NanoCockpit: Performance-optimized Application Framework for AI-based Autonomous Nanorobotics

2026-01-12

AI Summary

This work addresses the challenge of high system latency and poor control performance in resource-constrained nano-drones, which stems from the lack of an efficient software framework to exploit the parallelism of multi-core microcontrollers (MCUs), forcing image acquisition, computation, and communication tasks into a serial pipeline. To overcome this limitation, the paper presents the first high-performance application framework tailored for AI-driven nanorobots. Built upon coroutine-based multitasking, the framework establishes an end-to-end pipeline with zero serialization overhead, co-optimizing multi-buffered image capture, multi-core computation, inter-MCU communication, and Wi-Fi streaming. Experimental results demonstrate that the system achieves near-theoretical-optimal end-to-end latency, reduces average position error in closed-loop control by 30%, and increases task success rate from 40% to 100%, substantially enhancing real-time performance while simplifying the development model.

Technology Category

• Intelligent Robots: Multi-Robot Systems
• Computer Vision: Vision for Robotics & Autonomous Driving
• Machine Learning: Hardware-aware ML

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
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

Autonomous nano-drones, powered by vision-based tiny machine learning (TinyML) models, are a novel technology gaining momentum thanks to their broad applicability and pushing scientific advancement on resource-limited embedded systems. Their small form factor, i.e., a few 10s grams, severely limits their onboard computational resources to sub-\SI{100}{\milli\watt} microcontroller units (MCUs). The Bitcraze Crazyflie nano-drone is the \textit{de facto} standard, offering a rich set of programmable MCUs for low-level control, multi-core processing, and radio transmission. However, roboticists very often underutilize these onboard precious resources due to the absence of a simple yet efficient software layer capable of time-optimal pipelining of multi-buffer image acquisition, multi-core computation, intra-MCUs data exchange, and Wi-Fi streaming, leading to sub-optimal control performances. Our \textit{NanoCockpit} framework aims to fill this gap, increasing the throughput and minimizing the system's latency, while simplifying the developer experience through coroutine-based multi-tasking. In-field experiments on three real-world TinyML nanorobotics applications show our framework achieves ideal end-to-end latency, i.e. zero overhead due to serialized tasks, delivering quantifiable improvements in closed-loop control performance ($-$30\% mean position error, mission success rate increased from 40\% to 100\%).

Problem

• autonomous nanorobotics
• TinyML
• resource-constrained embedded systems
• real-time performance
• onboard computation

Innovation

• NanoCockpit
• TinyML
• nanorobotics
• coroutine-based multitasking
• latency optimization