{
  "annotation_id": "25492cda-75bc-465c-86a1-804c8deee43b",
  "date_created": "2026-02-17T05:53:12.387000Z",
  "date_modified": "2026-02-17T05:53:12.387000Z",
  "file_hash": "1cc76ead9ecc8176e785e5dcd90b112b620792ffa694b1dd59cdbb4265d65269",
  "private": false,
  "record": {
    "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\u0027s 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\\%).",
    "arxiv_id": "2601.07476",
    "authors": [
      "Elia Cereda",
      "Alessandro Giusti",
      "Daniele Palossi"
    ],
    "categories": [
      "cs.RO",
      "cs.SE",
      "cs.SY",
      "eess.SY"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "title": "NanoCockpit: Performance-optimized Application Framework for AI-based Autonomous Nanorobotics",
    "url": "https://arxiv.org/abs/2601.07476",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "54ac30a5-d152-4a0d-ab21-ddbd04905f5a",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}