Improved RRT path planning algorithm based on growth evaluation

Haixia Wanga; Xin Liu; Shibin Song; Boyang Li; Xiao Lu; Jun Nie; Xilin Zhao

doi:10.1117/12.2625448

12 December 2021 Improved RRT path planning algorithm based on growth evaluation

Haixia Wanga, Xin Liu, Shibin Song, Boyang Li, Xiao Lu, Jun Nie, Xilin Zhao

Proceedings Volume 12127, International Conference on Intelligent Equipment and Special Robots (ICIESR 2021); 1212723 (2021) https://doi.org/10.1117/12.2625448
Event: International Conference on Intelligent Equipment and Special Robots (ICIESR 2021), 2021, Qingdao, China

Abstract

Path planning algorithm is a key research problem in the application of autonomous mobile robots. RRT algorithm is one of the excellent methods for robot path planning. However, RRT algorithm has drawbacks such as high time consumption, high number of samples, low operational efficiency. On the basis of an adaptive resolution Octree Map, this paper presents an improved RRT path planning algorithm through establishing a growth point evaluation function. By adding the restriction through the evaluation function to random growing points of RRT, the random growth can be turned into purpose-oriented, and redundant growth points and routes can be eliminated. Besides, the growth path is reselected and rewired by the parent node and candidate nodes obtained through the growing process. Through reselection and rewiring, the route is continuously optimized and smoothed. Experiments are carried out for performance evaluation. Experiment results indicate that, comparing with the traditional RRT, RRT* and B-RRT*, the improved RRT algorithm can eliminate the redundant bifurcations of the growth tree reduce the number of sampling times, and greatly improves the growth efficiency.

Citation Download Citation

Haixia Wanga, Xin Liu, Shibin Song, Boyang Li, Xiao Lu, Jun Nie, and Xilin Zhao "Improved RRT path planning algorithm based on growth evaluation", Proc. SPIE 12127, International Conference on Intelligent Equipment and Special Robots (ICIESR 2021), 1212723 (12 December 2021); https://doi.org/10.1117/12.2625448

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KEYWORDS

Detection and tracking algorithms

Aerospace engineering

Mobile robots

Free space

Lithium

Lutetium

Network architectures

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