An Autonomous Free Airspace En-route Controller using Deep Reinforcement Learning Techniques

2020-07-03 · Joris Mollinga, Herke van Hoof

Air traffic control is becoming a more and more complex task due to the increasing number of aircraft. Current air traffic control methods are not suitable for managing this increased traffic. Autonomous air traffic control is deemed a promising alternative. In this paper an air traffic control model is presented that guides an arbitrary number of aircraft across a three-dimensional, unstructured airspace while avoiding conflicts and collisions. This is done utilizing the power of graph based deep learning approaches. These approaches offer significant advantages over current approaches to this task, such as invariance to the input ordering of aircraft and the ability to easily cope with a varying number of aircraft. Results acquired using these approaches show that the air traffic control model performs well on realistic traffic densities; it is capable of managing the airspace by avoiding 100% of potential collisions and preventing 89.8% of potential conflicts.

📄 PDF Abstract BibTeX arXiv:2007.01599

Code (0)

등록된 구현이 없습니다.

Tasks

Deep Reinforcement Learningreinforcement-learning Reinforcement Learning (RL)

Autonomous Air Traffic Controller: A Deep Multi-Agent Reinforcement Learning Approach

2019-05-02 · Marc Brittain, Peng Wei

Air traffic control is a real-time safety-critical decision making process in highly dynamic and stochastic environments. In today's aviation practice, a human air traffic controller monitors and directs many aircraft fl…

Decision Making Multi-agent Reinforcement Learning reinforcement-learning Reinforcement Learning+1

Optimizing Collision Avoidance in Dense Airspace using Deep Reinforcement Learning

2019-12-20 · Sheng Li, Maxim Egorov, Mykel Kochenderfer

New methodologies will be needed to ensure the airspace remains safe and efficient as traffic densities rise to accommodate new unmanned operations. This paper explores how unmanned free-flight traffic may operate in den…

Collision Avoidance Deep Reinforcement Learning reinforcement-learning Reinforcement Learning+1

A Future Capabilities Agent for Tactical Air Traffic Control

2026-01-07 · Paul Kent, George De Ath, Martin Layton, Allen Hart 외 arxiv

Escalating air traffic demand is driving the adoption of automation to support air traffic controllers, but existing approaches face a trade-off between safety assurance and interpretability. Optimisation-based methods s…

Reinforcement Learning

SECRM-2D: RL-Based Efficient and Comfortable Route-Following Autonomous Driving with Analytic Safety Guarantees

2024-07-23 · Tianyu Shi, Ilia Smirnov, Omar ElSamadisy, Baher Abdulhai

Over the last decade, there has been increasing interest in autonomous driving systems. Reinforcement Learning (RL) shows great promise for training autonomous driving controllers, being able to directly optimize a combi…

Autonomous Driving Reinforcement Learning (RL)

Intercepting Unauthorized Aerial Robots in Controlled Airspace Using Reinforcement Learning

2024-07-09 · Francisco Giral, Ignacio Gómez, Soledad Le Clainche

The proliferation of unmanned aerial vehicles (UAVs) in controlled airspace presents significant risks, including potential collisions, disruptions to air traffic, and security threats. Ensuring the safe and efficient op…

reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)