AI预判地形扰动，实现移动3D打印高精度作业
Terrain-Adaptive Mobile 3D Printing with Hierarchical Control
- 用AI预测地形干扰，提前补偿而非事后修正
- 在坡地和不平地面实现亚厘米级打印精度
- 适合野外自主建造场景，兼顾移动性与精度
移动式3D打印在非结构化地形上仍面临平台机动性与沉积精度之间的矛盾。现有基于龙门架的系统精度高但缺乏移动性，而移动平台难以在不平地面上保持打印质量。本文提出一种框架，将基于AI的扰动预测与多模态传感器融合及分层硬件控制紧密结合，形成闭环感知-学习-执行系统。AI模块通过惯性测量单元（IMU）、视觉和深度传感器学习地形到扰动的映射关系，实现主动补偿而非被动修正。该智能嵌入三层控制架构：路径规划、预测性底盘-机械臂协同以及精密硬件执行。在具有坡度和表面不规则性的户外地形上实验表明，系统在保持全平台移动性的同时实现了亚厘米级打印精度。该人工智能与硬件的融合为非结构化环境中的自主建造提供了实用基础。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。