IEEE Trans Haptics. 2021 Apr-Jun;14(2):359-370. doi: 10.1109/TOH.2020.3029043. Epub 2021 Jun 17.
Bone milling is one of the most widely used and high-risk procedures in various types of surgeries, and it is important to be noted that the experienced surgeon can perform such an operation safely. The objective of this article is to enhance the safety of the robot-assisted milling operation with the inspiration of human haptic perception. The emergence, coding and perception of the human haptic are introduced. Following this, a single axis accelerometer that measures the vibration of the surgical power tool is mounted in the robot arm, and the recorded acceleration signal is encoded as parallel stream of binary data. The data are subsequently inputted to the Hopfield network so as to identify the milling state. Inspired by human inference procedure, the fuzzy logic controller is introduced to control the robot to track the desired state when performing bone milling operations. A real-time implementation of the proposed method on a digital signal processing is also described. The experimental results in milling porcine spines prove that the robot accurately discriminates different milling states even when the additive noise is serious, and the safe motion control of the robot is also realized.
骨铣削是各种类型手术中最广泛使用且风险较高的程序之一，值得注意的是，经验丰富的外科医生可以安全地进行此类操作。本文的目的是通过借鉴人类触觉感知来提高机器人辅助铣削操作的安全性。介绍了人类触觉的产生、编码和感知。在此之后，将测量手术动力工具振动的单轴加速度计安装在机器人臂上，并将记录的加速度信号编码为二进制数据流。随后，将数据输入到霍普菲尔德网络中，以识别铣削状态。受人类推理过程的启发，引入模糊逻辑控制器来控制机器人在进行骨铣削操作时跟踪期望状态。还描述了在数字信号处理上对所提出方法的实时实现。在铣削猪脊柱的实验结果证明，即使在存在附加噪声的情况下，机器人也能准确地区分不同的铣削状态，并且还实现了机器人的安全运动控制。
