Hysteresis Compensator with Learning-based Pose Estimation for a Flexible Endoscopic Surgery Robot

The use of the tendon-sheath mechanism (TSM) is common in flexible surgery robots, because it can flexibly work in limited spaces and provides efficient power transmission. However, hysteresis from nonlinearities such as friction and backlash poses a challenge in controlling precise motion in the surgical instrument. Moreover, this hysteresis is also affected by changes in the various configurations of sheath which limits traditional model-based compensation approaches. Recently, feedback approach using an endoscopic camera is presented, but they use markers which are not appropriate for applying to a real surgical instruments. In this paper, a novel hysteresis compensator with learning-based pose estimation is proposed. Unlike previous studies, the proposed compensator can reduce hysteresis of the surgical instrument in various sheath configurations without using markers. In order to estimate an actual angle of the surgical instrument’s joint, we employ the learning-based pose estimation using a siamese convolutional neural network (SCNN). The proposed compensator reduces hysteresis by partially controlling the position command, similar to the instinctive adjustments that physicians make with their visual feedback. To validate the proposed method, a testbed was constructed considering several requirements of flexible surgery robots. As a result, the results show the proposed method reduces hysteresis to less than 10°, for various configurations of sheath. In addition, we confirmed that the learning-based pose estimation is sufficient to apply to the proposed compensator for reducing hysteresis in real-time.