グラフ畳み込み強化学習による複数UAVのコンフリクト解消
Multi-UAV Conflict Resolution with Graph Convolutional Reinforcement Learning
複数UAVの衝突回避をマルチエージェント強化学習として定式化し、グラフニューラルネットで協調通信しながら解消機動を生成する手法を提案した。
著者: Ralvi Isufaj, Marsel Omeri, Miquel Angel Piera
分類: cs.LG, cs.MA, cs.RO
原文アブストラクト
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.