Limited Information Model Predictive Control for Pursuit-evasion Games
Mukhtar Sani, Bogdan Robu, Ahmad Hably
Abstract
This paper explores the use of model predictive control (MPC) in dealing with the pursuit-evasion game (PEG) problem where players have incomplete information on their opponents. This is different from most cases in the literature where each player knows all the information (states information and dynamics) on the opponent. The burden caused by such demand for the opponent’s full information induces the need for more sensors during physical implementation as well as high computation time. However, we found that only the current positions, i.e. x−y coordinate of the opponent, are indispensable. Thus, knowing the orientation and the dynamics of the opponent are insignificant to the performance of the game. We propose a new method to exploit a two-player PEG in the presence and absence of obstacles, where each player can only rely on the current position information of its opponent. Several simulation results show that the PEG problem can be handled and obstacles can be avoided using the proposed control protocol. We also show that our approach is robust to measurement noise and can perform better, in terms of the computation than the approach with full information.