Search Results (107)

Autonomous navigation of a vehicle in an environment where there are obstacles is difficult due to low onboard sensing technology, high measuring noise, and external interference, which collectively result in poor tracking performance of the vehicle’s trajectory and compromise safety. In this paper, a UAV-assisted Distributed Sliding Mode Control (DSMC) is proposed to robustly and safely implement path tracking for autonomous ground vehicles (AGVs). The proposed system utilizes an aero-sensor layer for enhanced perception, such as obstacle sensing, reference path preview, and look-ahead trajectory information, and it shares this information with the vehicle via wireless communication. The fundamental scheme, called DSMC, is based on a conventional Sliding Mode Control (SMC) technique and uses UAV preview-based feedback. This allows anticipation of control actions to enhance tracking performance and achieve more timely, smoother obstacle avoidance than baseline SMC. The proposed method is designed to overcome the limitations of traditional SMC strategies, such as chattering and poor responsiveness. The proposed method features continuous nonlinear approximation and damping mechanisms to reduce chattering and improve response characteristics, thereby enhancing stability and reducing oscillations. Strict safety enforcement through constraint is always achieved by keeping the vehicle and obstacles separated by a minimum distance only; that is, a minimum distance is always guaranteed: a Constraint Barrier Function (CBF)-based constraint is used. By combining UAV-assisted perception with DSMC and CBF the system can guarantee its formal safety in the presence of disturbances and sensing uncertainties while maintaining accurate trajectory tracking. Based on our simulation results, the proposed UAV-assisted DSMC method is shown to be significantly superior to conventional SMC and Model Predictive Controller (MPC) in terms of tracking accuracy, control smoothness, and adherence to the safety margin. Our simulation results demonstrate that the proposed method significantly outperforms conventional SMC and MPC control. Specifically, it achieves a 22.9% reduction in RMSE (0.135 m vs. 0.175 m) and 63% lower mean control effort, and it strictly maintains the minimum safety distance under both static and dynamic obstacles. The algorithm runs in real-time with an average execution time of 1.85 ms (>200 Hz), making it highly suitable for embedded deployment. These results highlight the effectiveness of combining UAV-assisted preview, adaptive robust control, and formal safety constraints for reliable autonomous navigation in complex environments. Full article

In precision mold manufacturing, the machining of HRC52 hardened steel causes severe tool wear and high noise in multi-source sensor signals, making accurate remaining useful life (RUL) prognostics challenging. To address this, we propose a hybrid model based on a two-stage VB-to-RUL estimation strategy. The network first performs high-fidelity flank wear (VB) trajectory tracking; the RUL is then deduced via threshold mapping. The model integrates three components: a one-dimensional deep convolutional neural network (DCNN), a low-resolution self-attention (LRSA) module with 1D-to-2D spatiotemporal reconstruction, and a multi-channel bidirectional long short-term memory network (McBiLSTM). A Gaussian smoothing filter is first applied to denoise the 50 kHz signals, followed by physical-period sliding windows for feature extraction. A multi-strategy fusion pooling layer (mean, max, and last-quarter features) further improves prediction accuracy. Using the PHM 2010 milling cutter dataset under leave-one-out cross-validation, the proposed model achieves a mean absolute percentage error (MAPE) of 1.45% and a root mean square error (RMSE) of 2.76 μm, reducing prediction error by up to 75.6% compared to Transformer, LSTM, and GRU baselines. These results demonstrate that the model effectively extracts degradation features even during the accelerated wear stage, providing a potential solution for tool health monitoring and predictive maintenance under complex cutting conditions. Full article

Road network extraction and updating are crucial for urban development, map updating, and mobility applications. Existing trajectory-based methods often underutilize grid-level semantic information and neighborhood context, thereby limiting their robustness to noisy, heterogeneous, and cross-city trajectory conditions. This study proposes a supervised framework for trajectory-driven road network extraction by coupling intra-grid movement semantics with inter-grid neighborhood context. Multi-level features, including convex-hull shape descriptors, directional clustering, DTW-based (Dynamic Time Warping) heterogeneity, and neighborhood density differences, are used to train a Random Forest classifier for key-grid detection. The detected key grids are further processed through morphology-aware thinning and Kalman smoothing to generate a topology-preserving and vectorization-ready road skeleton. The model is trained on pedestrian trajectories from Shenzhen and directly transferred to vehicle trajectories in Wuhan and Changsha under a zero-shot setting. Experimental results show that the proposed method achieves longer correctly extracted road length and competitive length-based precision compared with raster-based reference methods, while feature-importance and ablation analyses confirm the complementary role of neighborhood context. The proposed pipeline is scalable, interpretable, and transferable, supporting trajectory-based road map updating and urban network analysis. Full article

