Random Finite Set Theory and Centralized Control of Large Collaborative Swarms
Name
1810.00696.pdf
Description
Accepted version
Size
3.73 MB
Format
Adobe PDF
Checksum (MD5)
11805cc86e5aa12b5779050ff85bc12d
Author(s)
Doerr, Bryce
Linares, Richard
Zhu, Pingping
Ferrari, Silvia
Date Issued
2021
Journal
Journal of Guidance Control and Dynamics
Publisher
American Institute of Aeronautics and Astronautics (AIAA)
Version
Author's final manuscript
Abstract
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 theMPCversion 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 L 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 realworld scenario. 2 2
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.2514/1.G004861
