基于改进蚁群算法的家庭机器人路径规划研究  

Path Planning of Family Robots Based on Improved Ant Colony Algorithm

作　　者：王学忠 徐丽萍[1] 李美莲[1] WANG Xuezhong;XU Liping(AnhuiSanlian University,Hefei 230601,China)

机构地区：[1]安徽三联学院,安徽合肥230601

出　　处：《皖西学院学报》2018年第5期46-50,共5页Journal of West Anhui University

基　　金：安徽三联学院校级自然科学重点项目(KJZD2018001);安徽高校自然科学研究重点项目(KJ2018A0601)

摘　　要：针对基本蚁群优化算法(ACO)在家用机器人路径规划中存在易早熟、收敛时间长、效率不高、获得路径的最佳值不稳定且不准确等现象,提出了一种改进的IACO计算方法。通过动态调整信息素增强系数Q值和启发因子α来提高收敛速度和效率;采用设置信息素阈值、改进信息素的计算方式来避免早熟;通过改进转移概率的计算方法和引入路径选择的随机机制使获得的路径最优解的值更加稳定、更加准确。从仿真数据结果看出,改进后的IACO算法在收敛速度、运行效率、最优路径的解方面较基本的ACO算法优越。Aiming at the problems of precocity,long convergence time,inefficiency,instability and inaccuracy of the basic ant colony optimization algorithm（ACO）in the path planning of home robots,an improved IACO algorithm is proposed.By dynamically adjusting the enhancement coefficient Q and heuristic factor alpha of pheromone,the convergence speed and efficiency are improved.By setting pheromone threshold and improving pheromone to avoid premature.By improving the calculation method of transfer probability and introducing the random mechanism of path selection,the value of optimal path solution is more stable and accurate.The simulation results show that the improved IACO algorithm is superior to the basic ACO algorithm in terms of convergence speed,operation efficiency and optimal path solution.

关 键 词：ACO算法 移动机器人 路径规划 

分 类 号：TP24[自动化与计算机技术—检测技术与自动化装置]