Robotics doi: 10.3390/robotics15050100

Authors: Teresa Sinico Daniele Businaro Giovanni Boschetti

Robotic waste sorting presents significant challenges, including object variability, cluttered environments, and the predominant reliance on deep learning and traditional computer vision techniques, which typically demand extensive datasets and task-specific training. This paper introduces a robotic waste sorting system that integrates the Gemini Vision–Language–Action (VLA) model with a KUKA LBR iiwa collaborative robot and an RGB-D camera. Our approach leverages the advanced reasoning capabilities of large, pre-trained VLA models to perform waste sorting, without requiring explicit training or dataset collection. Key contributions include the development of effective prompt engineering strategies for waste object identification, the assessment of the VLA’s performance in terms of inference time and accuracy, and the development of different grasping strategies for operation in cluttered scenarios. Our experimental tests demonstrated that the system’s inference time is between 2 and 4 s, which is suitable for collaborative robotic applications, and the system achieved a high overall classification accuracy of 89.64%. Crucially, we demonstrated that integration of RGB-D sensing enhanced the model’s ability to perceive object heights, resolve occlusions, and make informed grasping decisions in realistic, three-dimensional settings. We further validated multiple real-world grasping strategies, demonstrating tradeoffs between system efficiency and safety in heavily cluttered scenarios. This work establishes a practical and adaptable framework for deploying VLA-driven intelligence on commercial robotic platforms, highlighting the potential of VLAs for complex manipulation tasks beyond waste sorting.

Robotics doi: 10.3390/robotics15050099

Authors: Seena Joseph Wai Keung Fung Tony Punnoose Valayil Rajan Prasad Tim Bashford

In recent years, hand exoskeleton robots have attracted extensive attention from researchers and practitioners due to their potential to rehabilitate, assist, and enhance hand movements, particularly for stroke patients. With an ageing population increasingly affected by strokes, there is a growing demand for patient-centred interventions which place less demand on clinicians, especially wearable devices that can enhance hand function. Advances in artificial intelligence have opened new avenues for developing more reliable and adaptive assistive systems. This study presents a systematic literature review, following the PRISMA protocol on the design elements of hand exoskeleton robots, acknowledging the emerging perspectives on AI integration and ethical considerations. The study provides a comprehensive foundation for future research and development in rehabilitation technologies by systematically synthesising the current mechanical architecture, actuation, sensors, material, weight, and cost aspects of soft hand exoskeleton robots for rehabilitation. The results show important patterns and trade-offs in various design dimensions, providing useful information to direct the development of more accessible and efficient rehabilitation solutions in the future.

Robotics doi: 10.3390/robotics15050098

Authors: Chi Jie Tan Eiji Hayashi Abbe Mowshowitz Way Soong Lim

Genetic Programming (GP) for evolving Behavior Trees (BTs) in autonomous robots often suffer from premature convergence, even when adaptive mutation mechanisms are employed. This paper proposes a novel hybrid framework that integrates Large Language Model (LLM) supervision into GP, in which the LLM performs holistic population analysis, adaptively regulates mutation rates, and generates targeted BTs to proactively address behavioral gaps in the evolving population. Unlike conventional evolutionary operators, the LLM introduces high-level semantic guidance by seeding underrepresented behavioral archetypes, thereby complementing stochastic genetic variation with structured exploration. The proposed method is evaluated in a Unity-based multi-task robotic simulation environment. Experimental results show that the hybrid approach significantly outperforms baseline GP with standard adaptive mutation, achieving a 71.7% faster emergence of Complete Robots, a 65.2% faster emergence of Excellent Robots, and a 28% increase in behavioral diversity. Notably, the two systems exhibit opposite mutation dynamics: the LLM-guided system progressively reduces mutation rates to promote exploitation, whereas the baseline maintains a high mutation rate. In addition, the LLM generates approximately 40 targeted BTs per run, proactively seeding the population with underrepresented behavioral archetypes. These performance gains are obtained with only a 13% computational overhead.

Robotics doi: 10.3390/robotics15050097

Authors: Lander Ketelbuters Bart Engelen Ivo Dekker Karel Kellens

This paper highlights the state of the art in Cooperative Dual-Manipulation (CDM) and Cooperative Multi-Manipulation (CMM), comparing advances in modeling, control, planning, sensing, vision, and end-effector technologies. Methods originally established in CDM have been extended or adapted to support higher complexity of CMM. A historical timeline visualizes the steady growth of cooperative manipulation (CM) and the recent acceleration of CMM driven by rising process complexity and the need for more flexible automation strategies. CM is becoming increasingly relevant as industrial processes demand higher payload capacity, larger workspaces, and greater flexibility. In addition, this paper categorizes existing applications by cooperation type and application domain. Here, a clear dominance of simultaneous object manipulation tasks is visible (fixation-fixation). However, fixation-tooling tasks, where one manipulator grasps the product while another performs a tool operation, and tooling-tooling tasks, where multiple manipulators perform tool operations simultaneously, remain significantly underrepresented. A similar imbalance is found for rigid/non-deformable object manipulation and flexible/deformable object manipulation, respectively. Based on this review, several research gaps are identified: (i) reliable flexible object manipulation methods; (ii) CM strategies for disassembly (e.g., battery pack deconstruction); (iii) complexity in control and planning for multi-manipulator systems; (iv) pathways to industrial deployment beyond laboratory demonstrators; and (v) task-specific tooling and end-effector innovation.

Robotics doi: 10.3390/robotics15050096

Authors: Abderrahim Waga Said Benhlima Ali Bekri Fatima Zahrae Saber Jawad Abdouni Toufik Mzili Ahmed Regragui

Path planning in cluttered environments constitutes a critical challenge for mobile robotics. Although optimal solutions can be obtained by classical methods such as A*, they have the disadvantage of being computationally expensive in complex environments. In this paper, we propose a novel deep learning-based framework for 2D trajectory prediction in grid environments. The framework employs attention mechanisms specifically designed for path planning tasks. In particular, we design an Attention U-Net architecture that employs attention gates for effective path area focusing and residual connections for efficient feature selection. To validate our method, the Attention U-Net architecture is trained on 5000 randomly sampled 40 × 40 environments and tested on a separate test set of 200 environments. The experimental results show that the Attention U-Net architecture significantly outperforms the A* algorithm. It expands 62% fewer nodes (207.6 vs. 543.11) and achieves near-optimal path lengths (99.8% of optimal) and planning speed (0.78 ms vs. 1.19 ms). Furthermore, the Attention U-Net architecture achieves a 100% success rate for A* path planning with the attention heuristic, demonstrating the effectiveness of the attention heuristic for path planning.

Robotics doi: 10.3390/robotics15050095

Authors: Yassin Abdelmeguid Ammar Hasan

Multi-robot systems operating on battery power face fundamental constraints through which energy limitations directly impact mission success. The existing multi-agent reinforcement learning approaches optimize for task performance without explicit energy consideration, leading to inefficient consumption and depletion risk. This paper presents a framework for energy-aware multi-agent coordination that treats battery management as a safety constraint, rather than an optimization objective. We introduce Energy-Aware Multi-Agent Proximal Policy Optimization (EA-MAPPO) with energy-augmented observations and shaped rewards and extend it to Safe Energy-Aware MAPPO (SEA-MAPPO) combining predictive action masking with safety-oriented reward shaping. An experimental validation on the Georgia Tech Robotarium with 7 agents demonstrates that SEA-MAPPO reaches 95% goal completion 19× faster than standard MAPPO, requiring only 0.5 M environment steps versus 9.4 M. Throughout training, SEA-MAPPO reduces cumulative depletion events by 93% compared to MAPPO while maintaining superior energy efficiency. SEA-MAPPO achieves 100% goal completion versus 81.5% for MAPPO at the same training budget. Physical deployment on GTernal robots without fine-tuning achieves 100% goal completion with zero depletion events across 70 robot-trials, with the energy predictor achieving R2=0.89 with measured power consumption.

Robotics doi: 10.3390/robotics15050094

Authors: Andres de la Rosa-Garcia Alma Guadalupe Rodriguez-Ramirez Beatriz Alvarado Robles Israel Soto-Marrufo Diana Ortiz-Muñoz Victor Manuel Alonso-Mendoza David Luviano-Cruz Francesco Garcia-Luna

The COVID-19 pandemic highlighted the need for effective disinfection strategies in order to minimize human exposure and reduce the risk of contagion in indoor environments. Ultraviolet-C (UV-C) irradiation has proven to be an effective solution for inactivating a wide range of pathogens. However, traditional fixed UV-C systems suffer from limited coverage and lack operational flexibility. To address these limitations, this paper proposes an augmented reality (AR)-based teleoperation framework for an omnidirectional mobile robot equipped with a UV-C disinfection light. Unlike traditional toolchain integrations, our framework synergizes immersive spatial visualization of a reconstructed environment, operator-guided waypoint-based remote navigation, and real-time interaction with the disinfection payload in a single operational workflow. The system is implemented using a ROSMASTER X3 Plus robotic platform, which generates a three-dimensional representation of the environment through visual simultaneous localization and mapping using RTAB-Map. The result is a 3D map that is imported into the Unity game engine and deployed to a Meta Quest 3 head-mounted display, enabling immersive visualization and interaction. Communication between the AR interface and the robotic system is achieved via the ROS-TCP-Connection, allowing real-time data exchange and remote robot control. Through the AR interface, the operator can navigate the robot within the scanned environment and activate the UV-C light. Experimental validation conducted in a classroom demonstrates the feasibility of the proposed approach and shows measurable reductions in surface microbial load. These results indicate that our system-level integration of AR-assisted teleoperation with mobile UV-C robotics represents a feasible proof-of-concept for flexible, operator-guided disinfection of indoor spaces.

Robotics doi: 10.3390/robotics15050093

Authors: Rand N. Albustanji Shorouq Elmanaseer Ahmad A. A. Alkhatib

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Robotics doi: 10.3390/robotics15050092

Authors: Luigi D’Alfonso Alp Merzi Giuseppe Fedele

This paper presents a novel distributed coordination framework for multi-agent robotic swarms tailored for smart transportation applications. The proposed approach addresses the critical pre-transportation phase where a fleet of mobile robots, eventually with different sizes, must converge to fixed positions around an object to ensure effective caging within a user-defined prescribed time. By leveraging a time-varying diffeomorphic mapping based on an affine transformation, the strategy embeds prescribed-time guarantees within a swarm-inspired framework that maps agents between virtual and real reference frames. This methodology ensures the simultaneous achievement of precise target convergence, finite-time stability regardless of initial conditions, and inherent collision avoidance by explicitly considering the physical footprint of each robotic unit. The control protocol is first derived for scalar systems and subsequently extended to multidimensional robotic fleets using additional diffeomorphism-based techniques, which allow for the management of multiple non-interacting swarms to reduce network communication overhead.

Robotics doi: 10.3390/robotics15050091

Authors: Theodoros Papafotiou Emmanouil Tsardoulias Andreas Symeonidis

Precise 3D positioning in GPS-denied environments is a critical enabler of autonomous robotics, industrial automation, and smart logistics within the emerging cyber-physical landscape. This paper presents a distributed and reconfigurable architecture designed to benchmark and provide unified multimodal indoor localization for mobile edge nodes. Unlike rigid commercial solutions, our architecture employs a distributed, reconfigurable framework that allows the rapid interchange of Absolute Localization Methods (UWB, External RGB-D Vision) and Relative Localization Methods (Inertial Odometry, Visual Odometry). We evaluate these modalities individually and in hybrid configurations using a custom low-cost mobile edge node. Experimental results in a controlled environment demonstrate that while all-optical systems offer high precision, a cost-effective fusion of Ultra-Wideband (UWB) and Inertial Measurement Unit (IMU) data provides a robust balance of accuracy and reliability. Conversely, we identify significant limitations in monocular visual odometry within feature-poor indoor spaces. The developed platform serves as a reproducible foundation for researchers to prototype hybrid localization algorithms and assess the trade-offs between hardware cost and operational accuracy within complex cyber-physical ecosystems.

