Search Results (1,810)

Autonomous drone navigation in confined tubular environments remains a major challenge due to the constraining geometry of the conduits, the proximity of the walls, and the perceptual limitations inherent to such scenarios. We propose a reinforcement learning (RL) approach enabling a drone to navigate unknown three-dimensional tubes without any prior knowledge of their geometry, relying solely on local observations from a Light Detection and Ranging (LiDAR) sensor and a conditional visual detection of the tube center. In contrast, the Pure Pursuit algorithm, used as a deterministic baseline, benefits from explicit access to the centerline, creating an information asymmetry designed to assess the ability of RL to compensate for the absence of a geometric model. The agent is trained through a progressive curriculum learning strategy that gradually exposes it to increasingly curved geometries, where the tube center frequently disappears from the visual field. A turning-negotiation mechanism, based on the combination of direct visibility, directional memory, and LiDAR symmetry cues, proves essential for ensuring stable navigation under such partial observability conditions. Experiments show that the Proximal Policy Optimization (PPO) policy acquires robust and generalizable behavior, consistently outperforming the deterministic controller despite its limited access to geometric information. Validation in a high-fidelity three-dimensional environment further confirms the transferability of the learned behavior to continuous physical dynamics. In particular, this work introduces an explicit formulation of the turn negotiation problem in tubular navigation, coupled with a reward design and evaluation metrics that make turn-handling behavior measurable and analyzable. This explicit focus on turn negotiation distinguishes our approach from prior learning-based and heuristic methods. The proposed approach thus provides a complete framework for autonomous navigation in unknown tubular environments and opens perspectives for industrial, underground, or medical applications where progressing through narrow and weakly perceptive conduits represents a central challenge. Full article

Safe navigation in dynamic environments remains challenging because classical distance-based constraints ignore the coupling between a robot’s translational motion and attitude dynamics, and fixed safety margins are either over-conservative or risky under varying uncertainty and approach speed. This paper presents a Risk-Aware Model Predictive Control (RA-MPC) framework that addresses both limitations through two integrated components. First, we introduce Orientation–Motion Coupled Control Barrier Functions (O-MCBFs) that enforce unified safety constraints linking collision avoidance with attitude stability limits, preventing dangerous pose configurations during dynamic obstacle avoidance. Second, we develop Risk-Aware Adaptive Margins (RAAMs) that compute time-varying safety buffers based on relative velocity, robot braking capability, and prediction uncertainty, enabling context-dependent safety–efficiency trade-offs without manual parameter tuning. The proposed method integrates these components into a quadratic programming formulation within MPC, ensuring real-time computational tractability. Experimental results demonstrate higher success rates, smoother trajectories, and improved progress toward the goal, with no observed safety violations under the tested conditions. These findings indicate that coupling pose-space safety with risk-adaptive margins provides a principled and practical path to safe and efficient navigation in dynamic scenes. Full article

Traffic accidents involving Vulnerable Road Users (VRUs) frequently occur at unsignalized intersections on Japanese community roads. To prevent such accidents, collision avoidance systems need to predict VRUs’ movements throughout the entire road network while explicitly handling uncertainty degraded by sparse observations and frequent route branching at intersections. Based on this motivation, this study proposes a Vehicle-to-Network (V2N)-based comprehensive safety framework for estimation of VRU movement and prediction of future intersection entry for community roads. The framework integrates estimation results provided from Roadside Edges and Vehicle Edges at a Central Server. In addition, road geometry from map information is incorporated as pseudo-observations into the estimation, and multiple route hypotheses are explicitly managed to represent route branching at intersections. For intersection-entry prediction, entry certainty is calculated by integrating a predicted distribution. For evaluation of the proposed framework, we conduct Monte Carlo simulations on simplified grid road networks. The results show that the proposed framework maintains conservative estimation under sparse observations and improves prediction when additional observation information from surrounding vehicles becomes available. Furthermore, a simulation-based case study using an actual community road-network geometry shows the feasibility of the proposed framework for cooperative collision avoidance on actual community roads. Full article

