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arXiv · 2601.10208

Terrain-Adaptive Mobile 3D Printing with Hierarchical Control

Shuangshan Nors Li·J. Nathan Kutz
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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.

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Shuangshan Nors Li, J. Nathan Kutz. 2026-01-15. Terrain-Adaptive Mobile 3D Printing with Hierarchical Control. https://arxiv.org/abs/2601.10208

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