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

TL;DR

This paper addresses multi-robot coordination under localization uncertainty by proposing a distributed stochastic LQG framework that robustly coordinates agents while optimizing a defined performance criterion. It combines state estimation with control, accounting for measurement uncertainty to derive stability conditions for the networked robotic system. The work is validated through Robotrium simulation experiments, demonstrating practical feasibility in uncertain localization scenarios. The approach offers a scalable, robust solution for real-world MRS deployments where localization errors impact cooperative decision-making.

Abstract

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.

Figures (1)

• Figure 1: Magnetization as a function of applied field. It is good practice to explain the significance of the figure in the caption.