Vortex Artificial-Potential-Field Guided RRT for Path Planning of Mobile Robot

The rapidly-exploring random tree (RRT) has the problems of slow convergence, dense sampling nodes, and complicated path twists under the condition of dense obstacles and narrow channels. In this paper, a common variant of the RRT algorithm, RRT*, is designed with atificial potential fields (APF) to...

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Published inJisuanji kexue yu tansuo Vol. 15; no. 4; pp. 723 - 732
Main Author CAO Kai, CHEN Yangquan, GAO Song, GAO Jiajia
Format Journal Article
LanguageChinese
Published Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press 01.04.2021
Subjects
artificial potential fields (apf)
mobile robot
path planning
rapidly-exploring random tree (rrt)
vortex
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Summary:The rapidly-exploring random tree (RRT) has the problems of slow convergence, dense sampling nodes, and complicated path twists under the condition of dense obstacles and narrow channels. In this paper, a common variant of the RRT algorithm, RRT*, is designed with atificial potential fields (APF) to guide RRT* for path planning. First, vortex is used to constrain the repulsive field that diverges outward to form a vortex field along the tangential gradient. And vortex-APF (VAPF) is used to guide the sampling node to perform directional sampling in the RRT* deflection area, so as to reduce the execution time and accelerate the convergence speed. Simultaneously, the node rejection is used to remove high cost nodes and invalid nodes and a more centralized tree can be generated to reduce memory requirements. Finally, the extra nodes are pruned and the path is smoothed by vortex potential field to achieve the path optimization. Considering that the RRT algorithm has probabilistic randomness, 32 experi-ments are ca
ISSN:1673-9418
DOI:10.3778/j.issn.1673-9418.2004037