This paper investigates the conflicting multiple constraints and safety challenges in humanoid robot teleoperation for nonprehensile transportation tasks. The robot’s complex workspace and high degrees of freedom frequently conflict with highly dynamic task requirements, imposing stringent demands on coordinated motion. To address these issues, this paper proposes a Multiple-Constraint Safety-Critical Control Framework (MC-SCCF) featuring a hierarchical three-layer architecture. The top layer guarantees intrinsic safety against workspace boundaries using a continuously differentiable reachability surrogate model and an improved control barrier function (CBF)-based safe velocity filter for smooth deceleration. The middle layer maps user commands into pose-coupled reference trajectories to ensure task-level object safety, satisfying strict non-slip and non-toppling constraints. The bottom layer utilizes a quadratic programming (QP)-based inverse kinematics solver to achieve self-collision avoidance, coordinated motion, and optimal configuration while strictly enforcing joint and manipulability limits. Simulations and hardware experiments demonstrate that the MC-SCCF achieves real-time, high-precision reachability evaluation and successfully coordinates task dynamics with physical constraints, enhancing operational safety and the human–robot interaction experience. Full article

Background/Objectives: Post-stroke gait impairment is highly heterogeneous, which limits the effectiveness of standardized exoskeleton control strategies. Deep reinforcement learning offers a route to adaptive assistance, but its use in stroke rehabilitation is constrained by limited pathological gait data and the lack of interpretable transfer frameworks. We developed a data-efficient, pathology-informed reinforcement learning framework for personalized exoskeleton assistance under limited clinical gait data. Methods: The framework combines neuromuscular-inspired parametric augmentation (NIPA) with parameter-efficient transfer learning. NIPA synthesizes pathological gait trajectories by modeling weakness, stiffness or contracture, and abnormal synergies. A policy is first pretrained in simulation and then adapted to clinical gait data by freezing a shared feature extractor and fine-tuning the output heads. The framework was evaluated on a public clinical gait dataset of 50 stroke survivors using tracking error, reward, smoothness, generalization, and data efficiency as main outcomes. Results: The proposed method outperformed zero assistance, rule-based control, and reinforcement learning from scratch on the test set. Compared with scratch, it reduced total MSE from 14.8681 to 11.9369 ($p=5.96\times {10}^{-8}$) and improved reward from −21.2264 to −18.4798 ($p=3.76\times {10}^{-4}$). Hip MSE decreased from 5.9544 to 4.0143 ($p=7.51\times {10}^{-8}$) and knee MSE decreased from 6.5507 to 5.4507 ($p=1.51\times {10}^{-5}$), with significant improvements in repeated experiments. Conclusions: The proposed framework reduces reliance on large pathological training datasets and improves offline trajectory-level personalization under limited clinical data. It also provides an interpretable basis for quantitative characterization of post-stroke gait heterogeneity and may support individualized rehabilitation assessment and assistance planning. Full article

The Educational Competition Optimizer (ECO) shows promise on simple tasks but struggles with high-dimensional and complex landscapes due to rigid stage division and limited search operators. This paper proposes a Hybrid Strategy Enhanced ECO (HSECO) featuring: (i) a self-adaptive parameter evolution mechanism for individual-level flexibility, (ii) a multi-operator adaptive selection scheme switching between learning and differential evolution strategies based on real-time feedback, and (iii) an archive-assisted diversity preservation module to mitigate premature convergence. HSECO is validated on CEC2017, CEC2020 and CEC2022, and a continuous engineering benchmark. Statistical tests confirm its superiority over nine State-of-the-Art and parameter-free algorithms in accuracy, convergence speed, and robustness. Ablation and diversity analyses verify its balanced exploration–exploitation dynamics. Finally, HSECO is applied to a three-dimensional UAV path-planning problem, where path length, altitude variation, and turning smoothness are integrated into a single fitness function using a weighted-sum formulation. Therefore, from a metaheuristic optimization perspective, the UAV case is treated as a single-objective constrained optimization problem rather than a Pareto-based multi-objective problem. Experimental results show that HSECO obtains shorter, safer, and smoother trajectories with lower overall weighted fitness. Full article

