Speaker: Yang Gu, PhD Student, Computer Science Department, Carnegie Mellon University
Date: Thursday, October 13
Abstract: Robots need to track objects. Tracking in essence consists of using sensory information combined with a motion model to estimate the position of a moving object. Object tracking efficiency completely depends on the accuracy of the motion model and of the sensory information. When tracking is performed by a robot executing specific tasks acting over the object being tracked, such as a Segway RMP soccer robot grabbing and kicking a ball, the motion model of the object becomes complex, and dependent on the robot's actions. In this talk, I will describe our tracking approach that switches among target motion models as a function of one robot's actions.
Interestingly, when multiple team-members can actuate the object being tracked, the motion can become even more discontinuous and nonlinear. I will report our recent tracking approach that can use a dynamic multiple motion model based on a team coordination plan. I will present the multi-model probabilistic tracking algorithms in detail and present empirical results both in simulation and in a human-robot Segway soccer team.
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