To improve the safety of planned paths and the accuracy of tracking control for intelligent tracked vehicles, this paper investigates the application of a CILQR-based motion-planning and tracking-control framework to intelligent tracked vehicles. Firstly, based on an improved discrete-point quadratic smoothing algorithm and the adapted CILQR, collision-free multi-objective optimal path generation in dynamic environment is achieved. Secondly, based on the discretization error model of the intelligent tracked vehicle, an LQR-MPC hybrid control method is proposed based on switching strategy. Finally, an experimental platform is formed, and real-vehicle tests are carried out. Experimental results demonstrate the efficiency and accuracy of the proposed framework. The adapted CILQR algorithm significantly reduces computation time to approximately 1.5 ms per iteration, ensuring real-time performance. Furthermore, field tests confirm that the hierarchical LQR-MPC controller achieves robust tracking with an average lateral error of only 5.7 cm at a speed of 0.5 m/s, effectively validating the system’s capability in obstacle avoidance and precise trajectory tracking. Full article

In underwater reconnaissance and patrol, AUV has to sense and judge traversability in cluttered areas that include reefs, cliffs, and seabed infrastructure. A narrow sonar field of view, occlusion, and current-driven disturbances leave the vehicle with local, time-varying information, so decisions are made with incomplete and uncertain observations. A path-planning framework is built around two coupled components: spatiotemporal graph neural network prediction and conditional normalizing flow (CNF)-based probabilistic environment reconstruction. Forward-looking sonar and inertial navigation system (INS) measurements are fused online to form a local environment graph with temporal encoding. Cross-temporal message passing captures how occupancy and maneuver patterns evolve, which supports path prediction under dynamic reachability and collision-avoidance constraints. For regions that remain unobserved, CNF performs conditional generation from the available local observations, producing probabilistic completion and an explicit uncertainty output. Conformal calibration then maps model confidence to credible intervals with controlled miscoverage, giving a consistent probabilistic interface for risk budgeting. To keep pace with ocean currents and moving targets, edge weights and graph connectivity are updated online as new observations arrive. Compared with Informed Random Tree star (RRT*), D* Lite, Soft Actor-Critic (SAC), and Graph Neural Network-Probabilistic Roadmap (GNN-PRM), the proposed method achieves a near 100% success rate at 20% occlusion and maintains about an 80% success rate even under 70% occlusion. In dynamic obstacle scenarios, it yields about a 4% collision rate at low speeds and keeps the collision rate below 20% when obstacle speed increases to 3 m/s. Ablation studies further demonstrate that temporal modeling improves success rate by about 7.1%, CNF-based probabilistic completion boosts success rate by about 13.2% and reduces collisions by about 17%, while conformal calibration reduces coverage error by about 6.6%, confirming robust planning under heavy occlusion and time-varying uncertainty. Full article

Operating an aerial vehicle in a confined space, such as a vessel ballast tank, is a major challenge in terms of localization, perception, and control due to limited visibility, constrained maneuvering space, and the absence of reliable (if any) GNSS signals. This paper addresses the design considerations for a quadcopter in confined spaces, focusing on a novel perception system using 12 VL53L8CX time-of-flight (ToF) sensors from STMicroelectronics. These sensors are used for enhanced perception and collision avoidance while flying in confined spaces, making them a suitable alternative to bulky LiDAR systems, reducing weight, cost, and required computational power. These sensors are placed strategically around the quadcopter to cover 360° radial view within a 4 m range. Experiments are conducted to test the reliability and repeatability of the integrated system, along with its synchronization feature. Furthermore, the applicability is verified by flying in confined and cluttered spaces, both in simulation and the real world. This design and study aims to establish a baseline for lightweight, compact, and safe navigation for small drones in confined and featureless environments. Full article

This paper presents a safe guidance strategy for affine formations based on the Interference Vector Method (IVM) to address dynamic formation guidance and collision avoidance for Autonomous Surface Vessels (ASVs) in multi-obstacle environments. An affine formation control framework is first adopted to enable dynamic formation transformations for the Autonomous Surface Vessel (ASV) fleet. Building on this, an IVM-based obstacle avoidance method is developed, enabling the formation to evade both static and dynamic obstacles in real time. Furthermore, a course guidance law based on the Vector Field Method (VFM) and a speed magnitude guidance law based on Control Barrier Functions (CBFs) are proposed to simultaneously achieve formation guidance and prevent inter-vessel collisions. The proposed safe guidance strategy is rigorously validated through theoretical proofs and comprehensive numerical simulations. The simulation results further confirm the robustness of the obstacle avoidance algorithm under ideal perception conditions, as well as the practical applicability of the overall strategy in complex, obstacle-rich environments. Full article

