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

2026-01-19

AI Summary

This work addresses the challenge of autonomous navigation for nano-scale drones under stringent constraints of no GPS availability and sub-100-milliwatt power budgets. The authors propose a hybrid architecture that integrates lightweight classical control with data-driven perception. By leveraging quantized deep neural networks, neuromorphic event-based sensing, dense optical flow, optimized SLAM, and learning-based flight control, the system achieves robust visual navigation and relative pose estimation under ultra-low size, weight, and power (SWaP) conditions. The study systematically traces the evolution from geometric methods toward edge AI paradigms, significantly enhancing the full autonomy of nano-drones while highlighting persistent challenges in long-endurance operation, dynamic obstacle avoidance, and sim-to-real transfer.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Computer Vision: Vision for Robotics & Autonomous Driving
• Machine Learning: Learning on the Edge & Model Compression

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• nano-UAVs
• autonomous navigation
• SWaP constraints
• obstacle avoidance
• Sim-to-Real transfer

Innovation

• Edge AI
• neuromorphic control
• SWaP-constrained autonomy
• quantized neural networks
• hardware-software co-design