无需改造手术器械的机器人微创手术多轴力传感技术
Multi-Axis Force Sensing in Robotic Minimally Invasive Surgery With No Instrument Modification
AI总结：
本研究提出无需改造手术器械的机器人微创手术多轴力传感方案，通过新型套管设计与6轴光学力传感器结合，经模型与数据驱动校准，可精准测量器械末端受力与扭矩，适配不同手术器械。
摘要：
本文提出了一种无需改造手术器械的新型机器人微创手术多轴力传感方法，因此可适配不同的手术器械。研究人员将一款集成局部信号调理与数字电子电路的新型6轴光学力传感器，安装在da Vinci EndoWrist（达芬奇内腕）器械的近端轴上。同时提出了一种由内管和外管组成的新型套管设计：内管通过刚度可调的柔性板簧连接到机器人基座的套管接口，允许器械轴因末端受力而发生弯曲；外管则通过机械滤波作用，消除躯体受力对器械弯曲行为的影响。研究建立了传感原理的数学模型，并将其用于基于模型的校准，此外还探讨了一种基于浅层神经网络架构的数据驱动校准方法，该网络包含一个5节点隐藏层和一个5×1输出层。大量测试验证表明，该传感器能够在所需的分辨率、精度和量程要求内，成功测量施加在器械远端的横向力、力矩以及轴向扭矩。
英文摘要：
This paper presents a novel multi-axis force-sensing approach in robotic minimally invasive surgery with no modification to the surgical instrument. Thus, it is adaptable to different surgical instruments. A novel 6-axis optical force sensor, with local signal conditioning and digital electronics, was mounted onto the proximal shaft of a da Vinci EndoWrist instrument. A new cannula design comprising an inner tube and an outer tube was proposed. The inner tube is attached to the cannula interface to the robot base through a compliant leaf spring with adjustable stiffness. It allows bending of the instrument shaft due to the tip forces. The outer tube mechanically filters out the body forces from affecting the instrument bending behavior. A mathematical model of the sensing principle was developed and used for model-based calibration. A data-driven calibration based on a shallow neural network architecture comprising a single 5-nodes hidden layer and a 5x1 output layer is discussed. Extensive testing was conducted to validate that the sensor can successfully measure the lateral forces and moments and the axial torque applied to the instruments distal end within the desired resolution, accuracy, and range requirements.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
