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.

翻译：在非结构化地形上进行移动式3D打印仍然面临挑战，主要源于平台移动性与沉积精度之间的固有矛盾。现有的龙门架式系统虽能实现高精度但缺乏移动性，而移动平台在崎岖地面上难以维持打印质量。本文提出一种将人工智能驱动的扰动预测与多模态传感器融合及分层硬件控制深度集成的框架，构建形成感知-学习-执行的闭环系统。人工智能模块通过融合IMU、视觉与深度传感器的数据，学习从地形特征到机械扰动的映射关系，从而实现主动补偿而非被动校正。该智能核心被嵌入三层控制架构：路径规划层、预测性底盘-机械臂协调层以及精密硬件执行层。通过在包含斜坡与表面不平整的户外地形进行实验验证，本系统在保持平台完整移动能力的同时，实现了亚厘米级的打印精度。这种人工智能与硬件的深度融合为在非结构化环境中实现自主建造奠定了实用基础。