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

TL;DR

This paper reviews the latest algorithms, architectures, and hardware-software co-design strategies for enabling autonomous navigation in nano-UAVs under extreme SWaP constraints, highlighting recent advances and remaining challenges.

Contribution

It provides a comprehensive synthesis of sensing, computing, and control architectures tailored for ultra-low-power nano-UAVs, and critically analyzes emerging Edge AI paradigms and hardware-software co-design approaches.

Findings

Progress in visual navigation and pose estimation techniques.

Persistent challenges in obstacle avoidance and long-term endurance.

Gaps in transfer learning and robustness in dynamic environments.

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.