This article aims to optimize robotic actions for bone milling during laminectomy. The layer-by-layer spinal lamina milling operation during robot-assisted laminectomy is analyzed in detail to summarize related control tasks. A milling sound signal processing method based on band filters and the two-order Prony algorithm is proposed to overcome the frequency disturbance of the surgical power device's rotation motion caused by bone milling to accurately extract harmonic amplitudes. A depth estimation model based on the total value of major harmonics is fitted to monitor the actual depth when the ball end is cutting into an easily displaced bone. A milling feed speed optimization method based on the sound signal feedback and a fuzzy controller is designed to balance the efficiency and safety of bone milling. Milling experiments are carried out on artificial bone blocks and fresh swine cervical vertebrae. The results show that the depth monitoring accuracy is about 0.08 mm and the feed speed can be optimized effectively to match the actual bone mineral density and axial depth of cut. The proposed method can improve the depth accuracy and output a safe and fastest feed speed.

During bent bone grinding, the robot often needs to adjust its grinding depth and angle in real time to ensure surgical quality. Optical- or vision-based navigation methods are no longer applicable due to the interference caused by splashing cooling water in the operating field. Inspired by orthopedic surgeons, in this article, we present a tactile perception-based method to solve this problem. The effect of grinding parameters on the cutter's current and vibration is analyzed, and the current and vibration signals are further processed to predict depth and angle. Depth and angle estimation models were calibrated on homogeneous artificial bone. Grinding control experiments were carried out on femur models, and the statistical mean and standard deviation results of the measured depth show that the accuracy of depth control can be further improved by adding angle estimation and proper adjustment when depth is indirectly estimated based on sensing signals. The proposed approach relies solely on the estimation of the relative position and posture between the tool and the bone surface and is expected to improve the safety of automatic grinding with orthopedic robots.

Bone milling is widely used in many hard tissue surgical procedures, and the main concern is the mechanical damage to some important tissues induced by the high-speed rotating tool. In this study, the behavior of the human auditory system is analyzed. Following this, a commercially available microphone is mounted on the robot arm and then measures the sound generated from bone milling. Inspired by the bandpass filtering in the cochlea, the recorded sound pressure signal is decomposed into a set of subband signals by wavelet packet transform. Inspired by encoding and perception mechanisms in human auditory system, the average amplitude of the wavelet coefficients in each subband is calculated and inputted to a self-organizing feature map so as to classify different milling states. In order to increase the robustness to noise, the sum of the Manhattan distances between all winning neurons is calculated to select the optimum dimension of the map. The experimental results in milling in vitro porcine spines prove that the proposed method can determine which type of tissue is being cut when the suction noise exists, and the success classification rate is no less than 85%. Therefore, the safety of the robot-assisted milling surgery is improved.

One of the major challenge in spine surgery is successfully cutting the desired bony structure while avoiding injury to the vital anatomy such as the spinal cord. This paper presents a vibration signal processing method to discriminate different types of tissue in robot-assisted spine surgery. During bone milling process, the tissue vibration signal measured by a laser displacement sensor is decomposed into some frequency sub-bands through the wavelet packet transform, and the harmonic component whose frequency is an integer times of the spindle frequency is obtained. The wavelet energy of the 1st, 2ndand 3rdharmonics is then used as input vector to an artificial neural network for discriminating tissue. To verify the effectiveness of the proposed method, extensive milling experiments are carried out on porcine spines. The experimental results indicate that the method produces up to 100% discrimination accuracy for the vertebra being cut and the spinal cord, and yields the success discrimination rate of more than 80% for the adjacent bony structure and the muscle, so the safety of robot-assisted spine surgery is improved.