An Automated Thickness Evaluation Procedure Using an Integrated Structured Light 3D Camera in a Robotic Bioprinting Framework

2026-09-10

AI Summary

本文提出了一种全自动视觉方法，利用结构光3D相机和机器人正向运动学数据，精确测量复杂形状生物打印构造的厚度。

Abstract

Bioprinting is emerging as a tissue engineering technique to replace common treatment methods for large scale injuries. While thickness of the BioPrinted Constructs (BPCs) have shown to be of importance in the cell maturation and integration, the literature lacks a robust, automated, and quantitative method for measuring these metrics. In this paper, we propose a fully automated vision-based method for measuring the thickness of the BPCs with complex geometries. Leveraging the point cloud and RGB images of a structured light 3D camera, our proposed method performs an image-based segmentation for delineating the BPCs from the RGB images, accompanied by novel geometry-based thickness measurement algorithms performed on the point cloud scans. These algorithms combine the segmentation mask with the robot's forward kinematics data and a 3D point cloud scan to precisely measure the aforementioned metrics for complex-shaped BPCs. The proposed method was evaluated in simulation and experimental studies. In simulation studies, the algorithms were used to measure the thickness of some virtually created BPCs with known thickness. The comparison between the measured and true thicknesses demonstrates the high accuracy of the proposed method, achieving mean absolute errors between 0.025 mm and 0.057 mm in simulation at a spatial resolution of 0.1 mm x 0.1 mm per pixel. Furthermore, we successfully deployed the algorithms on our robotic bioprinting setup utilizing a structure light 3D camera, where complex patterns were printed and the developed methods utilized to accurately measure the thickness of printed BPCs.

Problem

• Bioprinting
• Thickness Measurement
• Automated Method
• Point Cloud
• Structured Light 3D Camera

Innovation

• structured light 3D camera
• automated thickness evaluation
• image-based segmentation
• geometry-based algorithms
• robotic bioprinting