Search Results (5,129)

Robotic teleoperation is a core enabling technology spanning remote surgery, industrial inspection, and virtual-reality applications. Despite growing deployment, operator experience assessment still relies almost exclusively on post hoc subjective questionnaires, which preclude real-time monitoring and adaptive intervention. Here, the wearable IMU chain is considered not only as a command interface but also as an implicit sensing channel for operator state. We test whether end-effector kinematics generated by the IMU-to-robot mapping contain information about ten post-task workload and user-experience dimensions, comprising NASA-TLX-inspired workload scales together with usability, responsiveness, realism, intuitiveness, and perceived performance. A secondary analysis of a publicly available dataset (16 participants, 144 motion recordings, simulated UR10e arm) is conducted through a three-stage pipeline: bivariate correlation analysis (Pearson and Spearman), multivariate regression (10 model families, 16 feature-set combinations, Leave-One-Subject-Out validation), and binary classification (median-split). Statistical validity is assessed via 1000-permutation nested testing. Target-specific regression models reach $R^2\ge 0.50$ on seven out of 10 subjective dimensions, with a peak of $R^2=0.787$ for usability; permutation testing confirms significance for eight out of 10 targets. Binary classification achieves AUC $\ge 0.75$ on nine out of 10 targets, with three dimensions reaching perfect AUC. SHAP analysis identifies temporal irregularity and distributional shape descriptors as the dominant kinematic explanatory families. These results support the feasibility of kinematics-based inference of operator experience and provide an offline proof of concept toward future real-time adaptive teleoperation systems. Full article

Reliable obstacle avoidance is essential for bionic robotic fish operating in complex underwater environments. However, when a robotic fish performs depth-keeping cruising near U-shaped obstacles, the traditional artificial potential field (APF) method is prone to local minima, which can cause the vehicle to become trapped and lead to obstacle avoidance failure. To address this problem, this paper proposes a virtual sentinel obstacle strategy based on the APF method. Virtual sentinel obstacles are deployed near the entrance of U-shaped obstacles, and corresponding deployment rules are formulated to prevent the robotic fish from entering the local minimum region. To further improve path planning performance, a two-stage fuzzy controller is developed to adjust heading rotation and cruising step size. The proposed method is evaluated through numerical simulations and physical experiments using a self-developed bionic robotic fish prototype. The results show that the virtual sentinel obstacle strategy prevents entrapment around the tested U-shaped obstacles, while fuzzy control shortens the path and improves smoothness. The physical experiments further verify the feasibility of the proposed strategy in a two-dimensional underwater obstacle avoidance scenario. These results indicate that combining virtual sentinel obstacles with APF-based planning provides a feasible approach for U-shaped obstacle avoidance by bionic robotic fish. Full article

Human-centric manufacturing is reshaping collaborative production systems by repositioning human capabilities, safety, experience, and well-being as central concerns in the design and operation of intelligent manufacturing. As manufacturing moves toward Industry 5.0, task allocation in assembly-oriented collaborative systems is no longer only a matter of productivity, cycle time, or resource utilization, but also a key mechanism for protecting worker safety, workload balance, ergonomic compatibility, and long-term well-being. Although existing reviews have addressed various aspects of collaborative manufacturing and ergonomics, a systematic synthesis of how ergonomics is embedded into task allocation for collaborative assembly remains limited. To address this gap, this paper systematically reviews 95 studies identified from WoS and Scopus using an expanded keyword-based search strategy, with April 2026 retained as the publication eligibility cutoff. Studies were included when ergonomics or related human-factor considerations materially influenced task allocation, task assignment, planning, scheduling, or line-balancing decisions in AI-enabled and robot-assisted collaborative manufacturing, with emphasis on assembly-related settings such as workstations, workcells, and assembly lines. The literature is analyzed from four perspectives: ergonomic objectives, allocation scenarios, temporal responsiveness, and computational approaches. Given the heterogeneity of modeling, optimization, simulation, and design studies, a narrative synthesis rather than meta-analysis was conducted. The results show that physiological ergonomics remains the dominant dimension, accounting for 70 of the 95 studies. Recent studies increasingly incorporate multidimensional ergonomic risks, fatigue progression, worker trust, human preference, and real-time human-state information into allocation decisions. The reviewed studies also indicate a transition from static and assessment-informed allocation toward adaptive, state-aware, and cyber-physical allocation. Finally, the review identifies future directions concerning multidimensional ergonomic modeling, assessment-to-decision transformation, real-time adaptive allocation, human-centric interaction, and transferable industrial validation for collaborative assembly systems. No review registration was undertaken. Full article

