Search Results (2,483)

Background: Reconstructing full muscle activation trajectories from sparse measurements is underdetermined: many activation patterns can explain similar joint moments, and purely mechanical inverse formulations can yield non-physiological solutions. Methods: We propose a synergy-informed, physics-constrained framework to reconstruct unmeasured muscle activations when only a subset of muscles is observed. A synergy reconstruction prior (SynRc) is obtained by identifying a synergy basis from proxy activations via non-negative matrix factorization (NMF) and estimating time-varying synergy excitations from measured channels. Unmeasured activations are then solved via bound-constrained multi-objective optimization that jointly minimizes (i) normalized joint-moment error between OpenSim forward-computed moments and inverse-dynamics moments and (ii) deviation from the SynRc prior, with an optional smoothness refinement stage. Results: Verification on synthetic OpenSim Arm26 (2-DOF) cases with known ground truth shows that $J_1$-dominant selections from the stage-I Pareto set reduce normalized joint-moment error from 0.154 (SynRc-only) to ≈0.138, at the cost of larger deviation from the synergy prior. These Pareto diagnostics expose identifiability and selection sensitivity under sparse measurements when ground truth is unavailable. Conclusions: The proposed framework makes mechanics–synergy trade-offs explicit and provides structured diagnostics and selection guidance for sparse-measurement scenarios. Full article

Unmanned vehicles are becoming increasingly essential in executing high-risk missions in unknown environments such as search and rescue. As the complexity of operational environments escalates, carrying out unmanned tasks becomes cumbersome or even infeasible for a single vehicle, hampered by limited perception and operational constraints. Aiming at enhancing the flexibility of unmanned operations under complicated scenarios, this study introduces AGC-NeRF, an innovative air–ground collaborative exploration framework that harnesses the functional complementarity of UAVs and UGVs—enabling a UGV to navigate through a complex scenario with the assistance of a UAV via referencing a neural radiance map. First, a UAV is employed to collect aerial images for reconstructing the environment to be explored by a UGV, leveraging its aerial perspective to achieve wide-area coverage and global environmental perception that is unattainable for a single UGV. Concurrently, an innovative image saliency evaluation approach is introduced to meticulously select landmarks that are contributive to the UGV’s navigation system, yielding a pre-trained NeRF model of the operation scene. Then, a landmark-aware 6-DOF ego-motion estimator and collision-free trajectory optimizer are designed for the UGV based on the NeRF map. Finally, an online replanning architecture is established which relies on a ground station for NeRF training and state optimization by synergizing the trajectory planner and the state estimator, which forms a dual-agent vision-only navigation pipeline. Simulations and experiments validate that AGC-NeRF enables reliable UGV trajectory planning and state estimation in unknown environments, demonstrating superior efficacy and robustness of the air–ground collaborative paradigm. Full article

In this study, a novel multilayer neuroadaptive output feedback controller is proposed for n-degree-of-freedom (n-DOF) serial hydraulic manipulators. This approach utilizes measurable position signals only while introducing a multilayer neuroadaptive observer to estimate modeling uncertainties and unknown states simultaneously. Notably, this controller can compensate for both endogenous uncertainties and exogenous disturbances simultaneously. Simulation results validate the feasibility and effectiveness of the developed controller, confirming its practical potential for hydraulic manipulator applications. Full article

Fused granular fabrication (FGF) offers high deposition efficiency and low material cost for large-scale mold production, but commonly yields parts with surface defects and dimensional deviations. This study develops a six-axis robotic post-processing workstation that integrates multi-DOF toolpath planning and real-time communication to flexibly machine FGF components with complex geometries. Using short-fiber-reinforced polypropylene (PP-GF), robotic milling experiments were performed, and spindle speed, feed rate, and cutting depth were systematically optimized to enhance surface quality and dimensional accuracy. The NSGA-III algorithm optimizes parameters, thereby increasing machining efficiency by 4.9% and reducing surface roughness by 12.35%. Results show that the proposed platform effectively improves the machining performance of FGF-printed parts, demonstrating its feasibility for high-precision post-processing. The work provides a practical technical route for the hybrid additive–subtractive manufacturing of large 3D-printed structures. Full article

