Abstract:Aiming at the actual multi-robot formation control problems such as obstacle avoidance, external disturbance and hysteresis, a control method based on distributed reference correction is proposed. By using the artificial potential field method and the disturbance observer, the obstacle avoidance problem of mismatched uncertainty is solved. The unreachable reference scene is defined to describe the passive correction behavior of multiple robots when trying to avoid obstacles. A distributed reference correction algorithm is designed for each robot to reduce the adverse effects of passive correction behavior and ensure the boundedness of position tracking error of each robot. Considering the known actuator hysteresis effect, a Bouc-Wen hysteresis compensator is added to the current control law. The effectiveness of obstacle avoidance is verified by Lyapunov stability theory. Finally, numerical simulation and comparison are carried out based on the multi-robot system to prove the effectiveness of the proposed algorithm and controller. Finally, numerical simulation and comparison are carried out based on multi-robot system in MATLAB environment to prove the effectiveness of the proposed algorithm and controller. The experimental results show that the method can form a stable multi-robot formation control under practical problems such as external interference and hysteresis, and avoid obstacles on the path without collision. The proposed distributed reference correction algorithm weakens the passive correction behavior and improves the stability of the system.

Abstract:Dynamic path planning is a critical factor in ensuring the safe flight of unmanned aerial vehicles (UAVs) in complex interference environments. To address the issues of high iteration counts, slow convergence, and dynamic obstacle avoidance in dynamic path planning, this paper proposes a UAV dynamic path planning method based on obstacle motion prediction and improved artificial potential fields (APF). First, for dynamic obstacles, a target detection algorithm based on laser radar and a motion prediction algorithm based on Kalman filtering are designed to estimate dynamic obstacle information. A velocity direction similarity detection method is introduced for local position evasion decisions. Secondly, for static obstacles, a simulated annealing algorithm is introduced to perturb the current state, coupled with a neighborhood optimization function based on target points for dynamic path planning. Simulation results show that the proposed algorithm reduces dynamic obstacle avoidance time by 69% when dealing with static obstacles and reduces obstacle avoidance distance by 19.7% and task duration by 23.6% when dealing with dynamic obstacles, thereby enhancing the safety and efficiency of UAV mission execution.

Abstract:Aiming at the problem that the search path of A* algorithm is not smooth and does not have dynamic obstacle avoidance in 3D environment, this paper proposes a fusion A* algorithm. Based on the A* algorithm, this algorithm first introduces pitch angle and yaw angle as search constraints, which shortens the time for path planning, and secondly uses variable weight evaluation functions and UAV range, flight height, and threat cost Finally, the smoothed A* algorithm is combined with the artificial potential field method, and the particle swarm algorithm is used to optimize the parameters involved in the A* algorithm and the artificial potential field method. The simulation results show that compared with the traditional A* algorithm, the fusion algorithm saves 5.34% of fuel consumption, improves the search efficiency, shortens the path length, the planned path is smoother, and can achieve real-time dynamic obstacle avoidance.

Abstract:Aiming at the problem of local minimum and unreachable target when using artificial potential field method for mobile robot path planning in the presence of complex obstacles. In this paper, an improved artificial potential field method based on concave obstacle patching is proposed for local path planning. Firstly, the concave obstacles are filled to prevent the robot from entering the local minimum area. Then, by adding the distance influence factor, the repulsion field function is improved to make the target point the smallest point in the global situation field, and prevent the robot from falling into the target unreachable area. Finally, the simulation results show that the improved artificial potential field method proposed in this paper can solve the local minimum problem and the target unreachable problem in the environment with complex obstacles. Compared with other algorithms, it can effectively reduce the path compensation and improve the planning efficiency.