Multi-UAV Conflict Resolution with Graph Convolutional Reinforcement Learning

TL;DR

This paper introduces a multi-agent reinforcement learning approach using graph neural networks to enable cooperative conflict resolution among multiple UAVs, addressing the limitations of pairwise conflict methods in increasing traffic scenarios.

Contribution

It presents a novel graph convolutional reinforcement learning algorithm for multi-UAV conflict resolution, allowing cooperative maneuver generation among multiple agents.

Findings

Agents successfully resolve multi-UAV conflicts in simulated scenarios.

The cooperative strategy improves conflict resolution effectiveness.

Model scales to scenarios with 3 and 4 UAVs.

Abstract

Safety is the primary concern when it comes to air traffic. In-flight safety between Unmanned Aircraft Vehicles (UAVs) is ensured through pairwise separation minima, utilizing conflict detection and resolution methods. Existing methods mainly deal with pairwise conflicts, however due to an expected increase in traffic density, encounters with more than two UAVs are likely to happen. In this paper, we model multi-UAV conflict resolution as a multi-agent reinforcement learning problem. We implement an algorithm based on graph neural networks where cooperative agents can communicate to jointly generate resolution maneuvers. The model is evaluated in scenarios with 3 and 4 present agents. Results show that agents are able to successfully solve the multi-UAV conflicts through a cooperative strategy.