The problem of path planning is one of the most crucial and challenging issues in the fields of intelligent systems and autonomous robotics. A robot’s ability to move quickly and easily from a starting position to a goal position without hitting anything is directly related to how useful the robot is in real life. This paper compares five advanced path-planning algorithms: A* (A-Star), D* Lite (Dynamic A-Star Lite), RRT* (Rapidly exploring Random Tree Star), PRM* (Probabilistic Roadmap Star), and APF-D (Adaptive Potential Field with Dynamic Awareness). The paper addresses the difficult problem in dynamic environments where objects enter, exit, and move around continuously within the robot environment, as it moves through the environment, which is becoming more prevalent in the real world, such as in warehouses, hospitals, and urban and outdoor environments. Six performance measures, namely path length, computation time, memory, optimality ratio, success rate, and replanning latency, are used to test our five algorithms on a standard simulator in Matlab. Experiments are conducted in four different conditions, from very quiet to very dynamic, with a high number of obstacles. Results indicate that A* fails to perform well in dynamic environments and performs nearly optimally in static environments, while APF-D and D* Lite adapt to changes in the environment much better. An experimental study was carried out by 50 independent simulations in static and dynamic environments, where in each simulation, the hybrid solution was evaluated. The APF-D algorithm showed a success rate of 92.8% in highly dynamic environments, which is better than that of A* (58.8%), RRT* (76.8%), and PRM* (70.5%), whereas the success rate of D* Lite was found to be 89.3%. Additionally, APF-D decreased the average time taken for replanning by around 25% in comparison to other graph-based algorithms. Full article

This study presents a radiation-aware path planning framework for mobile robots operating in hazardous environments containing radiation sources, shielding structures, and moving obstacles. The proposed method integrates A*-based global planning, chaotic particle swarm optimization (CPSO)-based route refinement, B-Spline trajectory smoothing, and exposure-dependent speed adaptation within a unified dynamic planning architecture. The framework represents the radiation field as a physically parameterized dose-rate map in mSv/h by combining inverse-square source decay with line-of-sight material attenuation through shielding materials. Route generation is therefore evaluated in terms of cumulative absorbed dose, path length, mission time, trajectory roughness, computational cost, success rate, and dynamic obstacle interaction. The proposed method is compared with Pure A*, Informed RRT*, A*-PSO, A*-CPSO, and a risk-aware A*+DWA baseline under identical seed sets and computational budgets. In static scenarios, the proposed method reduced cumulative absorbed dose by approximately 33.3%, 47.2%, and 21.4% compared with Pure A* under low-, medium-, and high-risk conditions, respectively. In dynamic scenarios, the corresponding dose reductions were approximately 32.5%, 41.4%, and 40.1%. Additional ablation, sensitivity, statistical significance, and latency analyses were conducted to isolate the contribution of each component and evaluate computational feasibility. The results show that the proposed framework provides a balanced trade-off between absorbed dose reduction, trajectory feasibility, mission time, and online replanning performance under the tested simulation conditions. Full article

