Estimation with Applications to Tracking and Navigation
Estimation with Applications to Tracking and Navigation
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
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Effective Multi-Model Motion Tracking using Action Models
International Journal of Robotics Research
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Autonomous robots use sensors to perceive and track objects in the world. Tracking algorithms use object motion models to estimate the position of a moving object. Tracking efficiency completely depends on the accuracy of the motion model and of the sensory information. Interestingly, when the robots can actuate the object being tracked, the motion can become highly discontinuous and nonlinear. We have previously developed a successful tracking approach that effectively switches among object motion models as a function of the robot's actions. If the object to be tracked is actuated by a team, the set of motion models is quite more complex. 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 real robot test. Our physical team is composed of a robot and a human in a real Segway soccer game scenario. We show how the coordinated plan allows the robot to better track a mobile object through the effective interaction with its human teammate.