基于融合A^(*)-蚁群优化算法的移动机器人全局优化  

Global optimization of mobile robot based on fusion A^(*)-ant colony optimization algorithm

作　　者：方文凯 廖志高[1,2] FANG Wenkai;LIAO Zhigao(School of Economics and Management,Guangxi University of Science and Technology,Liuzhou 545006,China;Guangxi Industry High Quality Development Research Center,Liuzhou 545006,China)

机构地区：[1]广西科技大学经济与管理学院,柳州545006 [2]广西工业高质量发展研究中心,柳州545006

出　　处：《现代制造工程》2024年第7期77-84,共8页Modern Manufacturing Engineering

基　　金：国家自然科学基金面上项目(71771157);广西自动检测技术与仪器重点实验室开放基金项目(YQ20208);2020年广西汽车零部件与整车技术重点实验室自主研究课题项目(2020GKLACVTZZ01)。

摘　　要：针对传统蚁群算法在室内移动机器人全局路径规划中,存在的搜索效率低下、路径不够平滑、易陷入局部最优及死锁状况等问题,设计出一种融合改进A^(*)算法的双向搜索蚁群优化算法。首先利用改进A^(*)算法在栅格环境中快速收敛得到初始路径,构建初始信息素矩阵,并引入障碍物因子来减少蚂蚁死锁状况的发生;其次设定双向搜索蚁群优化算法规则,并改进双向搜索中的启发函数模型,引入精英蚂蚁搜索策略和自适应信息素挥发因子策略;最后利用三阶贝塞尔曲线对路径进行平滑处理。通过Pycharm平台仿真结果表明,该算法融合了A^(*)算法全局搜索能力强及蚁群算法正反馈的特性,使得融合改进后算法比传统蚁群算法和麻雀算法在路径长度上优化12.85%和7.76%,搜索时间上优化38.17%和23.46%,迭代次数上优化67.71%和54.41%,全局路径优化效果较明显。Aiming at the problems of traditional ant colony algorithm in global path planning of indoor mobile robot,such as low search efficiency,unsmooth path,easy to fall into local optimum and deadlock,an ant colony optimization algorithm for bi-directional search with improved A^(*)algorithm was designed.Firstly,the improved A^(*)algorithm was used to quickly converge and obtain the initial path in the grid environment,the initial pheromone matrix was constructed,and the obstacle factor was introduced to reduce the occurrence of ant deadlock.Secondly,the rules of ant colony optimization algorithm for bi-directional search were set,the heuristic function model in bi-directional search was improved,and elite ant search strategy and adaptive pheromone volatilization factor strategy were introduced.Finally,the third-order Bezier curve was used to smooth the path.The simulation results on Pycharm platform show that this algorithm combines the strong global search ability of A^(*)algorithm and the positive feedback characteristics of ant colony algorithm,which makes the improved algorithm optimize the path length by 12.85%and 7.76%,the search time by 38.17%and 23.46%,and the iteration times by 67.71%and 54.41%compared with the traditional ant colony algorithm and the sparrow search algorithm,and the global path optimization effect is obvious.

关 键 词：移动机器人 A^(*)算法 蚁群算法 双向搜索路径 贝塞尔曲线 

分 类 号：TP242[自动化与计算机技术—检测技术与自动化装置]