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FairFly A Fair Motion Planner for Fleets of Autonomous UAVs in Urban Airspace

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Document pages: 6 pages

Abstract: We present a solution to the problem of fairly planning a fleet of UnmannedAerial Vehicles (UAVs) that have different missions and operators, such that noone operator unfairly gets to finish its missions early at the expense ofothers - unless this was explicitly negotiated. When hundreds of UAVs share anurban airspace, the relevant authorities should allocate corridors to them suchthat they complete their missions, but no one vehicle is accidentally given anexceptionally fast path at the expense of another, which is thus forced to waitand waste energy. Our solution, FairFly, addresses the fair planning questionfor general autonomous systems, including UAV fleets, subject to complexmissions typical of urban applications. FairFly formalizes each mission intemporal logic. An offline search finds the fairest paths that satisfy themissions and can be flown by the UAVs, leading to lighter online control load.It allows explicit negotiation between UAVs to enable imbalanced path durationsif desired. We present three fairness notions, including one that reducesenergy consumption. We validate our results in simulation, and demonstrate alighter computational load and less UAV energy consumption as a result offlying fair trajectories.

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