Low Repetition Coverage Path Planning Model Based on Improved ACO Algorithm

Aiming at the special needs of the covering operation of the coating floor paint robot, an improved ACO algorithm using the repetition rate as the optimization evaluation index is proposed in this paper. The algorithm improves the path selection method of the traditional ACO algorithm by introducing a second-level vision strategy, which reduces the impact of the uncertainty of the path repetition rate on the planning process. Meanwhile, the updating method of the global pheromone of the ACO algorithm is optimized, which enhances the searchability of the algorithm in the early stage as well as the convergence of the algorithm in the later stage. The simulation experiment results show that compared with the traditional ACO algorithm, the proposed improved ACO algorithm can realize the coverage path planning with low repetition rate and has good convergence.