Multi-model Tracking using Team Actuation Models

Robots need to track object. Object tracking efficiency completely depends on the accuracy of the motion model and of the sensory information. Interestingly, when multiple team members can actuate the object being tracked, 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 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 in a human-robot Segway soccer team. The team coordination plan allows the robot to much more effectively track mobile targets

Authors

• Yang Gu
• Manuela Veloso

Keywords

• Robot kinematics
• Target tracking
• Robot sensing systems
• Cognitive robotics
• Mobile robots
• Computer science
• Robot control
• Cognition
• Switches
• Wheels
• Actuator
• Motion Model
• Team Coordination
• Kalman Filter
• Simulation Test
• Termination Condition
• Target Model
• Particle Filter
• Velocity Estimation
• Objective Conditions
• Motion Tracking
• Single Tracking
• Global Coordinates
• Real-world Test
• Dynamic Bayesian Network
• Team Cooperation
• Ball Position
• Ball Velocity