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.

A Learningbased Inverse Kinematics Solver for a MultiSegment Continuum Robot in RobotIndependent Mapping Inverse kinematics (IK) is one of the most fundamental problems in robotics, as it makes use of the kinematics equations to determine the joint configurations necessary to reach a desired endeffector pose. In the field of continuum robot, solving the IK is relatively challenging, owing to kinematic redundancy with infinite number of solutions.In this paper, we present a simplified model to represent a multisegment continuum robot using virtual rigid links. Based on the model, its IK can be solved using a multilayer perceptron (MLP), a class of feedforward neural network (FNN). The transformation between virtual joint space to task space is described using DenavitHartenberg (DH) convention. Using 20,000 established training data for supervised learning, the MLP reaches a mean squared error of 0.022 for a dualsegment continuum robot. The trained MLP is then used to find the joints for different endeffector positions, and the results show a mean relative error of 2.90% can be on the robot configuration. Hence, this simplified model and its MLP provide a simple method to evaluate the IK solution of a twosegment continuum robot, which can also be further generalized and implemented in multisegment cases.