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Journal of System Simulation

Abstract

Abstract: To improve the path planning efficiency of warehouse mobile robots in static environments, and to solve the problems of slow convergence and local optimum of traditional Harris Hawk (HHO) algorithm in path planning, a Harris Hawk optimization algorithm based on Tent chaotic mapping fused with Cauchy's back-learning variant (TCLHHO) is proposed. The population diversity is increased by Tent Chaotic mapping to speed up convergence. An exponential prey escape energy updating strategy is proposed to balance the global search and local exploitation capabilities of the algorithm. The optimal individual is disturbed by Cauchy mutation operator and inverse learning strategy to expand the search range and enhance the global optimization capability. A two-dimensional grid mapping model is built according to the warehousing environment, and a comparison simulation experiment is carried out with Matlab. The results showed that the proposed algorithm had a better performance in planning speed, path length and number of turning points compared with other algorithms, which verifies the feasibility and robustness of the improved HHO algorithm for path planning in the intelligent storage environment.

First Page

1081

Last Page

1092

CLC

TP391.9; TP242

Recommended Citation

Lei Xu, Chen Jingyi, Chen Xiaoyang. Research on Path Planning of Warehouse Robot with Improved Harris Hawks Algorithm[J]. Journal of System Simulation, 2024, 36(5): 1081-1092.

DOI

10.16182/j.issn1004731x.joss.23-0024

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