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. Full article

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. Full article

Efficient RFID spatial exploration in dynamic warehouse environments is challenging due to occlusions, sensing geometry constraints, and the weak coupling between information acquisition and navigation decisions. Many existing inventory robots treat RFID sensing as a passive data source during exploration, without explicitly optimizing sensing pose or prioritizing inventory-driven frontiers, which can result in incomplete coverage and redundant traversal. This paper presents ArmTenna, an articulated mobile robotic platform that formulates RFID inventory exploration as an active perception problem. The system integrates dual 4-DOF robotic arms carrying directional UHF RFID antennas and a 2-DOF neck-mounted RGB-D camera, enabling adaptive interrogation of candidate regions. We propose a multi-modal frontier exploration framework that combines newly detected EPC tags, average RSSI values, and vision-based product detections into a composite utility function for goal selection. By embedding articulated antenna control directly into the frontier evaluation loop, the robot tightly couples sensing geometry with exploration decisions. Experimental validation with 150 tagged items across three separated warehouse zones shows that ArmTenna achieves up to 97% map coverage, compared to 72% for a baseline platform, while reducing missed-tag regions. These results demonstrate that integrating active sensing pose control with multi-modal frontier evaluation provides an effective and scalable solution for RFID-driven warehouse inventory automation. Full article

The covariance matrix performs a vital role for space-time adaptive processing (STAP) in airborne multiple-input multiple-output (MIMO) radar. As is known, the clutter-plus-noise covariance matrix (CPNCM), reflecting the statistical characteristics of radar echo, is a key component for MIMO-STAP. Commonly, an ideal CPNCM is impossible to obtain, and it must be estimated with sufficient snapshots. According to the RMB rule, MIMO-STAP requires many snapshots since MIMO radar has a high degree-of-freedom (DoF) due to its orthogonal transmit waveform. However, this is hard to satisfy in practice. This paper develops a novel covariance matrix estimation method under limited snapshots in airborne MIMO-STAP radar. Motivated by the random matrix theory, the proposed method enhances the CPNCM estimation by noise and clutter sample eigenvalues adjustment (EA). Concretely, the sample eigenvalues of noise are adjusted as noise power, and the ones of clutter are adjusted through minimizing the radar output power. Then, with the sample eigenvectors and adjusted sample eigenvalues, an effective CPNCM is formulated, and EA-MIMO-STAP is implemented reliably. Multiple experiments demonstrate that EA-MIMO-STAP has superior performance and robustness. Full article

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. Full article

Stroke-induced hand motor dysfunction severely limits activities of daily living (ADL). While conventional systems face challenges in portability and sustained actuation accuracy, this work addresses these limitations through an integrated adaptive control framework and a lightweight 10-degrees-of-freedom (DoFs) tendon-driven exoskeleton. The system employs a rigid–flexible coupling design with a wearable mass under 300 g, ensuring compatibility across various finger lengths. The system is implemented via a motor imagery-based brain–computer interface (MI-BCI); by processing 64-channel electroencephalogram (EEG) signals, the system adaptively maps motor intent into three discrete grasp intensity levels (20%, 50%, and 80% maximum voluntary contraction). To reduce cognitive load and enhance system stability during rehabilitation, we propose a novel “Force–Topology Coupling” control paradigm. This paradigm functions as a synergistic filter, leveraging the correlation between intended effort level (IEL) and grasp taxonomy to map intensity levels to ADL-specific grasps (lateral, precision, and power). Validation with healthy subjects demonstrated 0° to 90° joint mobility and the successful execution of 9 ADL tasks. The results verify the efficacy of utilizing adaptive MI-BCI modulation to trigger biomechanically precise assistance, establishing a foundational computational paradigm with significant potential for clinical stroke rehabilitation. Full article

As a step towards advancing adaptive DBS control for Parkinson’s disease, we have developed an automated algorithm that detects tremor continuously on a seconds-resolution time scale from a wearable accelerometer and present the feasibility study test results. Triaxial acceleration data were wirelessly streamed from an Apple Watch as well as acquired from an internal accelerometer in the implanted DBS device itself. The algorithm first determines if there is any high-power voluntary activity, such as walking, using the arm, or transitioning from sitting to standing; then, it identifies peaks in the 4–7 Hz Parkinsonian tremor frequency band. Peak detection for tremor activity was more accurate using the Apple Watch than the IPG. Peak and harmonic detection were also more accurate using continuous wavelet transforms than short-time Fourier transform. According to the repeated measures correlation, our detection algorithm correlated strongly with DBS intensity (Subject RZCH: r = −0.93, p = 3.6 × 10⁻⁵; 6KOZ: r = −0.97, p = 1.6 × 10⁻⁵, NU5U: r = −0.94, p = 0.057). Pearson’s correlation coefficient between our tremor detection algorithm and the currently accepted industry metric was found to be 0.57 (t-value = 8.5, dof = 148, p < 1 × 10⁻⁶). Our algorithm is distinctive in the capability to detect Parkinsonian tremor, with a high degree of clinical relevance, during daily living activities and is able to discriminate tremor from walking, using a convenient, commercial wrist-worn accelerometer. Full article

