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Controlling large swarms of robotic agents presents many challengesincluding, but not limited to, computational complexity due to a large numberof agents, uncertainty in the functionality of each agent in the swarm, anduncertainty in the swarm's configuration. The contribution of this work is todecentralize Random Finite Set (RFS) control of large collaborative swarms forcontrol of individual agents. The RFS control formulation assumes that thetopology underlying the swarm control is complete and uses the complete graphin a centralized manner. To generalize the control topology in a localized ordecentralized manner, sparse LQR is used to sparsify the RFS control gainmatrix obtained using iterative LQR. This allows agents to use information ofagents near each other (localized topology) or only the agent's own information(decentralized topology) to make a control decision. Sparsity and performancefor decentralized RFS control are compared for different degrees oflocalization in feedback control gains which show that the stability andperformance compared to centralized control do not degrade significantly inproviding RFS control for large collaborative swarms.

Decentralized Control of Large Collaborative Swarms using Random Finite Set Theory