Learning Tactic-Based Motion Models of a Moving Object with Particle Filtering

— Learning motion models of a moving object is a challenge for autonomous robots. We address the particular instance of parameter learning when tracking object motions in a switching multi-model system. We present a general algorithm of joint parameter-state estimation based on multimodel particle filter. We apply the approach to a specific balltracking problem and extend the algorithm to learn model parameters in a dynamic Bayesian network (DBN). We show empirical results in simulation and in a team robot soccer environment, as a substrate for applying the learned models to object tracking in a team. The learning capability allow the tracker to much more effectively track mobile objects.

• Type: Conference
• Year: 2007
• Where: CIRA
• Authors: Yang Gu, Manuela M. Veloso
• Keywords: CIRA 2007, Multimodel Particle filter, Object Motions, Robotics, Switching Multi-model System