Path planning of mobile robot based on adaptive ant colony algorithm

被引：22
|
作者：
Zheng, Yan [1 ]
Luo, Qiang [1 ,3 ]
Wang, Haibao [1 ,2 ]
Wang, Changhong [1 ]
Chen, Xin [1 ]
机构：
[1] Chongqing Three Gorges Univ, Sch Mech Engn, Wanzhou 404100, Peoples R China
[2] Chongqing Engn Technol Res Ctr Light Alloy & Proc, Chongqing, Peoples R China
[3] Chongqing Univ, Engn Res Ctr, Intelligent Mfg Pilot Technol, Chongqing, Peoples R China
来源：
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS | 2020年 / 39卷 / 04期
关键词：
Mobile robot; path planning; ant colony algorithm; heuristic information; global optimization;
D O I：
10.3233/JIFS-189018
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The traditional ant colony algorithm has some problems, such as low search efficiency, slow convergence speed and local optimum. To solve those problems, an adaptive heuristic function is proposed, heuristic information is updated by using the shortest actual distance, which ant passed. The reward and punishment rules are introduced to optimize the local pheromone updating strategy. The state transfer function is optimized by using pseudo-random state transition rules. By comparing with other algorithms' simulation results in different simulation environments, the results show that it has effectiveness and superiority on path planning.
引用
收藏
页码：5329 / 5338
页数：10
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