Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.
School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen 518055, China.
Sensors (Basel). 2023 Aug 21;23(16):7307. doi: 10.3390/s23167307.
Bone drilling is a common procedure in orthopedic surgery and is frequently attempted using robot-assisted techniques. However, drilling on rigid, slippery, and steep cortical surfaces, which are frequently encountered in robot-assisted operations due to limited workspace, can lead to tool path deviation. Path deviation can have significant impacts on positioning accuracy, hole quality, and surgical safety. In this paper, we consider the deformation of the tool and the robot as the main factors contributing to path deviation. To address this issue, we establish a multi-stage mechanistic model of tool-bone interaction and develop a stiffness model of the robot. Additionally, a joint stiffness identification method is proposed. To compensate for path deviation in robot-assisted bone drilling, a force-position hybrid compensation control framework is proposed based on the derived models and a compensation strategy of path prediction. Our experimental results validate the effectiveness of the proposed compensation control method. Specifically, the path deviation is significantly reduced by 56.6%, the force of the tool is reduced by 38.5%, and the hole quality is substantially improved. The proposed compensation control method based on a multi-stage mechanistic model and joint stiffness identification method can significantly improve the accuracy and safety of robot-assisted bone drilling.
骨钻削是骨科手术中的一种常见程序，经常尝试使用机器人辅助技术进行。然而，在机器人辅助操作中，由于工作空间有限，经常会遇到刚性、光滑和陡峭的皮质表面，这可能导致工具路径偏差。路径偏差会对定位精度、孔质量和手术安全产生重大影响。在本文中，我们认为工具和机器人的变形是导致路径偏差的主要因素。为了解决这个问题，我们建立了一个工具-骨相互作用的多阶段力学模型，并开发了机器人的刚度模型。此外，还提出了一种关节刚度识别方法。为了补偿机器人辅助骨钻削中的路径偏差，我们基于推导的模型和路径预测补偿策略，提出了一种力-位混合补偿控制框架。我们的实验结果验证了所提出的补偿控制方法的有效性。具体来说，路径偏差显著降低了 56.6%，工具的力降低了 38.5%，孔质量得到了显著提高。基于多阶段力学模型和关节刚度识别方法的补偿控制方法可以显著提高机器人辅助骨钻削的准确性和安全性。
