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INFORMATION-DRIVEN CONTROL OF MULTI-ROBOT NETWORKS FOR DYNAMIC TARGET TRACKING

dc.contributor.authorLiu, Cong
dc.contributor.chairFerrari, Silviaen_US
dc.contributor.committeeMemberHelbling, Elizabethen_US
dc.date.accessioned2024-04-05T18:36:19Z
dc.date.available2024-04-05T18:36:19Z
dc.date.issued2023-08
dc.description56 pagesen_US
dc.description.abstractRecently, research on multi-robot networks has attracted increasing attentiondue to their higher efficiency and robustness over single-robot systems. This work focuses on the control of multi-robot networks for tracking multiple human targets, which has promising applications in security and surveillance. Existing literature has shown that the information gain can guide sensors to make informative measurements. With this inspiration, an information gain based control algorithm was developed to optimize the tracking performance of multirobot networks. The proposed control algorithm considered target tracking, target exploration, and collision avoidance. In addition, this work validated the network control through physical experiments involving Unmanned Ground Vehicles (UGVs) and real human targets, for which four online sensing algorithms including UGV localization, target detection, target localization, and target classification were implemented.en_US
dc.identifier.doihttps://doi.org/10.7298/nxms-wj04
dc.identifier.otherLiu_cornell_0058O_11825
dc.identifier.otherhttp://dissertations.umi.com/cornell:11825
dc.identifier.urihttps://hdl.handle.net/1813/114456
dc.language.isoen
dc.subjectInformation Driven Controlen_US
dc.subjectMulti-Robot Networksen_US
dc.subjectRoboticsen_US
dc.subjectTarget Trackingen_US
dc.titleINFORMATION-DRIVEN CONTROL OF MULTI-ROBOT NETWORKS FOR DYNAMIC TARGET TRACKINGen_US
dc.typedissertation or thesisen_US
dcterms.licensehttps://hdl.handle.net/1813/59810.2
thesis.degree.disciplineMechanical Engineering
thesis.degree.grantorCornell University
thesis.degree.levelMaster of Science
thesis.degree.nameM.S., Mechanical Engineering

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