Decentralized Control of Large Collaborative Swarms using Random Finite Set Theory
Name
2003.07221.pdf
Description
Submitted version
Size
1.21 MB
Format
Adobe PDF
Checksum (MD5)
96e7c5b371bf3f7dd42fb51266142de8
Author(s)
Doerr, Bryce
Linares, Richard
Date Issued
2021
Journal
IEEE Transactions on Control of Network Systems
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Version
Original manuscript
Abstract
IEEE 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)-based control of large collaborative swarms for controlling individual agents. The RFS-based control formulation assumes a Gaussian Mixture Probability Hypothesis Density (GM-PHD) approximation and a complete topology for centralized swarm control. To generalize the control topology in a localized or decentralized manner, sparse LQR is used to sparsify the RFS-based 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-based 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 provi
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/10.1109/TCNS.2021.3059793
