Terrain-Adaptive Mobile 3D Printing with Hierarchical Control

TL;DR

This paper introduces a terrain-adaptive mobile 3D printing system that combines AI-based disturbance prediction with hierarchical control to achieve high-precision printing on uneven terrain while maintaining platform mobility.

Contribution

It presents a novel integrated framework that uses AI-driven disturbance prediction and hierarchical control for mobile 3D printing on unstructured terrain.

Findings

Achieved sub-centimeter printing accuracy on complex terrain.

Demonstrated full platform mobility during printing tasks.

Validated system effectiveness through outdoor experiments.

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.