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