Estimation with Applications to Tracking and Navigation
Estimation with Applications to Tracking and Navigation
Effective team-driven multi-model motion tracking
Proceedings of the 1st ACM SIGCHI/SIGART conference on Human-robot interaction
Multi-model motion tracking under multiple team member actuators
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Skill acquisition and use for a dynamically-balancing soccer robot
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Tactic-based motion modeling and multi-sensor tracking
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
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We consider tasks where robots act on the target that is visually tracked, such as kicking a ball or pushing an object. We introduce a principled approach to incorporate models of the robot-object interaction into the tracking algorithm to effectively improve the performance of the tracker. We first present the integration of a single robot behavioral model with multiple actions into our dynamic Bayesian probabilistic tracking algorithm. We then extend to multiple motion tracking models corresponding to known multi-robot coordination plans or from multi-robot communication. We evaluate our resulting informed-tracking approach empirically in simulation and using a setup Segway robot soccer task. The input of the multiple single and multi-robot behavioral models allows a robot to visually track mobile targets with dynamic trajectories more effectively.