The Mixing Algorithm of ACO and GA based Global Path Planning Method for Mobile Robot

被引：1

作者：

Cui, Shigang ^{[1
]}

Dong, Jianglei ^{[1
]}

Liang, Fan ^{[1
]}

机构：

[1] Tianjin Univ Technol & Educ, Tianjin Key Lab Informat Sensing & Intelligent Co, Tianjin 300222, Peoples R China

来源：

CURRENT DEVELOPMENT OF MECHANICAL ENGINEERING AND ENERGY, PTS 1 AND 2 | 2014年 / 494-495卷

基金：

中国国家自然科学基金;

关键词：

Ant colony algorithm(ACO); Genetic algorithm(GA); Global path planning; Robot;

D O I：

10.4028/www.scientific.net/AMM.494-495.1290

中图分类号：

TH [机械、仪表工业];

学科分类号：

0802 ;

摘要：

An ant colony algorithm is a stochastic searching optimization algorithm that is based on the heuristic behavior of the biologic colony. Its positive feedback and coordination make it possible to be applied to a distributed system. It has favorable adaptability in solving combinatorial optimization and has great development potential for its connotative parallel property. This study focused on global path planning with an ant colony algorithm in an environment based on grids, which explores a new path planning algorithm. How to present and update the pheromone of an ant system was investigated. The crossover operation of a genetic algorithm was used in the ant system for path optimization. Experimental results show that the algorithm has better path planning optimization ability than other algorithms.

引用

页码：1290 / +

页数：2

共 5 条

[1]

LIU Yuming, 2002, RES GLOBAL PATH PLAN

[2] Ant colony optimization with global pheromone evaluation for scheduling a single machine [J].