Robotics doi: 10.3390/robotics15050090

Authors: Qixue Zhong Jing Xing Jian Liu Luqing Luo

Accurate and robust localization and mapping in complex and dynamic environments remain a fundamental challenge for autonomous systems. LiDAR–Inertial–Visual Odometry (LIVO) integrates the complementary strengths of LiDAR geometry, visual appearance, and inertial motion constraints. However, existing LIVO systems still suffer from limited adaptability to sensor degradation, weak loop-closure robustness, and insufficient cross-modal consistency modeling. This paper presents a robust multi-sensor SLAM framework that integrates an uncertainty-aware LIVO front-end, a geometry-driven loop-closure module, and a cross-modal consistency factor-graph back-end. We develop an uncertainty-aware iterated error-state Kalman filter (iESKF) to tightly fuse LiDAR, visual, and inertial measurements, with measurement covariances dynamically adjusted according to innovation statistics, feature-matching quality, and observability. To improve global consistency, we propose a multi-channel Binary Triangle Constraint (mBTC) descriptor for LiDAR-based loop detection, which enhances robustness under viewpoint changes and appearance degradation. In addition, we introduce a cross-modal consistency factor to explicitly constrain the relative motion agreement between visual and LiDAR odometries. Extensive experiments on multiple public benchmarks demonstrate improved accuracy, loop-closure reliability, and long-term consistency compared with state-of-the-art LIVO systems.

Robotics doi: 10.3390/robotics15050089

Authors: Askar Seidakhmet Amandyk Tuleshov Assylbek Jomartov Akmurat Altynbek Jacek Cieślik Azizbek Abduraimov Aziz Kamal Madi Kaliyev Zair Ualiyev

Automating the pushing of bulk materials deposited through the hatch of a transport and packaging container (TPC) requires the development of a specialized robot equipped with an end effector capable of displacing material within a spatially constrained environment. This paper proposes a Cartesian robot design featuring a cellular end effector—comprising a grid of rectangular compartments—and a control system that enables the pushing of bulk material batches loaded via the hatch. This work presents experimental results regarding robotic workcell performance as a function of its end effector immersion depth and the material’s moisture content. Finite element modeling (FEM) of the end effector is detailed, determining the resistance forces encountered during movement at various immersion depths within the bulk material. Furthermore, an analysis of the container’s design was conducted to determine the final layer thickness of the bulk material after leveling five consecutive loaded portions. Newton–Euler equations are formulated to describe the movement dynamics of the end effector, considering variables such as immersion depth and material moisture. Additionally, a motion control algorithm was developed to accommodate varying displacements of the conical heap’s apex relative to the hatch center, integrated within the overall Cartesian robot control system. The derived results and recommendations facilitate the effective pushing and redistribution of loaded material batches within the container. Finally, finite element analysis and experimental validation confirm the structural strength and rigidity of the Cartesian robotic workcell, ensuring that the maximum elastic deflection of the end effector under peak dynamic load remains ≈ 1 mm.

Robotics doi: 10.3390/robotics15050088

Authors: Álvaro Navarro-Pérez Bladimir Bacca-Cortés Eduardo Caicedo-Bravo

Long-term localization in dynamic and changing environments remains a key challenge for autonomous vehicles. Semantic Simultaneous Localization and Mapping (SLAM) enhances traditional SLAM by integrating high-level semantic understanding, enabling robust mapping and localization even under complex scenarios. In this context, multi-modal sensor fusion—particularly the combination of LiDAR and camera data—has proven essential in leveraging complementary strengths: the geometric accuracy of LiDAR and the rich semantic cues from images. A significant advancement in this domain is the adoption of graph-based semantic localization frameworks, where semantic entities and spatial relationships are encoded in graph structures to improve map consistency, loop closure detection, and data association over time. This review presents a comprehensive survey of recent developments in Semantic SLAM, with a focus on long-term localization for autonomous vehicles using multi-modal fusion strategies. We categorize existing methods into traditional SLAM, vision-based, point-cloud-based, and graph-based techniques, emphasizing the role of semantic data association and loop closure in maintaining long-term consistency. Additionally, we discuss the integration of deep learning techniques for semantic segmentation and feature extraction. Finally, we analyze widely used datasets and evaluation metrics, identifying current limitations and proposing directions for future research on robust, scalable, and semantically enriched localization.

Robotics doi: 10.3390/robotics15050087

Authors: Reza Arablouei

We present an efficient incremental SLAM back-end that reduces computation while preserving accuracy close to that of a full incremental Gauss–Newton (GN) solver across benchmark pose-graph datasets. The method combines information-guided gating (IGG), which uses a log-determinant-based information surrogate to decide when broad updates are warranted, with selective partial optimization (SPO), which confines multi-iteration GN updates to variables that remain affected after each iteration. We provide a local perturbation analysis, showing that, under standard regularity conditions, the proposed approximation tracks full GN within a threshold-controlled neighborhood and recovers the same local minimizer and asymptotic convergence rate when the effective approximation error vanishes asymptotically. Experiments on benchmark pose-graph SLAM datasets show competitive final and increment-averaged accuracy together with substantial reductions in update and solve FLOPs. These results support IGG-SPO as a practically promising SLAM back-end for robots operating under limited onboard computational resources.

Robotics doi: 10.3390/robotics15050086

Authors: Sebastián Chajón Jörg Reiff-Stephan Norman Günther

Additive manufacturing enables the development of low-cost, self-built robotic systems; however, their performance is typically not characterized by validated metrics. The paper presents a photogrammetric concept intended for system-independent application to characterize planar positioning accuracy and repeatability without access to internal controller data. The method is based on a Raspberry Pi 4 camera system, image processing in Python 3.12.0 and OpenCV 4.12.0, and a universal additively manufactured robot tool attachment. Two position estimation strategies are investigated: a marker-based approach using ArUco markers and a markerless blob-analysis method based on a ruby sphere. Camera calibration is evaluated using different patterns, with a compact CharUco board exhibiting the lowest RMS reprojection error (~1 px). Experimental validation follows selected elements of ISO 9283:1998 and comprises 30 repetitions at five target poses for linear and axial motion strategies. The results show lower positional deviations for marker-based methods compared to the markerless approach, with a two-marker configuration yielding the lowest mean deviation under the investigated conditions. Sub-millimeter positioning accuracy and repeatability are achieved, and linear motion exhibits lower repeatability deviations than axial motion. The proposed approach provides a cost-effective and flexible solution for external robot characterization, particularly suited for self-built and resource-constrained systems.

Robotics doi: 10.3390/robotics15050085

Authors: Evan Rolland Ilian A. Bonev Evan Jones Pengpeng Zhang Cheng Sun Nanzhu Zhao

This article presents an approach to extend the capabilities of vat photopolymerization (VPP) 3D printing using a robotic arm, with a focus on robust path planning. The robotic cell consists of a Mecademic Meca500 six-axis robot mounted on a Zaber X-LRQ300AP linear guide. The kinematic chain is inverted to reflect the logic of VPP: the world reference frame is fixed to the robot’s tool (the build plate), while the tool frame is attached to the polymerization zone. A virtual degree of freedom for screen image rotation is introduced, bringing the system to eight degrees of freedom. Inverse kinematics are solved under constraints (pose tolerance, joint limits, collision avoidance, and continuity) and evaluated using multi-criteria metrics: manipulability, normalized joint-limit margin, and positional/angular sensitivity. The algorithm follows a deterministic coarse-to-fine search procedure: discrete sweeping of global part orientations, initial sampling with Halton sequences, abd feasibility filtering on a sparsified trajectory, followed by refinement and multi-criteria ranking. The pipeline successfully discarded infeasible orientations and identified feasible printing trajectories for six of the seven benchmark parts, while the remaining case highlights a limitation that may be addressed in future improvements.

Robotics doi: 10.3390/robotics15050084

Authors: Jun Wan Zihao Zhou Nuo Yun Kehong Wang Xiaoyong Zhang

In the context of Industry 5.0, human–robot collaboration increasingly demands intuitive, safe, and sensorless interaction for tasks such as hand-guided teaching and concurrent manipulation. However, conventional admittance control systems are prone to instability due to abrupt changes in human arm stiffness and their reliance on accurate dynamic models. To address these challenges, this paper proposes a sensorless external force estimation and variable admittance control method that models robot dynamic uncertainties and interaction forces as normally distributed stochastic quantities. An improved particle swarm optimization algorithm is introduced to calibrate the variance parameters, enhancing estimation accuracy and robustness. Furthermore, an energy-based variable admittance control strategy is developed, which preserves system passivity by adaptively adjusting inertia and damping gains based on real-time energy variations. The proposed method was validated on a redundant robot platform. Experimental results show that the external force and torque estimation errors remain below 3 N and 3 N.m, respectively, with lower detection delays and errors than those of a first-order generalized momentum observer in collision detection. Variable admittance experiments demonstrate that the system maintains passivity and stable interaction even under sudden arm stiffness changes. The approach is well-suited for industrial applications requiring safe, sensorless, and compliant human–robot collaboration.

Robotics doi: 10.3390/robotics15050083

Authors: Aislinn McAleenan Yinglun Jian Yan Jin Dan Sun Johnny Moore

Minimally invasive surgery (MIS) has transformed modern surgical operations by reducing pain, trauma, scarring and recovery time for the patient. However, precision, stability and accuracy continue to limit surgical performance. Robots can exhibit better precision and stability than humans and have the potential to improve MIS results. This work presents the design and development of a patented 3R1T parallel robot for MIS. The mechanism incorporates a coaxial spherical parallel architecture enabling three rotational degrees of freedom, combined with a remotely actuated translational fourth degree of freedom, therefore reducing the weight of the moving structure, decreasing inertial forces and increasing the system accuracy. The kinematic design is analyzed to achieve the required workspace, motor torque requirements are calculated, and a control system with integrated inverse kinematics is developed. A prototype was manufactured, and preliminary experiments were conducted to evaluate the orientation repeatability of the robot. Results demonstrated a repeatability of ±22.86 μm, commensurate with typical MIS constraints. This suggests that the proposed robot offers potential improvements in precision and control for minimally invasive surgical procedures, over traditional manual methods.

Robotics doi: 10.3390/robotics15040082

Authors: Paúl A. Chasi-Pesantez Eduardo J. Astudillo-Flores Valeria A. Dueñas-López Jorge O. Ordoñez-Ordoñez Eldad Holdengreber Luis Fernando Guerrero-Vásquez

This paper presents a low-cost retrofitting pipeline for a legacy industrial robot that uses a single RGB webcam and monocular 2D keypoint tracking to estimate human-arm posture angles θ(h) and map them to robot-axis joint targets qcmd(r) for A1–A3 on a KUKA KR5-2 ARC HW, while keeping the wrist orientation (A4–A6) fixed. Rather than targeting full six-DoF manipulation, the main contribution is an experimental characterization of how far monocular 2D posture-to-axis mapping can be used reliably for coarse placement and safeguarded low-speed demonstrations on a legacy robot platform. Vision-side accuracy was evaluated per axis against goniometer-based reference angles θref(h), showing low errors for A2–A3 within the tested range and larger errors for A1 due to monocular yaw/depth ambiguity and occlusions. The study also analyzes failure modes during simultaneous multi-joint motion, where performance degrades notably, especially for A2 and A3, and reports practical mitigation directions such as improved viewpoints, multi-view/depth sensing, and stricter dropout handling. Runtime behavior is additionally characterized through a loop timing budget, with an end-to-end latency of 185.44 ms and an effective loop frequency of 5.39 Hz, which is consistent with low-speed online operation within the demonstrated scope. The system was implemented in a fenced industrial cell with restricted access and emergency stop; no collaborative operation is claimed.

Robotics doi: 10.3390/robotics15040081

Authors: Natheer Almtireen Mutaz Ryalat

Robotics is increasingly positioned as an enabling technology for precision agriculture, where management actions must be spatially and temporally targeted under constraints on labour, input use, safety, and environmental impact. This review synthesises studies on agricultural field robotics and organises the literature along four complementary axes: task (monitoring, weeding, spraying, and harvesting), platform (UGV, UAV, gantry/fixed-structure, greenhouse robot, and hybrid systems), autonomy-stack module (perception, localisation, planning, control, actuation, safety, and human–robot interaction), and evaluation setting (lab, greenhouse, open-field single season, and open-field multi-season/multi-site). Across these dimensions, this review analyses how platform constraints shape sensing geometry, actuation capability, localisation reliability, energy/endurance, supervision burden, and safety requirements. It further examines enabling technologies that recur across tasks, including vision and multimodal perception under occlusion and illumination variability, localisation and mapping under weak or denied GNSS, uncertainty-aware planning in deformable and partially observed environments, and compliant end-effectors for contact-rich operations. Beyond cataloguing systems, this paper emphasises evaluation practice by synthesising core task-relevant metrics, comparing laboratory and field validation settings, and proposing a reporting checklist and benchmark ladder to improve reproducibility and cross-study comparability. This review identifies recurring bottlenecks in domain shift, long-term autonomy, calibration robustness, crop-safe actuation, and safety assurance near humans, and it concludes with a staged research roadmap linking near-term evaluation reform to longer-term credible multi-site autonomy. Overall, this paper provides a structured framework for interpreting agricultural robotic systems not only by application but also by deployment context, system maturity, and evaluation credibility.

