A Reinforcement Learning-based Scheduler for Minimizing Casualties of a Military Drone Swarm

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dc.contributor.authorJin, Hengen
dc.contributor.committeechairHou, Yiwei Thomasen
dc.contributor.committeememberLou, Wenjingen
dc.contributor.committeememberLiu, Qingyuen
dc.contributor.departmentComputer Scienceen
dc.date.accessioned2022-07-15T08:00:07Zen
dc.date.available2022-07-15T08:00:07Zen
dc.date.issued2022-07-14en
dc.description.abstractIn this thesis, we consider a swarm of military drones flying over an unfriendly territory, where a drone can be shot down by an enemy with an age-based risk probability. We study the problem of scheduling surveillance image transmissions among the drones with the objective of minimizing the overall casualty. We present Hector, a reinforcement learning-based scheduling algorithm. Specifically, Hector only uses the age of each detected target, a piece of locally available information at each drone, as an input to a neural network to make scheduling decisions. Extensive simulations show that Hector significantly reduces casualties than a baseline round-robin algorithm. Further, Hector can offer comparable performance to a high-performing greedy scheduler, which assumes complete knowledge of global information.en
dc.description.abstractgeneralDrones have been successfully deployed by the military. The advancement of machine learning further empowers drones to automatically identify, recognize, and even eliminate adversary targets on the battlefield. However, to minimize unnecessary casualties to civilians, it is important to introduce additional checks and control from the control center before lethal force is authorized. Thus, the communication between drones and the control center becomes critical. In this thesis, we study the problem of communication between a military drone swarm and the control center when drones are flying over unfriendly territory where drones can be shot down by enemies. We present Hector, an algorithm based on machine learning, to minimize the overall casualty of drones by scheduling data transmission. Extensive simulations show that Hector significantly reduces casualties than traditional algorithms.en
dc.description.degreeMaster of Scienceen
dc.format.mediumETDen
dc.identifier.othervt_gsexam:35276en
dc.identifier.urihttp://hdl.handle.net/10919/111255en
dc.language.isoenen
dc.publisherVirginia Techen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectDrone swarmen
dc.subjectCasualtyen
dc.subjectSchedulingen
dc.subjectAge of Informationen
dc.subjectReinforcement learningen
dc.titleA Reinforcement Learning-based Scheduler for Minimizing Casualties of a Military Drone Swarmen
dc.typeThesisen
thesis.degree.disciplineComputer Science and Applicationsen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.levelmastersen
thesis.degree.nameMaster of Scienceen

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