Adaptive Path Planning for Cleaning Robots Considering Dust Distribution

Abstract

Path-planning algorithms for cleaning robots typically focus on how the robots can cover an entire space while minimizing overlapping or uncleaned areas. However, when considering actual environments, the distribution of dust and dirt is not uniform and has some specific features according to the shape of the environment and human behaviors. Therefore, if a cleaning robot plans its path while taking this distribution into consideration, it can clean the area more efficiently. In this paper, we present a novel path-planning algorithm for cleaning robots that prioritizes regions with large quantities of dirt and sorts them. The effectiveness of the proposed algorithm was examined through experimental simulations.