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

Table 9

Statistical results for map 2 in a 30 × 30 environment.

Ten simulation statistics
30 × 30 environment map (Figure 7(b))	Iterations
	Algorithm	1	2	3	4	5	6	7	8	9	10	Average
	OVM-ACO	48	46	44	49	51	43	47	48	46	45	47
	IACO	47	49	51	48	47	43	48	45	46	48	47
	T-ACO	—	—	—	—	—	—	—	—	—	—	—
	S-IACOA	36	33	35	37	36	34	35	37	36	34	35
	Path length (m)
	OVM-ACO	50.48	50.48	50.48	50.48	50.48	50.48	50.48	50.48	50.48	50.48	50.48
	IACO	51.55	51.55	51.55	51.55	51.55	51.55	51.55	51.55	51.55	51.55	51.55
	T-ACO	—	—	—	—	—	—	—	—	—	—	—
	S-IACOA	49.36	49.36	49.36	49.36	49.36	49.36	49.36	49.36	49.36	49.36	49.36
	Simulation time (s)
	OVM-ACO	11.22	10.97	9.68	11.83	11.95	9.18	10.89	11.38	10.64	10.32	10.80
	IACO	10.39	11.15	11.81	10.66	10.47	9.81	10.43	9.97	10.05	10.94	10.57
	T-ACO	—	—	—	—	—	—	—	—	—	—	—
	S-IACOA	9.88	8.74	9.48	9.97	9.65	8.82	9.67	10.08	9.85	8.92	9.51