基于改进麻雀搜索算法及动态窗口法的路径规划  

Path Planning Based on Improved Sparrow Search Algorithm and Dynamic Window Approach

作　　者：邓立霞 张肖轶群 陈奂宇 刘海英[1] DENG Li-xia;ZHANG Xiao-yi-qun;CHEN Huan-yu;LIU Hai-ying(School of Information and Automation Engineering,Qilu University of Technology(Shandong Academy of Sciences),Jinan 250300,China)

机构地区：[1]齐鲁工业大学(山东省科学院)信息与自动化学院,济南250300

出　　处：《科学技术与工程》2023年第14期6096-6104,共9页Science Technology and Engineering

基　　金：山东省重点研发计划(2019GGX104079);山东省自然科学基金(ZR2018QF005);齐鲁工业大学(山东省科学院)国际合作研究专项(QLUTGJHZ2018019)。

摘　　要：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。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.

关 键 词：移动机器人 路径规划 麻雀搜索算法 动态窗口法 

分 类 号：TP242.6[自动化与计算机技术—检测技术与自动化装置]