Continuum robots possess highly flexible backbones, enabling remarkable adaptability and dexterity for motion in confined environments. However, this flexibility also introduces significant nonlinearities and uncertainties, making motion planning under physical constraints particularly challenging. To address this, a hybrid offline–online configuration planning framework is proposed in this work. Specifically, the configuration planning problem is formulated as a nonlinear optimization task that considers collision avoidance and structural constraints. A co-evolutionary strategy is incorporated into the differential evolution (DE) algorithm to decompose the target high-dimensional optimization problem. Then, an unscented Kalman filter (UKF)-based strategy is presented for real-time shape estimation using tip pose feedback for safe distance monitoring. Based on this shape feedback, an online configuration refiner is designed to locally adjust the preplanned configurations, thus leveraging the global perspective of the offline planning configuration to steer the continuum manipulator through constrained spaces. Validation and comparative experiments demonstrate the effectiveness of the proposed method, as well as its enhanced motion smoothness and safe motion performance in real-world environments. Full article

Motion planning for on-road autonomous driving requires generating locally accurate spatio-temporal trajectories over a finite horizon, while facing increasing uncertainty and interaction variability toward distant regions. However, most existing planners employ uniform planning accuracy along the horizon, which implicitly treats far-field predictions with the same fidelity as near-term execution. This uniform treatment often leads to unnecessary computational effort and reduced planning efficiency without improving near-field feasibility. This paper presents an economical motion planning framework that allocates planning accuracy according to the spatio-temporal distance from the ego vehicle. The framework preserves high-fidelity planning in the near field where execution is imminent, while progressively reducing resolution and solution depth in the far field where uncertainty dominates and replanning is expected. A two-stage architecture is adopted, combining a distance-aware search for coarse path and velocity generation with distance-sensitive numerical refinement that prioritizes near-field feasibility under receding horizon execution. The simulation results demonstrate improved computational efficiency and planning reliability compared with uniform resolution baselines. Real-world experiments validate the stable online replanning performance in dynamic environments. Full article

Accurate prediction of peak particle velocity (PPV) in open-pit mine blasting is critical for ensuring operational safety and effective vibration control. This study proposes a hybrid modeling approach that integrates the Centered Collision Optimization (CCO) algorithm with Extreme Gradient Boosting (XGBoost), enhanced by SHAP-based sensitivity analysis to improve model transparency and mechanistic interpretability. A comprehensive dataset was constructed based on 193 field-measured blasting records collected from the Panzhihua Iron Mine in China, incorporating nine key input parameters. Model performance was rigorously evaluated using four widely recognized metrics: coefficient of determination (R²), root mean square error (RMSE), mean absolute error (MAE), and variance accounted for (VAF). The results demonstrate that the CCO–XGBoost model achieves superior predictive performance, with R² = 0.967, RMSE = 0.110, MAE = 0.067, and VAF = 96.35%, outperforming conventional approaches. SHAP-based sensitivity analysis reveals that blast-to-monitor distance (R) is the dominant negative predictor of PPV, contributing 43% to the total influence, with its vibration attenuation effect intensifying significantly when R exceeds 54 m. Charge per hole (q) and total charge per delay (Q) are identified as the primary positive influencing factors, accounting for 24% and 20% of the total contribution, respectively: the positive promoting effect of q on PPV strengthens markedly when q exceeds 17 kg, while Q exerts a continuous positive increasing influence on PPV when it exceeds 253 kg. Compared to existing hybrid models, the CCO–XGBoost uniquely avoids local optima and ensures higher global stability. This study fills the gap by providing quantifiable engineering thresholds for practical vibration control, making the model directly applicable to on-site blasting optimization. Full article

