改进哈里斯鹰算法的仓储机器人路径规划研究

doi:10.16182/j.issn1004731x.joss.23-0024

摘要/Abstract

摘要：

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。

关键词: 移动机器人, 路径规划, 哈里斯鹰优化算法, 栅格地图, 多策略改进

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.

Key words: mobile robots, path planning, Harris Hawks optimization(HHO) algorithm, grid maps, multi-strategy improvement

中图分类号:

TP391.9

雷旭,陈静夷,陈潇阳 . 改进哈里斯鹰算法的仓储机器人路径规划研究[J]. 系统仿真学报, 2024, 36(5): 1081-1092.

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.

图/表 10

图1

图2

图3

表1

仿真实验算法参数设置

算法参数	参数值
种群规模N	30
搜索维度D	10(10 $×$ 10地图仿真实验)
	30(30 $×$ 30地图仿真实验)
	40(40 $×$ 40地图仿真实验)
最大迭代次数T	500
逃逸能量调控因子a	1.2
柯西反学习变异概率p	0.5
搜索空间上界b_U	10(10 $×$ 10地图仿真实验)
	30(30 $×$ 30地图仿真实验)
	40(40 $×$ 40地图仿真实验)
搜索空间下界b_L	1

表1

图4

图5

表2

基于30次实验下不同规模的离散栅格地图中5种算法性能对比

地图规模	算法	$L m e a n$	$P m e a n$	$S m e a n$	$T m e a n$	$I m e a n$
10	TCLHHO	13.543 2	4	0	0.323 8	17
	HHO	13.543 2	5	0	0.444 5	28
	TGWO	13.543 2	4	0	0.232 5	24
	GWO	13.543 2	6	0	0.251 3	34
	ACA	13.899 5	5	0	0.516 8	23
30	TCLHHO	43.459 9	11	0.578 4	0.367 5	136
	HHO	44.290 2	16	1.413 2	0.381 4	56
	TGWO	44.569 9	15	1.109 4	0.348 4	83
	GWO	45.226 2	21	0.673 1	0.324 6	67
	ACA	47.606 4	25	1.596 0	28.815 9	113
40	TCLHHO	56.364 1	18	1.332 6	0.615 2	128
	HHO	—	—	—	—	—
	TGWO	—	—	—	—	—
	GWO	63.077 2	29	9.648 9	0.4179	117
	ACA	57.283 7	23	1.351 1	79.6561	104

表2

图6

图7

表3

基于30次实验下不同规模的仓储环境中5种算法性能对比

地图规模	算法	$L m e a n$	$P m e a n$	$S m e a n$	$T m e a n$	$I m e a n$
10	TCLHHO	13.756 4	2	2.022 8	0.505 8	17
	HHO	13.969 7	2	2.697 2	0.569 7	32
	TGWO	15.202 2	3	4.849 1	0.233 7	20
	GWO	15.268 7	4	3.913 1	0.305 2	63
	ACA	14.041 0	4	1.341 6	0.827 1	13
30	TCLHHO	55.633 7	2	3.029 1	0.546 2	26
	HHO	56.709 5	4	0.572 9	0.568 1	38
	TGWO	—	—	—	—	—
	GWO	—	—	—	—	—
	ACA	57.229 8	15	8.366 7	26.476 4	342
40	TCLHHO	64.989 1	4	6.581 7	0.973 4	58
	HHO	71.341 1	9	2.468 4	1.085 0	43
	TGWO	—	—	—	—	—
	GWO	—	—	—	—	—
	ACA	68.825 9	18	10.279 1	78.314 9	137

表3

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