Cooperative estimation and fleet reconfiguration for multi-agent systems

This paper considers a multi-agent system which aim is to determine the maximum of some field. For that purpose, noisy measurements are collected by each agent and exchanged between neighboring agents. The maximization task, performed by gradient climbing, has to be robust to the presence of agents equipped with sensors providing outliers. For that purpose, an outlier detection scheme is used and the optimal configuration for agents with different sensor noise characteristics is evaluated. This gives insights to derive a practical distributed control law to achieve robust maximization. The stability of the system with this control law is analyzed. The resulting performance is illustrated on an example.