Nano-size drones hold enormous potential to explore unknown and complex environments. Their small size makes them agile and safe for operation close to humans and allows them to navigate through narrow spaces. However, their tiny size and payload restrict the possibilities for on-board computation and sensing, making fully autonomous flight extremely challenging. The first step towards full autonomy is reliable obstacle avoidance, which has proven to be technically challenging by itself in a generic indoor environment. Current approaches utilize vision-based or 1-dimensional sensors to support nano-drone perception algorithms. This work presents a lightweight obstacle avoidance system based on a novel millimeter form factor 64 pixels multi-zone Time-of-Flight (ToF) sensor and a generalized model-free control policy. Reported in-field tests are based on the Crazyflie 2.1, extended by a custom multi-zone ToF deck, featuring a total flight mass of 35g. The algorithm only uses 0.3% of the on-board processing power (210uS execution time) with a frame rate of 15fps, providing an excellent foundation for many future applications. Less than 10% of the total drone power is needed to operate the proposed perception system, including both lifting and operating the sensor. The presented autonomous nano-size drone reaches 100% reliability at 0.5m/s in a generic and previously unexplored indoor environment. The proposed system is released open-source with an extensive dataset including ToF and gray-scale camera data, coupled with UAV position ground truth from motion capture.
翻译:纳米规模的无人驾驶飞机拥有探索未知和复杂环境的巨大潜力,其规模小,使其在接近人类的地方运行更加灵活和安全,并允许它们通过狭小的空间航行。然而,其小尺寸和有效载荷限制了在船上进行计算和遥感的可能性,使完全自主的飞行具有极大的挑战性。完全自主的第一步是可靠的避免障碍,这在一般室内环境中本身证明在技术上具有挑战性。目前的方法使用基于视像或一维传感器来支持纳米探点感知算法。这项工作提供了一个轻量级障碍避免系统,其基础是新型的毫米因子64像素多区相机传感器(TF)和通用无模式控制政策。报告实地测试的基础是Crazarmflie 2.1,该测试由定制的多区到F甲板扩展,其总飞行质量为35克。 算法仅使用0.3%的机载处理电源(210苏思执行时间),其框架速为许多未来应用提供了极好的底基。在100码的全机载动力中不到10%的机载力,包括100码的甚级的甚级地面数据定位系统将操作和交付。