A Robot Spraying Path Planning Method for the Digital Camouflage Pattern

Abstract

At present, the robot spraying path of the digital camouflage generally refers to the manual spraying experience, and uses a regular strategy to generate the spraying path of each domain of the pattern. Such regular path planning method will produce many redundant paths which affect the efficiency of robot spraying operations. In this paper, we propose a new path planning method to solve this problem. Firstly we adopt the grid method to model the digital camouflage pattern, and then achieve local zone path planning through domain segmentation with color block aggregation, finally we use and improve the genetic algorithm to optimize the spraying path among the local zones. Eventually the path planning of the digital camouflage pattern is realized. The simulation results show that the method we proposed in this paper has a significant improvement compared to the sequential spraying method. The path length of the genetic algorithm is shortened by 31.7% and the path length of the improved genetic algorithm is shortened by 37.0%. Furthermore, the speed of convergence of the improved genetic algorithm is faster than the original genetic algorithm. The above results demonstrate that the method we proposed in this paper is feasible and efficient.