Search Results (477)

Wearable sensors are transformative tools for continuous gait assessment in daily life. Tripping, a leading cause of falls, is closely linked to inadequate foot clearance, making accurate foot height measurement critical for fall risk evaluation. Inertial measurement units offer a practical solution for foot trajectory reconstruction; however, conventional drift correction methods such as zero-velocity updates fail to adequately address cumulative height errors. Recent kinematic constraint-based approaches improve height accuracy but remain limited to offline processing and lack simultaneous activity classification. To address these gaps, we developed a real-time, single-IMU system for continuous foot height trajectory reconstruction with simultaneous classification of five locomotion activities deployed on a smartphone. Twenty healthy adults were recruited for model training and independent validation. Level walking maintained ground reference ($0.0$ cm, 95% CI: [$-1.8$, $1.8$] cm), cumulative height errors remained below 1.1 cm across ramp and stair negotiation with a mean absolute error of 0.42%, and obstacle clearance was quantified. The system achieved 96.08% overall classification accuracy with less than one gait cycle latency. Toe height was estimated through rigid-body transformation with comparable accuracy to the foot height. This framework provides a practical foundation for real-time gait intervention and fall prevention applications. Full article

To address unclear multi-constraint coupling mechanisms, energy estimation errors, and poor dynamic feasibility in autonomous underwater vehicle path planning, this paper proposes an energy-efficient velocity-kinodynamic rapidly exploring random tree star algorithm. The method integrates a velocity–energy model, a velocity–curvature model, and a velocity–safe-distance model into a planning framework. Four targeted improvements are incorporated: a potential-field-guided sampling, an adaptive step-length expansion, a kinodynamic feasibility constraint, and a multi-objective cost function. Simulation experiments across four scenarios and five start–goal tasks show that the proposed method eliminates all curvature violations, reduces propulsion energy by up to 49.4% and improves mean minimum obstacle clearance by 34.2% and 47.4% over the two baselines. Ablation studies confirm that each module contributes to overall performance. Full article

Cooperative motion control is a fundamental requirement for unmanned underwater vehicle (UUV) swarms operating in complex marine environments. Conventional swarm motion-control algorithms may suffer from limited convergence efficiency and redundant obstacle-avoidance maneuvers when the swarm is required to move toward multiple task-related regions. To address these issues, this study proposes a Vicsek-based distributed motion-control framework with static summoning points and threat-selective obstacle avoidance. First, static summoning points are introduced as predefined task-attraction locations, and a movement-cost-based assignment rule is used to divide the initially mixed swarm into task-oriented subclusters. Under a limited field-of-view constraint, a summoning factor is incorporated into the heading-update rule to balance local neighbor alignment and directional guidance toward the assigned summoning point. Then, an obstacle-avoidance strategy is developed by considering both the relative position of obstacles and the velocity direction of individuals. The detected obstacles are classified as current obstacles or potentially threatening obstacles, and avoidance maneuvers are triggered only when a current obstacle lies within the prescribed safety distance. Simulation results demonstrate that the proposed VSSPAO framework can improve convergence consistency, reduce convergence time, and decrease redundant obstacle-avoidance routes compared with the reference algorithms. The proposed method provides an interpretable and computationally simple distributed coordination mechanism for UUV swarm segmentation, task-oriented aggregation, and obstacle avoidance. Full article

