Hysteresis Compensator with Learningbased Pose Estimation for a Flexible Endoscopic Surgery Robot The use of the tendonsheath 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 modelbased 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 learningbased 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 instrumentxe2x80x99s joint, we employ the learningbased 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 10xc2xb0, for various configurations of sheath. In addition, we confirmed that the learningbased pose estimation is sufficient to apply to the proposed compensator for reducing hysteresis in realtime.

A ModelFree MethodBased Shape Reconstruction for CableDriven Continuum Manipulator Using Artificial Neural Network The robotassisted natural orifice transluminal surgery (NOTES) typically involves applying the lengthy and slender continuum instrumentsmanipulators to get access to the target lesion sites, and then performs complex operations. However, due to the strongnonlinearity of continuum robotic manipulators and the confined and tortuous anatomical paths, itxe2x80x99s difficult to establish accurate inverse kinematics (IK) model to achieve precise motion control and realtime shape sensing for accurately modeling their shapes. To tackle such difficulties, a modelfree method based on neural network have been proposed to solve the IK problem and reconstruct the shape of a continuum manipulator at the same time using the training results from the electromagnetic (EM) tracking approach. For the IK problem, the relationship between the tip position and the corresponding cable lengths can be learned and for the shape estimation problem, the mapping from the cable lengths to the shape of the continuum robot can be established. A dataset of 500 random continuum manipulator postures was used to train the neural network, with recorded EM sensorstracked positions logged synchronously to the cable lengths. Experiment results show the proposed modelfree method could achieve high accuracy and reliable inverse kinematics and shape reconstruction outcomes.