Path planning of unmanned vehicle based on artificial potential field method of layered monitoring domain

To solve the problems of traditional artificial potential field method in path planning, such as easily trapping into local minimum points, unreachable target points near obstacles and prone to collide under fixed step length, an adaptive Artificial Potential Field method based on Layered Monitoring Domain (APFLMD) was proposed. A layered monitoring domain model was designed to realize the adaptive variable speed by establishing the safe obstacle avoidance range, so as to improve the obstacle avoidance ability of vehicle. To avoid the unmanned vehicle trapping into the local minimum point area, the local minimum point detection was realized by using the aggregation of path points. Also, the tangent point of equipotential circle was designed, and the path was optimized by quadratic Bezier curve. When the target point was inside the repulsive equipotential circle, a virtual random guidance strategy was proposed to help the vehicle escape from the local minimum point. The distance factor was added to the repulsion field function to solve the problem of unreachable target points near obstacles. The simulation results showed that compared with the reference algorithm, the APFLMD algorithm in complex environment could reduce the vehicle driving time by 49. 23%, the path length by 19. 4%, the vehicle energy consumption by 19. 4%, and the path smoothness by 82. 12% respectively. © 2024 CIMS. All rights reserved.