Poboljšani mikrogenetički algoritmi u upravljanju robotskim sustavima

Dužanec, Darko

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doctoral thesis

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Poboljšani mikrogenetički algoritmi u upravljanju robotskim sustavima

Darko Dužanec (2010)

University of Zagreb
Faculty of Electrical Engineering and Computing
Department of Control and Computer Engineering

Cite this document

APA 6th Edition

Dužanec, D. (2010). Poboljšani mikrogenetički algoritmi u upravljanju robotskim sustavima (Doctoral thesis). Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:372168

MLA 8th Edition

Dužanec, Darko. "Poboljšani mikrogenetički algoritmi u upravljanju robotskim sustavima." Doctoral thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2010. https://urn.nsk.hr/urn:nbn:hr:168:372168

Chicago 17th Edition

Harvard

Dužanec, D. (2010). 'Poboljšani mikrogenetički algoritmi u upravljanju robotskim sustavima', Doctoral thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 13 December 2019, https://urn.nsk.hr/urn:nbn:hr:168:372168

Vancouver

Dužanec D. Poboljšani mikrogenetički algoritmi u upravljanju robotskim sustavima [Doctoral thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2010 [cited 2019 December 13] Available at: https://urn.nsk.hr/urn:nbn:hr:168:372168

IEEE

D. Dužanec, "Poboljšani mikrogenetički algoritmi u upravljanju robotskim sustavima", Doctoral thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2010. Available at: https://urn.nsk.hr/urn:nbn:hr:168:372168

Cite this item: https://urn.nsk.hr/urn:nbn:hr:168:372168

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Metadata

Title	Poboljšani mikrogenetički algoritmi u upravljanju robotskim sustavima
Author	Darko Dužanec
Mentor	Zdenko Kovačić (mentor)
Committee member	Stjepan Bogdan (predsjednik povjerenstva)
Committee member	Zdenko Kovačić (član povjerenstva)
Committee member	Darko Grundler (član povjerenstva)
Committee member	Mario-Osvin Pavčević (član povjerenstva)
Committee member	Marin Golub (član povjerenstva)
Granter	University of Zagreb Faculty of Electrical Engineering and Computing (Department of Control and Computer Engineering) Zagreb
Defense date and country	2010-10-14, Croatia
Scientific / art field, discipline and subdiscipline	TECHNICAL SCIENCES Computing Process Computing
Universal decimal classification (UDC)	004 GENERALLY Computer science and technology. Computing. Data processing
Abstract	U ovom radu istražena je mogućnost primjene genetičkog algoritma za rješavanje optimizacijskih problema u realnom vremenu. Proveden je veliki broj numeričkih eksperimenata temeljem kojih je utvrđen utjecaj pojedinih parametara na ponašanje GA s ciljem utvrđivanja optimalnih parametara algoritma. U ovom radu predstavljena je metoda određivanja optimalnih parametara mikrogenetičkog algoritma temeljem poznatih optimalnih parametara genetičkog algoritma. Provedena je analiza ponašanja mikrogenetičkog algoritma pomoću Markovljevih lanaca. Temeljem te analize razvijen je operator kontrakcije prostora rješenja koji povećava točnost rješenja genetičkog algoritma. Opisani algoritam primijenjen je za rješavanje problema inverzne kinematike hiperredundantnog robota. Problem inverzne kinematike definiran je kao optimizacijski problem i definiran je vektorski oblik kromosoma pa su i genetički operatori prilagođeni vektorskom obliku kromosoma. Vektorski oblik genetičkog algoritma primijenjen je i za vremensku optimizaciju CAN sabirnice kojom se smanjuje vrijeme kašnjenja poruka kod većih opterećenja sabirnice. Mikrogenetički algoritam za rješavanje problema inverzne kinematike implementiran je na nekoliko računalnih platformi, dok je genetički algoritam za analizu CAN sabirnice testiran na postojećem upravljačkom sustavu vatrogasnog vozila.
Abstract (english)	This paper analyses the possibility of using genetic algorithms to solve real time optimization problems. A large number of experiments are performed to determine the influence of the parameters of the GA with the goal to determine the optimal set of parameters. A method is introduced to determine optimal parameters of microgenetic algorithms based on known parameters of the optimal genetic algorithm. An analysis of the microgenetic algorithm based on Markov chains is shown. The conclusions of this analysis are used to introduce the new solution space contraction parameter which increases the precision of the solution. The described algorithm is used to solve the inverse kinematics problem of a hiperredundant robot. The inverse kinematics problem is defined in form of an optimization problem and the chromosome is defined as a vector. The genetic operators are modified to operate with vector chromosomes. The vector form of the genetic algorithm is used to optimize the CAN bus protocol with the goal to reduce jitter at higher bus loads. The microgenetic algorithm for solving the inverse kinematics problem is implemented on several platforms and the genetic algorithm for CAN bus optimization is tested on an existing decentralized control system of an firefighting vehicle.
Keywords
Keywords (english)
Language	croatian
URN:NBN	urn:nbn:hr:168:372168
Project	Number: 036-0363078-3016 Title: Planiranje i izvođenje zadataka u robotskim i autonomnim sustavima Leader: Stjepan Bogdan Jurisdiction: Croatia Funder: MZOS Funding stream: ZP
Project	Number: 036-0363078-3017 Title: Integrirano upravljanje robotskim sustavima u složenim okruženjima Leader: Zdenko Kovačić Jurisdiction: Croatia Funder: MZOS Funding stream: ZP
Study programme	Title: Computer Science Study programme type: university Study level: postgraduate Academic / professional title: Doktor znanosti (Doktor znanosti)
Catalog URL	http://lib.fer.hr/cgi-bin/koha/opac-detail.pl?biblionumber=34151
Type of resource	Text
Extent	177 str. ; 30 cm
File origin	Born digital
Access conditions	Closed access
Terms of use
Repository	FER Repository
Created on	2019-03-19 09:25:10