Vision Tracking System for In-Situ Tissue Bioprinting

Abstract

Maintaining printing accuracy despite the constant motion is one of the main difficulties in printing on moving surfaces. Traditional 3D printers are made for stationary platforms. Changing them to work on dynamic surfaces calls for the creation of new control algorithms and methodologies.

In-situ (from Latin: in its original place) printing refers to a process where bioprinting or additive manufacturing technologies are used directly at the site of application. The core component of our vision tracking system is a real-time algorithm powered by a Deep Learning model that monitors the printing surface – a harmed part of the human body. This system relies on advanced Computer Vision technologies to accurately track the moving surface's position and orientation.

Our experiments in simulations proved the concept of the robot's ability to adjust to a dynamic environment. A multi-camera system and a 6-DOF robot arm are included in the experimental setup of this research.