Team-Driven Multi-Model Motion Tracking with Communication

Interactions are frequently seen between the robot and the targets being tracked within the robotics community. Modeling the interactions using knowledge of robot cognition improves the performance of the tracker. Communication improves the performance of a multi-agent system. The focus of this paper is to present our solution to integrate the communication information into our team-driven multi-model motion tracking. We present the probabilistic tracking algorithm in detail and present empirical results both in simulation and in a Segway soccer team. The information from team communication allows the robot to much more effectively track mobile targets