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

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