de Boer, G, Wang, H orcid.org/0000-0002-6546-1241, Ghajari, M et al. (3 more authors) (2016) Force and Topography Reconstruction Using GP and MOR for the TACTIP Soft Sensor System. In: Alboul, L, Damian, D and Aitken, JM, (eds.) Towards Autonomous Robotic Systems (Lecture Notes in Computer Science). 17th Annual Conference, TAROS 2016, Sheffield, UK, June 26--July 1, 2016, Proceedings, 28-30 Jun 2016, Sheffield, UK. Springer ISBN 978-3-319-40378-6
Abstract
Sensors take measurements and provide feedback to the user via a calibrated system, in soft sensing the development of such systems is complicated by the presence of nonlinearities, e.g. contact, material properties and complex geometries. When designing soft-sensors it is desirable for them to be inexpensive and capable of providing high resolution output. Often these constraints limit the complexity of the sensing components and their low resolution data capture, this means that the usefulness of the sensor relies heavily upon the system design. This work delivers a force and topography sensing framework for a soft sensor. A system was designed to allow the data corresponding to the deformation of the sensor to be related to outputs of force and topography. This system utilised Genetic Programming (GP) and Model Order Reduction (MOR) methods to generate the required relationships. Using a range of 3D printed samples it was demonstrated that the system is capable of reconstructing the outputs within an error of one order of magnitude.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | © 2016, Springer International Publishing. This is an author produced version of a paper published in Towards Autonomous Robotic Systems (Lecture Notes in Computer Science). Uploaded in accordance with the publisher's self-archiving policy. The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-40379-3_7 |
Keywords: | Soft-sensing; Genetic Programming; Model Order Reduction |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mechanical Engineering (Leeds) > Institute of Functional Surfaces (Leeds) |
Funding Information: | Funder Grant number Leverhulme Trust RPG-2014-381 |
Depositing User: | Symplectic Publications |
Date Deposited: | 01 Jul 2016 12:39 |
Last Modified: | 18 Jul 2017 06:49 |
Published Version: | http://dx.doi.org/10.1007/978-3-319-40379-3_7 |
Status: | Published |
Publisher: | Springer |
Identification Number: | 10.1007/978-3-319-40379-3_7 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:101732 |