A Decomposition and a Scheduling Framework for Enabling Aerial 3D Printing

Abstract

Aerial 3D printing is a pioneering technology yet in its conceptual stage that combines frontiers of 3D printing and Unmanned aerial vehicles (UAVs) aiming to construct large-scale structures in remote and hard-to-reach locations autonomously. The envisioned technology will enable a paradigm shift in the construction and manufacturing industries by utilizing UAVs as precision flying construction workers. However, the limited payload-carrying capacity of the UAVs, along with the intricate dexterity required for manipulation and planning, imposes a formidable barrier to overcome. Aiming to surpass these issues, a novel aerial decomposition-based and scheduling 3D printing framework is presented in this article, which considers a near-optimal decomposition of the original 3D shape of the model into smaller, more manageable sub-parts called chunks. This is achieved by searching for planar cuts based on a heuristic function incorporating necessary constraints associated with the interconnectivity between subparts, while avoiding any possibility of collision between the UAV’s extruder and generated chunks. Additionally, an autonomous task allocation framework is presented, which determines a priority-based sequence to assign each printable chunk to a UAV for manufacturing. The efficacy of the proposed framework is demonstrated using the physics-based Gazebo simulation engine, where various primitive CAD-based aerial 3D constructions are established, accounting for the nonlinear UAVs dynamics, associated motion planning and reactive navigation through Model predictive control.