基于优化蚁群算法的机器人路径规划  

Robot path planning based on optimized ant colony algorithm

作　　者：郭琴 郑巧仙 GUO Qin;ZHENG Qiaoxian(School of Computer Science and Information Engineering,Hubei University,Wuhan 430062,China)

机构地区：[1]湖北大学计算机与信息工程学院,湖北武汉430062

出　　处：《湖北大学学报（自然科学版）》2023年第2期157-163,共7页Journal of Hubei University：Natural Science

基　　金：国家自然科学基金(61803149)资助。

摘　　要：路径规划是移动机器人设计中的关键环节,蚁群算法能高效解决路径规划问题,但它也存在一些弊端,如收敛速度慢、容易陷入局部最优解等.针对这些问题,本研究提出一种改进蚁群算法,在传统蚁群算法的基础上,改进状态转移规则,增加周围障碍物数量影响因子,令蚂蚁尽量避开障碍物;增加角度影响因子,使得蚂蚁行走的路径更加平滑;同时运用精英蚁群策略,来改进蚁群算法易陷入局部最优解的问题.仿真实验结果表明,该算法在多种环境下,都能找到最优路径,且有较快的收敛速度,本研究提出的优化蚁群算法具有一定的可靠性和高效性.Path planning is a key link in the design of mobile robots.Ant colony algorithm can efficiently solve the problem of path planning,but also has some disadvantages,such as slow convergence speed,easy to fall into the local optimal solution.In view of the above problems,we proposed an improved ant colony algorithm.On the basis of the traditional ant colony algorithm,it improved the state transition rules,increased the influence factor of the number of surrounding obstacles,and made ants avoid obstacles as much as possible.We increased the angle influence factor to make the ants take a smoother path.At the same time,the elite ant colony strategy was used to improve the problem that the ant colony algorithm was easy to fall into local optimal solution.Simulation results show that the algorithm can find the optimal path in a variety of environments,and has a fast convergence speed,which reflects the reliability and efficiency of the proposed optimization ant colony algorithm.

关 键 词：路径规划 蚁群算法 障碍物数量影响因子 角度影响因子 精英蚁群策略 

分 类 号：TP305.1[自动化与计算机技术—计算机系统结构]