalability under real-time compute limits; and limited physical experimentation. Finally, we outline research opportunities and open research questions, covering benchmarking and reproducibility, resource-aware planning, multi-robot coordination, 3D navigation, and emerging foundation models (LLMs/VLMs) for high-level semantic navigation. Collectively, this review provides a consolidated reference and practical guidance for future AMR path-planning research. Full article

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

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25 pages, 4540 KB

Open AccessArticle

Vision-Guided Grasp Planning for Prosthetic Hands with AABB-Based Object Representation

by Shifa Sulaiman, Akash Bachhar, Ming Shen and Simon Bøgh

Robotics 2026, 15(1), 22; https://doi.org/10.3390/robotics15010022 - 14 Jan 2026

Abstract

Recent advancements in prosthetic technology have increasingly focused on enhancing dexterity and autonomy through intelligent control systems. Vision-based approaches offer promising results for enabling prosthetic hands to interact more naturally with diverse objects in dynamic environments. Building on this foundation, the paper presents [...] Read more.

Recent advancements in prosthetic technology have increasingly focused on enhancing dexterity and autonomy through intelligent control systems. Vision-based approaches offer promising results for enabling prosthetic hands to interact more naturally with diverse objects in dynamic environments. Building on this foundation, the paper presents a vision-guided grasping algorithm for a prosthetic hand, integrating perception, planning, and control for dexterous manipulation. A camera mounted on the set up captures the scene, and a Bounding Volume Hierarchy (BVH)-based vision algorithm is employed to segment an object for grasping and define its bounding box. Grasp contact points are then computed by generating candidate trajectories using Rapidly-exploring Random Tree Star (RRT*) algorithm, and selecting fingertip end poses based on the minimum Euclidean distance between these trajectories and the object’s point cloud. Each finger’s grasp pose is determined independently, enabling adaptive, object-specific configurations. Damped Least Square (DLS) based Inverse kinematics solver is used to compute the corresponding joint angles, which are subsequently transmitted to the finger actuators for execution. Our intention in this work was to present a proof-of-concept pipeline demonstrating that fingertip poses derived from a simple, computationally lightweight geometric representation, specifically an AABB-based segmentation can be successfully propagated through per-finger planning and executed in real time on the Linker Hand O7 platform. The proposed method is validated in simulation, and experimental integration on a Linker Hand O7 platform. Full article

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

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23 pages, 4679 KB

Open AccessArticle

A Synergistic Rehabilitation Approach for Post-Stroke Patients with a Hand Exoskeleton: A Feasibility Study with Healthy Subjects

by Cristian Camardella, Tommaso Bagneschi, Federica Serra, Claudio Loconsole and Antonio Frisoli

Robotics 2026, 15(1), 21; https://doi.org/10.3390/robotics15010021 - 14 Jan 2026

Abstract

Hand exoskeletons are increasingly used to support post-stroke reach-to-grasp, yet most intention-detection strategies trigger assistance from local hand events without considering the synergy between proximal arm transport and distal hand shaping. We evaluated whether proximal arm kinematics, alone or fused with EMG, can [...] Read more.

Hand exoskeletons are increasingly used to support post-stroke reach-to-grasp, yet most intention-detection strategies trigger assistance from local hand events without considering the synergy between proximal arm transport and distal hand shaping. We evaluated whether proximal arm kinematics, alone or fused with EMG, can predict flexor and extensor digitorum activity for synergy-aligned hand assistance. We trained nine models per participant: linear regression (LINEAR), feedforward neural network (NONLINEAR), and LSTM, each under EMG-only, kinematics-only (KIN), and EMG+KIN inputs. Performance was assessed by RMSE on test trials and by a synergy-retention analysis, comparing synergy weights from original EMG versus a hybrid EMG in which extensor and flexor digitorum measure signals were replaced by model predictions. Results have shown that kinematic information can predict muscle activity even with a simple linear model (average RMSE around 30% of signal amplitude peak during go-to-grasp contractions), and synergy analysis indicated high cosine similarity between original and hybrid synergy weights (on average 0.87 for the LINEAR model). Furthermore, the LINEAR model with kinematics input has been tested in a real-time go-to-grasp motion, developing a high-level control strategy for a hand exoskeleton, to better simulate post-stroke rehabilitation scenarios. These results suggest the intrinsic synergistic motion of go-to-grasp actions, offering a practical path, in hand rehabilitation contexts, for timing hand assistance in synergy with arm transport and with minimal setup burden. Full article

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

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44 pages, 5363 KB

Open AccessReview

End-Effector-Based Robots for Post-Stroke Rehabilitation of Proximal Arm Joints: A Literature Review

by Sohrab Moayer, Redwan Alqasemi and Rajiv Dubey

Robotics 2026, 15(1), 20; https://doi.org/10.3390/robotics15010020 - 13 Jan 2026

Abstract

Experiencing weakness or paralysis on one side of the body is a common consequence of stroke, with approximately 8 out of 10 patients experiencing some degree of Hemiparesis. Rehabilitation through physiotherapy and occupational therapy is one of the primary methods used to alleviate [...] Read more.

