基于改进麻雀搜索算法及动态窗口法的路径规划

对于麻雀搜索算法收敛中期局部探索能力不足、在路径规划方面路径不平滑且动态避障能力差的缺点。首先针对麻雀搜索算法局部探索能力的不足，利用混沌映射初始化种群，并且利用上一代全局最优解与动态自适应权重优化发现者位置更新方式；然后，使用一种线性路径策略，减少折点与节点数量；最后，针对其路径不平滑，动态避障能力差的缺点，将优化后的麻雀搜索算法与动态窗口法融合。实验结果表明改进的麻雀搜索算法与动态窗口法融合算法平衡了全局与局部发掘能力，加快了寻路过程的收敛速度，优化了路径且避障能力显著提高。

Path planning based on improved sparrow search algorithm and dynamic window approach

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.