基于改进麻雀搜索算法及动态窗口法的路径规划.

For sparrow search algorithm convergence mid-term local exploration ability is not enough, in path planning path is not smooth and dynamic obstacle avoidance ability is poor disadvantages. Firstly, to solve the deficiency of local exploration ability of the sparrow search algorithm, the population was initialized using chaotic mapping and the discoverer position updated method was optimized using the previous generation global optimal solution with dynamic adaptive weights. Then, a linear path strategy was used to reduce the number of fold points and nodes. Finally, the optimized sparrow search algorithm was fused with the dynamic windowing method for its disadvantages of unsmooth path and poor dynamic obstacle avoidance ability. The experimental results show that the improved sparrow search algorithm and the dynamic window method fusion algorithm balance the global and local discovery capabilities, accelerate the convergence speed of the pathfinding process, optimize the path and significantly improve the obstacle avoidance capability. [ABSTRACT FROM AUTHOR]