To address the challenges of generating autonomous, high-quality control laws for high-performance Unmanned Aerial Vehicles (UAVs) performing long-horizon complex aerobatic maneuvers, specifically the difficulty of achieving energy-altitude closure, low-level control chattering, and low utilization of high-quality experience, this paper proposes an improved Soft Actor-Critic (SAC) algorithm incorporating a Quality-Aware Expert Pool (QAEP). Using the aerobatic loop maneuver as a representative scenario, this study explores the autonomous generation of control-surface manipulation policies comparable to those of skilled human pilots for agile fixed-wing UAVs. First, a singularity-free feature state representation and a rate-integrated action space are constructed. Combined with a symmetric shaping reward, these suppress control chattering at the physical level and achieve energy-altitude closure throughout the maneuver. Second, a dual-threshold expert pool driven by task reward and trajectory quality, together with a progressive mixed-sampling mechanism, is designed to effectively filter out low-quality samples and improve algorithmic convergence stability. Simulation experiments based on JSBSim with a high-fidelity F-16 model, which serves as a representative surrogate for a high-performance UAV, demonstrate that the proposed method generates maneuver strategies with manipulation quality comparable to that of skilled human pilots. The Dynamic Time Warping (DTW) similarity between the generated control commands and human expert demonstration data exceeds 0.97, the Manipulation Smoothness Index (MSI) is improved by 7.3%, and the loop completion rate under randomized initial conditions reaches 96.2%. These results suggest that the proposed framework enables human-like energy coordination and fine-grained control sequence generation in complex simulation environments, offering a promising approach to advancing maneuver intelligence and autonomous control capability in UAV systems. Full article

Neural implicit surface representations have yielded impressive results in 3D reconstruction, yet existing methods tend to introduce noise in smooth regions or fail to capture fine details in complex areas, primarily due to a lack of explicit spatial structure modeling. To address these limitations, we propose a geometry-adaptive surface reconstruction method based on Morton codes. By mapping 3D space onto octree traversal paths, this approach provides a natural spatial structural prior for the reconstruction process. For each query point, an implicit octree generates a unique root-to-leaf trajectory, yielding spatially adaptive weights that modulate multi-resolution geometric features. Specifically, low-frequency coarse features dominate in flat regions to suppress noise, whereas high-frequency fine features are activated in edge-rich areas to recover intricate geometry. Experimental results demonstrate competitive performance across multiple datasets, particularly in reconstructing sharp features and fine-grained geometric details. Full article

As an important option for energy storage projects, pumping stations can also generate electricity when the upstream has surplus water and the pump system operates as a turbine (PAT mode). When it switches from pump mode to PAT mode, the pump operation state changes significantly. This study adopts a numerical simulation to investigate the flow characteristics, time-frequency domain performance and chaotic features of pressure pulsation in a vertical mixed-flow pump device when it operates in different PAT modes. The results show that, when the pump operates in PAT mode, the flow in the straight passage remains smooth, but it deteriorates in the elbow-shaped draft tube, such as developing a spiral stream in the straight section, a disordered stream in the elbow section, and vortexes and flow separation at the beginning of the diffuser section, but it gradually becomes smooth after passing through the diffuser section. Under low-head PAT conditions, circumferential circulation cross flow occurs at the impeller inlet, reducing energy conversion efficiency. Under all PAT conditions, the flow on the blade surface near the hub is stable, but obvious vortexes happen near the shroud. As the head increases, the small-scale vortexes disappear on the mid-blade surface, and the flow becomes smoother on the blade surface near the shroud of the impeller. Except at the impeller outlet, pressure pulsation of the monitoring probes exhibits clear periodicity, with dominant frequencies corresponding to the rotational frequency, and its amplitudes decreasing from shroud to hub. Pressure pulsation under all PAT conditions is chaotic, and phase trajectories exhibit ring-shaped structures consisting of the ring circle and the ring surface. Differences in the circle spacing, size, and spatial position of the ring circle phase locus and ring surface phase locus are observed, and these variations are closely related to the PAT conditions. A correlative relationship exists between the chaotic correlation dimension and flow performance, which is of great significance for the condition monitoring and fault diagnosis of pump units. These findings not only enrich the theoretical research on the PAT mode of pumps, but also provide a reference for similar engineering applications and offer new insights into condition monitoring of hydraulic machinery. Full article

