深層ドローンレーシング：ドメインランダム化によるシミュレーションから現実への転移
Deep Drone Racing: From Simulation to Reality with Domain Randomization
CNNによる知覚と計画・制御を組み合わせ、ドメインランダム化でシミュレーション訓練したシステムを実機ドローンにファインチューニングなしで展開し、動的環境での高速自律レースを実現した。
著者: Antonio Loquercio, Elia Kaufmann, René Ranftl, Alexey Dosovitskiy, Vladlen Koltun, Davide Scaramuzza
分類: cs.RO
原文アブストラクト
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 (CNN). The resulting modular system is both platform- 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.
