机器人辅助椎板切除的骨铣削状态感知与运动控制

Bone milling state perception and motion control in robot-assisted laminectomy

中文关键词  机器人辅助手术  线性自抗扰控制  铣削深度监控  声信号处理  模糊控制  铣削进给速度优化

英文关键词  robot assisted surgery  linear active disturbance rejection control  milling depth monitoring  acoustic signal processing  fuzzy control  milling feed rate optimization

中文摘要

      本文旨在提高机器人辅助椎板切除时的骨铣削操作安全性. 首先, 提出一种基于激光信号和线性自抗扰控 制器的铣削深度监测与控制方法, 辨识了机器人的位置控制传递函数, 并通过分析椎板的铣削力、受迫振动和铣削 过程, 给出基于骨铣削声信号的铣削进给速度优化原理. 然后使用基于带通滤波器和普罗尼算法的声信号处理方 法, 用于手术动力装置主轴旋转频率改变时, 准确提取声信号中的主轴频率及其整倍数谐波的幅度值, 并使用声信 号谐波幅度偏差和偏差的微分作为输入的模糊控制器来优化机器人的铣削进给速度. 最后, 基于机器人辅助椎板切 除实验装置在仿椎板人造骨块进行铣削深度控制实验和铣削进给速度优化实验, 并在猪颈椎骨上进行椎板自动逐 层铣削实验. 结果表明, 铣削深度控制方法的控制精度为0.1 mm, 进给速度优化方法可有效适应骨密度和铣削深度 等参数变化. 所提方法可用于提高机器人辅助椎板切除过程中的铣削精度和安全性.

英文摘要

      This paper aims to improve the safety of bone milling operations during robot-assisted laminectomy. First, a milling depth monitoring and control method based on laser signals and linear active disturbance rejection controller is proposed, and the robot’s position control transfer function is identified. The milling feed rate’s optimization principle based on the bone milling acoustic signal is indicated by analyzing the milling force, forced vibration, and milling process of the lamina. Then an acoustic signal processing method based on the band-pass filter and the Prony algorithm accurately extracts the harmonic amplitude, whose frequency is an integer time of the surgical power device’s rotation frequency, when their frequency changes. The acoustic signal harmonic amplitude deviation and the differential of the deviation are used as the input of a fuzzy controller to optimize the milling feed rate. Finally, the milling depth control and feed rate optimization experiment on the artificial bone material with imitation lamina structure was carried out by a robot-assisted laminectomy experimental setup. Furthermore, the automatic milling experiment layer by layer on the porcine cervical vertebrae was carried out. The results show that the milling depth control method’s control accuracy is 0.1 mm. The feed rate optimization method can effectively adapt to the changes in bone density and milling depth. The proposed method can improve the milling accuracy and safety in the robot-assisted laminectomy process.