An Accuracy Enhancement Method for a Cable-Driven Continuum Robot With a Flexible Backbone

Abstract

In this paper, an integrated accuracy enhancement method based on both the kinematic model and the data-driven Gaussian Process Regression (GPR) technique is proposed for a Cable-Driven Continuum Robot (CDCR) with a flexible backbone. Different from the conventional continuum robots driven by pneumatic actuators, a segmented CDCR is developed in this work, which is a modular manipulator composed by a number of consecutive Cable-Driven Segments (CDSs). Based on the unique design of the backbone structure which merely allows 2-DOF bending motions, a two-variable Product-of-Exponential (POE) formula is employed to formulate the kinematic model of the CDCR. However, such an analytic kinematic model is unable to accurately describe the actual deflections of the backbone structure. Therefore, GPR is proposed to compensate the tip error of a CDCR. Compared with other machine learning methods, GPR requires less learning parameters and training data, which makes the learning process computationally efficient. To validate the effectiveness of the proposed integrated accuracy enhancement method, experiments on the actual testbed are conducted. Experimental results show that the CDCR's position and orientation errors are reduced by 68.72% and 51.74%, respectively. INDEX TERMS Cable-driven continuum robot, kinematic modeling, tip error compensation, Gaussian process regression.

Feedback control of cable-driven continuum robot based on flexible perception

“…In this paper, a motion capture system (Qualisys Track Manager, accuracy 0.5mm) was used to capture the coordinates of Markers and convert them into the actual bending angles of the joint module. The specific scheme is referred to the journal [10].…”

Shape Representation and Modeling of Tendon-Driven Continuum Robots Using Euler Arc Splines

“…Model calibration is used in [21] to account for the variations observed in a CC representation based on experimental data. Data-driven models are used to learn the position errors of a TDCR, while using a CC representation in [22], [23]. All the above works assume that the robot is operating in free space.…”

Design and Validation of a Novel Cable-Driven Hyper-Redundant Robot Based on Decoupled Joints

“…Joint types for the cable-driven hyper-redundant robot mainly can be classified into the 1-DOF joint and 2-DOF joint [12]. e 1-DOF joints mainly include the revolute joint [13,14], the flexible beam [15,16], and the cylindrical rolling joint [17,18], while the multi-DOF joints mainly include the universal joint [19,20], the flexible backbone [21,22], and the spherical rolling joint. Based on the flexible backbone, Li et al developed a 2-DOF flexible endoscope driven by multiple cables, which is more dexterous than rigid endoscopes [23].…”