Scalable Multi-Agent Computational Guidance with Separation Assurance for Autonomous Urban Air Mobility

Abstract

Electric vertical takeoff and landing vehicles are becoming promising for on-demand air transportation in urban air mobility (UAM). However, successfully bringing such vehicles and airspace operations to fruition will require introducing orders of magnitude more aircraft to a given airspace volume. Although there are existing solutions for communication technology, onboard computing capability, and sensor technology, the computation guidance algorithm to enable safe, efficient, and scalable flight operations for dense self-organizing air traffic still remains an open question. In this paper, a message-based decentralized computational guidance algorithm is proposed and analyzed for multiple cooperative aircraft by formulating this problem using multi-agent Markov decision process and solving it by Monte Carlo tree search algorithm. A novel coordination strategy is introduced by using the logit level- model in behavioral game theory. To achieve higher scalability, the airspace sector concept is introduced into the UAM environment by dividing the airspace into sectors, so that each aircraft only needs to coordinate with aircraft in the same sector. At each decision step, all of the aircraft will run the proposed computational guidance algorithm onboard, which can guide all the aircraft to their respective destinations while avoiding potential conflicts among them. For validation and demonstration, a free-flight airspace simulator that incorporates environment uncertainty is built in an OpenAI Gym environment. Numerical experiment results over several case studies, including the roundabout test problem, show that the proposed computational guidance algorithm has promising performance even with the high-density air traffic case.