To address the issues of insufficient robustness, large depth recovery errors, and poor scene adaptability currently present in the initialisation phase of event-based stereo visual inertial SLAM systems, we propose a global BA initialisation method based on an adaptive threshold robust kernel function, ES-ATRK. The algorithm first achieves spatio-temporal fusion of events and visual features. Event features are triangulated to obtain depth values that serve as the 3D map, whilst visual features provide 2D observations; both modalities jointly feed the Structure from Motion (SfM) pipeline, laying the foundation for global bundle adjustment (BA) optimisation. The core contribution lies in incorporating a robust kernel function into the global BA to suppress outlier interference and in designing an adaptive thresholding algorithm that dynamically determines the kernel threshold. Furthermore, the algorithm calculates an initial threshold based on the quantile distribution of residuals prior to BA optimisation, combined with validity checks and a multi-round iterative smoothing adjustment strategy, thereby achieving scene-adaptive thresholding. In over 85% of the test scenes on the VECtor dataset, its localisation accuracy improved by at least 10% compared to existing mainstream event-based SLAM methods, such as ESVIO and USLAM. In high-dynamic scenes, its ATE performance is approximately twice that of mainstream models such as ESIO, and it maintains excellent positioning accuracy and stability of three-axis errors in generalisation tests on the HKU and MVSEC datasets. Furthermore, in the large-scale outdoor testing scenarios of the DSEC dataset, ES-ATRK also demonstrates superior feature tracking and trajectory estimation performance. This method effectively enhances the robustness of initialisation and depth recovery performance in event-based stereo visual inertial SLAM systems, reduces overall positioning error, and offers greater adaptability in challenging scenarios such as low-texture, high-dynamic, and HDR environments. Full article

Traditional table tennis technique evaluation relies heavily on coaches’ subjective judgment, which limits the objectivity, consistency, and scalability of instructional feedback. To address this problem, this study proposes a multi-dimensional joint quantitative evaluation method for table tennis techniques based on OpenPose and YOLOv8 using consumer-grade high-frame-rate video. A total of 50 participants were recruited and divided into a high-level group and a low-level group. Standardized forehand drive and backhand push tasks were recorded using a synchronized dual-view camera setup. OpenPose was used to extract upper-body keypoint trajectories for kinematic analysis, while YOLOv8 was employed to detect and track the ball, racket, and net for outcome-related feature extraction. Based on these data, seven core indicators covering movement stability, coordination, timing, smoothness, and hitting effectiveness were selected to construct a quantitative scoring model, which was further optimized by ridge regression and validated against expert ratings from three senior athletes/coaches. The results show significant between-group differences in multiple technical dimensions, including impact accuracy, smoothness, trajectory consistency, and limb coordination ($p<0.001$). The model score was strongly correlated with expert ratings ($r=0.882$, $p<0.001$) and demonstrated high reliability ($ICC=0.915$). These findings indicate that the proposed framework can provide a low-cost, non-invasive, and practically effective solution for intelligent table tennis teaching, technical diagnosis, and skill-level evaluation. Full article

