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
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Effective team-driven multi-model motion tracking
Proceedings of the 1st ACM SIGCHI/SIGART conference on Human-robot interaction
The first segway soccer experience: towards peer-to-peer human-robot teams
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
Effective Multi-Model Motion Tracking using Action Models
International Journal of Robotics Research
Prioritized multihypothesis tracking by a robot with limited sensing
EURASIP Journal on Advances in Signal Processing - Special issue on signal processing advances in robots and autonomy
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Tracking in essence consists of using sensory information combined with a motion model to estimate the position of a moving object. Tracking efficiency completely depends on the accuracy of the motion model and of the sensory information. For a vision sensor like a camera, the estimation is translated into a command to guide the camera where to look. In this paper, we contribute a method to achieve efficient tracking through using a tactic-based motion model, combined vision and infrared sensory information. We use a supervised learning technique to map the state being tracked to the commands that lead the camera to consistently track the object. We present the probabilistic algorithms in detail and present empirical results both in simulation experiment and from their effective execution in a Segway RMP robot.