Search Results (131)

Tight-formation control is a key technology for unmanned surface vehicle (USV) swarms in harbor navigation, cooperative berthing, and operations in hazardous environments, yet achieving reliable obstacle avoidance while maintaining formation stability remains highly challenging. Although multi-agent reinforcement learning has shown strong potential in cooperative systems, parallel policy structures in many existing methods still struggle to achieve synchronized coordination in tight formations, leading to behavioral inconsistencies and unstable formation keeping. To address these challenges, an action-aware multi-agent soft actor–critic (AAMASAC) algorithm is proposed that introduces a hierarchical, action-aware decision mechanism. Within each time step, upper-layer actions are propagated as prior signals to lower-layer policies, establishing an ordered, intent-aligned decision flow that mitigates temporal inconsistency and enhances coordination efficiency. The architecture explicitly encodes inter-layer dependencies via a decision priority hierarchy and real-time behavioral information channels, enabling more accurate credit assignment and more stable value estimation and policy optimization. Across three representative validation scenarios, the AAMASAC algorithm significantly outperforms baseline methods in average reward, path-tracking accuracy, formation stability, and obstacle-avoidance performance. These results indicate that introducing a hierarchical model and action awareness effectively improves control accuracy and coordination in a USV swarm. Full article

To address the key limitations of existing laser SLAM (Simultaneous Localization and Mapping) navigation systems in orchards—insufficient safety margins, unsmooth trajectories, poor dynamic obstacle adaptability, and high energy consumption—this study proposes an A* (A-Star)-DWA (Dynamic Window Approach) collaborative optimization algorithm integrated into an orchard-specific laser SLAM framework. Three core enhancements were added to the global A* planner: (1) obstacle–vertex adjacency checks (maintaining ~1 m minimum safety distance, meeting 0.8–1.2 m orchard machinery requirements); (2) redundant node elimination (reducing unnecessary turns and energy use); (3) obstacle density metric integrated into the heuristic function (optimizing node expansion efficiency). For the local DWA planner, key parameters (azimuth weight, obstacle distance weight, prediction horizon, etc.) were calibrated to orchard scenarios and tracked robot kinematics, with a lightweight “deviate → avoid → rejoin global path” mechanism for real-time obstacle avoidance. A three-stage path smoothing process (Bresenham verification + cubic spline interpolation + curvature constraint optimization) further improved trajectory quality. The A*-DWA framework synergizes A*’s global optimality (overcoming DWA’s local minima) and DWA’s real-time obstacle avoidance (compensating for A*’s static limitation). Validations via Matlab/Gazebo/Rviz simulations and field tests in the “Xinli No. 7” pear orchard confirmed superior performance: 100% obstacle avoidance success rate (vs. 85.0–92.0% for comparative algorithms), 0.36–0.45 s response time (57.7–71.1% shorter), 1.05–1.15 m safety distance (far exceeding 0.60–0.82 m of existing methods); field tests show 10% lower energy consumption than traditional A*, 0.011 m mean lateral deviation (straight segments), and 65% reduced peak turning deviation (0.14 m). This work resolves multidimensional orchard navigation challenges, enhances agricultural robot efficiency, safety, and adaptability, and provides a practical basis for smart agriculture advancement. 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

To tackle the navigation challenge in dynamic and complex environments, this study designs a fusion planning framework that synergistically integrates enhanced A* algorithm with improved DWA, inspired by the biological dual-layer navigation mechanism of global path planning and local real-time obstacle avoidance. Firstly, the original global path from the conventional A* algorithm is smoothed and length-reduced through a three-stage optimization strategy involving redundant node removal and forward and reverse path relaxation, mimicking the behavioral logic of honeybees and desert ants that eliminate redundant routes to complete foraging and homing with minimal energy consumption. Secondly, an evaluation function integrating dynamic obstacle perception and adaptive weight adjustment is designed for the DWA to enhance the intelligence of local planning, drawing on the adaptive strategy of animals such as antelopes that adjust behavioral priorities according to environmental complexity to balance safety and efficiency. To comprehensively verify the performance of the proposed algorithm, simulation evaluations are performed in various scenarios, including 20 × 20 and 30 × 30 grid maps, with single and dual dynamic obstacles. Results demonstrate that our algorithm outperforms conventional methods in path length, smoothness, and safety. Further physical verification is carried out on a LiDAR-equipped mobile robot (Shenzhen Yuanchuangxing Technology Co., Ltd., Shenzhen, China) based on the ROS platform, confirming that the algorithm can stably achieve static path tracking and real-time obstacle avoidance in real indoor environments. Consequently, the developed hybrid algorithm delivers a viable and robust solution for autonomous mobile robots to navigate safely and efficiently in unpredictable and complex environments. Full article

