Effective Multi-Model Motion Tracking Under Multiple Team Member Actuators
Yang Gu, Manuela Veloso · 2009
Motivated by the interactions between a team and the tracked target, we contribute a method to achieve efficient tracking through using a play-based motion model and combined vision and infrared sensory information. This method gives the robot a more exact task-specific motion model when executing different tactics over the tracked target (e.g. the ball) or collaborating with the tracked target (e.g. the team member). Then we represent the system in a compact dynamic Bayesian network and use particle filter to keep track of the motion model and target state through sampling. The empirical results from the simulated and using the real robot agent show the efficiency of the multi-model tracking over single model tracking.