TY - GEN
T1 - An Improved Dynamic Step Size RRT Algorithm in Complex Environments
AU - Zhang, Yuwei
AU - Wang, Ruirong
AU - Song, Chunlei
AU - Xu, Jianhua
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - Rapidly exploring Random Tree(RRT) is an efficient path planning algorithm based on random sampling, which plays an important role in the robot field and autonomous driving field. However, due to the randomness of sampling, its results are usually not optimal. This paper proposes a dynamic step size RRT algorithm, which mainly improves the traditional RRT as follows. First, combined with the Artificial Potential Field(APF), the target makes heuristic guidance for the sampling process. And then, the step size is adaptively changed according to the density of obstacles. After that, a one-shot heuristic strategy is used to speed up the search process. Finally, a bi-directional pruning strategy is adopted to reduce the path length by merging points. The simulation results show that the improved RRT algorithm can find the target faster and better.
AB - Rapidly exploring Random Tree(RRT) is an efficient path planning algorithm based on random sampling, which plays an important role in the robot field and autonomous driving field. However, due to the randomness of sampling, its results are usually not optimal. This paper proposes a dynamic step size RRT algorithm, which mainly improves the traditional RRT as follows. First, combined with the Artificial Potential Field(APF), the target makes heuristic guidance for the sampling process. And then, the step size is adaptively changed according to the density of obstacles. After that, a one-shot heuristic strategy is used to speed up the search process. Finally, a bi-directional pruning strategy is adopted to reduce the path length by merging points. The simulation results show that the improved RRT algorithm can find the target faster and better.
KW - APF
KW - Bi-directional Pruning
KW - Dynamic Step Size
KW - RRT
UR - https://www.scopus.com/pages/publications/85125186362
U2 - 10.1109/CCDC52312.2021.9602069
DO - 10.1109/CCDC52312.2021.9602069
M3 - Conference contribution
AN - SCOPUS:85125186362
T3 - Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021
SP - 3835
EP - 3840
BT - Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 33rd Chinese Control and Decision Conference, CCDC 2021
Y2 - 22 May 2021 through 24 May 2021
ER -