Human motion prediction and tracking for indoor mobile relay networks

There has been a growing interest in robotic relay networks for applications in dangerous and hazardous environments. In such an environment, robotic teams are deployed to provide sensing and communication support for human activities. In order to provide discreet support as a human moves throughout the environment, it is crucial that the robotic team be able to not only react to the current perceived location of the human of interest, but also predict his/her future locations. In this work we present a novel approach to predicting human motion in indoor environments. Our approach consists of an offline pre-processing phase, in which we analyze a map of the indoor environment and identify distinct areas of interest, such as rooms, and a run-time phase, in which we maintain hypotheses of destinations and probabilistically update these hypotheses based on observations of the human's motion. Our approach creates a probability distribution over future locations for the human user at each time step in the future. Such predictions can be used for the robotic team to effectively track and relay information from the human user to the base station.