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.

翻译：纳米尺度无人飞行器（nano-UAVs）的自主导航受极端的尺寸、重量与功耗（SWaP）约束（重量<50克，机载处理器功耗低于100毫瓦）所支配，这使其与标准机器人范式存在根本性区别。本综述综合了专为这些低于100毫瓦计算能力而设计的传感、计算与控制架构的最新进展。我们批判性地分析了从经典几何方法向新兴“边缘人工智能”范式的转变，包括部署在超低功耗片上系统（SoCs）上的量化深度神经网络以及基于事件的神经形态控制。除算法外，我们评估了实现自主性所必需的软硬件协同设计，涵盖了稠密光流、优化的同步定位与建图（SLAM）以及基于学习的飞行控制等方面的进展。尽管在视觉导航与相对位姿估计方面已取得显著进步，但我们的分析揭示了在长期续航、动态环境中的鲁棒避障以及强化学习策略的“仿真到现实”迁移方面仍存在持续差距。本综述为弥合这些差距提供了路线图，倡导融合轻量级经典控制与数据驱动感知的混合架构，以实现全球定位系统（GPS）拒止环境下完全自主、敏捷的纳米无人机。