为提高静态环境下仓储移动机器人路径规划效率,解决传统哈里斯鹰(Harris Hawks optimization,HHO)算法在路径规划中存在收敛速度慢且易陷入局部最优的问题,提出了一种基于Tent混沌映射融合柯西反学习变异的哈里斯鹰优化算法(HHO algorithm based on Tent chaotic mapping hybrid Cauchy mutation and inverse learning,TCLHHO).通过Tent 混沌映射增加种群多样性,以提高算法的收敛速度;提出指数型的猎物逃逸能量更新策略,以平衡算法的全局搜索和局部开发能力;通过柯西反学习变异策略对最优个体进行扰动,扩大算法的搜索范围,增强全局搜索能力.根据真实仓储环境搭建二维栅格环境模型,并在Matlab中进行仿真对比实验.结果表明:该算法的规划速度、最优路径长度以及最优路径转折次数较对比算法具有较好的效果,验证了应用于智能仓储环境下改进的HHO路径规划问题的可行性和鲁棒性.
Research on Path Planning of Warehouse Robot with Improved Harris Hawks Algorithm
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.
mobile robotspath planningHarris Hawks optimization(HHO)algorithmgrid mapsmulti-strategy improvement
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维普
