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

Table 6

Simulation results of different environments.

Map environment	Algorithm	Path length (m)	Iterations	Simulation time (s)
20 × 20 environment map (Figure 7(a))	OMV-ACO	29.82	23	4.98
	IACO	29.82	30	5.55
	T-ACO	29.82	34	8.74
	S-IACO	29.82	13	3.58
20 × 20 environment map (Figure 7(b))	OMV-ACO	30.37	26	5.28
	IACO	30.37	19	4.48
	T-ACO	30.37	30	7.66
	S-IACO	30.37	11	3.52
20 × 20 environment map (Figure 7(c))	OMV-ACO	30.40	18	4.82
	IACO	30.40	13	4.09
	T-ACO	30.40	27	6.54
	S-IACO	30.40	11	3.66
30 × 30 environment map (Figure 5(a))	OMV-ACO	51.93	44	10.60
	IACO	49.36	51	11.20
	T-ACO	—	—	—
	S-IACO	49.36	34	9.10
30 × 30 environment map (Figure 5(b))	OMV-ACO	50.48	47	10.80
	IACO	51.55	47	10.57
	T-ACO	—	—	—
	S-IACO	49.36	35	9.51
30 × 30 environment map (Figure 5(c))	OMV-ACO	49.13	44	10.90
	IACO	44.53	46	11.58
	T-ACO	—	—	—
	S-IACO	44.53	39	9.70