A probabilistic approach to high-confidence cleaning guarantees for low-cost cleaning robots

Cleaning is widely regarded as one of the most relevant applications of autonomous service robots. The goal of robotic cleaning is to achieve low dirt levels in the whole environment. Low cost consumer robots, however, are typically prone to high motion and sensor uncertainties. Additionally, their cleaning units do not always remove the dirt entirely. As a result, there is a substantial probability that some parts of the environment are not cleaned sufficiently. In this paper, we propose an approach to robotic cleaning that guarantees that in the whole environment, the dirt levels after cleaning are reduced below a user-defined threshold with high confidence. We introduce a novel probabilistic model for jointly estimating the trajectory of the robot and the current dirt distribution in the environment. Based on this estimate, we adapt the future cleaning path during operation such that the robot re-visits areas in which high dirt levels are still likely. We demonstrate the effectiveness of our approach in extensive experiments carried out both in simulation and with a real vacuum cleaning robot, also in comparison to previous approaches.