Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/IROS40897.2019.8968460 | 2019 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS) |
Field | DocType | Volume |
Motion planning,Inverse,Mathematical optimization,Air traffic control,Optimal planning,Control engineering,Inverse reinforcement learning,Operator (computer programming),Principle of maximum entropy,Engineering,State space | Journal | abs/1903.10525 |
ISSN | Citations | PageRank |
2153-0858 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ekaterina V. Tolstaya | 1 | 1 | 3.44 |
| Alejandro Ribeiro | 2 | 2817 | 221.08 |
| Vijay Kumar | 3 | 7086 | 693.29 |
| Ashish Kapoor | 4 | 1833 | 119.72 |