Sample-based techniques for state estimation in RoboCup
by
Harr Chen
One of the major components of soccer-playing robotic dogs is the ball
tracking system. The robot needs to be able to accurately and reliably
estimate the position and velocity of the ball in spite of noisy
observations. In this talk I will present the extended Kalman filter
(EKF), a way of integrating prior estimates and current observations to
obtain an accurate picture of the ball's movement. I will also discuss
ways in which the EKF can be improved to work well with rapid significant
changes in the ball state (when the ball is kicked, for example), and ways
in which the EKF can be combined with other techniques to perform ball
tracking in tighter conjunction with state localization.
Advised by Dieter Fox
MGH 228
February 24, 2003
3:30 - 4:20 pm