3 research outputs found
Random Finite Set Theory and Optimal Control of Large Collaborative Swarms
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 (MPC) to overcome the aforementioned challenges. Computationally
efficient solutions are obtained via the Iterative Linear Quadratic Regulator
(ILQR). Information divergence is used to define the distance between the swarm
RFS and the desired swarm configuration. Then, a stochastic optimal control
problem is formulated using a modified L2^2 distance. Simulation results using
MPC and ILQR show that swarm intensities converge to a target destination, and
the RFS control formulation can vary in the number of target destinations. ILQR
also provides a more computationally efficient solution to the RFS swarm
problem when compared to the MPC solution. Lastly, the RFS control solution is
applied to a spacecraft relative motion problem showing the viability for this
real-world scenario.Comment: arXiv admin note: text overlap with arXiv:1801.0731
Decentralized Control of Large Collaborative Swarms using Random Finite Set Theory
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) control of large collaborative swarms for
control of individual agents. The RFS control formulation assumes that the
topology underlying the swarm control is complete and uses the complete graph
in a centralized manner. To generalize the control topology in a localized or
decentralized manner, sparse LQR is used to sparsify the RFS 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 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 control for large collaborative swarms.Comment: arXiv admin note: text overlap with arXiv:1810.0069