Autonomous Free Flight Operations in Urban Air Mobility With Computational Guidance and Collision Avoidance

Abstract

The use of electrical vertical takeoff and landing (eVTOL) aircraft to provide efficient, high-speed, on-demand air transportation within a metropolitan area is a topic of increasing interest, which is expected to bring fundamental changes to the city infrastructures and daily commutes. NASA, Uber, and Airbus have been exploring this exciting concept of Urban Air Mobility (UAM), which has the potential to provide meaningful door-to-door trip time savings compared with automobiles. However, the ability to manage many of these eVTOL aircraft safely in a congested urban area presents a challenge unprecedented in air traffic management. In order to enable safe and efficient autonomous on-demand free flight operations in UAM, a computational guidance algorithm with collision avoidance capability is designed and analyzed. The approach proposed in this paper is to formulate this problem as a Markov Decision Process (MDP) and solve it using an online algorithm Monte Carlo Tree Search (MCTS). For illustration, a high-density free flight airspace simulator is created to test the proposed algorithm's performance. Numerical experiment results show that this proposed algorithm has fewer conflicts and near mid-air collisions than Optimal Reciprocal Collision Avoidance (ORCA), a state-of-the-art collision avoidance strategy.