Aiming to address the challenge of achieving both global path optimality and dynamic obsta-cle 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 algo-rithm,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 be-tween global path optimality and real-time obstacle avoidance.Finally,the feasibility of the improved al-gorithm is validated through multiple sets of simulation experiments.
关键词
移动机器人/路径规划/动态权重因子/改进A*算法/融合算法
Key words
mobile robot/path planning/dynamic weighting factor/improved A* algorithm/fusion algorithm
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