Robotics doi: 10.3390/robotics15040080

Authors: Juan Diego Peña-Narvaez Rodrigo Pérez-Rodríguez Juan Carlos Manzanares Francisco Miguel Moreno Francisco Martín-Rico

In recent years, planning frameworks have enabled the creation and execution of plans in robots using classical planning approaches based on the Planning Domain Definition Language (PDDL). The dynamic nature of the environments in which these robots operate requires that execution plans adapt to new conditions, either by repairing plans to improve efficiency or because they are no longer valid. Determining the appropriate moment to initiate such repairs is the focus of our research. This paper presents a novel approach to this problem by using Large Language Models (LLMs) to make informed plan repair decisions during robot operation. Our approach introduces an LLM-based semantic evaluation heuristic that goes beyond the traditional heuristic methods employed in symbolic planning frameworks, while addressing the common hallucinations associated with task planning when relying solely on generative artificial intelligence. Our approach uses the semantic evaluation capabilities of LLMs to track environmental features and forecast hazards. This allows the system to proactively identify dangerous situations and adapt plans more efficiently. We experimentally demonstrate the validity of our approach using real robots in environments where both the environmental conditions and the goals to be achieved change dynamically.

Robotics doi: 10.3390/robotics15040079

Authors: Daniel Syniawa Levin Droste Bernd Kuhlenkötter

The increasing application of industrial robots in modern production systems contrasts with a persistently high programming complexity that requires specialized know-how and creates substantial entry barriers. This work addresses this problem by introducing a systematic approach to robot programming based on Large Language Models (LLMs) that automatically translates natural language task descriptions into executable robot programs. The solution follows a two-stage pipeline: in Stage 1, the LLM structures the input into coherent process steps, and in Stage 2 these process steps are transformed into C++ code using a high-level function library. The performance is evaluated in simulation for the automated electrical cabinet assembly use case with terminal blocks, which is a significant element of various production processes. The architecture, based on the Robot Operating System 2 (ROS2) and MoveIt2, further integrates a standardized AutomationML-based configuration management for dynamic parameter handling and persistent state storage. A graphical user interface visualizes intermediate results, enables manual interventions and enables a simple operation for potential users without programming experience. The evaluation of the presented approach shows a success rate of up to 95% for interpreting natural language instructions and generating code in the application scenario focused. The system reliably recognizes object attributes and correctly executes complex assembly instructions. In general, this work demonstrates how modern LLMs can bridge the semantic gap between human intent and robotic code for industrial applications. The developed high-level abstraction makes the system usable for non-programmers, highlights the potential for intuitive robot programming, and simultaneously identifies concrete technical challenges.

Robotics doi: 10.3390/robotics15040078

Authors: Tilla Egerhei Båserud Joakim Johansen Ajit Jha Ilya Tyapin

Reliable textile recycling requires automated unfolding to expose hidden hard components such as zippers, buttons, and metal fasteners, which otherwise risk damaging machinery and compromising downstream processes. This paper presents the design and implementation of an automated textile unfolding system based on a dual-arm robotic manipulation framework. The system uses two Interbotix WidowX 250s 6-DoF robotic arms and an Intel RealSense L515 LiDAR camera for visual perception. The unfolding process consists of three stages: initial dual-arm stretching to reduce major folds, refinement through a second stretch targeting the lower region, and a machine-learning stage that employs a YOLOv11 framework trained on depth-encoded textile images, followed by a depth-gradient-based estimator for fold direction. The system applies an extremity-based grasping strategy that selects leftmost and rightmost textile points from a custom error-corrected depth map, enabling robust grasp point selection, and a fold direction estimation method based on depth gradients around the detected fold. The most confident fold region is selected, an unfolding direction is determined using depth ranking, and the textile is manipulated until a flat state is confirmed through depth uniformity. Experiments show that depth correction significantly reduces spatial error in the robot frame, while segmentation and extremity detection achieve high accuracy across varied fold configurations, and the YOLOv11n-based model reaches 98.8% classification accuracy, while fold direction is estimated correctly in 87% of test cases. By enabling robust, largely autonomous textile unfolding, the system demonstrates a practical approach that could support safer and more efficient automated textile recycling workflows.

Robotics doi: 10.3390/robotics15040077

Authors: Claudio Loconsole

This Special Issue was conceived to explore how Artificial Intelligence can meaningfully empower robotic exoskeletons and prosthetic systems, enhancing modeling, control, perception, and real-world applicability to ultimately improve the quality of life of individuals that rely on these technologies [...]

Robotics doi: 10.3390/robotics15040076

Authors: Giovanni Mastrangelo Betsy Dayana Marcela Chaparro Rico Matteo Russo Marco Ceccarelli Daniele Cafolla

This paper presents a low-cost, lightweight wearable sensing module for real-time multi-degree-of-freedom motion analysis, which is validated using ankle movements from a representative case study. The system is based on a compact inertial measurement unit integrated into a custom-made enclosure and employs Kalman filter-based sensor fusion to estimate three-dimensional joint orientation. An experimental campaign involving sixteen healthy participants was conducted, and measurements were compared against a gold-standard optical motion capture system, Optitrack V120 Trio. Ankle kinematics were analysed across all anatomical planes, including dorsiflexion/plantarflexion, inversion/eversion, and adduction/abduction. Quantitative metrics, including cosine similarity consistently above 0.98 across all movements and root mean square error within 4° on average, demonstrate strong agreement between the angular measuring device and motion capture data, with errors remaining within clinically acceptable limits. The results confirm the feasibility of the proposed system as a reliable, portable, and affordable alternative to laboratory-based measurement technologies. Beyond ankle assessment, the sensing approach is applicable to a wide range of motion-assistive and rehabilitation systems, supporting continuous monitoring, personalised therapy, and future integration into intelligent wearable devices.

Robotics doi: 10.3390/robotics15040075

Authors: Simone Figorilli Simona Violino Simone Vasta Federico Pallottino Giorgio Manca Lorenzo Bianchi Corrado Costa

(1) Background: Robotics and advanced control architectures are increasingly central to the development of precision agriculture (PA), supporting automated, efficient, and data-driven farm management. This review offers a comprehensive analysis of scientific literature on robotic control systems applied to PA, focusing on technological progress, methodological approaches, and emerging research trends. (2) Methods: A systematic review was conducted according to PRISMA guidelines, combined with a bibliometric analysis using VOSviewer to examine term co-occurrences, thematic clusters, and topic evolution over time. Publications indexed in Scopus between 1976 and 2025 were analyzed. (3) Results: Results reveal a sharp growth in publications after 2010, with a strong acceleration from 2015 onward, reflecting advances in autonomous systems and the integration of artificial intelligence, sensor technologies, and distributed software frameworks. Three principal clusters emerged: algorithmic and control methods (e.g., neural networks, path tracking, simulation); sensing and infrastructure technologies (e.g., LiDAR, SLAM, IMU, ROS, deep learning-based perception); and agronomic applications, including crop monitoring, irrigation, yield estimation, and farm management. Citation trends indicate a shift from foundational control theory to AI-driven solutions. (4) Conclusions: Overall, control architectures are evolving toward modular, scalable, and interoperable systems enabling autonomous decision-making in complex agricultural environments.

Robotics doi: 10.3390/robotics15040074

Authors: Kaisei Hosoyama Qingjiu Huang

This paper proposes a hybrid control method for a 6-DOF robot arm using dynamic impedance to achieve stability, high precision, and robustness simultaneously. Conventional impedance control with fixed inertia, viscosity, and stiffness values lacks robustness against changes in working conditions. The proposed method designs an impedance model for the end-effector and performs position control by adding force-based displacement corrections to the target position for force-controlled axes. Dynamic impedance is realized by relating impedance characteristics to joint angles and angular velocities through the final value theorem and quadratic form transient response analysis. MATLAB/Simulink simulations of wall-wiping motion using an RPY-type 6-DOF robot verify the effectiveness of the proposed method.

Robotics doi: 10.3390/robotics15040073

Authors: Kashif Khalid Yasar Ayaz Umer Asgher Vladimír Socha Sara Ali Khawaja Fahad Iqbal

Real-time motion planning in cluttered and dynamically evolving environments remains challenging due to the need to ensure rapid convergence, collision avoidance, computational efficiency, and robustness against local minima under frequent changes. Although sampling-based planners such as RRTX* and ABIT* provide strong theoretical guarantees, their practical deployment in dense dynamic scenarios is often limited by high sampling overhead and computational latency. This paper proposes a Fast Converging Adaptive Algorithm (FCAA), a deterministic sampling-based framework integrating adaptive sampling density, temperature-controlled exploration, and dynamic step-size regulation within a unified heating and annealing mechanism. The temperature parameter governs both the spatial sampling band and incremental expansion radius, enabling controlled transitions between goal-directed expansion and stochastic exploration when stagnation occurs. The algorithm is evaluated using a two-stage protocol comprising intrinsic validation and benchmarking. Across 36 environments with obstacle densities ranging from 3% to 20% and velocities between −30 and +30 m/s, FCAA achieved a 100% success rate within the defined experimental design while maintaining path quality comparable to or better than RRTX* and ABIT*. Unlike the reference planners, which typically required tens of thousands of samples and seconds of computation, FCAA operated with substantially reduced sampling effort, typically tens of nodes, and planning times from 0.1 to 320 ms depending on scenario complexity. Within the simulation framework, the results indicate that the proposed temperature-regulated strategy enables fast and computationally efficient motion planning under dynamic constraints, making FCAA suitable for time-critical robotic navigation scenarios.

Robotics doi: 10.3390/robotics15040072

Authors: Diego Martinez-Baselga Diego Lanaspa Luis Riazuelo Luis Montano

Efficient navigation in crowded and dynamic environments is crucial for robot integration into human spaces. AVOCADO (AdaptiVe Optimal Collision Avoidance Driven by Opinion) generates collision-free velocities using Velocity Obstacles and adaptation to the cooperation estimation among agents. However, it assumes holonomic motion and cannot handle non-holonomic constraints, such as those of differential-drive robots. We propose DD-AVOCADO, an extension of AVOCADO that incorporates differential-drive kinematics to compute feasible and safe velocities. The method combines AVOCADO-based planning with a non-holonomic controller and accounts for tracking errors to avoid collisions. Simulation results across diverse scenarios show a significant reduction in collisions and efficient navigation in scenarios with cooperative and non-cooperative agents, and hardware experiments demonstrate its applicability in robot platforms. The method has the potential to be applied to other dynamic models.

Robotics doi: 10.3390/robotics15040071

Authors: Ionut Geonea Andrei Corzanu Cristian Copilusi Adriana Ionescu Daniela Tarnita

Lower-limb rehabilitation exoskeletons must balance biomechanical compatibility, structural safety, and low mass to enable practical, repeatable gait assistance. This paper proposes a planar pantograph-derived exoskeleton leg driven by a Chebyshev Lambda linkage and develops an integrated workflow from mechanism synthesis to manufacturable optimization and experimental verification. A mannequin-coupled multibody model was built in MSC ADAMS to evaluate joint kinematics, end-point (foot) trajectories, and joint reaction forces under multiple scenarios (fixed-frame, ramp, stair ascent, and inclined-plane walking). The extracted joint loads were transferred to a parametric finite element model in ANSYS Workbench 2019, where response surface surrogates and a multi-objective genetic algorithm (MOGA) were used to minimize mass under stiffness and strength constraints. For the optimized load-bearing link, the selected minimum-mass design reached a component mass of 0.542 kg while respecting the imposed structural limits, i.e., a maximum total deformation below 0.2 mm and a maximum equivalent (von Mises) stress below 50 MPa (e.g., ~0.188 mm deformation and ~39 MPa stress in the optimal candidate). A rapid prototype was manufactured by 3D printing and experimentally evaluated using CONTEMPLAS high-speed video tracking, providing measured XM(t) and YM(t) trajectories and joint-angle histories for quantitative comparison with simulations via RMSE metrics.

