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| Type | Journal Article |
| Scope | Discipline-based scholarship |
| Title | Deep Drone Racing: From Simulation to Reality With Domain Randomization |
| Organization Unit | |
| Authors |
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| Item Subtype | Original Work |
| Refereed | Yes |
| Status | Published in final form |
| Language |
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| Journal Title | IEEE Transactions on Robotics |
| Publisher | Institute of Electrical and Electronics Engineers |
| Geographical Reach | international |
| ISSN | 1552-3098 |
| Volume | 36 |
| Number | 1 |
| Page Range | 1 - 14 |
| Date | 2020 |
| Abstract Text | Dynamically changing environments, unreliable state estimation, and operation under severe resource constraints are fundamental challenges that limit the deployment of small autonomous drones. We address these challenges in the context of autonomous, vision-based drone racing in dynamic environments. A racing drone must traverse a track with possibly moving gates at high speed. We enable this functionality by combining the performance of a state-of-the-art planning and control system with the perceptual awareness of a convolutional neural network. The resulting modular system is both platform independent and domain independent: it is trained in simulation and deployed on a physical quadrotor without any fine-tuning. The abundance of simulated data, generated via domain randomization, makes our system robust to changes of illumination and gate appearance. To the best of our knowledge, our approach is the first to demonstrate zero-shot sim-to-real transfer on the task of agile drone flight. We extensively test the precision and robustness of our system, both in simulation and on a physical platform, and show significant improvements over the state of the art. |
| Related URLs | |
| Digital Object Identifier | 10.1109/tro.2019.2942989 |
| Other Identification Number | merlin-id:20301 |
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