Control of Large Swarms via Random Finite Set Theory

TL;DR

This paper introduces a novel control method for large robotic swarms using Random Finite Set theory and model predictive control, effectively managing uncertainty and computational complexity.

Contribution

It generalizes swarm state representation with RFS theory and formulates a stochastic optimal control problem to handle uncertainties in large swarms.

Findings

Swarm densities successfully converge to target destinations.

The RFS control approach adapts to multiple target destinations.

Simulation demonstrates effective control under uncertainty.

Abstract

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.