基于改进JPS和DWA算法的移动机器人路径规划OA

To tackle the problems of heightened search blindness,elevated memory usage,and redundant nodes in the output path resulting from the access of a large number of irrelevant extension nodes in traditional JPS algo-rithms,an improved JPS algorithm was proposed.This algorithm based on the priority of the target-point search di-rection and a dynamic-weight evaluation function.Firstly,according to the positions of the target point and the mo-bile robot,the priority of the direction of target point in the algorithm's path-finding process was enhanced.A dis-tance-based dynamic-weight evaluation function was introduced to mitigate the resource waste and efficiency loss caused by searching for irrelevant nodes.Meanwhile,the global path output by the improved JPS algorithm under-went secondary planning.Redundant nodes in the original path were eliminated,making the global path smoother.Secondly,the DWA algorithm was introduced and improved as a local path-planning algorithm.The improved DWA algorithm adopted a dynamic priority strategy based on collision distance to automatically avoid mobile robots on cross-paths.Finally,the improved JPS algorithm and DWA algorithm were separately simulated and verified.The results indicated that,compared with traditional jump-point algorithms,the improved algorithm in this study re-duced the average number of searched extended nodes by 60.0%,the average number of trajectory nodes by 43.6%,and the average number of path turning points by 23.9%.The improved DWA algorithm could effectively address the drawbacks and limitations of traditional DWA algorithms in dynamic environmental problems such as path conflicts,and improve the collaboration and adaptability of DWA algorithms in multi-robot path planning.