Persistent Coverage Control for a Group of Two-Wheeled Mobile Robots Based on Obstacles Prediction

In this paper, we consider a persistent coverage control problem for a group of two-wheeled mobile robots in an environment with unknown obstacles. The persistent coverage control is one of the multi-agent control method which provides efficient and persistent environmental monitoring. In typical persistent coverage control, the control inputs are calculated based on a given time-varying density function designed for monitoring and might not work well with unknown obstacles. For the coverage control in the unknown environment, we propose a persistent coverage control method utilizing the prediction of unknown obstacles by Gaussian process regression. The features of the proposed method is discussed based on simulation and experimental results.