  Controlling large swarms of robotic agents has many challenges including, but
not limited to, computational complexity due to the number of agents,
uncertainty in the functionality of each agent in the swarm, and uncertainty in
the swarm's configuration. This work generalizes the swarm state using Random
Finite Set (RFS) theory and solves the control problem using model predictive
control which naturally handles the challenges. This work uses information
divergence to define the distance between swarm RFS and a desired distribution.
A stochastic optimal control problem is formulated using a modified L2
distance. Simulation results show that swarm densities converge to a target
destination, and the RFS control formulation can vary in the number of target
destinations.