Mobile robot navigation remains challenging when fast convergence, collision avoidance and deployability must be satisfied simultaneously. The original Q-learning with Artificial Potential Field (QAPF) paradigm is extended in this paper with three coordinated mechanisms that together yield a reported-horizon convergence reduction of approximately four orders of magnitude (from $\sim 3\times {10}^6$ episodes to $\sim 200$ to 230 episodes under the present protocol) and an internal-ablation collision-rate reduction of approximately one order of magnitude ($6.2\%$ to $0.3\%$), and that open a new capability frontier covering dynamic obstacles, multi-robot coordination, energy-aware velocity modulation and embedded-deployable inference timing. The first mechanism is a potential-based reward-shaping schedule whose unclipped fixed-weight form follows the policy-invariant shaping theorem, while the implemented clipped and time-varying form is used as an empirically stable approximation. Under the present experimental protocol, the reported convergence horizon is reduced from the $\sim 3\times {10}^6$ episodes reported for the original QAPF formulation to approximately 200 to 230 episodes; this comparison is protocol-dependent and is not claimed as a controlled one-to-one runtime speedup. The second mechanism is a discrete Control Barrier Function (CBF)-inspired action filter (thediscrete filter described in this paper is inspired by the continuous-time CBF literature, but does not carry a forward-invariance proof; it is used as an empirical safety mechanism rather than as a formal Control Barrier Function in the formal continuous-time sense) with per episode visit memory by which the held-out collision rate is reduced from $6.2\%$ for QAPF alone to $0.3\%$ while $93.8\%$ task completion is maintained, where this collision-rate comparison is internal to the QAPF ablation because the prior QAPF reference does not report a comparable held-out collision metric. The third mechanism is a set of extensions to dynamic obstacles, two-robot cooperative navigation under a centralized scheme (with an explicit $\mathcal{O}(N^2)$ scaling-cost analysis and three decentralization strategies for fleets beyond the small-N regime), curriculum learning and energy-aware velocity modulation. Disturbance robustness tests, empirical timeout/stagnation detection for unreachable-goal cases, i7 reference inference timing with projected embedded-device latencies, multi-axis generalization over obstacle density and grid size, scalability analysis for centralized multi-robot coordination and a scope comparison against A* and RRT* are added by the revised evaluation. Across 30 independent seeds on held-out static maps, $94.5\pm 2.1\%$ success is achieved by adaptive QAPF while $93.8\pm 2.3\%$ success with $0.3\pm 0.4\%$ collisions is achieved by QAPF+CBF. Under a separate finite robustness suite, $85.0\pm 4.1\%$ success is retained by QAPF+CBF in the combined disturbance regime. The timing study indicates that the 20 Hz real-time threshold is comfortably exceeded by all methods on the measured i7 reference platform and by all projected embedded-device equivalents. The results show that a lightweight and safety-oriented navigation policy for grid-based mobile-robot settings can be provided by APF-guided tabular reinforcement learning when it is paired with a discrete safety filter and a clarified energy and robustness analysis. Full article

To address the limitations of conventional mobile robot path planning results in terms of geometric continuity, kinematic executability, and adaptability to dynamic environments, this study proposes a reference trajectory generation method oriented toward trajectory tracking. First, the A* algorithm is employed to search for an initial collision-free path, and key-point sparsification is applied to remove redundant nodes. Then, a geometrically continuous reference path is constructed using cubic B-splines. On this basis, by considering the kinematic constraints of the differential-drive mobile robot together with the local curvature characteristics of the path, a local trackability index is introduced, and the reference velocity is adaptively corrected under the maximum angular velocity constraint to improve trajectory executability and tracking smoothness. To address local path invalidation caused by dynamic obstacles, a collision-risk-triggered local replanning and trajectory stitching mechanism is further developed to achieve smooth transition between the original and updated trajectories. Simulation and real-world experimental results demonstrate that the proposed method can effectively reduce path redundancy, improve trajectory smoothness and executability, and achieve rapid local path updating and stable trajectory stitching in dynamic environments. Full article

Under strict spatial constraints and environmental interference, autonomous navigation of vessels in inland bridge-restricted waterways demands precise coordination between collision avoidance and trajectory tracking. To meet these operational demands, an integrated framework that directly combines spatiotemporal risk assessment with dynamic control execution is developed. Based on a digital traffic model integrating bridge piers and channel boundaries, collision risks are evaluated by combining trajectory-predicted time to safe distance with the velocity obstacle interval. Such a formulation quantifies the actual spatial difficulty of evasion rather than relying solely on temporal urgency. Driven by this continuous assessment, a time-series rolling strategy calculates feasible maneuvering intervals, generating trajectories that comply strictly with inland navigation rules and physical vessel limits. Subsequently, an adaptive model predictive control algorithm executes these commands, implicitly compensating for the localized hydrodynamic disturbances typical of bridge areas. The effectiveness of the architecture is validated through comprehensive simulations covering rule-based encounters and complex multi-vessel scenarios. Quantitative results indicate that under wind and current disturbances, the maximum route tracking deviation is constrained below 53 m, while the minimum encounter distance with target ships is consistently maintained above 51 m. These performance metrics confirm the capacity to execute safe, rule-compliant maneuvers while preserving high navigational precision in confined inland environments. Full article

