Adaptive patrolling by mobile robot for changing visitor trends

In this paper, a single mobile robot is used to patrol an indoor environment composed of rooms. The robot is required to monitor the rooms and detect visitors as many as possible. A key idea in the patrolling mission is to allow the robot to utilize information about visitor trends. Thus far, we have focused on a visiting probability as the trend. However, the visitor trends usually change over time. Therefore, the robot is further required to detect the changes and identify the correct visitor trends in each room. For this challenge, we present a novel change-point detection method. In this method, the Jensen-Shannon divergence is used as a dissimilarity measure between two distributions of the visiting probability. The change-point detection method is activated in the Bayesian learning for estimating the changed visiting probability. Consequently, the robot is allowed to adaptively patrol the changing environment. Through the simulation experiments, we discuss the effectiveness of the adaptive patrolling for the changes in the visitor trends.