Abstract:To address the problems that the traditional artificial potential field (APF) does not fully consider the variability of vehicle collision avoidance risk distribution and that falling into local extremum leads to path planning failure, this study proposes an adaptive elliptic scope APF based on gradient statistical mutation quantum genetic algorithm (GSM-QGA). Based on the traditional circular scope of the repulsive field, the study designs a calculation method for the dynamic elliptic scope of the repulsive potential field by analyzing the relative motion state of vehicles and obstacles. At the same time, through the analysis of the influencing factors of the potential field function, the velocity factor is introduced to complete the design of the repulsive potential field and gravitational potential field function. The GSM-QGA is used as the local optimum correction strategy for the improved artificial potential field. When the vehicle falls into the local extremum and moves back and forth, a pseudo-global map is constructed according to the current position of the vehicle, and a feasible path is planned to jump out of the local extremum range. The simulation results show that the path planned by the improved algorithm not only can effectively prevent vehicles from getting stuck in local extremum and reduce unnecessary obstacle avoidance operations of vehicles but also has advantages over traditional APF algorithm and APF algorithm based on fixed elliptic scope in terms of path smoothness and path length. The length of the planned path is shortened by 6.37% and 9.14%, respectively.