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.

中文翻译：

---

Deep Drone Racing：通过域随机化从模拟到现实

动态变化的环境、不可靠的状态估计以及在严重资源限制下的操作是限制小型自主无人机部署的基本挑战。我们在动态环境中自主、基于视觉的无人机竞赛的背景下应对这些挑战。赛车无人机必须高速穿越可能会移动大门的赛道。我们通过将最先进的规划和控制系统的性能与卷积神经网络的感知能力相结合来实现这一功能。由此产生的模块化系统既独立于平台又独立于领域：它经过模拟训练并部署在物理四旋翼上，无需任何微调。通过域随机化生成的大量模拟数据，使我们的系统对照明和门外观的变化具有鲁棒性。据我们所知，我们的方法是第一个在敏捷无人机飞行任务上演示零射击模拟到真实转移的方法。我们在模拟和物理平台上广泛测试了我们系统的精度和稳健性，并展示了对现有技术的显着改进。