基于改进遗传算法的自动导引小车路径规划及其实现平台  

AGV path planning based on improved genetic algorithm and implementation platform

作　　者：刘二辉[1] 姚锡凡[1] 

机构地区：[1]华南理工大学机械与汽车工程学院,广东广州510640

出　　处：《计算机集成制造系统》2017年第3期465-472,共8页Computer Integrated Manufacturing Systems

基　　金：国家自然科学基金资助项目(51675186;51175187);湛江市科技计划资助项目(2015A01001);广州市南沙区科技计划资助项目(2015CX005)~~

摘　　要：针对自动导引小车全局路径规划算法收敛慢和容易陷入局部最小值的问题,结合灰狼优化算法改进传统的精英保留策略,避免了传统精英保留策略使种群多样性变差的缺点,增强了全局搜索能力;为了防止染色体上的基因聚集到小的邻域内,提出了基于染色体信息熵的自适应变异和交叉概率的改进遗传算法,其中对于与障碍物相交的染色体片段采用邻域变异算子,使染色体片段快速避开障碍物。采用MATLAB GUI工具开发出基于改进遗传算法的移动机器人路径规划平台。实验结果表明,本文所提出的改进算法和开发平台能高效并可靠地求解复杂静态环境中的移动机器人路径规划问题。Aiming at the problem that Automated Guided Vehicle（AGV）global path planning algorithm easy to fall into local minimum and slow convergence,the traditional Elitism Tactic was enhanced with Grey Wolf Optimization（GWO）to prevent population diversity worse gradually and improve its global search capability.To prevent genes on chromosomes to cluster around a small neighborhood,an Improved Genetic Algorithm（IGA）enhanced with adaptive mutation and crossover probability based on chromosome information entropy was proposed,in which a neighborhood mutation operator was adopted for gene fragments that intersected the obstacles to be avoided quickly.An AGV path planning platform for the proposed IGA was developed by using Matlab GUI tools.Experimental results showed that the proposed IGA and the developed platform could efficiently and reliably solve complex static environment AGV path planning problems.

关 键 词：灰狼优化算法 信息熵 邻域变异 自适应变异 

分 类 号：TH16[机械工程—机械制造及自动化] TP24[自动化与计算机技术—检测技术与自动化装置]