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

Table 8

Statistical results for map 1 in a 30 × 30 environment.

Ten simulation statistics
30 × 30 environment map (Figure 7(a))	Iterations
	Algorithm	1	2	3	4	5	6	7	8	9	10	Average
	OVM-ACO	45	47	42	43	40	46	45	45	46	44	44
	IACO	51	54	50	51	47	50	48	50	53	52	51
	T-ACO	—	—	—	—	—	—	—	—	—	—	—
	S-IACOA	35	37	32	34	36	33	32	35	33	35	34
	Path length (m)
	OVM-ACO	51.93	51.93	51.93	51.93	51.93	51.93	51.93	51.93	51.93	51.93	51.93
	IACO	49.36	49.36	49.36	49.36	49.36	49.36	49.36	49.36	49.36	49.36	49.36
	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	10.92	11.64	9.78	10.83	9.66	11.18	10.29	10.38	10.88	10.54	10.60
	IACO	11.53	12.15	11.21	11.68	10.42	10.21	9.83	11.37	12.08	11.94	11.20
	T-ACO	—	—	—	—	—	—	—	—	—	—	—
	S-IACOA	9.43	9.74	8.38	9.07	9.55	8.92	8.57	9.38	8.86	9.27	9.10