{
  "annotation_id": "a13fa9eb-87a5-4645-a772-e9f8939aa666",
  "date_created": "2026-02-17T05:53:23.604000Z",
  "date_modified": "2026-02-17T05:53:23.604000Z",
  "file_hash": "6d828cd2a201e68794d4c2eb9e8475c3473de58270e15192da33a5cd4176c14c",
  "private": false,
  "record": {
    "abstract": "Mobile 3D printing on unstructured terrain remains challenging due to the conflict between platform mobility and deposition precision. Existing gantry-based systems achieve high accuracy but lack mobility, while mobile platforms struggle to maintain print quality on uneven ground. We present a framework that tightly integrates AI-driven disturbance prediction with multi-modal sensor fusion and hierarchical hardware control, forming a closed-loop perception-learning-actuation system. The AI module learns terrain-to-perturbation mappings from IMU, vision, and depth sensors, enabling proactive compensation rather than reactive correction. This intelligence is embedded into a three-layer control architecture: path planning, predictive chassis-manipulator coordination, and precision hardware execution. Through outdoor experiments on terrain with slopes and surface irregularities, we demonstrate sub-centimeter printing accuracy while maintaining full platform mobility. This AI-hardware integration establishes a practical foundation for autonomous construction in unstructured environments.",
    "arxiv_id": "2601.10208",
    "authors": [
      "Shuangshan Nors Li",
      "J. Nathan Kutz"
    ],
    "categories": [
      "cs.RO"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "title": "Terrain-Adaptive Mobile 3D Printing with Hierarchical Control",
    "url": "https://arxiv.org/abs/2601.10208",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "7f6d454d-a0d6-4c73-ab77-d85aff559d5d",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}