An improved ant colony algorithm for robot path planning

被引：362
|
作者：
Liu, Jianhua [1 ,2 ]
Yang, Jianguo [1 ]
Liu, Huaping [3 ]
Tian, Xingjun [2 ]
Gao, Meng [2 ]
机构：
[1] Donghua Univ, Coll Mech Engn, Shanghai 201620, Peoples R China
[2] Shijiazhuang Tiedao Univ, Coll Elect & Elect Engn, Shijiazhuang 050043, Hebei, Peoples R China
[3] Tsinghua Univ, Key Lab Intelligent Technol & Syst, Beijing 100084, Peoples R China
来源：
SOFT COMPUTING | 2017年 / 21卷 / 19期
关键词：
Mobile robot; Ant colony algorithm; Pheromone diffusion; Local path optimization; GENETIC ALGORITHMS; OPTIMIZATION; NETWORK; SYSTEM;
D O I：
10.1007/s00500-016-2161-7
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
To solve the problems of convergence speed in the ant colony algorithm, an improved ant colony optimization algorithm is proposed for path planning of mobile robots in the environment that is expressed using the grid method. The pheromone diffusion and geometric local optimization are combined in the process of searching for the globally optimal path. The current path pheromone diffuses in the direction of the potential field force during the ant searching process, so ants tend to search for a higher fitness subspace, and the search space of the test pattern becomes smaller. The path that is first optimized using the ant colony algorithm is optimized using the geometric algorithm. The pheromones of the first optimal path and the second optimal path are simultaneously updated. The simulation results show that the improved ant colony optimization algorithm is notably effective.
引用
收藏
页码：5829 / 5839
页数：11
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