This paper presents a Neural Backstepping control strategy for trajectory tracking of a differential-drive mobile robot. The proposed approach combines a dynamic-level backstepping controller with a lightweight single-hidden-layer adaptive neural network to compensate uncertain nonlinear dynamics through online adaptation. The backstepping component provides a Lyapunov-based stabilizing structure, whereas the neural approximator improves tracking performance without requiring deep architectures, offline training stages, or computationally demanding optimization procedures. The adaptive law for the neural output weights is derived from the stability analysis, ensuring bounded closed-loop signals and uniformly ultimately bounded tracking errors in the presence of bounded approximation uncertainties. The controller is evaluated through simulations using four reference trajectories: circular, lemniscate, Lissajous, and waypoint-based paths. The same control gains and neural network configuration are used in all cases, showing that the proposed scheme can track different trajectory geometries without trajectory-specific retuning. The simulation results show satisfactory tracking performance, with position RMSE values below $0.04$ m for all evaluated trajectories. These results indicate that the proposed Neural Backstepping controller provides a suitable balance between tracking accuracy, online adaptation capability, and implementation simplicity for differential-drive mobile robot trajectory tracking. Full article

With advances in robotic manipulation in recent years, tactile sensing has become increasingly important in scenarios where visual information is unreliable or insufficient. However, the development of learning-based tactile algorithms and policies is limited by the cost and time required to collect large-scale physical datasets. While robotic simulation offers an alternative for synthetic data generation, current simulation platforms, such as NVIDIA Isaac Sim, lack integrated capacitive tactile sensors. This paper presents a finite element method (FEM)-based digital twin of a capacitive tactile sensor and an extension for NVIDIA Isaac Sim that enables the real-time generation of synthetic tactile data directly within the simulation environment. The proposed system extracts nodal deformations from the Isaac Sim PhysX engine and uses a convolutional neural network (CNN) to predict synthetic tactile maps that replicate the response of the physical sensor. We further demonstrate adaptability by retraining the model from an initial sensor to a second capacitive sensor, the Robotiq TSF-85, operating under the same sensing principle. The complete generation pipeline executes in 8.04 ms per frame on the laptop configuration and 6.71 ms on the workstation, enabling real-time operation at 60 Hz on both, and at 120 Hz on the workstation. The similarity of the generated tactile data for the Robotiq TSF-85 is evaluated using complementary similarity metrics, achieving a mean Structural Similarity Index Measure (SSIM) of 0.727 ± 0.16 and a mean Pearson correlation of 0.87 ± 0.16 against real measurements. To demonstrate the utility of the proposed framework, a shape-recognition task (cylinder, sphere, cube) was performed using only synthetic tactile data and evaluated on real-world sensor data, achieving an accuracy of 69.3% with zero real training labels. By enabling integrated tactile simulation and synthetic data generation within Isaac Sim, this work provides a practical tool for tactile perception using capacitive tactile sensors. Full article

Since the onset of the COVID-19 pandemic, children and adolescents across Europe have experienced a significant rise in mental health challenges, including anxiety, depression, and behavioural disorders. Schools have observed a marked shift in social behaviour, with increased emotional instability, social withdrawal, and attention-related issues. Simultaneously, educational systems face a growing shortage of skilled professionals, particularly in psychological support roles. While digital technologies offer partial relief, their effectiveness is limited by concerns such as screen fatigue and a lack of embodied interaction. This paper conceptualises the use of social robots as a novel intervention tool in education, grounded in the 4E cognition framework—embodied, embedded, enacted, and extended—and informed by interdisciplinary research. Using design thinking and insights from empirical and practical work, we developed five use cases for integrating robots into the Swiss MindMatters mental health promotion programme. These five use cases demonstrate how robots can facilitate emotional learning, simulate social interactions, support conflict mediation, and reduce teacher workload. By combining physical presence with adaptive behaviour, social robots, which will serve as digital twins of pedagogical partners, offer a promising, ethically sensitive extension of classroom environments, fostering deeper engagement, social competence, and cognitive development in ways traditional technologies cannot fully replicate. Full article

