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.

[2023-08-21] Accepted Paper /doc/2023.06.0256/v1

[2024-02-05] Printed Article /doc/2023.06.0256/v2