Multi-model tracking using team actuation models

Abstract

Robots need to track object. Object tracking efficiency completely depends on the accuracy of the motion model and of the sensory information. Interestingly, when multiple team members can actuate the object being tracked, the motion can become highly discontinuous and nonlinear. We have previously developed a successful tracking approach that switches among target motion models as a function of one robot's actions. In this paper, we report on a tracking approach that can use a dynamic multiple motion model based on a team coordination plan. We present the multi-model probabilistic tracking algorithms in detail and present empirical results both in simulation and in a human-robot Segway soccer team. The team coordination plan allows the robot to much more effectively track mobile targets