纳米尺度自主导航：算法、架构与约束
Autonomous Navigation at the Nano-Scale: Algorithms, Architectures, and Constraints
AI总结：
本文综述了纳米尺度无人驾驶航空器在极端SWaP约束下的自主导航挑战，分析了从经典几何方法到边缘AI范式的转变，并探讨了混合架构在提升自主性和动态环境适应性中的作用。
摘要：
纳米尺度无人驾驶航空器（nano-UAV）的自主导航受极端尺寸、重量和功率（SWaP）约束（重量<50克， onboard处理器<100毫瓦）的支配，这使其根本区别于传统机器人范式。本文综述了为这些亚100毫瓦计算环境设计的传感、计算和控制架构的最新状态。我们批判性地分析了从经典几何方法向新兴
英文摘要：
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.
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。
