This paper presents a learning based approach for obtaining the inverse kinematics (IK) solution for continuum robots. The proposed model learns a particular global solution for IK problem by supervised learning without any prior knowledge about the system. We have developed an approach that solely relies on the sampling method and a unique IK formulation. The convergence of the solution, practically feasible sample data requirements and adaptability of the model is shown with simulations of a redundant continuum robot.

Learning Global Inverse Kinematics Solutions for a Continuum Robot

GEORGE THURUTHEL, THOMAS;FALOTICO, Egidio;CIANCHETTI, Matteo;LASCHI, Cecilia
2016

Abstract

This paper presents a learning based approach for obtaining the inverse kinematics (IK) solution for continuum robots. The proposed model learns a particular global solution for IK problem by supervised learning without any prior knowledge about the system. We have developed an approach that solely relies on the sampling method and a unique IK formulation. The convergence of the solution, practically feasible sample data requirements and adaptability of the model is shown with simulations of a redundant continuum robot.
978-3-319-33713-5
978-3-319-33714-2
978-3-319-33713-5
978-3-319-33714-2
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11382/509104
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