Practical robotic systems require control methods that remain reliable under limited computational resources, uncertain environments, and frequent changes in operating conditions. Although model-based control forms the foundation of high-performance robotics, real-world deployment is often hindered by model uncertainty, time-varying dynamics, and costly identification. As a result, low-order and intuitive control schemes remain dominant, yet such approaches often fail to sustain consistent performance under disturbances and parameter variations. Robust and adaptive control provide representative paradigms to address this gap, where a Disturbance Observer (DOB) suppresses uncertainty through disturbance rejection and a Parameter Adaptation Algorithm (PAA) improves model fidelity through online identification. However, direct integration of a DOB and a PAA often introduces functional interference, including mutual masking between disturbance compensation and parameter estimation, which compromises closed-loop stability. This paper proposes an Adaptive Disturbance Observer (ADOB) that integrates a DOB with online parameter adaptation. The ADOB updates the nominal model of the DOB in real time using a Recursive Least Squares (RLS)-based PAA, while a dual-filtering structure separates disturbance rejection and parameter identification. Stability is analyzed using hyperstability theory, where a smoothing mechanism enforces the slowly varying parameter assumption. Experiments on a one-Degree-of-Freedom (DOF) electromagnetic actuator and a three-DOF robotic manipulator demonstrate reductions in model uncertainty and tracking error compared with a conventional DOB. Full article

Ordered mesoporous silica (OMS) is widely investigated as a mineral carrier for bioactive compounds; however, the adsorption of poorly soluble flavonoids such as quercetin on unmodified silica remains limited, and the effect of cationic surfactant modification on adsorption performance is still insufficiently understood. This study evaluates the adsorption of quercetin on OMS modified with tetrabutylammonium bromide (TBA-Br) and hexadecyltrimethylammonium bromide (HDTMA-Br). Batch adsorption experiments were analyzed using various adsorption isotherm models, and the quality of fit was evaluated based on the coefficient of determination (R²) and the reduced chi-square statistic (χ²/DoF). The results indicated that quercetin adsorption followed a physisorption mechanism, predominantly governed by hydrophobic interactions and surface heterogeneity. Silica modified with HDTMA-Br exhibited a significantly higher maximum sorption capacity compared to OMS-TBA-Br, reaching g_max values of up to 5.2 mg·g⁻¹, whereas the maximum adsorption for OMS-TBA-Br did not exceed 4.2 mg·g⁻¹. The best fit of the experimental data was obtained for models accounting for the heterogeneous nature of the adsorbent surface, particularly the Tóth model. The obtained results clearly demonstrate that modification of OMS with a cationic surfactant possessing a long alkyl chain significantly enhances the adsorption capacity of silica toward quercetin, which is of considerable importance for the design of mineral carriers for bioactive compounds. Full article

Walking is one of many basic human motor functions, yet replicating it in robotic systems remains a complex problem. Historically, the design of walking mechanisms has relied on human intuition and iterative refining. Some well-known mechanisms, like Theo Jansen, have been invented by artists rather than engineers. In this paper, we present a novel, automated pipeline that includes dataset generation, filtering, and an optimization procedure for synthesizing 1-DOF geometrically feasible walking mechanisms. Four million mechanisms were simulated and evaluated for 25 mechanism types, for a total of 100 million mechanisms. Quantitative design criteria for walking motion were identified and applied to retain a total of 23,250 valid, stable walking mechanisms. We then apply a custom optimization process to adjust near-walking mechanisms whose joints run into the ground. A custom function is used to minimize the error related to ground intersection and step uniformity. The computational generation and optimization of walking linkages demonstrated in this work aims to systematically generate a large number of design concepts for walking mechanisms. While the focus of this work is on the synthesis of mechanisms for walking robots, the same methodology could be generalized to identify mechanisms for a wide range of applications beyond walking robots. Full article

Inspired by observations of manta ray gliding, this study designed and evaluated a more biologically accurate pectoral fin bending model. We assessed its hydrodynamic performance using six-degrees-of-freedom (6-DoF) Computational Fluid Dynamics (CFD) simulations, which were validated by tethered water tunnel experiments. Key findings reveal that symmetric bending significantly impacts longitudinal stability, increasing the pitch angle to nearly twice that of the flat-wing model (80° model) but compromising gliding efficiency. During this symmetric motion, the lift-to-drag ratio (K) minimum point is significantly delayed as the bending angle increases, following a negative quadratic trend. Conversely, asymmetric bending triggers a sharp 3.5-fold increase in the roll angle (80° vs. 30° model) and produces significant lateral displacement. Importantly, “roll-induced yaw” was confirmed as the dominant mechanism for lateral control, contributing up to 88.5% of the lateral force in the 80° model, despite minimal changes in the yaw angle. These findings reveal the intrinsic trade-offs between fin deformation, gliding efficiency, and attitude control, providing a theoretical basis for active configuration optimization and control strategies for bionic gliders. Full article