摘要
针对蚁群算法在全局路径规划时无目的搜索、收敛慢和规划的路径不平滑等问题,本文提出了一种融合A^(*)蚁群和动态窗口法(dynamic window algorithm,DWA)的平滑路径规划方法。首先,对于传统蚁群算法,利用改进A^(*)算法非均匀分配初始信息素,解决算法初期搜索无目的问题;给出算法自定义的移动步长和搜索方式,提高路径寻优效率;修改转移概率函数中的启发函数值并增加障碍物影响因子,在避免死锁现象的同时加快收敛速度;采用二次路径优化策略,使得路径更短更平滑;其次在动态窗口法的评价函数中引入动态避障评价子函数,提高路径的安全性。仿真实验结果表明,改进A^(*)蚁群算法较传统蚁群算法可减少8.75%的路径长度和59%的转折点数,融合优化动态窗口法后,移动机器人既能保证在静态环境下规划出全局最优的路径,又能实现动态环境下的路径规划,有效躲避环境中出现的动态障碍物。
Aiming at the problems of aimless search,slow convergence and unsmooth path planning of ant colony algorithm in global path planning,this paper proposes a smooth path planning method that combines A^(*)ant colony and dynamic window algorithm.First,for the traditional ant colony algorithm,the improved A^(*)algorithm is used to distribute initial pheromones unevenly to solve the aimless problem of initial search of the algorithm.The self-defined moving step size and searching method are given to improve the efficiency of path optimization.The heuristic function value in the transition probability function is modified and the obstacle influence factor is added to avoid deadlock and speed up the convergence.The secondary path optimization strategy is adopted to make the path shorter and smoother.Secondly,the dynamic obstacle avoidance evaluation sub function is introduced into the evaluation function of the dynamic window method to improve the path safety.The simulation results show that the improved A^(*)ant colony algorithm can reduce the path length by 8.75%and the turning points by 59%compared with the traditional ant colony algorithm.After the dynamic window method is integrated and optimized,the mobile robot not only ensures the global optimal path planning in the static environment,but also realizes the path planning in the dynamic environment,effectively avoids dynamic obstacles in the environment.
作者
赵倩楠
黄宜庆
Zhao Qiannan;Huang Yiqing(College of Electrical Engineering,Anhui University of Engineering,Wuhu 241000,China;Provincial Key Laboratory of Detection Technology and Energy Saving Devices,Wuhu 241000,China)
出处
《电子测量与仪器学报》
CSCD
北大核心
2023年第2期28-38,共11页
Journal of Electronic Measurement and Instrumentation
基金
安徽省自然科学基金(2108085MF220)
安徽高校协同创新项目(GXXT-2020-069)资助
关键词
蚁群算法
动态窗口算法
动态障碍物
路径规划
移动机器人
ant colony algorithm
dynamic window algorithm
dynamic obstacle
path planning
mobile robot
作者简介
赵倩楠,现为安徽工程大学研究生,主要研究方向为移动机器人路径规划、编队控制等。E-mail:1347082086@qq.com;通信作者:黄宜庆,现为安徽工程大学教授,主要研究方向为机器人与飞行器控制、移动机器人与路径规划。E-mail:yiqhuang@ahpu.edu.cn