Robotics doi: 10.3390/robotics15040070

Authors: Vegard Brekke Erlend Odd Berge Eirik Dybdahl Jayant Singh Ilya Tyapin

This paper presents an open-source ROS 2 navigation and sensor platform for quadruped robots, demonstrated on Boston Dynamics Spot in a laboratory environment. The platform integrates SLAM Toolbox for mapping and localisation, Navigation2 with MPPI and Smac Hybrid-A* for global path planning, and a frontier-based autonomous exploration module with practical handling of unreachable frontiers. The paper validates and verifies current, open-source algorithms deployed on off-the-shelf hardware. A greedy wavefront-based frontier selection method is presented that prioritizes Time-to-Closest-Viable-Frontier (TCVF) by terminating the search as soon as a feasible frontier is identified. On a real robot dataset replayed across five goal scenarios, the method reduces median selection latency from 94.31 ms to 51.08 ms (95th percentile: 109.54 ms to 56.99 ms), corresponding to a 1.85-times improvement in compute time compared to a standard implementation. The system also employs Zenoh middleware and Foxglove for remote monitoring and control, enabling flexible, high-bandwidth operation. The platform, including configuration files and launch scripts, is released openly to support future research and deployment on quadruped robots.

Robotics doi: 10.3390/robotics15040069

Authors: Ahmed A. Abdelrahman Basheer Al-Tawil Ayoub Al-Hamadi

Gaze estimation, a critical facet of understanding user intent and enhancing human–computer interaction, has seen substantial advancements with the integration of deep learning technologies. Despite the progress, the application of deep learning in gaze estimation presents unique challenges, notably in the adaptation and optimization of these models for precise gaze tracking. This paper conducts a thorough review of recent developments in deep learning-based gaze estimation, with a particular focus on the evolution from traditional methods to sophisticated appearance-based techniques. We examine the key components of successful gaze estimation systems, including input feature processing, neural network architectures, and the importance of data preprocessing in achieving high accuracy. Our analysis extends to a comprehensive comparison of existing methods, shedding light on their effectiveness and limitations within various implementation contexts. Through this systematic review, we aim to consolidate existing knowledge in the field, identify gaps in current research, and suggest directions for future investigation. By providing a clear overview of the state-of-the-art in gaze estimation and discussing ongoing challenges and potential solutions, our work seeks to inspire further innovation and progress in developing more accurate and efficient gaze estimation systems.

Robotics doi: 10.3390/robotics15040068

Authors: Pangcheng David Cen Cheng Cesare Luigi Blengini Rosario Francesco Cavelli Angela Ripi Marina Indri

In recent years, applications with robots collaborating actively with humans have been increasing. The transition from Industry 4.0 to 5.0 rearranges the focus of fully automated processes to a human-centered system that allows more customization and flexibility. In human-centered systems, the robot is expected to safely assist or provide support to the human operator, avoiding any unintentional harm, while the latter is focused on tasks that require human reasoning, since current decision-making systems still have some limitations. This survey reviews all the main functionalities required to make a robot (collaborative or not) act as an assistant for human operators, analyzing and comparing solutions proposed by the authors (based on previous works) and/or the ones available in the literature. In this way, it is possible to combine those functionalities and build a complete framework enabling safe mobile manipulation while interacting with humans. In particular, a mobile manipulator is used to receive requests from a user, navigate in a human-shared environment, identify the requested object, and grasp and safely deliver such an object to the user. The framework, which is completed by a user interface designed using Android Studio, is developed in ROS1, tested, and validated on a real mobile manipulator in real-world conditions.

Robotics doi: 10.3390/robotics15040067

Authors: Ahmad Aldaher Sergei Savin

In recent years, we have seen a constant increase in the capabilities of walking robots, leading to early cases of their practical use, and a much broader application is expected in the near future. However, creating a robust control design (in the presence of disturbances and model uncertainties) for walking robots still remains a challenge. One challenging source of uncertainty is the combination of the contact constraints and the lack of full state information, which can potentially lead to an offset (a steady-state error) in the robot’s position, interfering with tasks requiring high accuracy and deteriorating the overall performance of the robot. This is further exacerbated by the presence of multiplicative model uncertainties, common to mobile robots. In this work, we introduce an H∞ control formulation designed to attenuate this type of disturbance. The proposed method can handle norm-bounded multiplicative uncertainties in the state, control, and disturbance matrices using a full-state static feedback control. The resulting control design procedure is a single semidefinite program which provides a large computational advantage over the alternative dynamic feedback controller methods. We demonstrate the effectiveness of the method in comparison with the alternative formulations in simulation. We demonstrate that the method can be effectively tuned using a regularization term in the cost function. We show that the upper bounds on the H∞ gain of the closed-loop system can be effectively tightened post control design.

Robotics doi: 10.3390/robotics15040066

Authors: Masud Karim Md. Solaiman Mia Saifuddin Md. Tareeq Md. Hasanuzzaman

Autism Spectrum Disorder (ASD) is defined by ongoing difficulties in social communication, flexibility in behavior, and adaptive learning skills. Interventions that utilize robots have demonstrated potential in providing organized training for children with ASD; however, there is a lack of controlled studies that specifically examine the effects of reinforcement strategies. This research introduces a systematic interaction policy based on reinforcement, founded on the principles of Applied Behavior Analysis (ABA), and assesses its effectiveness through a randomized controlled experimental design with observation. The humanoid robot NAO was used in two different interaction scenarios, one involving a reinforcement condition (RC) and the other a non-reinforcement condition (RC), ensuring that the instructional material and environment were maintained, while only the availability of contingent positive feedback was altered. A total of 50 participants diagnosed with ASD Level 2 engaged in structured word-learning sessions. Learning outcomes were assessed using institutional performance criteria, average response time, and emotion analysis derived from a CNN-based facial expression model. Independent samples t-tests revealed statistically significant improvements in both performance scores (t(48) = 3.779, p < 0.05) and response times (t(48) = 3.758, p < 0.05) in the reinforcement condition compared to the non-reinforcement condition. The findings demonstrate that structured ABA-based reinforcement within robotic interaction significantly enhances learning efficiency and task engagement, contributing methodologically rigorous evidence to robot-assisted ASD intervention research.

Robotics doi: 10.3390/robotics15030065

Authors: Adam Mark Mazurick Alex Ferworn

When is an unknown, confined environment traversable for a specific ground robot using only touch? We answer by (i) giving an environment-anchored definition of traversability, expressed through the max-min value T★(E;A)=supπ∈ΠS→Ginfs∈[0,1]ϕ(π(s)), where the bottleneck margin ϕ aggregates the clearance, curvature (ρ≥Rmin), slope/step, and friction constraints, and (ii) introducing an on-policy, tactile certificate (TC) that maintains a conservative, monotone lower bound Tt using partial contact histories. The TC fuses pessimistic free-space from contacts and the body envelope, the M3 decaying contact memory as a risk prior, and local bend/FSR proxies; a certificate is issued when Tt>0 and the explored corridor graph connects S to G. Relative to Papers 1–2 (tactile traversal; offline software assurance), this work formalizes traversability itself and provides a tactile-only, online certificate computable during runs. In a retrospective analysis of 660 trials across Indoor/Outdoor/Dark lighting environments, (H1) the early TC margin predicts success and traversal time better than contact/dwell heuristics (higher AUC/R2), (H2) the TC predictivity is lighting-invariant, and (H3) speed-gating M3 by a TC margin recovers part of the CB-V speed gap without degrading success. Artifacts include the TC implementation, explored-corridor graphs, and per-trial TC time series added to the Paper-1 log bundle; these materials are available from the corresponding author upon reasonable request.

Robotics doi: 10.3390/robotics15030064

Authors: Yuanzhong Li Hiroyuki Ishii

Soft pneumatic actuators (SPAs) are widely used in robotic systems due to their inherent compliance and safety during human–robot interaction. However, their intrinsic softness often leads to insufficient stiffness and a low load-bearing capacity, which limit their applicability. In this work, a novel soft–rigid hybrid pneumatic actuator incorporating a spine-like internal structure is proposed to enhance the effective stiffness while preserving bending flexibility. Inspired by the biomechanical structure of the human spine, the embedded spine-like structure consists of interconnected rigid vertebrae integrated along the central axis of a soft pneumatic actuator. Static bending experiments under different base orientations and external loads are conducted to evaluate the actuator’s performance. The experimental results demonstrate that the proposed actuator exhibits improved posture retention, enhanced load-bearing capacity, and higher robustness against gravitational loading compared to a soft pneumatic actuator without a spine-like structure. These results confirm that the spine-like internal structure effectively increases the actuator’s effective stiffness, enabling stable bending behavior under various working conditions.

Robotics doi: 10.3390/robotics15030063

Authors: Luis Escobar David Akhihiero Jason N. Gross Guilherme A. S. Pereira

Underground mine inspection is a critical operation for safety and resource management. It presents unique challenges, including confined spaces, harsh environments, and the lack of reliable positioning systems. This paper presents the design, development, and evaluation of an Unmanned Aerial Vehicle (UAV) specifically engineered for supervised autonomous inspection in subterranean scenarios. Key technical contributions include mechanical adaptations for collision tolerance, an optimized sensor-actuator selection for navigation, and the deployment of a mission-governing state machine for seamless autonomous acquisition. Furthermore, we detail the data treatment workflow, employing a multi-modal point cloud registration technique that successfully integrates high-resolution visual-depth scans of critical mine pillars into a comprehensive, globally referenced map derived from Light Detection and Ranging (LiDAR) data of the entire workspace. We show experiments that illustrate and validate our approach in two real-world scenarios, a simulated coal mine used to train mine rescue teams and an operating Limestone mine.

Robotics doi: 10.3390/robotics15030062

Authors: Mishell Cadena-Yanez Danel Rico-Melgosa Ekaitz Zulueta Angela Bernardini Jorge Rodriguez-Guerra

Energy consumption optimisation has emerged as a critical need in Autonomous Mobile Robots (AMRs). Conventional A* implementations typically minimise path distance, neglecting energy-relevant factors such as directional changes and trajectory smoothness that significantly impact battery life and operational costs. This work proposes two energy-aware A* variants trained on empirical data from the KUKA KMP 1500 platform, where energy consumption is measured as battery SoC depletion: A*-RF, which integrates a Random Forest (RF) model directly into the cost function, and A*-MOD, which approximates the energy model through RF feature importance weights, achieving linear computational complexity O(nf). The RF model predicted energy consumption with an RMSE below 1.5% relative error, identifying travel distance and rotation angle as the dominant energy factors. Experimental validation across 42 path planning scenarios on a real industrial factory floor demonstrates that A*-MOD reduces energy consumption by up to 58.91% and improves operational autonomy by 2.21 times, with statistically significant improvements (p < 0.01) across all evaluated metrics.

Robotics doi: 10.3390/robotics15030061

Authors: Nilachakra Dash Bandita Sahu Kakita Murali Gopal Indrajeet Kumar Ramesh Kumar Sahoo

This paper highlights a communal fault detection and isolation framework integrating a convolutional neural network (CNN) with a finite-state machine (FSM). The proposed concepts ensure state-based controlled discriminate pattern recognition and enable the diagnosis of different anomalies in the mobile robot in a multi-robot environment. The framework processes the time-series sensor data through the convolution layer upon experiencing different types of fault and governs different states based on fault diagnosis and recovery. The proposed concept has been validated using a Python 3.11 and Webot environment featuring the shrimp robot in a multi-robot arena. The model obtained an accuracy of 97% in identifying and classifying faults, enabling automated recovery of faulty robots in the multi-robot environment. Experiments conducted on different simulators demonstrate that effective fault management can be achieved with low training loss.

Robotics doi: 10.3390/robotics15030060

Authors: Matteo Dall’Olio Edoardo Ida’ Marco Carricato

The rapid increase in the production of Waste Electrical and Electronic Equipment (WEEE) and batteries requires advanced automated disassembly solutions. While disassembly automation has progressed, the non-destructive removal of electrical cable connectors (ECCs) remains a critical unresolved challenge, particularly for battery packs where safety is paramount. This paper presents a critical review of the state-of-the-art in robotic ECC disassembly. To systematically assess the technological maturity of the field, the authors introduce a functional decomposition of the process into six fundamental tasks: detection, pose estimation, accessibility, motion planning, manipulation, and extraction. While detection, pose estimation, and manipulation are more advanced due to contributions from adjacent fields like assembly and inspection, accessibility, motion planning, and extraction are still at an early stage. Based on the identified gaps, the authors suggest that future developments could follow two main directions: leveraging comprehensive databases for applications with limited variability, or shifting the disassembly approach from the connector housing to the locking mechanism to achieve broader applicability.

