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Original scientific paper

AUTONOMOUS ROBOT LEARNING MODEL BASED ON VISUAL INTERPRETATION OF SPATIAL STRUCTURES

Marko Švaco   ORCID icon orcid.org/0000-0002-6761-4336 ; Faculty of Mechanical Engineering and Naval Architecture, University of Zagreb, Zagreb, Croatia
Bojan Jerbić   ORCID icon orcid.org/0000-0003-1811-5669 ; Faculty of Mechanical Engineering and Naval Architecture, University of Zagreb, Zagreb, Croatia
Filip Šuligoj ; Faculty of Mechanical Engineering and Naval Architecture, University of Zagreb, Zagreb, Croatia

Fulltext: english, pdf (3 MB) pages 13-28 downloads: 228* cite
APA 6th Edition
Švaco, M., Jerbić, B. & Šuligoj, F. (2014). AUTONOMOUS ROBOT LEARNING MODEL BASED ON VISUAL INTERPRETATION OF SPATIAL STRUCTURES. Transactions of FAMENA, 38 (4), 13-28. Retrieved from https://hrcak.srce.hr/135873
MLA 8th Edition
Švaco, Marko, et al. "AUTONOMOUS ROBOT LEARNING MODEL BASED ON VISUAL INTERPRETATION OF SPATIAL STRUCTURES." Transactions of FAMENA, vol. 38, no. 4, 2014, pp. 13-28. https://hrcak.srce.hr/135873. Accessed 21 Jul. 2019.
Chicago 17th Edition
Švaco, Marko, Bojan Jerbić and Filip Šuligoj. "AUTONOMOUS ROBOT LEARNING MODEL BASED ON VISUAL INTERPRETATION OF SPATIAL STRUCTURES." Transactions of FAMENA 38, no. 4 (2014): 13-28. https://hrcak.srce.hr/135873
Harvard
Švaco, M., Jerbić, B., and Šuligoj, F. (2014). 'AUTONOMOUS ROBOT LEARNING MODEL BASED ON VISUAL INTERPRETATION OF SPATIAL STRUCTURES', Transactions of FAMENA, 38(4), pp. 13-28. Available at: https://hrcak.srce.hr/135873 (Accessed 21 July 2019)
Vancouver
Švaco M, Jerbić B, Šuligoj F. AUTONOMOUS ROBOT LEARNING MODEL BASED ON VISUAL INTERPRETATION OF SPATIAL STRUCTURES. Transactions of FAMENA [Internet]. 2014 [cited 2019 July 21];38(4):13-28. Available from: https://hrcak.srce.hr/135873
IEEE
M. Švaco, B. Jerbić and F. Šuligoj, "AUTONOMOUS ROBOT LEARNING MODEL BASED ON VISUAL INTERPRETATION OF SPATIAL STRUCTURES", Transactions of FAMENA, vol.38, no. 4, pp. 13-28, 2014. [Online]. Available: https://hrcak.srce.hr/135873. [Accessed: 21 July 2019]

Abstracts
The main concept of the presented research is an autonomous robot learning model for which a novel ARTgrid neural network architecture for the classification of spatial structures is used. The motivation scenario includes incremental unsupervised learning which is mainly based on discrete spatial structure changes recognized by the robot vision system. The learning policy problem is presented as a classification problem for which the adaptive resonance theory (ART) concept is implemented. The methodology and architecture of the autonomous robot learning model with preliminary results are presented. A computer simulation was performed with four input sets containing 22, 45, 73, and 111 random spatial structures. The ARTgrid shows a fairly high (>85%) match score when applied with already learned patterns after the first learning cycle, and a score of >95% after the second cycle. Regarding the category proliferation, the results are compared with a more predictive modified cluster centre seeking algorithm.

Keywords
autonomous systems; machine learning; adaptive resonance theory

Hrčak ID: 135873

URI
https://hrcak.srce.hr/135873

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