Surgical robot navigation in a restricted pelvic workspace requires accurate target localization, directional consistency and robot-feasible execution. This study proposes a physically constrained dual-branch front-end network (PCD-Net) for DDH-oriented navigation using public CT-derived pelvis geometries. PCD-Net maps a 21-dimensional input comprising the target geometry, current joint state, workspace bounds and constraint parameters to a base-frame target position, principal insertion direction and seven-joint correction vector for MoveIt2 and OMPL planning. Training combines supervised pretraining with direction consistency, joint limit, correction magnitude and workspace gap penalties. Evaluation comprised 15 complete simulation trials per method and target sequence-level fivefold cross-validation of 999 samples from five complete sequences. PCD-Net achieved a position error of $0.736\pm 0.202$ mm, a direction error of $0.428\pm 0.130°$, a planning time of $0.0162\pm 0.0040$ s and successful execution in all 15 trials. In cross-validation, the complete constraint setting produced the lowest joint correction MAE ($0.755\pm 0.034$ rad) and temporal correction variation ($3.270\pm 0.589$ rad) while maintaining sub-millimeter position and sub-degree direction errors. Removing the joint limit penalty increased the violation rate by 42.35%. These results support PCD-Net as a lightweight, planner-compatible front end that balances geometric accuracy and joint-level feasibility. All evidence is simulation-based; phantom experiments, physical robot validation and evaluation using clinically characterized DDH cases remain necessary before surgical translation. Full article

To address high-precision trajectory tracking challenges in multi-joint robots facing model uncertainties, complex friction, and abrupt disturbances, this paper proposes a radial basis function (RBF) neural network-improved integral terminal sliding mode control scheme optimized by state-aware adaptive particle swarm optimization (ASPSO), denoted as ASPSO-optimized RBF-IITSMC. First, a fractional-memory integral terminal sliding surface incorporating a boundary-layer saturation mapping is constructed. The proposed terminal mapping is shown to be globally Lipschitz continuous, and an explicit approximation-error bound relative to the conventional terminal power mapping is established. Second, an RBF neural compensator driven by the sliding variable is incorporated into the reconstructed sliding dynamics to estimate lumped uncertainties and reduce the compensation burden on the robust feedback term. Furthermore, a state-aware adaptive PSO variant combining population-diversity monitoring and differential mutation is developed to jointly tune the 15-dimensional controller parameter vector. The practical finite-time reachability of the sliding variable and the uniform ultimate boundedness of the sliding variable and neural-weight estimation error are analyzed using a Lyapunov framework. Simulation results on a six-degree-of-freedom (6-DOF) robotic arm demonstrate improved tracking accuracy and disturbance-rejection performance, together with reduced high-frequency torque oscillations, compared with the evaluated baseline controllers. Full article

This paper addresses the high-precision trajectory tracking control problem for robotic manipulators operating in uncertain environments by proposing a novel fuzzy adaptive gain-tuning sliding-mode control (FAGT-SMC) algorithm. While conventional sliding-mode control offers strong robustness against matched uncertainties, its fixed-gain switching mechanism inevitably induces severe chattering phenomena, causing actuator wear and performance degradation in practical implementations. To overcome this fundamental limitation, this paper designs an intelligent gain adaptation framework that dynamically regulates the sliding-mode switching gain through a fuzzy inference system. The control system structure integrates a nominal equivalent control component derived from the robotic dynamics model with an adaptively tuned discontinuous switching term. Theoretical analysis establishes global stability through Lyapunov-based methods, proving uniform ultimate boundedness (practical stability) of tracking errors under bounded uncertainties and residual fuzzy approximation errors. The proposed FAGT-SMC algorithm effectively balances robustness and control smoothness; therefore, numerical simulations demonstrate effectiveness for advanced robotic applications requiring both precision and adaptability in dynamic operating conditions. Full article

