Complete coverage path planning algorithm for multiple agricultural robots

Multi-robot complete-coverage path planning (MCCPP) is an important direction in developing intelligent agricultural robots. Firstly, to address the problem that the existing region decomposition algorithm has too many subregions and contains concave subregions, this paper adopts the improved Maklink line to convexly decompose the workspace to obtain the minimum number of convex subregions. Secondly, the current MCCPP algorithm suffers from duplicate coverage of connection paths, uneven task allocation, and failure to consider the robot's extra energy consumption. This paper adopts the Dijkstra algorithm to plan the shortest non-duplicated connected paths between any subregions; improves the existing objective function by combining with the actual; and retains the high-quality gene fragments for chromosome crossover according to the breakpoints. Finally, the improved Non-dominated Sorting Genetic Algorithm (NSGA-II) is simulated in real planting areas, and the total connected paths and planting area balance are optimised compared to the traditional NSGA-II.