Decentralized Control of Large Collaborative Swarms Using Random Finite Set Theory

Abstract

Controlling large swarms of robotic agents presents many challenges including but not limited to computational complexity due to a large number of agents, uncertainty in the functionality of each agent in the swarm, and uncertainty in the swarm's configuration. The contribution of this work is to decentralize random finite set (RFS)-based control of large collaborative swarms for controlling individual agents. The RFS-based control formulation assumes a Gaussian mixture probability hypothesis density approximation and a complete topology for centralized swarm control. To generalize the control topology in a localized or decentralized manner, a sparse linear quadratic regulator (LQR) is used to sparsify the RFS-based control gain matrix obtained using iterative LQR. This allows agents to use information of agents near each other (localized topology) or only the agent's own information (decentralized topology) to make a control decision. Sparsity and performance for decentralized RFS-based control are compared for different degrees of localization in feedback control gains which show that the stability and performance compared to centralized control do not degrade significantly in providing RFS-based control for large collaborative swarms.