Safe and Scalable Real-Time Trajectory Planning Framework for Urban Air Mobility

TL;DR

This paper introduces a real-time, decentralized trajectory planning framework for Urban Air Mobility that ensures safety and scalability by combining reachability analysis and MDP decision-making, validated through simulation.

Contribution

It presents a novel decentralized framework integrating data-driven reachability and MDP for safe, scalable UAM trajectory planning, with safety enhancements like reward shaping and action shielding.

Findings

Successfully simulated up to 32 aircraft with low NMACs

Demonstrated real-time performance and safety in UAM scenarios

Framework scales effectively with increasing aircraft numbers

Abstract

This paper presents a real-time trajectory planning framework for Urban Air Mobility (UAM) that is both safe and scalable. The proposed framework employs a decentralized, free-flight concept of operation in which each aircraft independently performs separation assurance and conflict resolution, generating safe trajectories by accounting for the future states of nearby aircraft. The framework consists of two main components: a data-driven reachability analysis tool and an efficient Markov Decision Process (MDP) based decision maker. The reachability analysis over-approximates the reachable set of each aircraft through a discrepancy function learned online from simulated trajectories. The decision maker, on the other hand, uses a 6-degrees-of-freedom guidance model of fixed-wing aircraft to ensure collision-free trajectory planning. Additionally, the proposed framework incorporates reward shaping and action shielding techniques to enhance safety performance. The proposed framework is evaluated through simulation experiments involving up to 32 aircraft in a UAM setting, with performance measured by the number of Near Mid Air Collisions (NMAC) and computational time. The results demonstrate the safety and scalability of the proposed framework.