Neural Network and Jacobian Method for Solving the Inverse Statics of a Cable-Driven Soft Arm With Nonconstant Curvature

Giorelli, Michele, Renda, Federico, Calisti, Marcello , Arienti, Andrea, Ferri, Gabriele and Laschi, Cecilia (2015) Neural Network and Jacobian Method for Solving the Inverse Statics of a Cable-Driven Soft Arm With Nonconstant Curvature. IEEE Transactions on Robotics, 31 (4). pp. 823-834. ISSN 1552-3098

Full content URL: https://doi.org/10.1109/TRO.2015.2428511

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Item Type:Article
Item Status:Live Archive

Abstract

The solution of the inverse kinematics problem of soft manipulators is essential to generate paths in the task space. The inverse kinematics problem of constant curvature or piecewise constant curvature manipulators has already been solved by using different methods, which include closed-form analytical approaches and iterative methods based on the Jacobian method. On the other hand, the inverse kinematics problem of nonconstant curvature manipulators remains unsolved. This study represents one of the first attempts in this direction. It presents both a model-based method and a supervised learning method to solve the inverse statics of nonconstant curvature soft manipulators. In particular, a Jacobian-based method and a feedforward neural network are chosen and tested experimentally. A comparative analysis has been conducted in terms of accuracy and computational time.

Keywords:Manipulators, Jacobian matrices, Kinematics, Mathematical model, Shape, Neural networks
Subjects:H Engineering > H671 Robotics
Divisions:College of Science > Lincoln Institute for Agri-Food Technology
ID Code:46171
Deposited On:23 Aug 2021 16:00

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