A Layered HMM for Predicting Motion of a Leader in Multi-Robot Settings

Abstract

We focus on a mobile robot that must learn another robot's motion model from observations to track it in a given map. This problem has several real-world applications such as self-driving cars being electronically towed by other cars and for telepresence robots. Our context is a nested particle filter, a generalization of the traditional particle filter, that allows both self-localization and tracking of another robot simultaneously. While the robot's observations are used to weight nested particles, the problem arises during the propagation step of the nested particles during which a motion model is needed. We introduce a novel layered hidden Markov model for this problem and present an on-line algorithm which learns the HMM parameters from observations gathered during the run. We demonstrate significantly improved tracking accuracy on using this new model to predict the motion of a leading mobile robot, in comparison to pre-defined and random motion models as previously used in literature.