A jump point search improved ant colony hybrid optimization algorithm for path planning of mobile robot

被引：0

作者：

Chen, Tao ^{[1
,2
]}

Chen, Suifan ^{[1
]}

Zhang, Kuoran ^{[1
]}

Qiu, Guoting ^{[2
]}

Li, Qipeng ^{[1
]}

Chen, Xinmin ^{[2
]}

机构：

[1] Zhejiang Univ Sci & Technol, Coll Mech & Energy Engn, 318 Liuhe Rd, Hangzhou 310023, Peoples R China

[2] Chinese Acad Sci, Ningbo Inst Mat Technol & Engn, 1219 Zhongguan West Rd, Ningbo 315201, Peoples R China

来源：

INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS | 2022年 / 19卷 / 05期

关键词：

Mobile robot; path planning; jump point search algorithm; ant colony optimization algorithm; turn cost factor;

D O I：

10.1177/17298806221127953

中图分类号：

TP24 [机器人技术];

学科分类号：

080202 ; 1405 ;

摘要：

To improve the finding path accuracy of the ant colony algorithm and reduce the number of turns, a jump point search improved ant colony optimization hybrid algorithm has been proposed in this article. Firstly, the initial pheromone concentration distribution gets from the jump points has been introduced to guide the algorithm in finding the way, thus accelerating the early iteration speed. The turning cost factor in the heuristic function has been designed to improve the smoothness of the path. Finally, the adaptive reward and punishment factor, and the Max-Min ant system have been introduced to improve the iterative speed and global search ability of the algorithm. Simulation through three maps of different scales is carried out. Furthermore, the results prove that the jump point search improved ant colony optimization hybrid algorithm performs effectively in finding path accuracy and reducing the number of turns.

引用

页数：9

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