Acoustic Signal-Based Robotic Cutting Depth Control Under Uncertain Bone Layer Distribution

Due to the limited operation field and the deformation of the bone being cut, experienced surgeons usually will capture the bone layer and cutting depth information by analyzing the cutting acoustic signal (CAS). However, surgeons lose this sensing ability in robotic operations because they are no longer near the surgical area. This article aims to make the robot possess an acoustic signal-based monitoring ability such as the surgeon to estimate the proportion of different bone tissues and control cutting depth well. A quality index is designed to choose the optimal acoustic harmonic amplitude by analyzing the recorded acoustic signal in the idling and bone-cutting stages. Two mappings are fit between the optimal amplitude and the cutting depth on cancellous and cortical bones in advance. When the cutting area is located at mixed bone layers, the cutting depth can be further calculated based on the pre-obtained mappings and the bone layer proportion identified by the pre-trained regression network. A controller based on depth error is used to track the desired depth, while adaptive selection rules based on the proportion of cortical bones are introduced to optimize controller parameters to adapt the bone layer. Validation experiments of the proposed method via automatic cutting on thin artificial bone plates and pig scapula samples test the depth control accuracy and scope of application. The proposed method can improve the operation accuracy and safety of robotic bone milling.