发表机构
University of Texas at Austin; Terasaki Institute for Biomedical Innovation（德克萨斯大学奥斯汀分校; 寺崎生物医学创新研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出一种基于物理的闭环机器人生物打印框架，集成3D视觉与控制器，实现近实时参数调整，快速收敛至目标厚度，误差小于0.5毫米，优于开环方法，用于治疗容积性肌肉缺失。
AI 中文摘要
机器人生物打印和直写成型（DIW）正被探索用于治疗容积性肌肉缺失（VML）。尽管先前的研究已表明参数选择对打印结果的重要性，但现有方法往往依赖于耗时且耗材的设计实验方法，或需要大型、精心整理的数据集来训练机器学习模型。在本文中，我们提出了一种基于物理的闭环机器人生物打印系统，能够实现近实时的参数自适应。该系统集成了3D点云相机和全自主的基于视觉的算法，以对打印结构进行定量评估。该评估结果被输入到控制器中，控制器调整打印参数以达到期望的珠状厚度。为评估该框架的性能，测试了四种实验配置，每种配置重复三次。在这些测试中，打印从任意初始参数值开始，控制器负责调整参数以达到期望厚度。系统在所有试验中均收敛，从打印开始平均5.2秒内实现了低于0.5毫米的跟踪误差。不同测试中收敛压力的低标准差（平均0.04巴）证明了其鲁棒性和可重复性。还进行了关闭控制器的额外实验，以便与开环DIW生物打印进行直接比较，进一步证实了所提出的闭环框架在实现期望珠状几何形状方面的有效性。
英文摘要
Robotic bioprinting and Direct Ink Writing (DIW) are being explored towards the treatment of Volumetric Muscle Loss (VML). While previous studies have shown the importance of proper parameter selection on the print outcome, existing approaches often rely on time- and material-intensive design of experiments methods, or require large, well-curated datasets for training machine learning models. In this paper, we propose a physics-based closed-loop robotic bioprinting system capable of near real-time parameter adaptation. The system integrates a 3D point cloud camera and fully autonomous vision-based algorithms to provide quantitative evaluation of printed constructs. This evaluation is fed into a controller that adjusts printing parameters to achieve a desired bead thickness. To assess the framework's performance, four experimental configurations were tested, each repeated three times. In these tests, printing began from an arbitrary initial parameter value, and the controller was tasked with adjusting the parameters to reach the desired thickness. The system converged in all trials, achieving a tracking error below 0.5 mm within an average of 5.2 seconds from the start of printing. The low standard deviation of the converged pressure over different tests (0.04 bar on average) demonstrates robustness and repeatability. Additional experiments were conducted with the controller turned off, enabling direct comparison with open-loop DIW bioprinting, further confirming the effectiveness of the proposed closed-loop framework in achieving the desired bead geometry.
CommentsThis paper has been accepted for publication and presentation in 2026 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2026)
