跳点优化蚁群算法的移动机器人路径规划  

Mobile robot path planning based on jump point optimization ant colony algorithm

作　　者：孙凌宇[1] 王威 秦红亮 刘文瀚 Sun Lingyu;Wang Wei;Qin Hongliang;Liu Wenhan(College of Mechanical Engineering,Hebei University of Technology,Tianjin 300401,China)

机构地区：[1]河北工业大学机械工程学院,天津300401

出　　处：《电子测量技术》2023年第9期48-53,共6页Electronic Measurement Technology

基　　金：国家自然科学基金联合基金(U1913211);河北省应用基础研究计划重点基础研究项目(17961820D)资助。

摘　　要：传统的蚁群算法(ACA)在路径规划中难以克服路径次优及收敛慢等问题。针对这些问题,提出一种跳点优化蚁群算法(JPOACA)。通过引入跳点搜索(JPS)算法价值函数,筛选出低成本的邻域节点,然后运用ACA的多邻域性扩展JPS算法的邻域,扩大JPOACA的视野,增加低成本邻域数量,在低成本的JPS算法邻域内设计夹角启发信息函数和步长启发信息函数,提高算法的路径寻优能力,最后采用在跳点处补充信息素,最优路径的跳点处额外增加信息素的信息素补充方式,提高融合算法的收敛速度。仿真结果表明,JPOACA规划出的路径光滑更好性,且收敛速度、对复杂地形的适应能力均有显著提升。The traditional ant colony algorithm(ACA)is difficult to overcome the problems of suboptimal path and slow convergence in path planning.To solve these problems,a jump point optimization ant colony algorithm(JPOACA)is proposed.By introducing the value function of jump point search(JPS)algorithm,low-cost neighborhood nodes are selected,and then the multi neighborhood of ACA is used to expand the neighborhood of JPS algorithm,expand the vision of JPOACA,increase the number of low-cost neighborhoods,design angle heuristic information function and step size heuristic information function in the low-cost JPS algorithm neighborhood,improve the path optimization ability of the algorithm,and finally supplement pheromones at the jump points,In order to improve the convergence speed of the fusion algorithm,a pheromone supplement method is added to the hops of the optimal path.The simulation results show that the path planned by JPOACA is smooth and better,and the convergence speed and adaptability to complex terrain are significantly improved.

关 键 词：路径规划 蚁群算法 夹角启发信息 步长启发信息 

分 类 号：TP242.6[自动化与计算机技术—检测技术与自动化装置]