Information seeking and model predictive control of a cooperative multi-robot system

In this paper, we propose a cooperative multi-robot control system, operating in an unfamiliar or unstructured environment. We focus on a robust model predictive control (robust-MPC) framework that enables robotic agents to operate in uncertain environments, and study the effect of observation uncertainties that arise from sensor noise on cooperative control performance. The proposed system relies on cooperative observation based on an information-seeking theory, in which the system not only can compensate uncertainty, but also takes actions to mitigate it. We carry out a case study that demonstrates a multi-robot collision avoidance scenario in an unknown environment. Simulation results show that the combination of robust-MPC methods and cooperative observation enables the cooperative multi-robot system to move efficiently and reach the goal faster than an uncooperative scenario.