Local Trajectory Planning for Obstacle Avoidance of Unmanned Tracked Vehicles Based on Artificial Potential Field Method

A trajectory planning method for local obstacle avoidance based on an improved artificial potential field (APF) method is proposed, which is aimed at the problem for dual motor driven unmanned tracked vehicles avoiding dynamic and static obstacles in unstructured environments. Firstly, in traditional artificial potential fields, by adding virtual target points, unmanned tracked vehicles can avoid large obstacles and reach the target point in off-road environments. Secondly, a water droplet type repulsive potential field function for static obstacles and an improved dynamic obstacle potential field function including relative velocity function and relative acceleration function are established in the proposed improved APF method to improve the smoothness of lane changing obstacle avoidance paths. The simulation results of overtaking and obstacle avoidance in the same direction show that the change in heading angle is reduced by 42.9%, and the lateral displacement is reduced by 39.5%. Finally, a trajectory planning method based on improved APF for obstacle avoidance and lane changing of the unmanned tracked vehicle is constructed, which also considers the speed planning with kinematic and dynamic constraints. For obstacle avoidance under lateral meeting condition, the collaborative simulation results of Prescan-Adams-Matlab/Simulink show that the change in heading angle is reduced by 84%, and the lateral displacement is almost zero. Under complex working conditions with multiple static and dynamic obstacles, the results of hardware in loop (HIL) simulation testing and vehicle experiments show that the number of drastic changes in turning radius and heading angle of the vehicle is significantly reduced, and the maximum amplitude was reduced by 63.2% and 37.5% respectively, making the vehicle’s obstacle avoidance and lane changing safer, smoother, and more efficient.