Research on Path Optimization Method for Warehouse Inspection Robot

Figures & data

Table 1. Comparison of several common optimization algorithms.

Algorithms	single body/swarm body	Solution method	Inferior solution acceptance method
HPSO	Swarm body	Update global optimal solution with group optimal solution	Accept inferior solutions through hybridization
ACO	Swarm body	Using the solution with thicker pheromones and smaller spacing as the optimal solution	Accept inferior solutions with probability
TSO	Single body	Selecting optimal solutions to generate neighborhoods	There is a chance of obtaining suboptimal or inferior solutions
SA	Single body	Better solutions must be accepted	Accept inferior solutions with probability
HPSO-ACO	Swarm body	Receive the optimal solution based on pheromones and city spacing	Accept inferior solutions through hybridization

Figure 1. The principle structure of the ACO.

Figure 2. The principle structure of the HPSO.

Figure 3. Solution flow chart of HPSO-ACO.

Figure 4. Coordinates of 30 patrol points.

Figure 5. ACO iteration results.

Figure 6. ACO path planning.

Figure 7. HPSO iteration results.

Figure 8. HPSO path planning.

Figure 9. HPSO-ACO process iteration results.

Figure 10. HPSO-ACO path planning.

Table 2. Comparison of Experimental results of three models.

Model	Number of iterations	Calculation time	Shortest path length
ACO	55	7.145 s	1659.62 m
HPSO	939	23.542 s	1743.45 m
HPSO-ACO	42	6.424 s	1622.87 m

Data availability statement

Due to the nature of this research, participants of this study did not agree for their data to be shared publicly, so supporting data are not available.