Collective Mobile 3D Printing: An Active Sensing Approach for Improved Autonomy

Abstract

Three-dimensional (3D) construction printing is an emerging alternative to conventional construction methods. Common gantry- and robotic arm-based systems impose scalability limitations based on the printer size. Several research efforts proposed using multiple mobile 3D printers towards large-scale printing, relying however on unrealistic assumptions of continuous communication among all agents. Here, we explore an active sensing framework allowing individual agents to assess other agents’ progress without directly communicating with them. Our approach leverages environmental modifications introduced by each agent during printing to track the structure evolution. We focus on heat conduction in the structure, which we discretize as a 2D lattice embodying its topology. Using on-board sensors, agents measure temperature and heat at their location, which they use to infer structure’s topology. From the input-output time-series and prior knowledge of the printing task, an agent identifies the system state-matrix by using a subspace identification method and solving an inverse eigenvalue problem. We demonstrate the validity of our approach through numerical simulations, establishing conditions for successful inference. We highlight the potential of the framework in facilitating information flow among agents through the physical medium, paving the way for decentralized collective mobile 3D printing.