Scalable and Probabilistically Complete Planning for Robotic Spatial Extrusion

Understand

There is increasing demand for automated systems that can fabricate 3D structures.

• Robotic spatial extrusion has become an attractive alternative to traditional layer-based 3D printing due to a manipulator's flexibility to print large, directionally-dependent structures.
• However, existing extrusion planning algorithms require a substantial amount of human input, do not scale to large instances, and lack theoretical guarantees.
• In this work, we present a rigorous formalization of robotic spatial extrusion planning and provide several efficient and probabilistically complete planning algorithms.