Scalable decentralised decision making and optimisation in heterogeneous teams

George M. Mathews, Hugh F. Durrant-Whyte, Mikhail Prokopenko

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

8 Citations (Scopus)

Abstract

This paper considers the scenario where multiple autonomous agents must cooperate in making decisions to minimise a common team cost function. A distributed optimisation algorithm is presented. This allows each agent to incrementally refine their decisions while intermittently receiving updates from the team. A convergence analysis provides quantitative requirements on the frequency agents must communicate that is prescribed by the problem structure. The general problem requires every agent to have a model of every other agent in the system. To overcome this, a specific subset of systems, called Partially Separable, is defined. These systems only require each agent to have a combined summary of the rest of the system. This leads to the definition of an infinitely scalable system, which may contain an infinite number of agents while ensuring the local decisions will converge to the optimal team decision. Examples are given for reconnaissance or information gathering tasks.

Original languageEnglish
Title of host publication2006 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI
Place of PublicationPiscataway, N.J
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages383-388
Number of pages6
ISBN (Print)1424405661, 9781424405664
DOIs
Publication statusPublished - 2006
Externally publishedYes
Event2006 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI - Heidelberg, Germany
Duration: 3 Sep 20066 Sep 2006

Other

Other2006 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI
CountryGermany
CityHeidelberg
Period3/09/066/09/06

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