Multi-UAV Conflict Resolution with Graph Convolutional Reinforcement Learning
This addresses safety issues in air traffic for UAV operations, but it is incremental as it extends existing methods to handle more than two UAVs.
The paper tackles multi-UAV conflict resolution by modeling it as a multi-agent reinforcement learning problem, and results show that agents successfully solve conflicts in scenarios with 3 and 4 UAVs through cooperative strategies.
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.