In complex, semi-structured orchard environments, mobile inspection robots often suffer from excessive turning points, low search efficiency, limited trajectory-tracking accuracy, and poor adaptability to dynamic obstacles. To address these issues, this study proposes an integrated autonomous navigation method that employs an improved A* algorithm for global path planning, a Fuzzy-Weighted Dynamic Window Approach (FW-DWA) for local path optimization, and a model predictive control (MPC)-based trajectory-tracking controller. First, a dynamic heuristic-weight adjustment strategy is introduced into the conventional A* algorithm, in which a correction factor adaptively tunes the heuristic weight; a two-stage node optimization procedure then removes hazardous and redundant nodes to improve path smoothness and safety. Second, the FW-DWA, grounded in fuzzy control theory, uses goal distance and obstacle distance to update the weights of the heading, clearance, and velocity evaluation functions in real time, thereby enhancing obstacle avoidance in dynamic environments. Finally, a discrete kinematic model is established to design the MPC Controller, which achieves high-precision tracking through receding-horizon optimization and feedback correction. Experiments conducted in real orchards demonstrate that the proposed method reduces path length by 5.79%, shortens planning time by 3.64%, and increases the minimum safety distance by 50%. Comparative results further show that the MPC Controller attains a mean position error of 0.032 m and a mean heading error of 3.14°, clearly outperforming a conventional Proportional–Integral–Derivative (PID) controller. These findings provide an effective solution for reliable autonomous navigation of orchard inspection robots and offer a valuable reference for smart agricultural robotics applications. Full article

To address the common limitations of conventional dual-layer path planning methods, such as slow global convergence, delayed local obstacle avoidance response, and insufficient inter-layer integration, this paper proposes an enhanced collaborative planning framework combining the Improved Dung Beetle Optimizer (IDBO) and the Improved Dynamic Window Approach (IDWA). First, the proposed IDBO solves the problems of population aggregation and unbalanced exploration–exploitation of traditional algorithms by optimizing the initialization strategy and reconstructing the position update mechanism. Second, in the local path planning stage, the IDWA introduces an adaptive evaluation function embedded with obstacle motion prediction and a global path-tracking factor, which breaks through the limitations of traditional local algorithms, such as fixed weights and lack of environmental adaptability, while resolving the contradictions of poor inter-layer coupling and path redundancy in traditional dual-layer frameworks. The results of comparative simulation experiments show that the average path length is reduced by 6.5% and the running time is decreased by 9.1%. This framework effectively overcomes the problems of delayed local response and insufficient inter-layer integration in traditional dual-layer path planning. Full article

Interest in the adoption of autonomous vehicles (AVs) continues to grow. It is essential to design new software architectures that meet stringent real-time, safety, and scalability requirements while integrating heterogeneous hardware and software solutions from different vendors and developers. This paper presents a lightweight, modular, and scalable architecture grounded in Service-Oriented Architecture (SOA) principles and implemented in ROS 2 (Robot Operating System 2). The proposed design leverages ROS 2’s Data Distribution System-based Quality-of-Service model to provide reliable communication, structured lifecycle management, and fault containment across distributed compute nodes. The architecture is organized into Perception, Planning, and Control layers with decoupled sensor access paths to satisfy heterogeneous frequency and hardware constraints. The decision-making core follows an event-driven policy that prioritizes fresh updates without enforcing global synchronization, applying zero-order hold where inputs are not refreshed. The architecture was validated on a 1:10-scale autonomous vehicle operating on a city-like track. The test environment covered canonical urban scenarios (lane-keeping, obstacle avoidance, traffic-sign recognition, intersections, overtaking, parking, and pedestrian interaction), with absolute positioning provided by an indoor GPS (Global Positioning System) localization setup. This work shows that the end-to-end Perception–Planning pipeline consistently met worst-case deadlines, yielding deterministic behaviour even under stress. The proposed architecture can be deemed compliant with real-time application standards for our use case on the 1:10 test vehicle, providing a robust foundation for deployment and further refinement. Full article

