Miniaturized autonomous unmanned aerial vehicles (UAVs) are an emerging and trending topic. With their form factor as big as the palm of one hand, they can reach spots otherwise inaccessible to bigger robots and safely operate in human surroundings. The simple electronics aboard such robots (sub-100mW) make them particularly cheap and attractive but pose significant challenges in enabling onboard sophisticated intelligence. In this work, we leverage a novel neural architecture search (NAS) technique to automatically identify several Pareto-optimal convolutional neural networks (CNNs) for a visual pose estimation task. Our work demonstrates how real-life and field-tested robotics applications can concretely leverage NAS technologies to automatically and efficiently optimize CNNs for the specific hardware constraints of small UAVs. We deploy several NAS-optimized CNNs and run them in closed-loop aboard a 27-g Crazyflie nano-UAV equipped with a parallel ultra-low power System-on-Chip. Our results improve the State-of-the-Art by reducing the in-field control error of 32% while achieving a real-time onboard inference-rate of ~10Hz@10mW and ~50Hz@90mW.

翻译：微型化自主无人机（UAVs）正成为新兴热点趋势。这类手掌大小的机器人能够抵达大型机器人无法触及的区域，并在人类环境中安全运行。其搭载的电子设备功耗低于100毫瓦，使其兼具廉价与吸引力，但同时也对实现机载复杂智能算法提出重大挑战。本研究采用新型神经架构搜索技术，自动识别出多个针对视觉位姿估计任务的帕累托最优卷积神经网络。我们的工作展示了经实际场景验证的机器人应用如何具体利用NAS技术，根据小型无人机特定硬件约束自动高效优化CNN。我们在配备超低功耗并行片上系统的27克Crazyflie纳米无人机上部署了多个NAS优化CNN，实现闭环运行。相较于现有技术，我们将实地控制误差降低32%，同时实现~10Hz@10mW和~50Hz@90mW的实时机载推理速率。