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.
Highlights
A Cable-Driven Continuum Robot (CDCR) with a flexible backbone can realize continuous deformation in order to adapt to congested and complex environments
DESIGN FOR THE CABLE-DRIVEN CONTINUUM ROBOT WITH A FLEXIBLE BACKBONE In this work, a segmented cable-driven scheme is employed for the CDCR with a flexible backbone
Gaussian Process Regression (GPR) is employed to compensate the tip error of the CDCR based on the formulated kinematic model
Summary
A Cable-Driven Continuum Robot (CDCR) with a flexible backbone can realize continuous deformation in order to adapt to congested and complex environments. In order to improve the accuracy of the kinematic model, various machine learning methods are employed for flexible continuum robots. The flexible backbone employs a unique mechanical structure that has high tensile and torsional stiffness but low bending stiffness, so that each Cable-Driven Segment (CDS) merely allows 2-DOF bending movements. Such a backbone structure design simplifies the kinematic modeling process and makes the resultant kinematic model more accurate than other continuum robots. Resulting from the unique flexible backbone structure, the kinematic modeling analysis of the CDCR is greatly simplified compared with other continuum robots which have complex movements
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