Robotics doi: 10.3390/robotics15030059

Authors: Federica Serra Cristian Camardella Antonio Frisoli Daniele Leonardis

This research presents a new glove-shaped wearable device, designed to deliver torsional cues on the wrist as a tactile guidance tool. The device integrates four tactile modules that apply modulated shallow torque to the anatomical wrist articulation, providing torsional hints for both ulnar–radial deviation and flexion–extension degrees of freedom (DOF). The aim of this research is to evaluate whether this new type of stimulation can convey accurate directional cues on 2-DOF wrist movements, with the main target application as a guidance and support tool in virtual motor rehabilitation. Effectiveness was tested in virtual reality (VR) serious games designed to exercise wrist movements through a virtual navigation task. The glove-shaped haptic device was introduced to guide the user by directional cues provided through the shallow-torques approach. Results showed that the tactile sensations were effective in conveying accurate directional cues, reliably guiding subjects’ wrist movements on 2-DOF. This research highlights the potential of a compact, non-bulky glove-shaped device for providing clear directional cues at the wrist across 2-DOF. The shallow-torque approach, combining the natural interaction of force feedback with hardware simplicity and lightness closer to vibrotactile devices, has the potential of scalability on other body segments, and shows promise for applications in rehabilitation, postural guidance, and virtual interaction.

Robotics doi: 10.3390/robotics15030058

Authors: Enrique Coronado

Generative Artificial Intelligence (GenAI), particularly Foundation Models (FMs), has recently become a key component of Industry 5.0. Despite growing interest in integrating these technologies into industrial environments, comprehensive analyses of the socio-technical opportunities and challenges of deploying these emerging AI systems in real-world settings remain limited. This article proposes a socio-technical conceptual perspective, termed Responsible Agentic Robotics (RAR), which structures the lifecycle deployment of agentic AI-enabled robotic systems around three core layers: context, design, and value. Additionally, this article presents a brief review of 21 peer-reviewed studies published between 2023 and 2025 (post-ChatGPT era) on FMs and agentic AI-enabled Human–Robot Collaboration (HRC) in industrial assembly/disassembly environments. The results indicate that existing research remains predominantly technology-centric, with a strong emphasis on enhancing robot autonomy, while comparatively limited attention is devoted to human-centered and responsible practices. Moreover, empirical evaluations of human, social, and sustainability dimensions, such as worker empowerment, human factors, well-being, inclusivity, resource utilization, and environmental impact, are rarely conducted and poorly discussed. This article concludes by identifying key socio-technical gaps, outlining future research directions.

Robotics doi: 10.3390/robotics15030057

Authors: Daniel Greve Christian Kreischer

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Robotics doi: 10.3390/robotics15030056

Authors: Aman Behal

It is my pleasure to present the Special Issue “Adaptive and Nonlinear Control of Robotics”, which brings together nine original research contributions exploring state-of-the-art control strategies for robotic systems operating under nonlinear dynamics, uncertain parameters, reconfiguration, or complex physical constraints [...]

Robotics doi: 10.3390/robotics15030055

Authors: Matthew Lisondra Beno Benhabib Goldie Nejat

Rapid advancements in foundation models, including Large Language Models, Vision-Language Models, Multimodal Large Language Models, and Vision-Language-Action models, have opened new avenues for embodied AI in mobile service robotics. By combining foundation models with the principles of embodied AI, where intelligent systems perceive, reason, and act through physical interaction, mobile service robots can achieve more flexible understanding, adaptive behavior, and robust task execution in dynamic real-world environments. Despite this progress, embodied AI for mobile service robots continues to face fundamental challenges related to the translation of natural language instructions into executable robot actions, multimodal perception in human-centered environments, uncertainty estimation for safe decision-making, and computational constraints for real-time onboard deployment. In this paper, we present the first systematic review of foundation models in mobile service robotics, following the preferred reporting items for systematic reviews and meta-analysis (PRISMA) guidelines. Using an OpenAlex literature search, we considered 7506 papers for the years spanning 1968–2025. Our detailed analysis identified four main challenges and how recent advances in foundation models, related to the translation of natural language instructions into executable robot actions, multimodal perception in human-centered environments, uncertainty estimation for safe decision-making, and computational constraints for real-time onboard deployment, have addressed these challenges. We further examine real-world applications in domestic assistance, healthcare, and service automation, highlighting how foundation models enable context-aware, socially responsive, and generalizable robot behaviors. Beyond technical considerations, we discuss ethical, societal, human-interaction, and physical design and ergonomic implications associated with deploying foundation-model-enabled service robots in human environments. Finally, we outline future research directions emphasizing reliability and lifelong adaptation, privacy-aware and resource-constrained deployment, as well as the governance and human-in-the-loop frameworks required for safe, scalable, and trustworthy mobile service robotics.

Robotics doi: 10.3390/robotics15030054

Authors: Chunxin Yang Ruoyu Jia Qitong Guo Xiaohang Shi Masahiro Hirano Yuji Yamakawa

Most existing research in human pose estimation focuses on predicting joint positions, paying limited attention to recovering the full 6D human pose, which comprises both 3D joint positions and bone orientations. Position-only methods treat joints as independent points, often resulting in structurally implausible poses and increased sensitivity to depth ambiguities—cases where poses share nearly identical joint positions but differ significantly in limb orientations. Incorporating bone orientation information helps enforce geometric consistency, yielding more anatomically plausible skeletal structures. Additionally, many state-of-the-art methods rely on large, computationally expensive models, which limit their applicability in real-time scenarios, such as human–robot collaboration. In this work, we propose STAG-Net, a novel 2D-to-6D lifting network that integrates Graph Convolutional Networks (GCNs), attention mechanisms, and Temporal Convolutional Networks (TCNs). By simultaneously learning joint positions and bone orientations, STAG-Net promotes geometrically consistent skeletal structures while remaining lightweight and computationally efficient. On the Human3.6M benchmark, STAG-Net achieves an MPJPE of 41.8 mm using 243 input frames. In addition, we introduce a lightweight single-frame variant, STG-Net, which achieves 50.8 mm MPJPE while operating in real time at 60 FPS using a single RGB camera. Extensive experiments on multiple large-scale datasets demonstrate the effectiveness and efficiency of the proposed approach.

Robotics doi: 10.3390/robotics15030053

Authors: Francesco Durante

This paper presents a bench-top occupational power-assist robot aimed at reducing biomechanical effort during repetitive material handling. The prototype adopts a SCARA-like structure with three degrees of freedom and provides assistance on the vertical (z) axis through a three-phase brushless DC (BLDC) motor driven in field-oriented control with inner-loop current regulation. The user interacts with the robot through a single handle-mounted load cell. The measured interaction force is converted, via a calibration-based mapping, into a motor current reference that enforces a prescribed force-sharing ratio. In this way, the drive’s embedded current loop acts as the low-level torque regulator, and the system can share gravitational and inertial loads without additional environment force sensing or explicit high-level impedance/admittance dynamics. A coupled electro-mechanical model is derived and used to select the assistance gain and to verify feasibility in simulation. A pilot experimental campaign with eight participants and two payloads (0.5 kg and 1.5 kg) was carried out on sinusoidal and random tracking tasks. With assistance enabled, the operator contribution was reduced to about 15% of the total load, and the mean bicep brachii EMG amplitude decreased by about 60%, while tracking accuracy was generally preserved and often improved.

Robotics doi: 10.3390/robotics15030052

Authors: Nizar Rokbani

Surgical robots require sub-millimeter accuracy and reliable inverse kinematics across anatomies. Population-based metaheuristics address this, but static parameters may limit achieving the needed precision for clinical use. This study introduces the Rough Sets Meta-Heuristic Schema (RSMS) for dynamic, context-aware control. RSMS categorizes agents (Elite, Boundary, Poor) via Rough Set discretization based on fitness and distribution, allocating resources accordingly without problem-specific heuristics. To demonstrate the approach’s effectiveness, RSMS was implemented within Particle Swarm Optimization and evaluated as a surgical robotics inverse kinematics solver and path planner. In simulations using three surgical problems, RS-PSO allowed upgrading of the performance of the standard PSO in terms of consistent convergence and success in tight search spaces. Statistical tests confirmed these improvements. Using a 7-DOF KUKA LBR iiwa robot and surgical benchmarks of landmark acquisition, spiral trajectory tracking, and constrained path, RS-PSO achieved success rates of 100%, 67%, and 100%, respectively, meeting surgical requirements. The results demonstrate clinical gains in accuracy, consistency, and reproducibility for minimally invasive surgery. These findings support the practical advantages of RS-PSO and, more importantly, show that the RS-MH framework can be used as a general, reusable tool to improve the robustness, precision, and reproducibility of many swarm-based meta-heuristics for surgical robotics and other applications.

Robotics doi: 10.3390/robotics15030051

Authors: Giuliano Fabris Lorenzo Scalera Alessandro Gasparetto

Energy efficiency is a challenging research topic in robotics, since it can reduce operating costs and increase production sustainability. In this paper, we present a strategy for energy-efficient trajectory planning in redundant robotic systems. The proposed approach aims at optimizing the solution of inverse kinematics at each of the waypoints that define the considered task, so as to minimize the energy consumption. The approach is validated with simulations and bespoke experiments on two different robotic systems with seven and eight degrees of freedom (DOFs). Two test cases are considered, i.e., a point-to-point motion and a pick-and-place task. The experimental results quantify the energy saving capabilities of the proposed approach up to 82.54% and 94.28% with the seven-DOF and eight-DOF robots, respectively, with respect to reference cases.

Robotics doi: 10.3390/robotics15030050

Authors: Afonso M. Timóteo Ramiro S. Barbosa Isabel S. Jesus

This paper presents the design and implementation of an integrated robotic system capable of detecting objects through computer vision and making decisions based on logic strategies to perform physical tasks. For that, the system uses a robotic arm to play the Tic-Tac-Toe game utilizing a Q-learning algorithm to determine optimal moves. The system can be controlled using a graphical interface that enables real-time monitoring, facilitating seamless interaction between the user and the robotic arm. Three algorithms with different decision strategies were developed: a random decision algorithm, the MiniMax algorithm, and Q-learning, a reinforcement-learning algorithm. The results obtained highlight the control of the robotic arm using kinematic equations, the training of a robust YOLOv5 model, and the effective learning capability of a Q-learning algorithm. The proposed system presents practical implementation of the robotic system which can be used as a basis for further projects and for teaching robotics.

Robotics doi: 10.3390/robotics15030049

Authors: Federico Neri Gaetano Lettera Giacomo Palmieri Massimo Callegari

In Industry 5.0, the transition from fixed traditional automation to flexible human–robot collaboration (HRC) needs interfaces that are both intuitive and efficient. This paper introduces a novel, multimodal control system for autonomous object handling, specifically designed to enhance natural user interaction in dynamic work environments. The system integrates a 6-Degrees of Freedom (DoF) collaborative robot (UR5e) with a hand-eye RGB-D vision system to achieve robust autonomy. The core technical contribution lies in a vision pipeline utilizing deep learning for object detection and point cloud processing for accurate 6D pose estimation, enabling advanced tasks such as human-aware object handover directly onto the operator’s hand. Crucially, an Automatic Speech Recognition (ASR) is incorporated, providing a Natural Language Understanding (NLU) layer that allows operators to issue real-time commands for task modification, error correction and object selection. Experimental results demonstrate that this multimodal approach offers a streamlined workflow aiming to improve operational flexibility compared to traditional HMIs, while enhancing the perceived naturalness of the collaborative task. The system establishes a framework for highly responsive and intuitive human–robot workspaces, advancing the state of the art in natural interaction for collaborative object manipulation.

Robotics doi: 10.3390/robotics15030048

Authors: Mohammad Jahani Moghaddam Filippo Arrichiello

In this paper, we propose a distributed admittance control framework for joint manipulation of objects by multiple robotic arms that addresses the challenges of human–robot interaction. The system is developed to control the joint transportation of an object by N Franka Emika Panda robots (validated with up to four in simulations) using external human force estimation in a distributed manner without relying on centralized computation or force sensors. We integrate a hybrid observer by combining a distributed force estimator with a nonlinear disturbance observer (NDOB) to achieve accurate human force estimation and minimize estimation errors in simulations. Adaptive radial basis function neural networks (RBFNNs) are employed to dynamically adjust the damping and inertia parameters, enhancing the system’s adaptability and stability. Event-based communication minimizes network bandwidth usage, while consensus protocols ensure synchronization of state estimates across robots. Unlike conventional methods, the proposed observer operates in a fully sensorless manner: no human-force measurements are required. The estimation relies solely on locally available robot states, maintaining high accuracy while reducing system complexity. The framework demonstrates scalability to multiple robots, enhancing robustness in distributed settings. Simulation results show superior performance in terms of path tracking, force estimation accuracy, and communication efficiency compared to centralized approaches. Specifically, the event-triggered strategy reduces communication messages by approximately 70% compared to always-connected mode while maintaining comparable RMSE in position (9.97×10−5 vs. 7.39×10−5) and velocity (2.52×10−5 vs. 3.76×10−5), outperforming periodic communication.

