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Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/22723

Title: Data-driven bending angle prediction of soft pneumatic actuators with embedded flex sensors
Authors: Elgeneidy, Khaled
Lohse, Niels
Jackson, Michael R.
Keywords: Soft grippers
Pneumatic actuators
Neural networks
Regression analysis
Image processing
Issue Date: 2016
Publisher: © IFAC. Hosting by Elsevier Ltd.
Citation: ELGENEIDY, K., LOHSE, N. and JACKSON, M., 2016. Data-driven bending angle prediction of soft pneumatic actuators with embedded flex sensors. IFAC-PapersOnLine, in press
Abstract: In this paper, resistive flex sensors have been embedded at the strain limiting layer of soft pneumatic actuators, in order to provide sensory feedback that can be utilised in predicting their bending angle during actuation. An experimental setup was prepared to test the soft actuators under controllable operating conditions, record the resulting sensory feedback, and synchronise this with the actual bending angles measured using a developed image processing program. Regression analysis and neural networks are two data-driven modelling techniques that were implemented and compared in this study, to evaluate their ability in predicting the bending angle response of the tested soft actuators at different input pressures and testing orientations. This serves as a step towards controlling this class of soft bending actuators, using data-driven empirical models that lifts the need for complex analytical modelling and material characterisation. The aim is to ultimately create a more controllable version of this class of soft pneumatic actuators with embedded sensing capabilities, to act as compliant soft gripper fingers that can be used in applications requiring both a ‘soft touch’ as well as more controllable object manipulation.
Description: Closed access until published online in IFAC-PapersOnLine. This paper was presented at MECHATRONICS 2016: 7th IFAC Symposium on Mechatronic Systems & 15th Mechatronics Forum International Conference Loughborough University 5th - 8th September 2016.
Sponsor: The reported work has been partially funded by the EPSRC Centre for Innovated Manufacturing in Intelligent Automation (EP/IO33467/1).
Version: Accepted for publication
URI: https://dspace.lboro.ac.uk/2134/22723
Publisher Link: http://www.sciencedirect.com/science/journal/24058963
ISSN: 2405-8963
Appears in Collections:Closed Access (Mechanical, Electrical and Manufacturing Engineering)

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