A fuzzy approach to qualification in design exploration for autonomous robots and systems

Jeremy Morse, Dejanira Araiza-Illan, Kerstin Eder, Jonathan Lawry, Arthur Richards

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

1 Citation (Scopus)
250 Downloads (Pure)

Abstract

Autonomous robots must operate in complex and changing environments subject to requirements on their behaviour. Verifying absolute satisfaction (true or false) of these requirements is challenging. Instead, we analyse requirements that admit flexible degrees of satisfaction. We analyse vague requirements using fuzzy logic, and probabilistic requirements using model checking. The resulting analysis method provides a partial ordering of system designs, identifying trade-offs between different requirements in terms of the degrees to which they are satisfied. A case study involving a home care robot interacting with a human is used to demonstrate the approach.

Original languageEnglish
Title of host publication2017 IEEE International Conference on Fuzzy Systems, FUZZ 2017
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages6
ISBN (Electronic)9781509060344
ISBN (Print)9781509060351
DOIs
Publication statusPublished - Aug 2017
Event2017 IEEE International Conference on Fuzzy Systems, FUZZ 2017 - Naples, Italy
Duration: 9 Jul 201712 Jul 2017

Publication series

Name IEEE International Fuzzy Systems Conference (FUZZ-IEEE)
PublisherIEEE
ISSN (Print)1098-7584
ISSN (Electronic)1558-4739

Conference

Conference2017 IEEE International Conference on Fuzzy Systems, FUZZ 2017
CountryItaly
CityNaples
Period9/07/1712/07/17

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    Morse, J., Araiza-Illan, D., Eder, K., Lawry, J., & Richards, A. (2017). A fuzzy approach to qualification in design exploration for autonomous robots and systems. In 2017 IEEE International Conference on Fuzzy Systems, FUZZ 2017 [8015456] ( IEEE International Fuzzy Systems Conference (FUZZ-IEEE)). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/FUZZ-IEEE.2017.8015456