Scalable Coverage Path Planning for Cleaning Robots Using Rectangular Map Decomposition on Large Environments

Abstract

The goal of coverage path planning is to create a path that covers the entire free space in a given environment. Coverage path planning is the most important component of cleaning robot technology, because it determines the cleaning robot's movement. When the environment covered by a cleaning robot is extremely large and contains many obstacles, the computation for coverage path planning can be complicated. This can result in significant degradation of the execution time for coverage path planning. Not many studies have focused on the scalability of coverage path planning methods. In this paper, we propose a scalable coverage path planning method based on rectangular map decomposition. The experimental results demonstrate that the proposed method reduces the execution time for coverage path planning up to 14 times when compared with conventional methods.