基于改进蚁群算法的机器人路径规划研究  

Research on robot path planning based on improved ant colony algorithm

作　　者：邵琪 时维国[1] SHAO Qi;SHI Weiguo(School of Electronics and Information Engineering,Dalian Jiaotong University,Dalian 116028,China)

机构地区：[1]大连交通大学自动化与电气工程学院,大连116028

出　　处：《现代制造工程》2023年第6期46-51,共6页Modern Manufacturing Engineering

基　　金：辽宁省教育厅科研项目(LJKMZ20220828);人工智能四川省重点实验室开放基金资助项目(2020RYJ04)。

摘　　要：针对传统蚁群算法存在收敛速度慢、易陷入局部最优以及规划路径存在冗余拐点等一系列问题,提出了一种改进的蚁群算法,用于移动机器人在栅格环境中的路径规划。首先,采用初始信息素角度导向分布策略,增强起点到终点线路上的初始信息素浓度,降低算法初期搜索路径的盲目性;其次,为进一步提高机器人移动的安全性,在状态转移概率的基础上引入一种含导向作用的安全因子,避免算法搜索产生曲折路径;最后,提出信息素增量自适应t分布变异策略,以增加种群的多样性,避免算法后期陷入局部最优。同时建立了信息素更新规则和路径评价机制,用来引导蚂蚁以最佳综合指标靠近最佳路径。栅格环境仿真表明,在不同环境下改进的蚁群算法能使机器人获得全局最佳路径,并且具有较高的路径规划实时性和稳定性。Aiming at a series of problems of traditional ant colony algorithm,such as slow convergence speed,easy to fall into local optimum and redundant inflection points in planning path,an improved ant colony algorithm was proposed for path planning of mobile robot in grid representation environment.Firstly,the initial pheromone angle oriented distribution strategy was adopted to enhance the initial pheromone concentration on the route from the beginning to the end,and reduce the blindness of the initial search path of the algorithm;secondly,in order to further improve the safety of robot movement,a safety factor with guidance was introduced on the basis of state transfer probability to avoid zigzag path generated by algorithm search;finally,the pheromone increment adaptive t-distribution mutation strategy was proposed to increase the diversity of the population and avoid the algorithm falling into local optimization in the later stage.At the same time,pheromone update rules and path evaluation mechanism were established to guide ants to approach the optimal path with the best comprehensive index.The grid environment simulation shows that the improved ant colony algorithm can make the robot obtain the global optimal path under different environments,and has high real-time and stability of path planning.

关 键 词：蚁群算法 t分布变异 路径规划 

分 类 号：TP242[自动化与计算机技术—检测技术与自动化装置]