Research on Global Path Planning of Robot Based on Ant Colony Algorithm and Gaussian Sampling

Table 10

Statistical results for map 3 in a 30 × 30 environment.

Ten simulation statistics
30 × 30 environment map (Figure 7(c))	Iterations
	Algorithm	1	2	3	4	5	6	7	8	9	10	Average
	OVM-ACO	43	44	42	46	41	45	46	43	44	42	44
	IACO	46	49	50	45	47	45	46	43	48	43	46
	T-ACO	—	—	—	—	—	—	—	—	—	—	—
	S-IACOA	40	37	39	41	43	38	39	37	36	40	39
	Path length (m)
	OVM-ACO	49.13	49.13	49.13	49.13	49.13	49.13	49.13	49.13	49.13	49.13	49.13
	IACO	44.53	44.53	44.53	44.53	44.53	44.53	44.53	44.53	44.53	44.53	44.53
	T-ACO	—	—	—	—	—	—	—	—	—	—	—
	S-IACOA	44.53	44.53	44.53	44.53	44.53	44.53	44.53	44.53	44.53	44.53	44.53
	Simulation time (s)
	OVM-ACO	10.87	10.95	10.57	11.83	10.52	11.08	11.29	10.66	10.59	10.62	10.90
	IACO	11.94	12.38	12.41	11.58	12.02	11.21	11.68	10.37	12.22	9.98	11.58
	T-ACO	—	—	—	—	—	—	—	—	—	—	—
	S-IACOA	9.92	9.03	9.54	10.02	10.65	9.82	9.87	9.15	8.96	10.03	9.70