Based on the Heuristic Bias Method of Efficient Algorithm of RRT - Connect
Fan Yang, Jinyang Fan · 2023
Aiming at the problems of Rapidly Exploring Random Tree (RRT) algorithm with a large number of redundant points and slow convergence in the sampling stage, An efficient RRT-Connect algorithm based on Heuristic Bias RRT-Connect (HB-RRT-Connect) is proposed. The algorithm divides the sampling process into global random sampling and biased target sampling, and determines the next sampling method by judging the environment of the previous sampling point, so as to realize the heuristic biased sampling. At the same time, the concept of double optimized dynamic step is also introduced to improve the expansion efficiency. The simulation results show that the proposed algorithm has less convergence time and less number of nodes than other algorithms, and can effectively improve the convergence speed.