A scalable information theoretic approach to distributed robot coordination
Name
A_Scalable_Information_Theoretic_Approach_to_Distr.pdf
Description
Accepted version
Size
619.38 KB
Format
Adobe PDF
Checksum (MD5)
7004c8284ae93b9af8b440578810744d
Author(s)
Julian, Brian J.
Angermann, Michael
Schwager, Mac
Rus, Daniela
Date Issued
September 2011
Publisher
IEEE
Citation
Julian, Brian J., Angermann, Michael, Schwager, Mac and Rus, Daniela. 2011. "A scalable information theoretic approach to distributed robot coordination."
Version
Author's final manuscript
Abstract
This paper presents a scalable information theoretic approach to infer the state of an environment by distributively controlling robots equipped with sensors. The robots iteratively estimate the environment state using a recursive Bayesian filter, while continuously moving to improve the quality of the estimate by following the gradient of mutual information. Both the filter and the controller use a novel algorithm for approximating the robots' joint measurement probabilities, which combines consensus (for decentralization) and sampling (for scalability). The approximations are shown to approach the true joint measurement probabilities as the size of the consensus rounds grows or as the network becomes complete. The resulting gradient controller runs in constant time with respect to the number of robots, and linear time with respect to the number of sensor measurements and environment discretization cells, while traditional mutual information methods are exponential in all of these quantities. Furthermore, the controller is proven to be convergent between consensus rounds and, under certain conditions, is locally optimal. The complete distributed inference and coordination algorithm is demonstrated in experiments with five quad-rotor flying robots and simulations with 100 robots. © 2011 IEEE.
MIT Department
Lincoln Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/iros.2011.6095127
