一种CABG手术辅助机器人运动补偿方法研究

Motion compensation of the CABG surgery assisted robot

针对非体外循环心脏动脉旁路移植手术中辅助机器人的运动控制问题,提出基于多测量耦合模型的多步预测控制算法,该算法增加了加速度测量并采用卡尔曼滤波器作为状态观测器进行信息融合处理,增强了对机器人运动状态的估计,进而提高心脏运动信号的跟踪性能。同时超前的N步预测增加了系统的带宽。实验结果表明,使用了耦合模型的多传感器信息融合多步预测控制算法的机器人系统将跟踪相对运动误差减小了20%。

Aimming at the motion control of assisted robot in the off pump Coronary Artery Bypass Graft （CABG）surgery, we propose a coupled model based multi-sensor fused N step predictive control algorithm in this paper. The method takes Kalman fil- ter as its state observer to improve the state estimation and thus enhance the tracking performance. Furthermore, The N step pre- dictions enlarge the system bandwidth. The comparison experiment results shows that tracking error from the coupled model based model predictive control algorithm are 20% less than that using position only model.