The primary objective of this research is to develop a probabilistic nonlinear model predictive control structure (NMPC) that efficiently operates uncrewed surface vehicles (USVs) in an environment that has probabilistic disturbances, such as wind, waves, and currents of the water, while simultaneously maneuvering the vehicle in a way that avoids stationary or moving stochastic obstacles in its path. The proposed controller structure considers the mean and covariances of the inputs or state variables of the vehicle in the cost function to handle probabilistic disturbances, where an extended Kalman filter (EKF) is utilized to calculate the mean, and the covariances are calculated dynamically via a linear matrix equality based on this mean and obtained system matrices with successive linearization for every sampling instance. The proposed control structure deals with non-zero-mean probabilistic disturbances such as water current via an innovative approach that treats the mean of the disturbance as a deterministic part, which is estimated by a disturbance observer and eliminated by a control term in the controller in addition to the control signal obtained via MPC optimization; the effect of the remaining zero-mean part is handled over its covariance during the probabilistic MPC optimization. The probabilistic constraints are also dealt with by converting them to deterministic constraints, as in linear probabilistic MPC. However, unlike the linear MPC, these constraints updated each sampling instance with the information obtained via successive linearization. The control structure incorporates the velocity obstacle (VO) method for collision avoidance. In order to ensure stability, the proposed NMPC adopts a dual-mode strategy, and a stability analysis is presented. In the second mode, an LQG design that ensures stability in the existence of non-zero mean disturbance is also provided. The simulation results demonstrate that the proposed probabilistic NMPC framework effectively handles probabilistic disturbances as well as both stationary and moving obstacles, ensuring collision avoidance while reaching the desired position and orientation through optimal path tracking, outperforming the conventional NMPC. Full article

With the growing demand for safe obstacle avoidance and precise trajectory tracking in the autonomous navigation of unmanned surface vessels (USVs), this paper investigates an adaptive differential evolution approach for integrated path planning and tracking control. In the path planning stage, an elite archive mechanism is first incorporated into the mutation process, and the scaling factor F and crossover rate $CR$ are adaptively adjusted to enhance population diversity and global search capability. Then, the International Regulations for Preventing Collisions at Sea (COLREGs) are embedded into the algorithmic framework to reinforce collision avoidance performance in complex encounter scenarios. A multi-objective fitness function combining six performance criteria is subsequently constructed to evaluate individual path points, thereby identifying high-quality solutions that ensure both safe navigation and route efficiency. In the tracking control stage, the optimally generated reference trajectory is then employed as the input command for the vessel’s motion control subsystem. A fuzzy logic system is introduced to approximate unknown nonlinear dynamics, and an adaptive fuzzy logic controller is designed to guarantee accurate tracking of the planned path. Finally, simulation tests are used to show the algorithm’s efficiency and usefulness. Full article

Autonomous navigation is a core enabler of smart agriculture, where path planning and trajectory tracking control play essential roles in achieving efficient and precise operations. Path planning determines operational efficiency and coverage completeness, while trajectory tracking directly affects task accuracy and system robustness. This paper presents a systematic review of agricultural robot navigation research published between 2020 and 2025, based on literature retrieved from major databases including Web of Science and EI Compendex (ultimately including 95 papers). Research advances in global planning (coverage and point-to-point), local planning (obstacle avoidance and replanning), multi-robot cooperative planning, and classical, advanced, and learning-based trajectory tracking control methods are comprehensively summarized. Particular attention is given to their application and limitations in typical agricultural scenarios such as open-fields, orchards, greenhouses, and hilly slopes. Despite notable progress, key challenges remain, including limited algorithm comparability, weak cross-scenario generalization, and insufficient long-term validation. To address these issues, a scenario-driven “scenario–constraint–performance” adaptive framework is proposed to systematically align navigation methods with environmental and operational conditions, providing practical guidance for developing scalable and engineering-ready agricultural robot navigation systems. Full article

Based on an enhanced artificial potential field approach, this paper presents a control method for obstacle avoidance in vehicle platoons within Intelligent Vehicle-Infrastructure Cooperative Systems (I-VICS). To enhance safety during maneuvers, an inter-vehicle obstacle avoidance potential field model is established. By integrating virtual forces and a consistency control strategy into the control law, the proposed method effectively handles obstacle avoidance for vehicles operating at large inter-vehicle distances (80–110 m). Experimental validation using real-world trajectory data shows a 34% improvement in trajectory smoothness, as quantified by a proposed Vehicle Trajectory Stability (VTS) metric, leading to significantly safer avoidance maneuvers. A coordinated multi-vehicle obstacle avoidance strategy is further devised using a rotating potential field method, enabling collaborative and safe overall motion planning. Moreover, a path tracking strategy based on virtual force design is introduced to enhance platoon stability and reliability. Future work will focus on collision avoidance for vehicle platoons with varying inter-vehicle distances and will extend the consistency control and cooperative avoidance strategies to longer vehicle platoon to further improve overall traffic safety. Full article

