A path planning method for mobile robots based on multi-strategy fusion to improve the Harris hawk optimization algorithm
Aiming at the problems of many folding points,slow convergence speed and easy to fall into local optimal solutions when mobile robots perform path planning in multi-obstacle indoor environments,a mobile robot path planning method based on multi-strategy fusion to improve the Harrishawk optimization algorithm is proposed.Firstly,Tent chaotic mapping initialization and adaptive positive cosine algorithm are used to improve the diversity of the initial population distribution and enhance the global search ability.Secondly,simulated annealing energy strategy is used to improve the behavioral selection of Harris hawk optimization algorithm and strengthen the convergence speed of the algorithm.And then Cauchy function and improved Levy flight optimization algorithm position updating behaviors are used to improve the algorithm's optimization search performance and efficiency.Finally,ablation and comparison experiments are used to validate the path planning performance of the mobile robot in map scenarios with different levels of complexity.The experimental results show that the multi-strategy fusion to improve the Harris hawk optimization algorithm in the mobile robot path planning problem can not only effectively reduce the number of folding points to obtain a better path smoothness,but also achieve faster convergence speed and shorter moving paths.
mobile robotpath planningmulti-strategy fusionHarris hawk optimization algorithm
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