Abstract
Autonomous navigation for nano-scale unmanned aerial vehicles (nano-UAVs) is governed by extreme Size, Weight, and Power (SWaP) constraints (with the weight < 50 g and sub-100 mW onboard processor), distinguishing it fundamentally from standard robotic paradigms. This review synthesizes the state-of-the-art in sensing, computing, and control architectures designed specifically for these sub- 100mW computational envelopes. We critically analyse the transition from classical geometry-based methods to emerging "Edge AI" paradigms, including quantized deep neural networks deployed on ultra-low-power System-on-Chips (SoCs) and neuromorphic event-based control. Beyond algorithms, we evaluate the hardware-software co-design requisite for autonomy, covering advancements in dense optical flow, optimized Simultaneous Localization and Mapping (SLAM), and learning-based flight control. While significant progress has been observed in visual navigation and relative pose estimation, our analysis reveals persistent gaps in long-term endurance, robust obstacle avoidance in dynamic environments, and the "Sim-to-Real" transfer of reinforcement learning policies. This survey provides a roadmap for bridging these gaps, advocating for hybrid architectures that fuse lightweight classical control with data-driven perception to enable fully autonomous, agile nano-UAVs in GPS-denied environments.
Abstract (translated)
纳米级无人飞行器（nano-UAV）的自主导航受极端尺寸、重量和功耗(SWaP)限制的影响，其重量小于50克且机载处理器功率低于100毫瓦，这与标准机器人范式有根本区别。这篇综述总结了为这些低至100毫瓦计算能力设计的传感、计算及控制架构的最新进展。我们批判性地分析了从传统几何方法向新兴“边缘AI”（Edge AI）范式的转变，包括在超低功耗片上系统(SoCs)上部署量化深度神经网络以及基于事件的神经形态控制。除了算法之外，还评估了实现自主性的硬件-软件协同设计需求，涵盖了密集光流、优化的同时定位与地图构建(SLAM)和学习型飞行控制的进步。 尽管在视觉导航和相对姿态估计方面已经取得了显著进展，但我们的分析揭示了长期续航能力不足、动态环境中的鲁棒性避障以及强化学习策略的“仿真到实际”迁移等方面的持续差距。本调查提供了弥合这些差距的道路图，倡导融合轻量级经典控制与数据驱动感知的混合架构，以实现在没有全球定位系统(GPS)支持环境中完全自主且敏捷的纳米无人机飞行。
URL
https://arxiv.org/abs/2601.13252
