Distributed Aggregation in the Presence of Uncertainty: A Statistical Physics Approach

We present a statistical physics inspired approach to modeling, analysis, and design of distributed aggregation control policies for teams of homogeneous and heterogeneous robots. We assume high-level agent behavior can be described as a sequential composition of lower-level behavioral primitives. Aggregation or division of the collective into distinct clusters is achieved by developing a macroscopic description of the ensemble dynamics. The advantages of this approach are twofold: 1) the derivation of a low dimensional but highly predictive description of the collective dynamics and 2) a framework where interaction uncertainties between the low-level components can be explicitly modeled and control. Additionally, classical dynamical systems theory and control theoretic techniques can be used to analyze and shape the collective dynamics of the system. We consider the aggregation problem for homogeneous agents into clusters located at distinct regions in the workspace and discuss the extension to heterogeneous teams of autonomous agents. We show how a macroscopic model of the aggregation dynamics can be derived from agent-level behaviors and discuss the synthesis of distributed coordination strategies in the presence of uncertainty.