Research on path planning of vehicle dynamic obstacle avoidance based on improved RRT algorithm
Ma Jinhong, Jie Luo, Hao Li, Jinmin Hu · Sixth International Conference on Electromechanical Control Technology and Transportation (ICECTT 2021) · 2022
In view of the situation that the traditional path planning algorithms have the disadvantages of local optimal solution or the generated path is not smooth, on the one hand, this paper combines Artificial Potential Field with Rapidly Randomexploring Trees to improve the efficiency of path planning and solve the problem of local optimal solution; on the other hand, in order to ensure that the algorithm can be used in vehicle obstacle avoidance, the calculation formula of safe distance to obstacles is modified to make the obstacle avoidance process conform to vehicle driving behavior. At the meanwhile, in the exploring rules of RRT algorithm, vehicle dynamics constraints are added to make the path smooth and meet the maximum steering limit. In addition, driving distance information is added to each node of the path to deal with dynamic obstacles. Finally, the simulation results show that the path planned by this method can guide the vehicle to avoid dynamic obstacles smoothly.