摘要
针对当前移动机器人的一些路径规划算法存在的局限性,提出了一种基于改进蚁群优化和遗传优化的融合算法。利用改进的信息素更新技术和路径节点选择技术使算法尽快找到优化路径,来形成融合算法的初始种群,机器人每前进一步,蚂蚁就对局部路径重新搜索,并处理随机出现的障碍物;然后利用遗传算法(GA)对种群个体进行全局优化,从而能使机器人沿一条全局优化的路径到达终点。仿真结果表明了该融合算法的可行性和有效性。
In order to overcome the limitation of the current path planning algorithms for mobile robot, a fusion algorithm based on ant colony optimization and genetic optimization was proposed. First, this method used pheromone update and path node selection technology to find optimization paths quickly so as to form initial population, and the ant executed a local search again once the robot went forward and dealt with random obstacles. Second, it optimized the individuals of the population by using Genetic Algorithm （GA） in the global scope, which could make the robot move on a globally optimal path to the ending node. The simulation results indicate the feasibility and effectiveness of the proposed method.
出处
《计算机应用》
CSCD
北大核心
2013年第11期3111-3113,3128,共4页
journal of Computer Applications
基金
国家自然科学基金资助项目(61125305)
关键词
蚁群优化
遗传算法
移动机器人
路径规划
信息素
Ant Colony Optimization （ACO）
Genetic Algorithm （GA）
mobile robot
path planning
pheromone