Robotics doi: 10.3390/robotics15020047

Authors: Tagor Hossain Peng Shi Levente Kovacs

Accurate apple detection and precise three-dimensional (3D) localisation are essential for autonomous robotic harvesting in orchard environments, where occlusion, illumination variation, depth noise, and the similar colour appearance of fruits and surrounding leaves present significant challenges. This paper proposes a dual-detector vision framework combined with depth-aware back-projection to achieve robust apple detection and metric 3D localisation in real time. The method integrates the complementary strengths of YOLOv8 and Mask R-CNN through confidence-weighted fusion of bounding boxes and pixel-wise union of segmentation masks, producing stabilised two-dimensional (2D) apple representations under visually ambiguous conditions. The fusion results are converted into dense 3D representations through depth-guided projection within the camera coordinate system representing the visible fruit surface. A depth-consistency weighting strategy assigns higher influence to depth-reliable pixels during centroid computation, thereby suppressing noisy or occluded depth measurements and improving the stability of 3D fruit centre estimation, while local intensity normalisation standardises neighbourhood-level pixel intensities to reduce the impact of shadows, highlights, and uneven lighting, enabling more consistent segmentation and detection across varying illumination conditions. Experimental results demonstrate an accuracy of 98.9%, an mAP of 94.2%, an F1-score of 93.3%, and a recall of 92.8%, while achieving real-time performance at 86.42 FPS, confirming the suitability of the proposed method for robotic harvesting in challenging orchard environments.

Robotics doi: 10.3390/robotics15020046

Authors: Calin Vaida Ionut Zima Florin Graur Bogdan Gherman Vasile Bulbucan Paul Tucan Alexandru Pusca Florin Zaharie Pierre Mougenot Adrian Pisla Damien Chablat Nadim Al Hajjar Doina Pisla

Background: Flexible surgical instruments for Robot-Assisted Minimally Invasive Surgery (RAMIS) face a critical limitation: the inability to rotate the distal head while the instrument is in a bent configuration, which restricts the maneuverability in narrow surgical workspaces. Methods: This paper presents a novel 4-degree-of-freedom (DOF) flexible laparoscopic instrument with a 10 mm diameter, incorporating a 3D-printed flexible element. The design enables independent bending (0–90°), continuous distal head rotation (360°), gripper actuation (0–60°), and rod rotation (180°). A constant-curvature kinematic model was developed. The instrument was manufactured using PolyJet 3D printing technology and integrated with the ATHENA parallel robot for proof-of-concept experimental validation. Results: Experimental tests demonstrated successful independent 360° distal head rotation across the full bending range (0–90°), validated through simulated surgical procedures including stomach retraction. Quantitative characterization using optical motion capture revealed a maximum angular deflection of 79.85° at 670 g applied load, with tip displacements of 74.95 mm (X) and 91.18 mm (Y). The measured grasping force was approximately 2 N, tip position repeatability was ±2.86 mm, and fatigue testing demonstrated no degradation after 500 bending cycles, confirmed by digital microscope inspection. The instrument performed multiple manipulation tasks, including elastic band transfer, wire path navigation, spring manipulation, and tissue grasping. Conclusions: The proposed instrument addresses a significant white spot in surgical robotics by adding an additional functional capability enabling grasper reorientation without repositioning the entire instrument.

Robotics doi: 10.3390/robotics15020045

Authors: Antonio Di Tecco Daniele Leonardis Edoardo Ragusa Antonio Frisoli Claudio Loconsole

AI-driven assistance can help the user perform complex teleoperated tasks, introduce autonomous patterns, or adapt the workbench to objects of interest. On the other hand, the level of assistance should be responsive to the user’s response and adapt accordingly to promote a positive and effective experience. Envisaging this final goal, this article investigates whether physiological signals can be used to estimate the user’s performance and response in a teleoperation setup, with and without AI-driven assistance. In more detail, a teleoperated pick-and-place task was performed with or without AI-driven assistance during the grasping phase. A deep-learning algorithm for affordance detection provided assistance, helping participants align the robotic hand with the target object. Physiological and kinematic data were measured and processed by machine learning models to predict the effects of AI assistance on task performance during teleoperation. Results showed that AI-driven assistance, as expected, affected pick-and-place performance. Beyond this, the assistance affected the participant’s fatigue level, which the machine learning models could predict with an average accuracy of 84% based on the physiological response. In addition, the success or failure of the pick-and-place task could be predicted with an average accuracy of 88%. These findings highlight the potential of integrating deep learning with biometric feedback and gesture-based control to create more intuitive and adaptive HRI systems.

Robotics doi: 10.3390/robotics15020044

Authors: Sayan Mandal Stefan Ainetter Friedrich Fraundorfer

Segmenting individual timber logs in robotic grasping scenarios poses significant challenges due to cluttered arrangements, overlapping geometries, and visually uniform textures, requiring instance segmentation models that balance accuracy and computational efficiency. In this work, we study the integration of the EfficientViT-SAM backbone into the MP-Former framework to analyze its impact on segmentation accuracy, inference speed, and cross-dataset generalization in autonomous forestry applications. Our contributions are threefold: (1) we benchmark Mask2Former and MP-Former with different variants of Swin Transformer as backbones on the TimberSeg 1.0 dataset, (2) we study the use of the EfficientViT-SAM-XL architecture as an alternative encoder backbone to analyze its impact on inference speed and segmentation accuracy, and (3) we use an In-house dataset as a hold-out test set, comprising 113 images and 923 annotations in the annotated subset and 50 images in the unannotated subset, for evaluating model generalization under real-world deployment scenarios. On the TimberSeg 1.0 dataset, our top-performing model, EfficientViT-SAM-XL1 MP-Former, achieves an mAP of 61.05, outperforming the Swin-B Mask2Former of the TimberSeg 1.0 paper by +3.52 mAP, while running at 12 FPS (+3.53 FPS gain). When tested on our In-house dataset, the model attains an mAP of 67.06. Notably, it matches the memory efficiency of TimberSeg’s strongest baseline, despite having nearly double the number of parameters, demonstrating its practical viability for robotic applications in forestry environments.

Robotics doi: 10.3390/robotics15020043

Authors: Amirreza Fahim Golestaneh

Biological muscles generate tension from the combined contribution of the passive elastic recoil and the actively controlled contractile mechanisms. Understanding and replicating these passive and active tensions is necessary and beneficial for designing soft robotic actuators that emulate muscle-like behavior. In the current work, the aim is to develop a mathematical framework for modeling both the passive and active tensions in a biological muscle as functions of muscle length and contraction velocity. We will describe the passive tension by a nonlinear monotonically increasing function of length with threshold behavior in order to capture the experimentally observed stiffening occurring in stretched biological muscles. We will model the active tension using the superposition of Gaussian functions that relate bell-shaped tension-length with a flat plateau over the optimal length of the sarcomere. The parameters of this Gaussian representation of the active tension-length relation are determined from formulating a least-squares optimization problem, such that a Characteristic (indicator) function is approximated globally over the optimal length range of the sarcomere by summation of some Gaussian functions. The closed-form formulations for the required integrals are derived using the integral of the product of two Gaussian functions over Rn as well as the error function which enables efficient parameter identification. We will also propose a symmetric tension–velocity relation that distinguishes three phases of concentric, eccentric and isometric contractions, and is parametrized directly by measurable quantities of isometric tension and maximum shortening velocity. The passive and active tensions are finally combined into a unified comprehensive tension model in which the exponentially modeled passive tension is added up to the active contribution, formulated as the product of the activation level, a normalized length-dependent factor and a normalized velocity-dependent factor. The resulting model reproduces canonical tension-length and tension-velocity relations and provides an analytically tractable comprehensive tension model that can be embedded in the dynamics of soft and continuum robot actuators inspired by biological muscles.

Robotics doi: 10.3390/robotics15020042

Authors: Ryosuke Hakamata Mitsuru Endo Yusuke Sugahara

Fly-casting is a throwing technique in which a flexible rod is used to cast a lightweight line. In skilled fly-casting, a phenomenon known as the rebound phenomenon is observed, where the residual vibration of the rod tip is suppressed by the re-acceleration of the rod handle during the rod-stop phase. This vibration suppression plays an essential role in the casting performance; however, an engineering method for this phenomenon has not been established. Therefore, the purpose of this study is to propose a trajectory-shaping method by interpreting the rebound phenomenon as a vibration suppression control problem for flexible systems with nonzero initial conditions. The proposed method applies a conventional shaping framework to rod systems by introducing a second-order approximation and repeatedly shaping the input trajectory to suppress the approximation errors. Through simulations using a rod model, it was shown that the shaped trajectory yields the characteristic re-acceleration of the rod-handle angular velocity during the rod-stop phase, consistent with the rebound phenomenon. Through experiments using a robotic prototype, it was confirmed that the rod tip vibration amplitude is suppressed by over 80% in two types of casting. These results are useful for further studies on the engineering realization of fly-casting.

Robotics doi: 10.3390/robotics15020041

Authors: Jincheng Li Chenghao Lin Amna Mazen Youssef A. Bazzi

As the global population ages, there is a growing need for assistive technologies to help older adults maintain their independence. This work presents a cost-effective autonomous socially assistive robot designed for object retrieval and delivery, enhancing accessibility in home environments. The system is built on the Robot Operating System (ROS) framework and integrates three key components: the Pioneer P3-DX mobile robot for autonomous navigation, the ReactorX-200 robotic arm for pick-and-place operations, and the Kinect v2 RGB-D camera for object detection and localization. Users interact with the robot through natural language processing by issuing voice commands to retrieve various objects. Microsoft Azure-powered speech recognition processes these commands to extract keywords and then localize requested objects on a predefined building map. Pioneer P3-DX, equipped with a Hokuyo LiDAR, enables autonomous navigation and obstacle avoidance, while Kinect v2, integrated with the YOLOv8 algorithm, facilitates object recognition and localization. The robot retrieves and delivers the user’s requested objects while following the shortest available path. Experimental evaluations in a home environment demonstrate the system’s effectiveness in identifying and retrieving requested objects. The subsystems achieve a success rate of 85–95% across more than 50 runs, highlighting their strong performance. The proposed approach provides a proof of concept for future advancements in assistive robotics, demonstrating the seamless integration of advanced technologies into a cost-effective and user-friendly platform.

Robotics doi: 10.3390/robotics15020040

Authors: Bing Ma Zebin Ji Yi Qin Xinye Zhu Tianjiao An

To address the issue that the performance-index function encompassing both dynamic and control processes in traditional adaptive dynamic programming (ADP) cannot be optimized as time approaches infinity, this paper proposes an optimal tracking control method for a modular manipulator based on a nonzero-sum game. The dynamic model of the modular manipulator is established using the Newton–Euler iterative method. By treating each module of the manipulator as a player in a nonzero-sum game and employing adaptive dynamic programming, the trajectory-tracking problem is transformed into an optimal-control problem. The critic neural network approximates the performance-index function without control input to derive the optimal tracking control policy. A stability theorem proves the stability of the closed-loop system, and an experimental platform validates the accuracy and optimality of the proposed method.

Robotics doi: 10.3390/robotics15020039

Authors: Kaoru Ohno Hikaru Nagano Yasuyoshi Yokokohji

Reliable detection of contact states, such as the “mating” of connectors, is crucial for high-quality teleoperated assembly. Conventional systems relying solely on visual and continuous force feedback often fail to convey these discrete high-frequency transients due to the limited high-frequency rendering capabilities. This study investigates the effectiveness of augmenting visual and force feedback with vibrotactile and auditory cues for detecting connector mating. We conducted three experiments: (1) a mating detection task using recorded multimodal data (N=10), (2) a modality contribution analysis (N=10), and (3) a real-time robot connector insertion task (N=10). Results from the real-time task demonstrated that the proposed multimodal feedback significantly reduced the maximum contact force exerted after mating compared to the baseline visual-force condition (p<0.001), thereby enhancing physical safety. Furthermore, vibrotactile and auditory cues were found to be redundant yet complementary, providing robust cues even when one modality is compromised. Although subjective mental workload increased due to sensory integration, the significant improvement in detection clarity and safety justifies the multimodal approach. We conclude that providing transient vibrotactile and auditory cues is a highly effective strategy for compensating for the limitations of conventional force feedback in teleoperated assembly.

