Robotic Path Planning Based on Improved Ant Colony Algorithm

被引：4
|
作者：
Liu, Tingting [1 ]
Song, Chuyi [1 ]
Jiang, Jingqing [1 ,2 ]
机构：
[1] Inner Mongolia Univ Nationalities, Coll Math, Tongliao 028000, Peoples R China
[2] Inner Mongolia Univ Nationalities, Coll Comp Sci & Technol, Tongliao 028000, Peoples R China
来源：
ADVANCES IN NEURAL NETWORKS - ISNN 2019, PT I | 2019年 / 11554卷
基金：
中国国家自然科学基金;
关键词：
Ant colony algorithm; Path planning; Pheromone; Mobile robot;
D O I：
10.1007/978-3-030-22796-8_37
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Ant colony algorithm is an intelligent bionic optimization algorithm. Its self-organization and intelligence provide guiding for studying the global path planning problem. Based on this, an improved ant colony algorithm is proposed to solve the problem of robotic path planning and improved the convergence speed. The environment model is established by grid method and the traditional ant colony algorithm is improved. The heuristic factor and pheromone updating strategy of the algorithm are improved to enhance the precision of the algorithm and the ability of later convergence. Simulation experiments show that the improved algorithm has a faster convergence speed to achieve the optimal path compared with other algorithms. It shows that the improved algorithm is effective and reliable.
引用
收藏
页码：351 / 358
页数：8
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