Print Email Facebook Twitter Autonomous conflict resolution for high-density urban operations using deep reinforcement learning Title Autonomous conflict resolution for high-density urban operations using deep reinforcement learning Author Santana, Ricardo (TU Delft Aerospace Engineering) Contributor Sharpanskykh, Alexei (mentor) Degree granting institution Delft University of Technology Programme Aerospace Engineering Date 2023-04-03 Abstract Low-altitude, high-density air traffic is expected to grow in the coming decades with several companies being certified to initiate urban operations for both freight and passenger transport. However, traditional human-centered Air Traffic Control operations (ATCos) are not scalable to handle the increased demand to maintain safe separation between aircraft. Thus, new autonomous solutions to resolve potential conflicts between aircraft must be developed, allowing humans to supervise and understand machine actions. We propose a centralized Deep Reinforcement Learning (DRL)-based framework to provide speed advisories to vehicles, ensuring safe and efficient operations. We used Proximal Policy Optimization (PPO) to train the intelligent controller and show that our framework is capable of handling challenging merging point settings. We evaluated our model extensively using a custom OpenAI Gym environment and proved that it can achieve a 99% success rate in conflict resolution across multiple merging point configurations. Subject Conflict ResolutionDeep Reinforcement LearningArtificial intelligenceUrban Air Mobility To reference this document use: http://resolver.tudelft.nl/uuid:9f14ecaf-c26b-4f22-aa4e-d834f1b5c40c Part of collection Student theses Document type master thesis Rights © 2023 Ricardo Santana Files PDF MSc_Thesis_Ricardo_Santana.pdf 10.13 MB Close viewer /islandora/object/uuid:9f14ecaf-c26b-4f22-aa4e-d834f1b5c40c/datastream/OBJ/view