Open Access

ARTICLE

A Path Planning Algorithm Based on Improved RRT Sampling Region

Xiangkui Jiang^*, Zihao Wang, Chao Dong

College of Automation, Xi’an University of Posts and Telecommunications, Xi’an, 710121, China

* Corresponding Author: Xiangkui Jiang. Email:

Computers, Materials & Continua 2024, 80(3), 4303-4323. https://doi.org/10.32604/cmc.2024.054640

Received 03 June 2024; Accepted 03 August 2024; Issue published 12 September 2024

Abstract

For the problem of slow search and tortuous paths in the Rapidly Exploring Random Tree (RRT) algorithm, a feedback-biased sampling RRT, called FS-RRT, is proposed based on RRT. Firstly, to improve the sampling efficiency of RRT to shorten the search time, the search area of the random tree is restricted to improve the sampling efficiency. Secondly, to obtain better information about obstacles to shorten the path length, a feedback-biased sampling strategy is used instead of the traditional random sampling, the collision of the expanding node with an obstacle generates feedback information so that the next expanding node avoids expanding within a specific angle range. Thirdly, this paper proposes using the inverse optimization strategy to remove redundancy points from the initial path, making the path shorter and more accurate. Finally, to satisfy the smooth operation of the robot in practice, auxiliary points are used to optimize the cubic Bezier curve to avoid path-crossing obstacles when using the Bezier curve optimization. The experimental results demonstrate that, compared to the traditional RRT algorithm, the proposed FS-RRT algorithm performs favorably against mainstream algorithms regarding running time, number of search iterations, and path length. Moreover, the improved algorithm also performs well in a narrow obstacle environment, and its effectiveness is further confirmed by experimental verification.

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Keywords

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