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.
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Acknowledgements
We would like to thank The Leverhulme Trust (Grant number: RPG-2014-381) for funding this work.
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de Boer, G., Wang, H., Ghajari, M., Alazmani, A., Hewson, R., Culmer, P. (2016). Force and Topography Reconstruction Using GP and MOR for the TACTIP Soft Sensor System. In: Alboul, L., Damian, D., Aitken, J. (eds) Towards Autonomous Robotic Systems. TAROS 2016. Lecture Notes in Computer Science(), vol 9716. Springer, Cham. https://doi.org/10.1007/978-3-319-40379-3_7
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DOI: https://doi.org/10.1007/978-3-319-40379-3_7
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