Autonomous Navigation at the Nano-Scale: Algorithms, Architectures, and Constraints

• URL: http://arxiv.org/abs/2601.13252v2
• Date: Wed, 04 Feb 2026 12:08:13 GMT
• Title: Autonomous Navigation at the Nano-Scale: Algorithms, Architectures, and Constraints
• Abstract summary: This review synthesizes the state-of-the-art in sensing, computing, and control architectures for nano-UAVs.<n>We critically analyse the transition from classical geometry-based methods to emerging "Edge AI" paradigms.
• Score: 3.8673630752805437
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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