Experiencing weakness or paralysis on one side of the body is a common consequence of stroke, with approximately 8 out of 10 patients experiencing some degree of Hemiparesis. Rehabilitation through physiotherapy and occupational therapy is one of the primary methods used to alleviate these conditions. However, physiotherapy, provided by a therapist, is not always readily available. Rehabilitation robots have been studied as alternatives and supplements to conventional therapy. These robots, based on their interaction with the user, can be categorized as end-effector and exoskeleton-based robots. This work aims to examine end-effector rehabilitation robots targeting hemiplegic arm’s proximal joints (shoulder and elbow) for post-stroke recovery. Additionally, we analyze their mechanical design, training modes, user interfaces, and clinical outcomes, highlighting trends and gaps in these systems. Furthermore, we suggest design considerations for home-based therapy and future integration with tele-rehabilitation, based on our findings. This review uniquely focuses on end-effector robots for proximal joints, synthesizing design trends and clinical evidence to guide future development. Full article

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

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38 pages, 5190 KB

Open AccessArticle

Discrete-Time Computed Torque Control with PSO-Based Tuning for Energy-Efficient Mobile Manipulator Trajectory Tracking

by Patricio Galarce-Acevedo and Miguel Torres-Torriti

Robotics 2026, 15(1), 19; https://doi.org/10.3390/robotics15010019 - 9 Jan 2026

Abstract

Mobile manipulator robots have an increasing number of applications in industry because they extend the workspace of a fixed base manipulator mounted on a mobile platform, making it important to further investigate their control and optimization. This paper presents an implementation proposal for [...] Read more.

Mobile manipulator robots have an increasing number of applications in industry because they extend the workspace of a fixed base manipulator mounted on a mobile platform, making it important to further investigate their control and optimization. This paper presents an implementation proposal for a coupled base–arm dynamics computed torque controller (CTC) for trajectory tracking of a differential-drive mobile manipulator, which considers the dynamics of the fixed base manipulator and the mobile base in a coupled way and compares its performance with that of a Proportional Derivative (PD) controller. Both controllers are tuned using Particle Swarm Optimization (PSO) with a cost function that aims to simultaneously reduce the control energy and the end-effector tracking error for different types of trajectories, and they operate in discrete time, thus accounting for inherent process delays. Simulation and laboratory implementation results show the superior performance of the CTC in both cases: in simulation, the average end-effector positioning error is reduced by 51.55% and the average RMS power by 46.44%; in the laboratory experiments, the average end-effector positioning error is reduced by 43.29% and the average RMS power by 53.49%, even in the presence of possible model uncertainties and system disturbances. Full article

(This article belongs to the Special Issue Motion Planning for Autonomous and Intelligent Mobile Robots in Unstructured Environments)

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Graphical abstract

23 pages, 15741 KB

Open AccessArticle

A Hierarchical Trajectory Planning Framework for Autonomous Underwater Vehicles via Spatial–Temporal Alternating Optimization

by Jinjin Yan and Huiling Zhang

Robotics 2026, 15(1), 18; https://doi.org/10.3390/robotics15010018 - 9 Jan 2026

Abstract

Autonomous underwater vehicle (AUV) motion planning in complex three-dimensional ocean environments remains challenging due to the simultaneous requirements of obstacle avoidance, dynamic feasibility, and energy efficiency. Current approaches often decouple these factors or exhibit high computational overhead, limiting applicability in real-time or large-scale [...] Read more.

Autonomous underwater vehicle (AUV) motion planning in complex three-dimensional ocean environments remains challenging due to the simultaneous requirements of obstacle avoidance, dynamic feasibility, and energy efficiency. Current approaches often decouple these factors or exhibit high computational overhead, limiting applicability in real-time or large-scale missions. This work proposes a hierarchical trajectory planning framework designed to address these coupled constraints in an integrated manner. The framework consists of two stages: (i) a current-biased sampling-based planner (CB-RRT*) is introduced to incorporate ocean current information into the path generation process. By leveraging flow field distributions, the planner improves path geometric continuity and reduces steering variations compared with benchmark algorithms; (ii) spatial–temporal alternating optimization is performed within underwater safe corridors, where Bézier curve parameterization is utilized to jointly optimize spatial shapes and temporal profiles, producing dynamically feasible and energy-efficient trajectories. Simulation results in dense obstacle fields, heterogeneous flow environments, and large-scale maps demonstrate that the proposed method reduces the maximum steering angle by up to 63% in downstream scenarios, achieving a mean maximum turning angle of 0.06 rad after optimization. The framework consistently attains the lowest energy consumption across all tests while maintaining an average computation time of 0.68 s in typical environments. These results confirm the framework’s suitability for practical AUV applications, providing a computationally efficient solution for generating safe, kinematically feasible, and energy-efficient trajectories in real-world ocean settings. Full article

(This article belongs to the Special Issue SLAM and Adaptive Navigation for Robotics)

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