Preliminary Study on Shape Sensing for Continuum Robot Affected by External Load Using Piecewise Fitting Curves This paper proposes a new method of shape sensing for continuum robot affected by external load using piecewise fitting curves. This scheme will integrate posture sensors into continuum robot, and then the shape is reconstructed by piecewise fitting Bezier curves. The simulation analysis is used to analyze the curvature and radius change of each section of the continuum robot, when the end of the bending continuum robot with constant curvature is subjected to external load. The layout scheme of sensors is determined according to the curvature and radius of each section. The shape of image recognition is compared with the shape of piecewise fitting cubic Bezier curves in simulation. The maximum tracking error is 1.4 mm and the minimum tracking error is 0.006 mm. The simulation results verify the accuracy and feasibility of this method.

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.