Multi-UAV Conflict Resolution with Graph Convolutional Reinforcement Learning

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 multiagent 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.

Comprehensive Review of Drones Collision Avoidance Schemes: Challenges and Open Issues

“…Conflicting aircraft can communicate and establish cooperative approaches. Reference [2] proposes the idea of compound conflicts to define a multi-UAV conflict based on Multi-Agent Reinforcement Learning (MARL), considering conflicts with tight geographical and temporal bounds.…”

“…T HE worldwide market for commercial and civil Unmanned Aircraft Systems (UAS) is anticipated to expand significantly. According to Single European Sky Air Traffic Management Research (SESAR), the European drone market will surpass 10 billion yearly by 2035 and 15 billion by 2050 [1], [2]. Moreover, based on the characteristics of the missions and application fields, small UAS and very low-level airspace operations would provide the most incredible market value.…”

“…Therefore, developing and deploying conflict management systems are prerequisites for UAS integration in civil airspace. Specifically, the National Aeronautics and Space Administration (NASA) in the United States (US) intends to develop a UAS Traffic Management (UTM) system that will let multiple UAS fly at low altitudes alongside other airspace users [2], [3].…”

An ontology-based system to avoid UAS flight conflicts and collisions in dense traffic scenarios

“…This is a limitation when UASs with different payloads share the same airspace. Isufaj et al (2022) proposed an avoidance method based on changing UAV heading with a reinforcement algorithm that is simulated with up to four concurrent UAVs. Our proposal combines heading and speed changes, based upon knowledge available and inferred, to improve avoidance in dense traffic scenarios, reaching up to nine UAVs.…”

From Single Aircraft to Communities: A Neutral Interpretation of Air Traffic Complexity Dynamics

“…However, the presence of AC1 complicates this decision. Following the evolution of trajectories, AC1 will eventually create a compound conflict [24] with AC2 and AC3. This is not shown yet by using only the Strength indicator; however, when measuring single aircraft complexity as previously described, we can observe the following complexity situation shown in Figure 7.…”