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With worldwide implementation, millions of surgeries are assisted by surgicalrobots. The cable-drive mechanism on many surgical robots allows flexible,light, and compact arms and tools. However, the slack and stretch of the cablesand the backlash of the gears introduce inevitable errors from motor poses tojoint poses, and thus forwarded to the pose and orientation of theend-effector. In this paper, a learning-based calibration using a deep neuralnetwork is proposed, which reduces the unloaded pose RMSE of joints 1, 2, 3 to0.3003 deg, 0.2888 deg, 0.1565 mm, and loaded pose RMSE of joints 1, 2, 3 to0.4456 deg, 0.3052 deg, 0.1900 mm, respectively. Then, removal ablation andinaccurate ablation are performed to study which features of the DNN modelcontribute to the calibration accuracy. The results suggest that raw jointposes and motor torques are the most important features. For joint poses, theremoval ablation shows that DNN model can derive this information fromend-effector pose and orientation. For motor torques, the direction is muchmore important than amplitude.

Ablation Study on Features in Learning-based Joints Calibration of Cable-driven Surgical Robots