Improved PRM Path Planning in Narrow Passages Based on PSO

被引：7

作者：

Chai, Qisen ^{[1
]}

Wang, Yujun ^{[1
]}

He, Yong ^{[1
]}

Xu, Caijing ^{[1
]}

Hong, Zhisong ^{[1
]}

机构：

[1] Southwest Univ, Sch Comp & Informat Sci, 2 Tiansheng Rd, Chongqing, Peoples R China

来源：

PROCEEDINGS OF 2022 IEEE INTERNATIONAL CONFERENCE ON MECHATRONICS AND AUTOMATION (IEEE ICMA 2022) | 2022年

关键词：

path planning; PRM; PSO; narrow passage;

D O I：

10.1109/ICMA54519.2022.9855913

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Probabilistic roadmap (PRM) has been successfully applied in mobile robot path planning. However, the task of path planning is still a challenging job when narrow passages exist in the configuration space. In this paper, we propose a new approach that combines the technique of particle swarm optimization (PSO) with PRM. By sharing the information of free space with the sampling points which were originally deployed in the area obstacles, these sampling points can explore the free area in the later move and thus increase connectivity of the undirected graph without increasing the total sampling time. Based on the simulation results, the proposed method improves both the utilization rate of sampling points and the success rate of narrow passages path planning.

引用

页码：41 / 46

页数：6

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