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. This work generalizes the swarm state using random finite set (RFS) theory and solves a centralized control problem with a quasi-Newton optimization through the use of model predictive control (MPC) to overcome the aforementioned challenges. This work uses the RFS formulation to control the distribution of agents assuming an unknown or unspecified number of agents. Computationally efficient solutions are also obtained via the MPC version of the iterative linear quadratic regulator (ILQR), a variant of differential dynamic programming. Information divergence is used to define the distance between the swarm RFS and the desired swarm configuration through the use of the modified distance. Simulation results using MPC and ILQR show that the swarm intensity converges to the desired intensity. Additionally, the RFS control formulation is shown to be very flexible in terms of the number of agents in the swarm and configuration of the desired Gaussian mixtures. Lastly, the ILQR and the Gaussian mixture probability hypothesis density filter are used in conjunction to solve a spacecraft relative motion problem with imperfect information to show the viability of centralized RFS control for this real-world scenario.
Random Finite Set Theory and Centralized Control of Large Collaborative Swarms
Journal of Guidance, Control, and Dynamics ; 44 , 3 ; 505-521
2020-11-24
17 pages
Article (Journal)
Electronic Resource
English
Collaborative Patrolling Swarms in Stochastically Expanding Environments
Springer Verlag | 2017
|British Library Conference Proceedings | 2009
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