Construction sites are beginning to deploy material transport robots to relieve labor constraints; however, planning and validating robot operations within complex, evolving building layouts remain difficult. This study proposes a BIM-enabled simulation framework that connects building information to enable an objective pre-deployment evaluation. The method extracts navigable spaces, delivery points and material quantities from BIM, formulates dispatching as a capacity-constrained vehicle routing problem, and couples it with an A* algorithm for pathfinding. A greedy next-stop decision rule with a tunable correction factor governs whether the robot continues visiting additional stops or returns to the staging area. The experimental results show a more than 20% improvement in the travel efficiency of the robot compared with conventional algorithms. These results conclude that the framework offers a practical tool for what-if analysis and data-driven planning of robotized construction logistics and is readily extensible to diverse materials and project types. Full article

The solid backfilling support robot is key equipment for intelligent backfill mining, but its dual-top-beam structure, multiple closed-loop linkages and hydraulically actuated compaction mechanism make posture representation, inverse actuator mapping and control execution strongly coupled. This study develops a unified kinematic modelling and mechanical–hydraulic-control co-simulation workflow for a ZC5160/30/50 solid backfilling hydraulic support. Closed-loop vector equations are derived for the main support mechanism, rear top-beam mechanism and compaction mechanism, and the mappings among actuator strokes, posture angles and key node positions are established. An engineering posture-index system is constructed for roof contact, support adjustment and backfilling–compaction operation. Forward and inverse kinematic modules are implemented in MATLAB/Simulink and checked using an ADAMS virtual prototype. Representative workspace sampling further shows that all 15 inverse–forward verification cases converge and satisfy actuator stroke constraints, while the residual Jacobians remain full rank with maximum condition numbers of 9.135–9.950. An ADAMS-AMESim-MATLAB/Simulink co-simulation platform is then used to evaluate actuator tracking and PID-based posture-control feasibility. The numerical comparison shows good consistency for the main rigid-body posture indices, whereas conveyor-related relative-position indices show larger deviations because the suspended conveyor motion is affected by gravity in the virtual prototype. Under two target posture cases, the top-beam and compaction-mechanism angle deviations remain within ±0.3°, and the height deviation is below 5 mm. The proposed workflow provides a simulation basis for posture perception, actuator planning and control-system design of solid backfilling support robots; the reported results should be interpreted as model-level numerical consistency and co-simulation feasibility rather than physical prototype accuracy. Full article

To address the challenges of lumped model uncertainties and tracking error constraints in
biomimetic rehabilitation robot control, this paper proposes a novel high-order integral
barrier function to construct an adaptive neural tracking control scheme. Radial basis
function neural networks (NNs), inspired by the receptive field mechanism of motor
neurons, feature local activation and can accurately approximate the nonlinear dynamics
of such bionic rehabilitation devices. Distinct from traditional integral barrier Lyapunov
functions, the presented high-order integral barrier function can accommodate both timevarying
and time-invariant error constraints, while simplifying the controller derivation
and ensuring full differentiability of virtual control laws throughout the backstepping
framework. Supported by the derived barrier function theorems, the tracking error of
the robot is theoretically proven to stay within predefined safe boundaries and converge
exponentially to a compact neighborhood of the origin. Finally, comparative numerical
simulations on a biomimetic rehabilitation robot validate the effectiveness of the proposed
theorem and constrained adaptive neural control strategy Full article

This article examines the challenge of trajectory inference for an unknown system affected by external disturbances, with the objective of reconstructing the trajectory of a quadrotor using a reference model. The proposed methodology extends the Closed-Loop Output Error (CLOE) scheme through two complementary contributions: an identified gain, incorporated into the reference model to guarantee the Hurwitz condition of the closed-loop error dynamics, and a set of sliding-mode correction terms that further accelerate error convergence and enhance robustness against bounded disturbances. The stability of both contributions is formally established via Lyapunov-based analysis. The proposed approach is validated through realistic simulations carried out in the CoppeliaSim robotics environment, considering both constant and time-varying trajectory scenarios. Results show that the hybrid approach improves trajectory inference accuracy and convergence speed, maintaining resilience under adverse conditions, making it a promising alternative for autonomous quadrotor monitoring. Full article