融合改进哈里斯鹰和改进动态窗口的机器人动态路径规划

针对动态环境的移动机器人路径规划问题，提出了一种改进哈里斯鹰算法（IHHO）与改进动态窗口算法（IDWA）的融合算法（IHHO-IDWA）。首先，针对哈里斯鹰算法后期搜索性能不足等问题，提出了融合自适应混沌和核心种群动态划分策略、融合黄金正弦策略以及动态云最优解扰动策略来提高算法的性能。其次，针对动态窗口算法存在规划的路径长和易陷入死锁等问题，提出了三个改进策略：增加子函数，保证算法能够规划出更短的路径；提出自适应权重策略，平衡算法局部避障能力和全局搜索性能；设定初始航向角，避免路径冗余。最后，通过测试函数、CEC2014函数的数值实验和静态、动态路径规划实验，验证了IHHO和IDWA性能有明显提升；通过50×50大型动态地图验证了融合算法较对照组算法规划的路径缩短了11.51%，证明了该方法的优越性。

Research on mobile robot dynamic path planning based on improved Harris hawk algorithm and improved dynamic window algorithm

Aiming at the path planning problem of mobile robot in dynamic environment, this paper proposed a fusion algorithm(IHHO-IDWA) of improved Harris hawk algorithm(IHHO) and improved dynamic window algorithm(IDWA). First of all, this paper proposed a fusion adaptive chaos and core population dynamic division strategy, a fusion golden sine strategy and a dynamic cloud optimal disturbance resolution strategy to improve the performance of and solve the lack of search performance in the late stage of the Harris eagle algorithm. In addition, in view of the problems that the dynamic window algorithm had a long planned path and easied to fall into deadlock, this paper proposed three improvement strategies to solve them. Firstly, it added sub-functions to ensure that the algorithm could plan a shorter path. Secondly, it proposed an adaptive weight strategy to balance the local obstacle avoidance ability and global search performance of the algorithm. Thirdly, it set the initial heading angle to avoid path redundancy. Finally, through numerical experiments of test functions, CEC2014 functions, and static and dynamic path planning experiments, it is verified that the performance of IHHO and IDWA in this paper has been significantly improved; through the 50×50 large-scale dynamic map, it is verified that the path planned by the fusion algorithm is shorter than that planned by the control group algorithm 11.51%, proving the superiority of the IHHO-IDWA.