This paper presents a dedicated dataset for the measurement and prediction of VHF signal propagation path error, aiming to mitigate their adverse effects on the ranging and positioning accuracy of terrestrial navigation systems. The Automatic Identification System (AIS), as a critical maritime collision-avoidance technology, enables terrestrial-based positioning using coastal AIS stations, offering significant advantages in terms of deployment and maintenance costs. However, propagation path error remains one of the primary sources of positioning inaccuracies, and no specialized datasets have yet been developed to support its systematic measurement and prediction. To address this limitation, a comprehensive data acquisition and processing framework for AIS-related VHF-band propagation path error is proposed. Based on this framework, a multidimensional dataset is constructed, incorporating temperature, relative humidity, air pressure, instantaneous wind speed, salinity, and measured propagation path error. The measured propagation path error data are collected using a self-developed additional secondary phase correction system. Hydrometeorological parameters obtained from authoritative sources at the same time and location are integrated with the measured data to form experimental samples with rich feature representations. Data cleaning and preprocessing procedures are further applied to improve dataset quality. The final dataset comprises 1,296,000 samples and is suitable for training and evaluating machine learning and deep learning models for VHF signal propagation path error prediction, thereby supporting enhanced positioning accuracy and the improved reliability of maritime navigation systems. Full article

Maritime autonomous systems require robust predictive capabilities to anticipate vessel motion and environmental dynamics. While transformer architectures have revolutionized AIS-based trajectory prediction and demonstrated feasibility for sonar frame forecasting, their application to maritime radar frame prediction remains unexplored, creating a critical gap given radar’s all-weather reliability for navigation. This survey reviews predictive modeling approaches relevant to maritime radar, with emphasis on transformer architectures for spatiotemporal sequence forecasting, where existing representative methods are analyzed according to data type, architecture, and prediction horizon. Our review shows that, while the literature has demonstrated transformer-based frame prediction for sonar sensing, no prior work addresses transformer-based maritime radar frame prediction, thereby defining a clear research gap and motivating concrete research directions for future work in this area. Full article

In traditional industry setups, Automated Guided Vehicles (AGVs) follow trajectories planned together with the layout of the storage or production facility and supported by fixed markers on the floor or on the walls. Traffic rules manage the avoidance of multiple vehicles, while fleet management gets movement and transportation commands completed as soon as possible. In contrast, recent developments in navigation and advanced computing, sensor, and communication capabilities make their free movement safe and manageable. Detailed route planning and scheduling can guarantee that the vehicles keep a safe distance in time and space. A recent challenge of electric AGVs is that their charging may take several hours, which must be factored into their schedule. This has made minimal energy demand a key objective alongside earliest delivery and strictly meeting the deadlines. This paper presents a method for detailed routing and scheduling of AGV fleets to minimize energy consumption while considering battery levels and charging times. The optimization method is illustrated by a case study where multiple delivery tasks are performed by synchronized movement of vehicles on a complex warehouse layout. In the optimal solution, the scheduled waiting times for collision avoidance are utilized by the vehicles to pre-charge their batteries. Full article

This paper proposes a closed-form feedback control framework for rear- and front-wheel-drive car-like vehicles, aiming to solve trajectory tracking and collision avoidance tasks operating in complex environments. This method constructs novel risk assessment functions that incorporate motion information, enabling accurate risk assessment and reducing the conservatism in collision avoidance. Consequently, the proposed framework can effectively handle various on-road situations, including lane-following and obstacle avoidance, parking maneuvers, and navigation through intersections. Lyapunov-based analysis proves the stability of the designed closed-form control scheme. Simulation results in various typical scenarios demonstrate that the proposed method can achieve safe, stable, and smooth trajectory tracking, with improved performance metrics such as reduced tracking error and control effort, verifying its feasibility and effectiveness. Full article

This paper applies the recently developed Prediction-Based Control Barrier Functions (PB-CBFs) to the obstacle avoidance problem for multirotor air taxis in Urban Air Mobility (UAM). Unlike conventional Control Barrier Functions (CBFs), PB-CBFs incorporate escape path predictions into the formulation, facilitating safe controller design for dynamical systems with high relative degree and enabling safety under strict control constraints. We first review the PB-CBF framework, then formulate the safety requirements specific to the collision avoidance problem and derive the corresponding invariance conditions. Finally, we validate our approach through simulation of the obstacle avoidance scenario, demonstrating the efficacy of PB-CBFs in ensuring safety in UAM operations and providing additional insight into the mechanism by which predictions are leveraged to enforce safety. Full article