面向自主微型无人机的鲁棒且高效的基于深度的避障
Robust and Efficient Depth-based Obstacle Avoidance for Autonomous Miniaturized UAVs
AI总结：
针对纳米无人机机载资源受限问题，提出基于64像素多区域ToF传感器和无模型控制策略的轻量级避障系统，以极低算力与功耗实现室内高可靠自主飞行并开源数据。
摘要：
纳米级无人机在探索未知和复杂环境方面具有巨大潜力。它们体积小，因此灵活且适合在人类附近安全运行，并能够穿过狭窄空间。然而，其极小的尺寸和载荷限制了机载计算与传感的可能性，使完全自主飞行极具挑战性。迈向完全自主的第一步是可靠的避障，而在一般室内环境中，这本身已被证明在技术上颇具挑战。现有方法采用基于视觉或一维传感器来支持纳米无人机感知算法。本工作提出了一种轻量级避障系统，该系统基于新型毫米级外形的64像素多区域飞行时间（ToF）传感器以及一种通用的无模型控制策略。所报告的现场测试基于Crazyflie 2.1，并通过定制的多区域ToF扩展板进行扩展，其总飞行质量为35克。该算法仅使用0.3%的机载处理能力（执行时间为210微秒），帧率为15fps，为许多未来应用提供了良好基础。运行所提出的感知系统所需功率不到无人机总功率的10%，其中包括承载和运行传感器。所展示的自主纳米级无人机在一个一般的、此前未探索过的室内环境中，以0.5m/s的速度达到100%的可靠性。所提出的系统以开源形式发布，并附带一个内容丰富的数据集，其中包括ToF和灰度相机数据，以及来自动作捕捉系统的UAV位置真值。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
