Multi-model motion tracking under multiple team member actuators

Autonomous robots need to track objects. Object tracking relies on predefined robot motion and sensory models. Tracking is particularly challenging if the robots can actuate on the object to be tracked, as the motion can become highly discontinuous and nonlinear. We have previously developed a successful tracking approach that switches among target motion models as a function of one robot's actions. In this paper, we consider the object to be effected by a team of agents. We contribute on our team-based tracking method that can use a dynamic multi-motion model based on a team coordination plan. We present the multi-target multi-model probabilistic tracking algorithm in detail and present empirical results both in simulation and in a human-robot Segway soccer team. The team coordination plan allows the robot to much more effectively track mobile targets.

[1]  Brett Browning,et al.  Turning Segways into soccer robots , 2005, Ind. Robot.

[2]  Dieter Fox,et al.  Map-Based Multiple Model Tracking of a Moving Object , 2004, RoboCup.

[3]  Wolfram Burgard,et al.  People Tracking with Mobile Robots Using Sample-Based Joint Probabilistic Data Association Filters , 2003, Int. J. Robotics Res..

[4]  Thiagalingam Kirubarajan,et al.  Estimation with Applications to Tracking and Navigation , 2001 .

[5]  Y. Bar-Shalom Tracking and data association , 1988 .

[6]  Brett Browning,et al.  Turning Segways into soccer robots , 2004, 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566).

[7]  Neil J. Gordon,et al.  A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking , 2002, IEEE Trans. Signal Process..

[8]  J. Vermaak,et al.  A hybrid approach for online joint detection and tracking for multiple targets , 2005, 2005 IEEE Aerospace Conference.

[9]  Brett Browning,et al.  Segwayrmp robot football league rules , 2005 .

[10]  Neil J. Gordon,et al.  Editors: Sequential Monte Carlo Methods in Practice , 2001 .

[11]  Brett Browning,et al.  STP: Skills, tactics, and plays for multi-robot control in adversarial environments , 2005 .

[12]  Timothy J. Robinson,et al.  Sequential Monte Carlo Methods in Practice , 2003 .

[13]  Neil J. Gordon,et al.  A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking , 2002, IEEE Trans. Signal Process..

[14]  Alfred O. Hero,et al.  Multi-target Sensor Management Using Alpha-Divergence Measures , 2003, IPSN.

[15]  Yang Gu Tactic-Based Motion Modeling and Multi-Sensor Tracking , 2005, AAAI.