Robotics doi: 10.3390/robotics15020038

Authors: Nat Wannawas Jyotindra Narayan Warakom Nerdnoi Arsanchai Sukkuea

Functional Electrical Stimulation (FES) can restore motor function; however, achieving precise multi-joint control remains challenging due to nonlinear muscle dynamics and fatigue. Reinforcement Learning (RL) offers a promising solution, but practical deployment is hindered by the need for patient-specific calibration. This study investigates offline RL approaches for controlling planar arm movements using heterogeneous datasets, aiming to enable zero-shot transfer to new users. We develop a biomechanical arm model in MuJoCo and evaluate four RL algorithms coupled with three offline techniques: conservative Q learning (SAC-CQL and QBR-CQL), Randomized Ensemble (QBR-REM), and distributional RL (IQNBR). Across all conditions, IQNBR demonstrates robust learning and superior control performance, achieving an average RMSE of 3.8±0.6 cm, even when trained on mixed-quality data. These results highlight the potential of distributional RL as a base learning method to build generic FES controllers that can operate without exhaustive calibration, with broader implications for controlling robots with human-like actuation systems.

Robotics doi: 10.3390/robotics15020037

Authors: Ryosuke Tsumura Kiyoshi Yoshinaka

Robotic ultrasound (US) has emerged as a promising solution to enhance the accuracy, consistency, and accessibility of US examinations. Although many studies have proposed various technologies toward full automation, patient acceptance of the robotic US system has not been discussed. This study explored the patient’s acceptability of robotic US through a questionnaire survey. The 480 participants were asked about their willingness to use a medical facility that offers robotic US examinations. The results showed that the score was 3.61 out of 6, slightly above the average of 3.5. As the reasons for wanting to undergo the robotic US examination, many participants pointed to “time” and “cost”. The most common reason for not wanting to use it was “vague anxiety” that patients feel somewhat anxious and “object anxiety” such as a fear of danger due to malfunction. These negative reasons were relatively low among participants who had found diseases via US examinations and among those who already had experience using robots in medical and nursing care fields. This suggests that patients need to increase their knowledge and experience of US examination and robotics to increase their willingness to use automated robotic US.

Robotics doi: 10.3390/robotics15020036

Authors: Federico Guerra Edoardo Ida’ Marco Carricato Sunil Agrawal

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.

Robotics doi: 10.3390/robotics15020035

Authors: Tiago Pereira Carlos Viegas Salviano Soares Nuno Ferreira

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.

Robotics doi: 10.3390/robotics15020034

Authors: Edward J. Haug Vincent De Sapio

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.

Robotics doi: 10.3390/robotics15020033

Authors: 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 Doina Pisla

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.

Robotics doi: 10.3390/robotics15020032

Authors: Ameur Touir Mohsen Denguir Achraf Gazdar Safwan Qasem

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.

Robotics doi: 10.3390/robotics15020031

Authors: Mohamad Al Mdfaa Raghad Salameh Geesara Kulathunga Sergey Zagoruyko Gonzalo Ferrer

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.

Robotics doi: 10.3390/robotics15020030

Authors: Aditya Dilip Patil Siavash Farzan

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.

Robotics doi: 10.3390/robotics15010029

Authors: Zhenxi Cui Jiacong Chen Xin Xu Henry K. Chu

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.

Robotics doi: 10.3390/robotics15010028

Authors: Liam O’Brien Hao Xu

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.

Robotics doi: 10.3390/robotics15010027

Authors: Meher Madhu Dharmana Pramod Sreedharan

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.

Robotics doi: 10.3390/robotics15010026

Authors: Gerardo Flores Héctor M. Becerra Juan Pablo Ramirez-Paredes Alexandre Santos Brandão

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Robotics doi: 10.3390/robotics15010025

Authors: Cuizhi Fei Qiaoling Meng Hongliu Yu Xuhua Lu

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.

Robotics doi: 10.3390/robotics15010024

Authors: Simona Casini Pietro Ducange Francesco Marcelloni Lorenzo Pollini

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.

Robotics doi: 10.3390/robotics15010023

Authors: Mubarak Badamasi Aremu Gamil Ahmed Sami Elferik Abdul-Wahid A. Saif

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.

Robotics doi: 10.3390/robotics15010022

Authors: Shifa Sulaiman Akash Bachhar Ming Shen Simon Bøgh

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.

Robotics doi: 10.3390/robotics15010021

Authors: Cristian Camardella Tommaso Bagneschi Federica Serra Claudio Loconsole Antonio Frisoli

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.

Robotics doi: 10.3390/robotics15010020

Authors: Sohrab Moayer Redwan Alqasemi Rajiv Dubey

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.

Robotics doi: 10.3390/robotics15010019

Authors: Patricio Galarce-Acevedo Miguel Torres-Torriti

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.

Robotics doi: 10.3390/robotics15010018

Authors: Jinjin Yan Huiling Zhang

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.

Robotics doi: 10.3390/robotics15010017

Authors: Shifa Sulaiman Amarnath Harikumar Simon Bøgh Naresh Marturi

Autonomous experimental platforms increasingly rely on robust, vision-guided robotic manipulation to support reliable and repeatable laboratory operations. This work presents a modular motion-execution subsystem designed for integration into self-driving laboratory (SDL) workflows, focusing on the coupling of real-time visual perception with smooth and stable manipulator control. The framework enables autonomous detection, tracking, and interaction with textured objects through a hybrid scheme that couples advanced motion planning algorithms with real-time visual feedback. Kinematic analysis of the manipulator is performed using the screw theory formulations, which provide a rigorous foundation for deriving forward kinematics and the space Jacobian. These formulations are further employed to compute inverse kinematic solutions via the Damped Least Squares (DLS) method, ensuring stable and continuous joint trajectories even in the presence of redundancy and singularities. Motion trajectories toward target objects are generated using the RRT* algorithm, offering optimal path planning under dynamic constraints. Object pose estimation is achieved through a a vision workflow integrating feature-driven detection and homography-guided depth analysis, enabling adaptive tracking and dynamic grasping of textured objects. The manipulator’s performance is quantitatively evaluated using smoothness metrics, RMSE pose errors, and joint motion profiles, including velocity continuity, acceleration, jerk, and snap. Simulation results demonstrate that the proposed subsystem delivers stable, smooth, and reproducible motion execution, establishing a validated baseline for the manipulation layer of next-generation SDL architectures.

Robotics doi: 10.3390/robotics15010016

Authors: Azamat Mustafa Rustem Kaiyrov Yerik Nugman Mukhagali Sagyntay Nurtay Albanbay Algazy Zhauyt Zharkynbek Turgunov Ilyas Dyussebayev Yang Lei

Additive manufacturing technologies for metals are developing rapidly. Among them, wire arc additive manufacturing (WAAM) has become widespread due to its accessibility. However, parts produced using WAAM require surface post-processing; therefore, hybrid technologies have emerged that combine additive and subtractive processes within a single compact manufacturing complex. Such systems make it possible to organize single-piece and small-batch production, including for the repair and restoration of equipment in remote areas. For this purpose, hybrid equipment must be lightweight, compact for transportation, provide sufficient workspace, and be capable of folding for transport. This paper proposes the concept of a multifunctional metal 3D printer based on hybrid technology, where WAAM is used for printing, and mechanical post-processing is applied to obtain finished parts. To ensure both rigidity and low mass, a 3-UPU parallel manipulator and a worktable with two rotational degrees of freedom are employed, enabling five-axis printing and machining. The printer housing is foldable for convenient transportation. The kinematics of the proposed 3D printer are investigated as an integrated system. Forward and inverse kinematics problems are solved, the velocities and accelerations of the moving platform center are calculated, singular configurations are analyzed, and the workspace of the printer is determined.

Robotics doi: 10.3390/robotics15010015

Authors: Weixing Xia Shihui Shen Ping Wang Jinjin Yan

For multi-robot cooperative coverage, an effective spatial division strategy is essential to ensure balanced and spatially continuous task regions for each robot. Traditional grid-based partitioning approaches like DARP (Divide Areas based on Robots’ Positions) and TASR (Task Allocation based on Spatial Regions) often generate discontinuous sub-regions and imbalanced workloads, particularly in irregular or fragmented task spaces. To mitigate these issues, this paper introduces HGTA (Hexagonal Grid-based Task Allocation), a novel method that employs hexagonal tessellation for environmental representation. The hexagonal grid structure provides uniform neighbor connectivity and minimizes boundary fragmentation, yielding smoother partitions. HGTA integrates a multi-stage wavefront expansion algorithm with an iterative region-correction mechanism, jointly ensuring spatial contiguity and load equilibrium across robots. Extensive evaluations in 2D environments with varying obstacle densities and robot distributions show that HGTA enhances spatial continuity—achieving improvements of 18.2% in connectivity and 17.8% in boundary smoothness over DARP, and 7.5% and 9.5% over TASR, respectively—while also improving workload balance (variance reduction up to 18.5%) without compromising computational efficiency. The core contribution lies in the synergistic coupling of hexagonal tessellation, wavefront expansion, and dynamic correction, a design that fundamentally advances partition smoothness and convergence speed. HGTA thus offers a robust foundation for multi-robot cooperative coverage, area surveillance, and underwater search applications where connected and balanced partitions are critical.

Robotics doi: 10.3390/robotics15010014

Authors: Christopher Trent Neville-Dowler Charlie Williams Yuting Zhu Kean C. Aw

Limb amputations are a growing global challenge. Electrically powered prosthetic hands are heavy, expensive, and battery dependent. Body-powered prostheses offer a simpler and lighter alternative; however, existing designs require high body forces to operate, exhibit poor aesthetics, and have limited dexterity. This study aims to present a design of a hydraulically actuated soft bending finger with a simple and scalable manufacturing process. This is then realised into a five-fingered body-powered prosthetic hand that is lightweight, comfortable, and representative of a human hand. The actuator was formed from two silicone materials of different stiffness (Stiff Smooth-Sil 950 and flexible Ecoflex 00-30) and reinforced with double-helix fibres to generate bending under internal hydraulic pressure. A shoulder-mounted hydraulic system has been designed to convert scapular elevation and protraction into actuator pressure. Finite element analysis and physical tests were performed to examine the bending and blocking force performance of the actuators. The physical actuators achieved bending angles up to 230 degrees at 60 kPa and blocking forces of 5.9 N at 100 kPa. The prosthetic system was able to grasp and hold a 320-g water bottle. The results demonstrate a soft actuator design that provides simple and scalable manufacturing and shows how these actuators can be incorporated into a body-powered prosthesis. This study provides a preliminary demonstration of the feasibility of human-powered prosthetics and necessitates continued research. This work makes progress towards an affordable and functional body-powered prosthetic hand that can improve the lives of transradial amputees.

Robotics doi: 10.3390/robotics15010013

Authors: Zoltán Forgó Ferenc Tolvaly-Roșca

The accelerating industrial demand for high-speed manipulation has necessitated the development of robotic architectures that effectively balance dynamic performance with workspace size. While serial SCARA robots offer large workspaces and parallel Delta robots provide high acceleration, existing architectures fail to combine these benefits effectively for specific four-degree-of-freedom (4-DoF) Schoenflies motion tasks. This study introduces and characterizes a novel 4-DoF parallel topology, having a symmetrical build-up, which is distinguished by its use of modular 2-DoF linear drive units. The research methodology entails the structural synthesis of the kinematic chain followed by kinematic analysis using vector algebra to derive closed-form inverse geometric models. Additionally, the Jacobian matrix is formulated to evaluate velocity transmission and systematically classify singular configurations, while the dexterity index is defined to assess the rotational capabilities of the mechanism. Numerical simulations of pick-and-place trajectory were also conducted, varying trajectory curvature to analyze kinematic behavior. The results demonstrate that the proposed modular architecture yields a highly symmetric and homogeneous workspace that can be scaled simply by adjusting the drive module lengths. Furthermore, the singularity and dexterity analyses reveal a substantial, singularity-free operational workspace, although tighter trajectory curvatures were found to impose higher velocity demands on the joints. In conclusion, the proposed mechanism successfully achieves the targeted Schoenflies motion, offering a solution for automated industrial tasks.