This study presents a mathematical and kinematic modeling framework for analyzing trajectory behavior in a virtual reality (VR) welding simulator. Twenty novice participants performed repeated welding trials across three sessions, with torch trajectories recorded at 50 Hz in the task space. The proposed framework combines trial-level performance descriptors with derivative-based dynamic features, including spectral arc length (SPARC), log-normalized jerk (LNJ), and the number of velocity peaks (NVP), to characterize movement smoothness, intermittency, and longitudinal trajectory organization in a computer-simulated manual welding task. The results showed that spatial welding error decreased most clearly during the earliest stage of practice, with mean absolute lateral error declining from approximately 2.8 mm in the first trial to approximately 1.7 mm by the third trial. This early improvement was then broadly preserved across subsequent sessions. In contrast, smoothness- and fragmentation-related metrics exhibited more variable temporal patterns, indicating that improvements in task-space accuracy were not necessarily accompanied by uniform reorganization of movement dynamics. Associations between spatial error and kinematic features remained limited, suggesting that geometric task accuracy and dynamic trajectory organization represent complementary aspects of simulated manual performance. Overall, the findings show that high-frequency trajectory analysis in VR provides a useful basis for the mathematical modeling of dynamic behavior in simulated welding systems and supports the use of computer simulation for process-level investigation of manual task execution. Full article

Continuous wrist angle estimation based on surface electromyography (sEMG) is often affected by signal variability and prediction instability. Although regression models provide instantaneous outputs, their predictions may exhibit temporal fluctuations and limited robustness due to the non-stationary nature of sEMG signals. To address this issue, we propose an uncertainty-aware probabilistic fusion post-processing framework for continuous wrist motion estimation. The proposed approach decouples regression and uncertainty modeling, enabling plug-in compatibility with feature-based regression models. A local Gaussian process regression (LGPR) model is employed to estimate predictive uncertainty from a sliding feature window. The instantaneous regression output is then fused with the LGPR prediction through a Bayesian-inspired Gaussian formulation, resulting in a closed-form adaptive gain that dynamically adjusts smoothing strength according to predictive variance. Experimental results from both open-loop wrist joint motion estimation and closed-loop myoelectric control tasks demonstrate that our method outperforms existing methods in key performance indicators, including task completion time, trajectory smoothness, and trajectory tracking error. Full article

To address the challenges of unstable target localization and poor multi-module coordination in automated green pepper harvesting—caused by occlusions from branches and leaves, as well as varying lighting conditions—this paper presents the design and implementation of a modular robotic picking system. At the perception level, the system integrates a YOLOv8 detector with a RealSense D435i camera to identify and locate the calyx–ectocarp junctions of green peppers. An integrated multi-target tracking and filtering framework is proposed, which fuses multi-feature association, trajectory smoothing and coordinate denoising strategies to suppress depth noise and trajectory jitter, thereby enhancing the stability and accuracy of 3D localization. At the control and execution level, a depth-first picking sequence strategy with ID freeze-state management is implemented within a multithreaded software–hardware co-design architecture. This approach avoids task conflicts and duplicate operations while supporting continuous multi-fruit harvesting. Field experiments under natural outdoor lighting and varying occlusion levels demonstrate that the proposed system achieves recognition rates of 91.57% and 80.29% and harvesting success rates of 82.85% and 77.68% for non-occluded and lightly occluded fruits, respectively. The average picking cycle per pepper fruit is 9.8 s. This system provides an effective technical solution for addressing stability control challenges in the automated harvesting process of green peppers. Full article

Navigation is an essential ability for autonomous systems, and efficient motion planning for mobile robots is a central topic for autonomous vehicle design and service robotics. Most path-planning algorithms produce reference paths with sharp or discontinuous turns, inducing several drawbacks during mission execution, such as unexpected inertial stress and strain on the mechanical structure, passenger discomfort, and unsafe and unpredictable deviation of the real trajectory with respect to the reference planned one. Oppositely, smooth and feasible trajectories are often desired in real-time navigation for nonholonomic mobile robots where the surrounding environment can have a dynamic and complex shape with obstacles. In this paper, we propose a novel technique for the generation of smooth, collision-free, and near time-optimal paths for nonholonomic mobile robots. The proposed method exploits the features of a set of tunable bump functions, with the goal of pursuing smooth reference curves with tunable features (such as curvature, or jerk) yet seeking a reasonable length minimality, thus combining the advantages of the two most adopted techniques, namely Bezier interpolation and Dubins curves. After a thorough description of the analytical methods, the paper is primarily concerned with the design and tuning methods of the path-planning algorithm. Both a graphical method and numerical investigations and examples are performed to fully exploit the algorithm potentialities and to show the efficiency of the proposed strategy. Full article