Learning-'N-Flying: A Learning-based, Decentralized Mission Aware UAS Collision Avoidance Scheme
This addresses the safety challenge for urban air mobility by enabling autonomous UAS from different operators to perform complex missions without collisions, though it appears incremental as it builds on prior work like Learning-to-Fly.
The paper tackles the problem of safe collision avoidance for multiple unmanned aircraft systems (UAS) in dense urban airspaces by proposing Learning-'N-Flying (LNF), a decentralized framework that combines learning-based decision-making and convex optimization. The result shows online computation times in milliseconds and failure rates below 1% in worst-case scenarios, improving to near 0% under relaxed conditions.
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.