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Bacterial Evolutionary Algorithm-Trained Interpolative Fuzzy System for Mobile Robot Navigation

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Τίτλος: Bacterial Evolutionary Algorithm-Trained Interpolative Fuzzy System for Mobile Robot Navigation
Συγγραφείς: Ferenc Ádám Szili, János Botzheim, Balázs Nagy
Πηγή: Electronics, Vol 11, Iss 1734, p 1734 (2022)
Στοιχεία εκδότη: MDPI AG
Έτος έκδοσης: 2022
Συλλογή: Directory of Open Access Journals: DOAJ Articles
Θεματικοί όροι: interpolative fuzzy system, bacterial evolutionary algorithm, neuro-activity, path planning, Electronics, TK7800-8360
Περιγραφή: This paper describes the process of building a transport logic that enables a mobile robot to travel fast enough to reach a desired destination in time, but safe enough to prevent damage. This transport logic is based on fuzzy logic inference using fuzzy rule interpolation, which allows for accurate inferences even when using a smaller rule base. The construction of the fuzzy rule base can be conducted experimentally, but there are also solutions for automatic construction. One of them is the bacterial evolutionary algorithm, which is used in this application. This algorithm is based on the theory of bacterial evolution and is very well-suited to solving optimization problems. Successful transport is also facilitated by proper path planning, and for this purpose, the so-called neuro-activity-based path planning has been used. This path-planning algorithm is combined with interpolative fuzzy logic-based speed control of the mobile robot. By applying the described methods, an intelligent transport logic can be constructed. These methods are tested in a simulated environment and several results are investigated.
Τύπος εγγράφου: article in journal/newspaper
Γλώσσα: English
Relation: https://www.mdpi.com/2079-9292/11/11/1734; https://doaj.org/toc/2079-9292; https://doaj.org/article/05bfb58eebd2453ca3fd3ffcff235323
DOI: 10.3390/electronics11111734
Διαθεσιμότητα: https://doi.org/10.3390/electronics11111734
https://doaj.org/article/05bfb58eebd2453ca3fd3ffcff235323
Αριθμός Καταχώρησης: edsbas.CE6134B6
Βάση Δεδομένων: BASE
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  – Url: https://doi.org/10.3390/electronics11111734#
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  Data: Bacterial Evolutionary Algorithm-Trained Interpolative Fuzzy System for Mobile Robot Navigation
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  Data: <searchLink fieldCode="AR" term="%22Ferenc+Ádám+Szili%22">Ferenc Ádám Szili</searchLink><br /><searchLink fieldCode="AR" term="%22János+Botzheim%22">János Botzheim</searchLink><br /><searchLink fieldCode="AR" term="%22Balázs+Nagy%22">Balázs Nagy</searchLink>
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  Data: Electronics, Vol 11, Iss 1734, p 1734 (2022)
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  Data: MDPI AG
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  Data: 2022
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  Data: Directory of Open Access Journals: DOAJ Articles
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  Data: <searchLink fieldCode="DE" term="%22interpolative+fuzzy+system%22">interpolative fuzzy system</searchLink><br /><searchLink fieldCode="DE" term="%22bacterial+evolutionary+algorithm%22">bacterial evolutionary algorithm</searchLink><br /><searchLink fieldCode="DE" term="%22neuro-activity%22">neuro-activity</searchLink><br /><searchLink fieldCode="DE" term="%22path+planning%22">path planning</searchLink><br /><searchLink fieldCode="DE" term="%22Electronics%22">Electronics</searchLink><br /><searchLink fieldCode="DE" term="%22TK7800-8360%22">TK7800-8360</searchLink>
– Name: Abstract
  Label: Description
  Group: Ab
  Data: This paper describes the process of building a transport logic that enables a mobile robot to travel fast enough to reach a desired destination in time, but safe enough to prevent damage. This transport logic is based on fuzzy logic inference using fuzzy rule interpolation, which allows for accurate inferences even when using a smaller rule base. The construction of the fuzzy rule base can be conducted experimentally, but there are also solutions for automatic construction. One of them is the bacterial evolutionary algorithm, which is used in this application. This algorithm is based on the theory of bacterial evolution and is very well-suited to solving optimization problems. Successful transport is also facilitated by proper path planning, and for this purpose, the so-called neuro-activity-based path planning has been used. This path-planning algorithm is combined with interpolative fuzzy logic-based speed control of the mobile robot. By applying the described methods, an intelligent transport logic can be constructed. These methods are tested in a simulated environment and several results are investigated.
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  Data: https://www.mdpi.com/2079-9292/11/11/1734; https://doaj.org/toc/2079-9292; https://doaj.org/article/05bfb58eebd2453ca3fd3ffcff235323
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  Data: 10.3390/electronics11111734
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