Robotics doi: 10.3390/robotics15010012

Authors: Dauren Bizhanov Marco Ceccarelli Kassymbek Ozhikenov Nursultan Zhetenbayev

This paper presents the design, prototype, and experimental evaluation of the AlmatyExoElbow, a lightweight cable-driven robotic exoskeleton that is intended to support elbow joint rehabilitation. The device provides two active degrees of freedom for flexion/extension and pronation/supination. It also incorporates a sensor-based control system for accurate motion tracking. The mechanical structure is fabricated using 3D-printed PLA plastic, resulting in a compact, modular, and comfortable design suitable for prolonged use. The control architecture is based on an Arduino Nano microcontroller integrated with IMU sensors, enabling the real-time monitoring of elbow motion and the precise reproduction of physiologically relevant movement patterns. The results of experimental testing demonstrate smooth and stable operation, confirming reliable torque transmission through antagonistic cable mechanisms. Overall, the proposed design achieves a balanced combination of functionality, portability, and user comfort, highlighting its potential for upper-limb rehabilitation applications in both clinical and home-based settings.

Robotics doi: 10.3390/robotics15010011

Authors: Arturo Franco-López Mauro Maya Alejandro González Liliana Félix-Ávila César-Fernando Méndez-Barrios Antonio Cardenas

This study investigates the kinematic behavior of a reconfigurable Delta parallel robot aiming to enhance its performance in real industrial applications such as high-speed packaging, precision pick-and-place operations, automated inspection, and lightweight assembly tasks. While Delta robots are widely recognized for their speed and accuracy, their practical use is often limited by workspace constraints and singularities that compromise motion stability and control safety. Through a detailed analysis, it is shown that classical Jacobian-based performance indices are unsuitable for resolving the redundancy introduced by geometric reconfiguration, as they may lead the system toward singular or ill-conditioned configurations. To overcome these limitations, this work introduces an adjustable singularity-sensitive performance index designed to penalize extreme velocity and force singular values and enables tuning between velocity and force performance. Simulation results demonstrate that optimizing the reconfiguration parameter using this index increases the usable workspace by approximately 82% and improves the uniformity of manipulability across the workspace. These findings suggest that the proposed approach provides a robust framework for enhancing the operational range and kinematic safety of reconfigurable Delta robots, while remaining adaptable to different design priorities.

Robotics doi: 10.3390/robotics15010010

Authors: Zhumadil Baigunchekov Giuseppe Carbone Med Amine Laribi Rustem Kaiyrov Li Qian Zhadyra Zhumasheva

“Experiments were conducted at DIMEG, University of Calabria, located in the main campus in Arcavacata di Rende, Italy.” This article focuses on the study of the geometry, direct and inverse kinematics, workspace, and singularity of a novel 3-PRRS parallel manipulator (PM) with a redundantly actuated architecture. The PM consists of three active revolute joints and three passive prismatic redundant input joints, all located on a fixed platform. The constant and variable parameters characterizing the PM’s geometry and kinematics are determined. The direct kinematics problem is formulated as a 16th-degree polynomial, while the inverse kinematics problem is solved in closed form. A comparison of the direct and inverse kinematics is provided, and the correctness of the solutions is validated through numerical examples. The equations of motion for the moving platform are derived, and the PM’s workspace is defined based on the inverse kinematics. This work demonstrates how the passive prismatic input joints, specifically included in the design, contribute to an enlarged workspace—particularly in the vertical direction—compared to traditional 3-RRS PM architecture.

Robotics doi: 10.3390/robotics15010009

Authors: Ziyang Wang Zhihai Li Hongpeng Yu Duotao Pan Songjie Peng Shenlin Liu

Efficient and intuitive programming strategies are essential for enabling robots to adapt to small-batch, high-mix production scenarios. Mixed reality (MR) and programming by demonstration (PbD) have shown great potential to lower the programming barrier and enhance human–robot interaction by leveraging natural human guidance. However, traditional offline programming methods, while capable of generating industrial-grade trajectories, remain time-consuming, costly to debug, and heavily dependent on expert knowledge. Conversely, existing MR-based PbD approaches primarily focus on improving intuitiveness but often suffer from low trajectory quality due to hand jitter and the lack of refinement mechanisms. To address these limitations, this paper introduces a coarse-to-fine human–robot collaborative programming paradigm. In this paradigm, the operator’s role is elevated from a low-level “trajectory drawer” to a high-level “task guider”. By leveraging sparse key points as guidance, the paradigm decouples high-level human task intent from machine-level trajectory planning, enabling their effective integration. The feasibility of the proposed system is validated through two industrial case studies and comparative quantitative experiments against conventional programming methods. The results demonstrate that the coarse-to-fine paradigm significantly improves programming efficiency and usability while reducing operator cognitive load. Crucially, it achieves this without compromising the final output, automatically generating smooth, high-fidelity trajectories from simple user inputs. This work provides an effective pathway toward reconciling programming intuitiveness with final trajectory quality.

Robotics doi: 10.3390/robotics15010008

Authors: Woojin Son Seunghyeon Lee Taejoo Kim Geonhwa Son Yukyung Choi

We address the problem of 6D object pose estimation in aerosol environments, where RGB and depth sensors experience correlated degradation due to scattering and absorption. Handling such spatially varying degradation typically requires depth restoration, but obtaining ground-truth complete depth in aerosol conditions is prohibitively expensive. To overcome this limitation without relying on costly depth completion, we propose RA6D, a framework that integrates attention-guided reliability modeling with feature distillation. The attention map generated during RGB dehazing reflects aerosol distribution and provides a compact indicator of depth reliability. By embedding this attention as an additional feature in an Attention-Guided Point cloud (AGP), the network can adaptively respond to spatially varying degradation. In addition, to address the scarcity of aerosol-domain data, we employ clean-to-aerosol feature distillation, transferring robust representations learned under clean conditions. Experiments on aerosol benchmarks show that RA6D achieves higher accuracy and significantly faster inference than restoration-based pipelines, offering a practical solution for real-time robotic perception under severe visual degradation.

Robotics doi: 10.3390/robotics15010007

Authors: Fatimah Alahmed Muhammad Hawwa Uthman Baroudi

The construction and facilities management sectors are increasingly adopting automation technologies to improve productivity and reduce manual labor. In parallel, decorative and informational floor-marking is widely used in indoor environments such as schools, exhibition halls, and public spaces to support organization, wayfinding, and visual communication. While robotic systems have been developed for floor and layout marking, many existing solutions rely on specialized infrastructure or offer limited flexibility in the range of patterns that can be produced. This paper presents the development of a prototype of a mobile, wheeled robot capable of autonomously executing diverse designs on surfaces such as fields and floors. The robot’s potential applications include use on indoor floors and exhibition halls. It marks the ground using a plotting pen while navigating and avoiding obstacles within its environment. Additionally, the robot can produce a range of drawings, including letters and signage, and its capabilities can be extended to create decorative patterns as well as marks for floor-based games. This robot was constructed entirely from cost-effective, commercially available components. Experimental evaluation demonstrates repeatable motion and drawing performance, with measured standard deviations of approximately 1.6 mm in forward motion and 3 mm in lateral motion during representative grid-based traversal. These results indicate that the proposed approach achieves a level of accuracy and consistency sufficient for decorative floor-marking and similar applications, without reliance on external localization infrastructure.

Robotics doi: 10.3390/robotics15010006

Authors: Marco Ceccarelli Erwin-Christian Lovasz

In this Special Issue, we aim to promote and circulate recent mechanism design developments and achievements in the international field of robotics, ranging from theoretical contributions to experimental and practical applications [...]

Robotics doi: 10.3390/robotics15010005

Authors: Andrei Dutceac Constantin I. Vizitiu

Wildfires pose a growing global threat, demanding rapid, scalable, and autonomous response strategies. This study proposes HG-MADQN (Heuristic-Guided Multi-Agent Deep Q-Network), a hybrid framework that integrates reinforcement learning with biologically inspired pheromone-based heuristics to achieve adaptive fire detection and suppression using drone swarms. The system models a decentralized swarm operating in a grid-based environment, where each drone combines learned policies with heuristic guidance derived from a dual-pheromone mechanism (a fire-attraction field guiding suppression and a coverage-repulsion field promoting exploration). The proposed hybrid approach ensures efficient coordination, minimizes redundant movements, and maintains continuous area coverage without centralized control. Simulation experiments conducted on dynamic wildfire scenarios demonstrate that HG-MADQN significantly outperforms traditional heuristic, Lévy-Flight, and reinforcement learning (MADQN) algorithms. It achieves faster containment, reduced burned area, and lower resource consumption, while exhibiting strong robustness across multiple swarm sizes and fire configurations. The results confirm that hybridizing learned and heuristic decision models enables a balanced exploration–exploitation trade-off, leading to improved scalability and resilience in cooperative fire suppression missions.

Robotics doi: 10.3390/robotics15010004

Authors: Zuguang Liu Md Suruz Miah

As collaborative robots (cobots) become more prevalent in industry, there is growing need for autonomous cobots that can cooperatively navigate shared workspaces. Reliable navigation and safety become especially critical when intermittent communication failures occur, potentially due to environmental factors or network disruptions. This paper contributes to the development of a navigation scheme for a team of autonomous networked cobots under intermittent communication. In particular, the paper proposes a decentralized control approach enabling cobots to cooperatively transport an object across an industrial environment despite intermittent communication. The navigation scheme is decentralized in the sense that each cobot computes its control actions locally using only information from neighboring cobots, without relying on a central coordinator, and applies actuator commands independently based on local sensor feedback and inter-robot communication. The work presented herein provides a comprehensive framework for autonomous multi-cobot cooperative object transportation tasks, including the design of the control, navigation, and communication systems. The communication network among the cobots is modeled using directed graphs, with the graph Laplacian matrix representing the connectivity among the cobots. The proposed method is first validated using a commercial robot simulator. Its performance is then evaluated on physical cobots operating in an indoor environment with various complexities.

Robotics doi: 10.3390/robotics15010003

Authors: Lorenzo Toccaceli Andrea Botta Giovanni Colucci Luigi Tagliavini Carmen Visconte Giuseppe Quaglia

Recent advances in mobile robotics have emphasized the need for systems capable of operating in unstructured environments, combining obstacle negotiation, stability, and adaptability. This study presents the preliminary design and testing of Brush.Q, an articulated ground robot featuring a novel structure distinct from existing wheel-legged robots, equipped with compliant brush-like wheels composed of multiple spokes. The main contribution is the experimental analysis of suspension capability across different wheel geometric profiles, combined with the assessment of obstacle-climbing performance. A simplified prototype was constructed to evaluate the effects of wheel rotation direction, spoke number, and spoke tapering. Results show that reducing the number of spokes improves obstacle-climbing at the expense of suspension, while higher spoke count and compliant geometry enhance suspension and stability. Spoke tapering improves obstacle climbing in the backward-facing configuration but consistently reduces suspension. Overall, these findings highlight the critical role of wheel geometry and the potential for reconfigurable spoked wheels to enhance adaptability and versatility in unstructured terrains.

Robotics doi: 10.3390/robotics15010002

Authors: Muhammad Hamza Asif Nizami Zaid Ahsan Shah Charles Young Jonathan Clark

Tumbling, rolling, and somersaults are alternate forms of locomotion used by animals and robots to navigate rough terrains. In this paper, we present a Two-Fold Symmetric (TFS) mechanism that demonstrates dynamic tumbling and leaping using a single actuator. The dynamics of the proposed mechanism are captured by a hybrid dynamic model with discrete states based on the nature of ground contact. By changing the shape parameters of a trapezoidal actuation signal, various dynamic responses and gaits are attained. Simulations and hardware experiments demonstrate tumbling and leaping/hopping. It is shown that the mechanism demonstrates gait versatility and attains speeds up to 3.0 Body Lengths per second and can jump up to a height of 60% of its total height, all using a single actuator that sets it apart from contemporary tumbling robots.

Robotics doi: 10.3390/robotics15010001

Authors: Luke Q. Gardner Katie Varela Larry L. Howell Spencer P. Magleby

Lamina Emergent Torsional (LET) arrays can be used to replace creases in origami-based mechanisms. They can be made of planar materials, which makes them compatible with many designs. However, LET arrays can take up a lot of area and can exhibit significant parasitic motion, which makes them less ideal for some applications, such as in origami-based robotics and deployable space structures. This work presents a compact variation of the conventional LET array, which resolves these issues. An experimental method for fabricating these compact LET arrays, or C-LET arrays, from carbon fiber-reinforced polymer is given. Deflection models for C-LET array torsion segments, with and without interference with other torsion segments, are given. Bending stress and shear stress equations are provided, and the deflection models are combined into a final model that can solve for the deflections of multiple torsion segments in series. The concepts described are demonstrated in a prototype origami-based deployable reflectarray incorporating C-LET arrays. The prototype demonstrates that C-LET arrays provide the desired motion while maximizing the usable area of the deployable reflectarray.