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基于视觉的工具路径补偿提高机器人生物打印精度。

Precision improvement of robotic bioprinting via vision-based tool path compensation.

机构信息

Department of Mechanical and Manufacturing Engineering, Schulich School of Engineering, University of Calgary, Calgary, AB, T2N 1N4, Canada.

Deparement of Biomedical Engineering, Schulich School of Engineering, University of Calgary, Calgary, AB, T2N 1N4, Canada.

出版信息

Sci Rep. 2024 Aug 1;14(1):17764. doi: 10.1038/s41598-024-68597-z.

Abstract

Robotic 3D bioprinting is a rapidly advancing technology with applications in organ fabrication, tissue restoration, and pharmaceutical testing. While the stepwise generation of organs characterizes bioprinting, challenges such as non-linear material behavior, layer shifting, and trajectory tracking are common in freeform reversible embedding of suspended hydrogels (FRESH) bioprinting, leading to imperfections in complex organ construction. To overcome these limitations, we propose a computer vision-based strategy to identify discrepancies between printed filaments and the reference robot path. Employing error compensation techniques, we generate an adjusted reference path, enhancing robotic 3D bioprinting by adapting the robot path based on vision system data. Experimental assessments confirm the reliability and agility of our vision-based robotic 3D bioprinting approach, showcasing precision in fabricating human blood vessel segments through case studies. Significantly, it minimizes the printing layer width disparity to just 0.15 mm compared to the 0.6 mm in traditional methods, and it decreases the average error for curved filaments to 7.0 mm from the previous 12.7 mm in conventional printing. While these results underscore the significant potential of our innovation in creating precise biomimetic constructs, further investigation is necessary to tackle challenges such as accurately distinguishing closely stacked layers using a vision system, especially under varying lighting conditions. These limitations, coupled with issues of computational complexity and scalability in larger-scale bioprinting, emphasize the importance of enhancing the reliability of the vision-based approach across various conditions. Nonetheless, our innovation demonstrates substantial promise in creating precise biomimetic constructs and paves the way for future advancements in vision-guided robotic bioprinting, including the integration of multi-material printing techniques to enhance versatility.

摘要

机器人 3D 生物打印是一项快速发展的技术,在器官制造、组织修复和药物测试方面有应用。虽然器官的分步生成是生物打印的特征,但在自由形态可逆嵌入悬浮水凝胶(FRESH)生物打印中,存在非线性材料行为、层移和轨迹跟踪等挑战,这导致复杂器官构建中存在不完美。为了克服这些限制,我们提出了一种基于计算机视觉的策略,以识别打印丝和参考机器人路径之间的差异。通过采用误差补偿技术,我们生成了一个调整后的参考路径,通过根据视觉系统数据调整机器人路径来增强机器人 3D 生物打印。实验评估证实了我们基于视觉的机器人 3D 生物打印方法的可靠性和灵活性,通过案例研究展示了在制造人血管段方面的精度。值得注意的是,与传统方法中的 0.6 毫米相比,它将打印层宽度差异最小化到仅 0.15 毫米,并且将弯曲丝的平均误差从传统打印中的 12.7 毫米降低到 7.0 毫米。虽然这些结果突出了我们的创新在创建精确仿生结构方面的巨大潜力,但需要进一步研究以解决使用视觉系统准确区分紧密堆叠层的问题,特别是在不同光照条件下。这些限制,加上在更大规模的生物打印中计算复杂性和可扩展性的问题,强调了增强基于视觉的方法在各种条件下可靠性的重要性。尽管如此,我们的创新在创建精确仿生结构方面具有很大的潜力,并为未来的视觉引导机器人生物打印的发展铺平了道路,包括集成多材料打印技术以提高多功能性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/be39/11291724/908ff638d2e7/41598_2024_68597_Fig1_HTML.jpg

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