摘要
针对单独使用A^(*)或DWA算法难以同时实现全局路径最优和动态避障的问题,提出了一种基于A^(*)算法与DWA算法的优化融合策略。通过引入环境复杂度动态权重因子,优化A^(*)算法评价函数,提高算法的适应性;采用冗余点去除策略对A^(*)算法生成的全局路径进行优化,以提高路径效率;考虑移动机器人周围环境状况,引入距离自适应系数对DWA算法的评价函数进行优化,提高了局部路径规划的性能;以优化后A^(*)算法生成的全局路径中的关键节点作为DWA算法的临时目标点进行路径规划,实现全局路径最优化与实时避障的兼顾。最后,通过多组仿真实验验证了改进算法的可行性。
Aiming to address the challenge of achieving both global path optimality and dynamic obstacle avoidance when using A^(*)or DWA algorithms individually,a novel optimization fusion strategy based on the integration of A^(*)and DWA algorithms is proposed.The approach involves introducing a dynamic weight factor for environmental complexity to optimize the A^(*)algorithm’s evaluation function and enhance its adaptability.Redundant point removal strategy is employed to optimize the global path generated by the A^(*)algorithm,thereby improving path efficiency.Considering the surrounding environment of the mobile robot,a distance adaptive coefficient is introduced to optimize the evaluation function of the DWA algorithm,enhancing the performance of local path planning.The optimized key nodes from the A^(*)algorithm's generated global path are used as temporary target points for the DWA algorithm,achieving a balance between global path optimality and real time obstacle avoidance.Finally,the feasibility of the improved algorithm is validated through multiple sets of simulation experiments.
作者
姜海猛
张志安
潘孝斌
JIANG Haimeng;ZHANG Zhi’an;PAN Xiaobin(School of Mechanical Engineering,Nanjing University of Science and Technology,Nanjing 210094,China)
出处
《机械与电子》
2024年第10期15-21,共7页
Machinery & Electronics
