A Multi-Robot Coverage Path Planning Algorithm for the Environment With Multiple Land Cover Types

Many scholars have proposed different single-robot coverage path planning (SCPP) and multi-robot coverage path planning (MCPP) algorithms to solve the coverage path planning (CPP) problem of robots in specific areas. However, in outdoor environments, especially in emergency search and rescue tasks, complex geographic environments reduce the task execution efficiency of robots. Existing CPP algorithms have hardly considered environmental complexity. This article proposed an MCPP algorithm considering the complex land cover types in outdoor environments to solve the related problems. The algorithm first describes the visual fields of the robots in different land cover types by constructing a hierarchical quadtree and builds the adjacent topological relations among the cells in the same and different layers in the hierarchical quadtree by defining shared neighbor direction based on Binary System. The algorithm then performs an approximately balanced task assignment to the robots considering the moving speeds in different land cover types using the azimuth trend method we proposed to ensure the convergence of the task assignment process. Finally, the algorithm improves Spanning Tree Covering (STC) algorithm to complete the CPP in the area where each robot belongs. This study used a classification image of the real outdoor environment to the verification of the algorithm. Results show that the coverage paths planned by the algorithm are reasonable and efficient and its performance has obvious advantages compare with the current mainstream MCPP algorithm.