Network Inference From Local Measurements: Application to Coordination of Groups of Mobile Three-Dimensional Printers

In recent years, three-dimensional (3D) construction printing has emerged as a viable alternative to conventional construction methods. Particularly promising for large scale construction are collective printing systems consisting of multiple mobile 3D printers. However, the design of these systems typically relies on the assumption of continuous communication between the printers, which is unrealistic in dynamically changing construction environments. As a first step toward decentralized collective 3D printing, we explore an active sensing framework allowing individual agents to reconstruct the shape of the structure, toward assessing other agents' progress in the absence of direct communication. In this vein, the shape of the structure is discretized as a 2D lattice embodying its topology, such that the problem is equivalent to the inference of a network. We leverage environmental modifications introduced by each agent through the printing of new layers to track the structure evolution. We demonstrate the validity of a sequential approach based on system identification through numerical simulations. Our work paves the way to decentralized collective 3D construction printing, as well as other applications in collective behavior that rely on the physical medium to transfer information among agents.