Improved RRT algorithm for path planning in field environment based on target bias probability
Jin Ma, Jiajia Miao, Xinyue Yao, Xiaolong Li, Kerui Xia, Shuyan Wang, Yiqun Liu, Pinjia Zhang · 4th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2022) · 2022
With the advancement of unmanned driving technology, unmanned vehicles' application environment has gradually moved from the structured road surface to the unstructured and complex field environment. The traditional path planning approaches cannot match the path planning needs of unmanned vehicles in the field environment due to the complicated and changing terrain. In this paper, a fast random tree search algorithm is used to find a viable path in an environmental map quickly. By introducing the target probability bias value and the path post-processing method based on the maximum turning radius constraint of the unmanned vehicle into the basic RRT algorithm, and then simulating and comparing the simulation analysis on the 3D raster map of the constructed field environment. The applicability of the RRT algorithm based on the target probability in the field environment is verified, and the algorithm has higher search efficiency, smoother paths planned by the algorithm, and more goal-oriented path generation.