With worldwide implementation, millions of surgeries are assisted by surgical robots. The cable-drive mechanism on many surgical robots allows flexible, light, and compact arms and tools. However, the slack and stretch of the cables and the backlash of the gears introduce inevitable errors from motor poses to joint poses, and thus forwarded to the pose and orientation of the end-effector. In this paper, a learning-based calibration using a deep neural network is proposed, which reduces the unloaded pose RMSE of joints 1, 2, 3 to 0.3003 deg, 0.2888 deg, 0.1565 mm, and loaded pose RMSE of joints 1, 2, 3 to 0.4456 deg, 0.3052 deg, 0.1900 mm, respectively. Then, removal ablation and inaccurate ablation are performed to study which features of the DNN model contribute to the calibration accuracy. The results suggest that raw joint poses and motor torques are the most important features. For joint poses, the removal ablation shows that DNN model can derive this information from end-effector pose and orientation. For motor torques, the direction is much more important than amplitude.

翻译：摘要：随着全球范围内的推广实施，数百万台手术正由手术机器人辅助完成。许多手术机器人采用的线驱机构能够实现柔性、轻量化且紧凑的机械臂与器械。然而，缆索的松弛与拉伸以及齿轮的反向间隙，会导致电机位姿到关节位姿产生不可避免的误差，进而传递至末端执行器的位姿与姿态。本文提出一种基于深度神经网络的学习式标定方法，将关节1、2、3空载位姿均方根误差分别降低至0.3003度、0.2888度、0.1565毫米，负载位姿均方根误差分别降低至0.4456度、0.3052度、0.1900毫米。随后开展移除消融与不准确消融实验，以研究DNN模型中哪些特征对标定精度有贡献。结果表明：原始关节位姿与电机力矩是最重要的特征。对于关节位姿，移除消融显示DNN模型可从末端执行器位姿与姿态中推导该信息；对于电机力矩，方向的重要性远大于幅值。