联合力、声音信号的机器人钻骨操作

在近些年临床机器人辅助骨科手术中，机器人主要用来实现钉道定位，而后续钻削操作依然需要医生完成，因此本文主要针对机器人自主钻削展开研究。首先基于双层自适应模糊控制器实现皮质骨层钻削力控制操作，解决了钻削过程中的非线性时变问题，平衡了钻削进给速度和骨组织热损伤等问题。然后在机器人钻削过程中，分别基于时域和时频分析得到力特征...

Robotic Bone-drilling Operation by Force and Acoustic Signals

In clinic robot-assisted bone surgeries of recent years, robots are mainly used in the screw path positioning, and the subsequent drilling procedure is still performed by surgeon. Therefore, the autonomous drilling procedure based on robot is the main research content in this paper. Firstly, force control for the cortical bone drilling is realized by a two-layer adaptive fuzzy controller. The nonlinear time-varying problem in the drilling is solved, and the drilling feed velocity and the thermal damage to bone tissue can be balanced. Then, force and acoustic features are extracted by time domain analysis and time-frequency analysis respectively during robotic drilling. The state of the drilled bone is recognized by combining those force and acoustic features, and the switch from force control to velocity control is realized. In this way, the drilling safety is guaranteed, which means that the inner cortical bone won’t be punctured by the drilling tool and the nervous tissue won’t be damaged. Finally, experiments of the force control and the drilled bone state recognition are carried out on the swine bone. Results of the force control show that the drilling force error is limited to [-2 N;2 N] by the two-layer adaptive fuzzy controller, and its influence on robotic drilling can be ignored since the drilling force is usually more than 20 N. Results of the drilled bone state recognition show that the state of inner cortical bone can be detected by acoustic features earlier than by force features, and the recognition robustness is improved by force features. Thus, the effectiveness of the proposed drilling control and state recognition strategy is proved.