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

In 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. / Master of Science / Drones 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.

Identiferoai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/111255
Date14 July 2022
CreatorsJin, Heng
ContributorsComputer Science, Hou, Yiwei Thomas, Lou, Wenjing, Liu, Qingyu
PublisherVirginia Tech
Source SetsVirginia Tech Theses and Dissertation
LanguageEnglish
Detected LanguageEnglish
TypeThesis
FormatETD, application/pdf
RightsIn Copyright, http://rightsstatements.org/vocab/InC/1.0/

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