基于改进A^(*)与DWA算法融合的路径规划仿真设计  

Path Planning Simulation Design Based on Improved A^(*)and DWA Algorithm Integration

作　　者：肖岳平 阳倩 苏焱鸿 黄守鹏 XIAO Yueping;YANG qian;SU Yanhong;HUANG Shoupeng(College of Electrical and Information Engineering,Hunan Institute of Engineering,Xiangtan 411104,China)

机构地区：[1]湖南工程学院电气与信息工程学院,湘潭411104

出　　处：《湖南工程学院学报（自然科学版）》2023年第2期30-37,共8页Journal of Hunan Institute of Engineering(Natural Science Edition)

基　　金：湖南省自然科学基金资助项目(2023JJ50029);湖南省教育厅一般项目(22C0427);湖南工程学院青年科研项目(21015).

摘　　要：针对移动机器人在全局路径规划中不能进行动态实施避障、在局部路径规划中效率低等一系列问题.本文基于Gazebo的3D仿真环境,引入权重系数控制A^(*)(A-star)算法的启发函数,减少全局规划路径的拐点,获取全局最优路径,提高算法效率;并选取全局最优路径上动态变化点作为DWA(Dynamic Window Approach)的关键点进行动态避障.仿真实验结果表明:基于改进A*与DWA的融合算法,既保证全局路径规划最优,且调整速度权值,并兼顾安全性和高效性.Aiming at a series of problems that mobile robots cannot dynamically implement obstacle avoidance in global path planning and have low efficiency in local path planning.Based on the 3D simulation environment of Gazebo,this paper introduces the heuristic function of the weight coefficient control A^(*)(A-star)algorithm to reduce the inflection point of the global planning path,obtains the global optimal path and improve the efficiency of the algorithm.In addition,the dynamic change point on the global optimal path is selected.to be is used as the key point of dynamic window approach(DWA)for dynamic obstacle avoidance.The simulation experiment results show that the fusion algorithm based on the improved A^(*)and DWA can ensure the optimal global path planning and adjust the speed weight,taking both safety and efficiency into account.

关 键 词：改进A*算法 路径规划 DWA算法 Gazebo环境开发 

分 类 号：TP242[自动化与计算机技术—检测技术与自动化装置]