Given the undoubtable similarities between the dynamic behavior of the vehicular traffic flow in terms of its response to boundary condition alterations dictated in the form of obstacles, and the specific case of supersonic compressible fluid flow fields, the current manuscript addresses developing a target trajectory for the overtaking maneuver of autonomous vehicles. The path-planning is pursued in analogy to the governing principles of the supersonic compressible fluid flow fields, with the specific definition of a physically meaningful dimensionless group, namely the Traffic Mach number (M_T), which grants the initial access point to the said set of fundamental equations. This practical application is a follow-up to the primarily established proof-of-concept level introduction and analysis of the more general case of collision avoidance for autonomously driven vehicles in accordance with the supersonic compressible fluid flow field, where the Traffic Mach number was first introduced. The proposed trajectory is then taken to the next block of the investigation, namely the tracking and control aspects of the maneuvering vehicle’s dynamics. The path tracking controller is designed based on sliding mode control technique and the algorithm is applied on a 7-DOF simulation model, used for validation and discussion of results. The proposed method is shown to be suitable for overtaking maneuvers of autonomous vehicles, whilst meeting the criteria for a relative velocity from the constant-velocity vehicle ahead of the road in the supersonic regime based on the defined Traffic Mach number. The results are then presented, first, in the scope of the aerodynamics field configuration and their verifications, followed by the vehicle dynamics remarks showing the practicality of the proposed method in terms of vehicle motion. It is observed that the distance corresponding to the delayed maneuver maximizes at highest velocities of the ego vehicle, consistent with the highest M_T values, yet in all simulated cases, the control system of the vehicle model was capable of performing the maneuver based on the assigned trajectories through the present model. Full article

In order to solve the problem of obstacle avoidance for unmanned surface vehicles (USV), based on the classic RRT algorithm and Velocity Obstacle principle, an improved RRT algorithm is proposed. For the situation of the extension direction of the parent node inside the collision cone in the EXTEND operation, ‘obstacle repellent vector’ and ’collision risk index’ are presented, making the extension direction of the search tree have the tendency to move away from obstacle. Meanwhile for the problem of the real time performance of the algorithm and path oscillation, ‘target attraction vector’ and waypoint corner constraint are introduced to accelerate the convergence of the algorithm and improve the quality of path point. Path planning experiment results show that the improved algorithm has better real-time character. Path tracking experiment results based on 3-DOF ship nonlinear dynamic model reveal that the collision-free paths generated by improved RRT algorithm are smoother and the navigation time is shorter, which are of great significance for practical engineering application. Full article

This paper presents a modular, high-fidelity simulation framework for the autonomous navigation of omnidirectional mobile robots using visual localization with AprilTags. The proposed system integrates realistic robot dynamics, a dual-layer path planning architecture, and interchangeable trajectory tracking controllers, all within the ROS Noetic and Gazebo simulation environment. AprilTags are employed as low-cost fiducial markers for map construction, eliminating the need for LiDAR or GPS. The architecture supports global path planning via the A* algorithm and local reactive replanning to avoid unexpected obstacles while preserving trajectory continuity. Three control strategies—Lyapunov-based, null-space Lyapunov, and proportional–integral (PI) control—are implemented and evaluated in multiple maze-like environments. Experimental results in the simulation demonstrate accurate trajectory tracking, successful visual mapping, and effective obstacle avoidance under realistic conditions. Full article

There have been many attempts to model the flow of vehicular traffic in analogy to the flow of fluids. Given the evident change in distance between vehicles driving in platoons, the compressibility of traffic flow is inferred and, considering the reaction time-scales of the driver (human or autonomous), it is argued that this compressibility is increased as relative velocities increase—giving the lag in imposed redirection by the driver and the controller units a higher relative importance. Therefore, a supersonic compressible flow field has been opted for as the most analogous base flow. On this point, added to by the overall extreme similarities of the two above-mentioned flows, the non-dimensional group of the traffic Mach number M_T has been defined in the present research, providing the possibility of calculating a suggested flow field and its corresponding shockwave systems, for any given obstacle ahead of the traffic flow. This suggested flow field is then taken as the basis to obtain trajectories designed for avoiding collision with the obstacle, and in compliance with the physics of the underlying analogous fluid flow phenomena, namely the internal supersonic compressible flow around a double wedge. It should be noted that herein we do not model the traffic flow but propose these trajectories for more optimal collision avoidance, and therefore the above-mentioned similarities (explained in detail in the manuscript) suffice, without the need to rely on full analogies between the two flows. The manuscript further analyzes the applicability of the proposed analogy in the path-planning process for an autonomous passenger vehicle, through dynamics and control of a full-planar vehicle model with an autonomous path-tracking controller. Simulations are performed using realistic vehicle parameters and the results show that the fluid flow analogy is compatible with the vehicle dynamics, as it is able to follow the target path generated by fluid flow calculations with minor deviations. Simulation results demonstrate that the proposed method produces smooth and dynamically consistent trajectories that remain stable under varying traffic scenarios. The controller achieves accurate path tracking and rapid convergence, confirming the feasibility of the fluid-flow analogy for real-time vehicle control. Full article