Planning effective collision avoidance routes is a crucial measure for ensuring ship safety. However, position uncertainty caused by sensor noise, communication delays, and sudden changes in the maneuvering of target vessels severely restricts the reliability of traditional collision avoidance methods. To address this, this study integrates the velocity obstacle method and conditional value at risk theory to design a ship collision avoidance framework under position uncertainty. The position uncertainty of the target vessel is modeled using a Gaussian distribution. By fusing multi-source sensor data from radars and the Automatic Identification System through Bayesian inference, the posterior estimate of the vessel’s position is dynamically updated, thereby constructing an uncertainty velocity obstacle region. The Gaussian posterior distribution of the position is incorporated into a stochastic loss function to formulate a stochastic optimization model that balances navigation efficiency and collision risk. The model is solved using the sample mean approximation method and strictly complies with the International Regulations for Preventing Collisions at Sea. The results of two sets of multi-vessel encounter simulations demonstrate that, compared with traditional methods, the proposed method achieves superior performance in terms of total path length and algorithm runtime. It is capable of generating compliant collision avoidance strategies in complex dynamic crossing scenarios, attaining optimal comprehensive performance with respect to safety, economy, and regulatory compliance. Full article

When a rigid object is manipulated by dual arms to form a closed chain, the dual-arm motion must satisfy closed-chain constraints. Although synchronized motion can be achieved by strictly tracking predefined global trajectories, the presence of dynamic obstacles necessitates reactive local planning. However, existing local planning methods designed for single-arm manipulators cannot guarantee synchronization between dual arms. To address this limitation, we propose a dual-arm reactive synchronized motion controller (SMC) by incorporating closed-chain constraints on dual-arm slack velocities based on spherical geometric velocity constraints, and by implementing a flexible master-slave arm switching strategy. As a result, the proposed controller achieves synchronized dual-arm control while preserving excellent motion performance, including manipulability enhancement, obstacle avoidance, and compliance with joint angle and velocity constraints. Simulations and experiments on a humanoid upper-body robot validate the effectiveness of the proposed approach. Full article

Unmanned Aerial Vehicle (UAV) tracking of ground moving targets holds significant applications in domains such as intelligent transportation, logistics distribution, and environmental monitoring, placing greater demands on efficient and stable path-planning methods for vehicular tracking. This study investigates a UAV path tracking approach based on a deep reinforcement learning algorithm, Proximal Policy Optimization (PPO). Starting from the kinematic characteristics of UAVs and ground vehicles, a 3D path planning model was constructed that considers spatial coordinates, velocity, and attitude constraints. A well-designed objective function—including tracking error minimization, energy optimization, and safety distance constraints—was incorporated. By designing the state space, action space, and reward function, the PPO algorithm is capable of adaptive learning in complex environments. Compared with traditional Artificial Potential Field (APF), Q-learning, and TD3 algorithms, PPO better balances exploration and exploitation and demonstrates stronger learning stability and global optimization capability in dynamic multi-obstacle scenarios. Simulation results show that PPO-based UAV path planning outperforms Q-learning and other comparative algorithms in terms of tracking accuracy, convergence speed, and robustness. In specific scenarios, Q-learning achieves a trajectory error of approximately 1 m, TD3 and APF exhibit errors around 0.3 m with noticeable oscillations, and PPO achieves an error of about 0.2 m. The UAV can follow the vehicle trajectory smoothly, with a more continuous path and rapidly converging, stable error curves, indicating the promising application potential of PPO in intelligent UAV control. The PPO-based UAV-tracking path planning method effectively enhances the UAV’s intelligent decision-making and path optimization capabilities, providing new technical approaches and a research foundation for intelligent UAV traffic and cooperative control systems. Full article

This paper presents a closed-form tracking and collision avoidance framework for multi-agent differential drive robots. Existing reactive methods often rely on purely geometric proximity, leading to conservative detours and local minima. A state-dependent adaptive avoidance strategy is developed to dynamically modulate repulsive forces using the time-derivative of fractional barrier risk functions, alleviating unnecessary evasive maneuvers. Within a convergence vector field (CVF) architecture, an active safety-corrected tracking mechanism orthogonally strips hazardous velocity projections from the spatial error. This mitigates the inherent conflict between target tracking and obstacle repulsion. A matrix projection-based Lyapunov approach demonstrates the finite-time convergence of the vehicle orientation, bounded tracking errors, and collision-free properties of the closed-loop system, with effectiveness further validated through simulations. Full article

