Collective additive manufacturing is a new paradigm for construction, where a team of mobile robots can collectively 3D print large-scale buildings and infrastructure. One of the main issues to solve to make this paradigm a standard is the possibility of limited communication between the printing robots, which would therefore not be able to coordinate to print the structure.
In their new paper, PhD Candidate Mohammad Tuqan, Postdoctoral Researcher Alain Boldini, and Prof. Maurizio Porfiri proposed a new way to solve the problem of coordination with no connectivity between the printing robots. Specifically, robots can leverage the printing process itself to infer the shape of the printed structure and therefore track other robots’ progress.
Read the full paper “Network inference from local measurements: application to coordination of groups of mobile 3D printers” published in Journal of Dynamic Systems, Measurement, and Control here.
Image credit: Mohammad Tuqan, Alain Boldini, and Maurizio Porfiri.