Cooperative Visual Pursuit Control with Learning of Position Dependent Target Motion via Gaussian Process

This paper considers a pursuit control based on cooperative target motion estimation by robotic networks equipped with visual sensors. First, we propose a cooperative pursuit control law with a vision-based observer using visual sensor networks, called networked visual motion observer. Then, we learn position dependent target motion by a Gaussian process and integrate it within the proposed control law. Second, we show that all rigid bodies converge to desired relative poses when at least one robot can obtain visual information of the target. Furthermore, we prove that the total estimation and control error is ultimately bounded with high probability when integrating a GP model. Finally, we demonstrate the effectiveness of the proposed control law through simulations.