Research on Path Planning of Warehouse Robot with Improved Harris Hawks Algorithm

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

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.

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

CLC Number:

TP391.9

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.

Figures/Tables 10

Fig. 1

Fig. 2

Fig. 3

Table 1

Simulation experiment algorithm parameter setting

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

Table 1

Fig. 4

Fig. 5

Table 2

Performance comparison of 5 algorithms in discrete raster maps of different scales based on 30 experiments

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

Table 2

Fig. 6

Fig. 7

Table 3

Performance comparison of 5 algorithms in regular raster maps of different scales based on 30 experiments

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

Table 3

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