We envision a system that concisely describes the rules of air traffic control, assists human operators and supports dense autonomous air traffic around commercial airports. We develop a method to learn the rules of air traffic control from real data as a cost function via maximum entropy inverse reinforcement learning. This cost function is used as a penalty for a search-based motion planning method that discretizes both the control and the state space. We illustrate the methodology by showing that our approach can learn to imitate the airport arrival routes and separation rules of dense commercial air traffic. The resulting trajectories are shown to be safe, feasible, and efficient.

Original document

The different versions of the original document can be found in:

Back to Top

Document information

Published on 01/01/2019

Volume 2019, 2019
DOI: 10.1109/iros40897.2019.8968460
Licence: CC BY-NC-SA license

Document Score


Views 0
Recommendations 0

Share this document

claim authorship

Are you one of the authors of this document?