Abstract
ABSTRACT With the increasing popularity of intelligence, many enterprises’ warehouse inspection work is completed through robots. However, due to the multiple target points of warehouse inspection, the low efficiency of planning intelligent robot inspection paths is a problem that needs to be solved. In order to solve the above problems, this paper proposes an HPSO-ACO algorithm based on hybrid particle swarm optimization (HPSO) to optimize the parameters of the ant colony optimization (ACO) algorithm, and establishes a path optimization model for intelligent inspection robots in warehouse management. Compared with HPSO algorithm and ACO algorithm, the experimental results show that the proposed method has faster convergence speed, fewer iterations, and shorter optimal path under the same conditions, which provides a theoretical reference for path optimization for inspection robot.
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