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

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