Development of Decentralized Task Allocation Algorithms for Multi-Agent Systems with Very Low Communication

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dc.contributor.advisorHerrmann, Jeffrey Wen_US
dc.contributor.advisorOtte, Michael Wen_US
dc.contributor.authorBapat, Akshay Vinayen_US
dc.contributor.departmentSystems Engineeringen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.date.accessioned2020-07-14T05:34:49Z
dc.date.available2020-07-14T05:34:49Z
dc.date.issued2020en_US
dc.description.abstractExisting decentralized task allocation algorithms perform poorly under very low communication. Although previous work has considered task allocation algorithms in the presence of imperfect communication, the case of very low communication has not yet been addressed. In this thesis, we present two new algorithms: the Spatial Division Playbook Algorithm and the Travelling Salesman Playbook Algorithm, which cater to the cases when the instantaneous probability (p) of a successful message between agents satisfies p << 0.01. These algorithms work by assuming that communications may not happen, but then derive advantages whenever communications are successful. We compare these algorithms experimentally with three state-of-the-art algorithms - ACBBA, PIA and DHBA - across multiple communication levels and multiple numbers of targets, based on three communication models: Bernoulli model, Gilbert-Elliot model and Rayleigh Fading model. Our results show that the algorithms perform better than the other algorithms and reduce the time required to ensure all targets are visited.en_US
dc.identifierhttps://doi.org/10.13016/rd2s-mzoy
dc.identifier.urihttp://hdl.handle.net/1903/26310
dc.language.isoenen_US
dc.subject.pqcontrolledRoboticsen_US
dc.subject.pquncontrolledAutonomous Agentsen_US
dc.subject.pquncontrolledDistributed Robot Systemen_US
dc.subject.pquncontrolledTask Planningen_US
dc.titleDevelopment of Decentralized Task Allocation Algorithms for Multi-Agent Systems with Very Low Communicationen_US
dc.typeThesisen_US

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