  Urban Air Mobility, the scenario where hundreds of manned and Unmanned
Aircraft System (UAS) carry out a wide variety of missions (e.g. moving humans
and goods within the city), is gaining acceptance as a transportation solution
of the future. One of the key requirements for this to happen is safely
managing the air traffic in these urban airspaces. Due to the expected density
of the airspace, this requires fast autonomous solutions that can be deployed
online. We propose Learning-'N-Flying (LNF) a multi-UAS Collision Avoidance
(CA) framework. It is decentralized, works on-the-fly and allows autonomous UAS
managed by different operators to safely carry out complex missions,
represented using Signal Temporal Logic, in a shared airspace. We initially
formulate the problem of predictive collision avoidance for two UAS as a
mixed-integer linear program, and show that it is intractable to solve online.
Instead, we first develop Learning-to-Fly (L2F) by combining: a) learning-based
decision-making, and b) decentralized convex optimization-based control. LNF
extends L2F to cases where there are more than two UAS on a collision path.
Through extensive simulations, we show that our method can run online
(computation time in the order of milliseconds), and under certain assumptions
has failure rates of less than 1% in the worst-case, improving to near 0% in
more relaxed operations. We show the applicability of our scheme to a wide
variety of settings through multiple case studies.
