Design Optimisation of a Magnetic Field Based Soft Tactile Sensor

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@Article{deBoer:2017:sensors,

• author = "Gregory {de Boer} and Nicholas Raske and Hongbo Wang and Mazdak Ghajari and Peter Culmer and Robert Hewson",
• title = "Design Optimisation of a Magnetic Field Based Soft Tactile Sensor",
• journal = "sensors",
• year = "2017",
• volume = "17",
• number = "11",
• pages = "2539",
• month = nov,
• note = "Special Issue Tactile Sensors and Sensing)",
• keywords = "genetic algorithms, genetic programming, tactile sensing, sensitivity, optimisation, magnetic fields, force measurement",
• publisher = "MDPI",
• bibsource = "OAI-PMH server at eprints.whiterose.ac.uk",
• oai = "oai:eprints.whiterose.ac.uk:124902",
• URL = "http://eprints.whiterose.ac.uk/124902/1/sensors-17-02539-v2.pdf",
• DOI = "doi:10.3390/s17112539",
• size = "20 pages",
• abstract = "This paper investigates the design optimisation of a magnetic field based soft tactile sensor, comprised of a magnet and Hall effect module separated by an elastomer. The aim was to minimise sensitivity of the output force with respect to the input magnetic field; this was achieved by varying the geometry and material properties. Finite element simulations determined the magnetic field and structural behaviour under load. Genetic programming produced phenomenological expressions describing these responses. Optimisation studies constrained by a measurable force and stable loading conditions were conducted; these produced Pareto sets of designs from which the optimal sensor characteristics were selected. The optimisation demonstrated a compromise between sensitivity and the measurable force, a fabricated version of the optimised sensor validated the improvements made using this methodology. The approach presented can be applied in general for optimising soft tactile sensor designs over a range of applications and sensing modes.",
• notes = "Also known as \cite{oai:eprints.whiterose.ac.uk:124902}",

}

Genetic Programming entries for Greg de Boer Nicholas Raske Hongbo Wang Mazdak Ghajari Pete Culmer Robert Hewson

Citations