Computer vision and pursuit–evasion game theoretical controls for ground robots

Abstract

A hardware-in-loop control framework with robot dynamic models, pursuit–evasion game models, sensor and information solutions, and entity tracking algorithms is designed and developed to demonstrate discrete-time robotic pursuit–evasion games for real-world conditions. A parameter estimator is implemented to learn the unknown parameters in the robot dynamics. For visual tracking and fusion, several markers are designed and selected with the best balance of robot tracking accuracy and robustness. The target robots are detected after background modeling, and the robot poses are estimated from the local gradient patterns. Based on the robot dynamic model, a two-player discrete-time game model with limited action space and limited look-ahead horizons is created. The robot controls are based on the game-theoretic (mixed) Nash solutions. Supportive results are obtained from the robot control framework to enable future research of the robot applications in sensor fusion, target tracking and detection, and decision making.