For complex tasks such as search and recovery in uncharted maritime areas, the use of heterogeneous unmanned swarms (UAVs and USVs) is highly promising, yet effective cross-domain cooperative trajectory planning remains a key challenge, often leading to mission delays. This paper proposes a rapid Cooperative Cross-domain Path Planning framework (CCPP) and its associated algorithm for heterogeneous UAV–USV swarms. The framework first establishes a visual-fusion modeling pipeline, converting visual pose estimation, uncertainties, and semantic dynamic obstacles into a planning representation with robust safety margins and time-varying risk fields. A hybrid velocity-path co-optimization algorithm is then designed to simultaneously generate curvature-feasible trajectories and speed profiles under heterogeneous kinematics and explicit temporal constraints. In the end, an adaptive interpretable decision tree acts as a meta-strategy for online replanning and real-time adjustment of modes and weights. To address the critical issue of uneven arrival time distribution, this paper introduces, inspired by economic inequality analysis, a normalized Gini coefficient-based arrival time consistency index to quantify and optimize coordination timing. Comprehensive experiments validate the effectiveness of the proposed approach in enhancing cooperative efficiency and real-time adaptability. Full article

Redundant robot manipulators are widely used in constrained operations and tasks in complex environments. However, when multiple task constraints and inequality constraints coexist, motion planning becomes significantly more difficult. In high-dimensional configuration spaces, conventional planners are prone to local minima and may generate trajectories that are difficult to execute in real time. To address these issues, this paper proposes a hierarchical, redundancy-driven real-time replanning framework. First, we perform Cartesian sampling on the task-constraint manifold to reduce the search dimension and generate multiple candidate joint configurations for each Cartesian sample via a redundancy mapping. During connection, manipulability and executability margin are used as evaluation metrics, so that redundant degrees of freedom are explicitly exploited in tree expansion and configuration selection. Second, at the local execution layer, we employ a null-space manipulability optimization strategy to continuously improve dexterity while keeping the primary task unchanged and combine it with a priority-based hard inequality constraint filtering mechanism to project the nominal motion onto the feasible set under joint limits, velocity bounds, and safety-distance constraints in real time. Unlike existing approaches that treat global planning and local control as loosely coupled modules, the proposed framework unifies redundancy reconfiguration, feasibility maintenance, and topological replanning within a single closed-loop structure, thereby reinterpreting local minima as event-triggered topology-switching conditions. To handle the mismatch between dynamic environments and real-time perception, we further introduce a feasibility-margin monitoring mechanism that triggers event-based replanning based on changes in manipulability, constraint scaling, and safety distance, enabling fast topology-level switching and escape from local minima. Simulation and experimental results show that the proposed method effectively restores manipulability through redundancy-driven configuration adjustment and achieves a higher success rate of local recovery under dynamic obstacle intrusion. In forced replanning scenarios, the framework further demonstrates faster environmental response and lower replanning overhead while maintaining better task-constraint stability compared with existing approaches. Full article

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. Full article

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. Full article

To enhance the navigation efficiency and obstacle avoidance capability of omnidirectional mobile robots in unstructured and complex environments, this paper conducts research on intelligent path planning and management for X-type Mecanum-wheeled mobile robots with the improved Proximal Policy Optimization–Gated Recurrent Unit (PPO-GRU) model on the basis of robot kinematics modeling and deep reinforcement learning. First, by performing kinematic modeling of the X-type Mecanum-wheeled chassis and designing a high-dimensional state space along with a multi-factor composite reward function, the agent training environment for the robot–environment interaction control is established, laying the environmental foundation for in-depth research on path planning. Second, based on the construction of a Proximal Policy Optimization (PPO) path planning model, the PPO model is integrated with Gated Recurrent Units (GRUs) to form an improved PPO-GRU path planning model, thereby achieving an end-to-end path planning strategy. Finally, using a self-developed kinematic simulation platform for the X-type Mecanum-wheeled robot, the rationality and robustness of the proposed path planning model are investigated through ablation experiments, comparative experiments, dynamic environment tests, and tests considering key real-world phenomena. The research results indicate that the improved PPO-GRU path planning model increases the path planning success rate to 96%, reduces the average number of collisions by 82.7%, and achieves an average linear velocity reaching 84.5% of the maximum speed set in the environment. While attaining high-precision and robust planning management for autonomous navigation paths, it significantly improves the response speed of the agent’s autonomous navigation path planning. Full article