Team-Driven Multi-Model Motion Tracking with Communication

Interactions are frequently seen between the robot and the targets being tracked within the robotics community. Modeling the interactions using knowledge of robot cognition improves the performance of the tracker. Communication improves the performance of a multi-agent system. The focus of this paper is to present our solution to integrate the communication information into our team-driven multi-model motion tracking. We present the probabilistic tracking algorithm in detail and present empirical results both in simulation and in a Segway soccer team. The information from team communication allows the robot to much more effectively track mobile targets

Authors

• Yang Gu
• Manuela Veloso

Keywords

• Target tracking
• Robot kinematics
• Robot sensing systems
• Mobile robots
• Cognitive robotics
• Orbital robotics
• Intelligent robots
• Computer science
• Cognition
• Multiagent systems
• Motion Tracking
• Focus Of This Paper
• Information Communication
• Detailed Algorithm
• Multi-agent Systems
• Tracking Algorithm
• Team Communication
• Team Members
• Dynamic Model
• Actuator
• Infrared Imaging
• Gaussian Noise
• Transition Probabilities
• Team Sports
• Simulation Test
• Motion Model
• Position Estimation
• Particle Filter
• Tracking Problem
• Vision Sensors
• Limited Field Of View
• Kinds Of Messages
• Dynamic Bayesian Network
• Actuator Model
• Real-world Test
• Tracking Scenarios
• Team Players
• Sensor Measurements
• Target Velocity