Distributed Linear Quadratic Gaussian for Multi-Robot Coordination with Localization Uncertainty

This paper addresses the problem of distributed coordination control for multi-robot systems (MRSs) in the presence of localization uncertainty using a Linear Quadratic Gaussian (LQG) approach. We introduce a stochastic LQG control strategy that ensures the coordination of mobile robots while optimizing a performance criterion. The proposed control framework accounts for the inherent uncertainty in localization measurements, enabling robust decision-making and coordination. We analyze the stability of the system under the proposed control protocol, deriving conditions for the convergence of the multi-robot network. The effectiveness of the proposed approach is demonstrated through experimental validation using Robotrium simulation experiments, showcasing the practical applicability of the control strategy in real-world scenarios with localization uncertainty.