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

arXiv cs.RO / 4/23/2026

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Key Points

The NanoCockpit framework targets autonomous nano-drone systems that use vision-based TinyML models but are constrained by very low-power, sub-100mW MCU compute budgets.
It provides an efficient software layer for time-optimal pipelining across multi-buffer image acquisition, multi-core computation, intra-MCU data exchange, and Wi‑Fi streaming, which the authors say is often missing in current setups.
NanoCockpit uses coroutine-based multi-tasking to both improve throughput and minimize end-to-end latency, aiming to reduce serialized-task overhead.
Experiments on three real-world TinyML nanorobotics applications on Bitcraze Crazyflie demonstrate ideal end-to-end latency and measurable control improvements, including a 30% reduction in mean position error and mission success rising from 40% to 100%.
By abstracting these performance-critical concurrency and communication concerns, the framework is intended to simplify the developer experience while improving closed-loop performance on resource-limited embedded platforms.

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 tens of grams, severely limits their onboard computational resources to sub-100mW microcontroller units (MCUs). The Bitcraze Crazyflie nano-drone is the 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 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%).