An integrated particle filter and potential field method for cooperative robot target tracking

Abstract

A fundamental challenge for robotic target-tracking systems is to cope with cases in which the target is not seen for long periods of time. An additional challenge in multiple-robot systems is to coordinate robot activity to best track targets with limited visibility. We describe a novel technique that combines a particle filter target model with a potential field robot controller. Robots are attracted to points sampled from the particle cloud that models the probability distribution over the target's position, subject to environmental constraints. We show how this method can be used as a coordination strategy whereby a team of robots cooperatively minimize the uncertainty in the pose of a tracked target. Simulation results are presented