Application of an autonomous multi-agent system using Proximal Policy Optimisation for tactical deconfliction within the urban airspace

Rodolphe Fremond, Yan Xu, Gokhan Inalhan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)

Abstract

The present paper formalises the development of a Multi-agent Reinforcement Learning (MARL) solver for U-space Service Providers (USSPs) supporting the tactical conflict resolution and exhibited in the Air Mobility Urban - Large Experimental Demonstration (AMU-LED) project. It relies on an Advantage Actor Critic (A2C) model with a Proximal Policy Optimisation (PPO) learning baseline. The application of the autonomous system is demonstrated under a synthetic (with live and virtual) air/unmanned traffic management (ATM/UTM) environment. The Unmanned Aircraft Systems (UASs) are flying in cruise phase at low altitudes, whose respective flight plan generates intersections for enforcing a high collision frequency. The study adopts a step-wise complexity approach of scenarios that confront two agents' observation methods and showcases a practical case of tactical conflict resolution. The experiments show encouraging deconfliction performance with promising prospects for seeing a such solver deployed.

Original languageEnglish
Title of host publication2022 IEEE/AIAA 41st Digital Avionics Systems Conference, DASC 2022 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665486071
DOIs
Publication statusPublished - 2022
Externally publishedYes
Event41st IEEE/AIAA Digital Avionics Systems Conference, DASC 2022 - Portsmouth, United States
Duration: 18 Sept 202222 Sept 2022

Publication series

NameAIAA/IEEE Digital Avionics Systems Conference - Proceedings
Volume2022-September
ISSN (Print)2155-7195
ISSN (Electronic)2155-7209

Conference

Conference41st IEEE/AIAA Digital Avionics Systems Conference, DASC 2022
Country/TerritoryUnited States
CityPortsmouth
Period18/09/2222/09/22

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

Keywords

  • Actor Critic
  • Multi-Agent System
  • Proximal Policy Optimization
  • Reinforcement Learning
  • U-space Service Providers
  • Urban Air Mobility

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