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 language | English |
---|---|
Title of host publication | 2022 IEEE/AIAA 41st Digital Avionics Systems Conference, DASC 2022 - Proceedings |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
ISBN (Electronic) | 9781665486071 |
DOIs | |
Publication status | Published - 2022 |
Externally published | Yes |
Event | 41st IEEE/AIAA Digital Avionics Systems Conference, DASC 2022 - Portsmouth, United States Duration: 18 Sept 2022 → 22 Sept 2022 |
Publication series
Name | AIAA/IEEE Digital Avionics Systems Conference - Proceedings |
---|---|
Volume | 2022-September |
ISSN (Print) | 2155-7195 |
ISSN (Electronic) | 2155-7209 |
Conference
Conference | 41st IEEE/AIAA Digital Avionics Systems Conference, DASC 2022 |
---|---|
Country/Territory | United States |
City | Portsmouth |
Period | 18/09/22 → 22/09/22 |
Bibliographical note
Publisher Copyright:© 2022 IEEE.
Keywords
- Actor Critic
- Multi-Agent System
- Proximal Policy Optimization
- Reinforcement Learning
- U-space Service Providers
- Urban Air Mobility