基于独狼蚁群混合算法的移动机器人路径规划  

Mobile robot path planning based on the wolf ant colony hybrid algorithm

作　　者：张毅[1] 权浩 文家富[1] ZHANG Yi;QUAN Hao;WEN Jiafu(Research Center of Information Accessibility Project and Robotics,Chongqing University of Posts and Telecommunications,Chongqing 400065,China)

机构地区：[1]重庆邮电大学信息无障碍工程与机器人技术研发中心

出　　处：《华中科技大学学报（自然科学版）》2020年第1期127-132,共6页Journal of Huazhong University of Science and Technology(Natural Science Edition)

基　　金：重庆市科学技术委员会资助项目(CSTC2015jcyjBX0066);重庆市自然科学基金面上项目(CSTC2019jcyj-msxmX0686)

摘　　要：针对移动机器人路径规划问题,提出了一种基于独狼搜索机制的自适应精英蚁群混合算法.首先,在精英蚁群算法中引入了独狼视场机制并设计了自适应增强函数,用以改进精英蚁群算法搜索机制中蚁群的寻径能力.然后,为了消除混合算法中的停滞现象,引入了独狼逃跑策略并构造了一种信息素优化机制,用来提高混合算法的全局搜索能力,帮助寻径个体突破当前的路径停滞问题,避免算法陷入局部最优.最后通过仿真分析和测试,进行了针对性的对比试验,结果表明:混合算法在多种环境下的路径规划均拥有较好的收敛速度和高效的寻径能力.To solve the problem of mobile robot path planning,an adaptive elite ant colony hybrid algorithm based on the unique wolf search mechanism is proposed.Firstly,the visual area search mechanism of wolf is introduced in the elite ant colony algorithm and the adaptive enhancement function is designed to improve the ability of the ant colony to find the path in the elite ant colony algorithm search mechanism.Secondly,to eliminate the stagnation phenomenon in the hybrid algorithm,the wolf escape strategy and constructs a pheromone optimization mechanism are introduced,which is used to improve the global search ability of the hybrid algorithm,help the path-seeking individual to break through the current path stagnation problem and avoid the algorithm falling into local optimum.Finally,through the simulation analysis and the physical experiment test,a targeted comparison experiment was carried out.The results show that the hybrid algorithm has better convergence speed and efficient path finding ability in path planning under various environments.

关 键 词：蚁群算法 独狼算法 路径规划 移动机器人 视场搜索 

分 类 号：TP242.6[自动化与计算机技术—检测技术与自动化装置]