Szczegóły publikacji
Opis bibliograficzny
Advanced control algorithms for mobile robot / Agata NAWROCKA, Marcin NAWROCKI, Andrzej KOT // W: ICCC 2017 [Dokument elektroniczny] : 18th International Carpathian Control Conference : Sinaia, Romania, May 28–31, 2017 : proceedings / eds. Dorin Șendrescu, [et al.]. — Wersja do Windows. — Dane tekstowe. — [Piscataway] : IEEE, cop. 2017. — Dysk Flash. — W bazie Web of Science ISBN: 978-1-5090-4862-5. — e-ISBN: 978-1-5090-5825-9. — S. [1–4]. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. [4], Abstr. — ISBN: 978-1-5090-4862-5. — Toż w: https://ieeexplore-1ieee-1org-1000047zs0135.wbg2.bg.agh.edu.pl/stamp/stamp.jsp?tp=&arnumber=7970435. — W bazie Web of Science zakres stron: 412-415
Autorzy (3)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 107207 |
|---|---|
| Data dodania do BaDAP | 2017-07-25 |
| DOI | 10.1109/CarpathianCC.2017.7970435 |
| Rok publikacji | 2017 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Institute of Electrical and Electronics Engineers (IEEE) |
| Konferencja | International Carpathian Control Conference 2017 |
Abstract
Among the issues discussed in this article the main problem concerns a neuro-fuzzy system (ANFIS) for controlling a mobile robot, which is considered one of the methods of artificial intelligence. The first part discusses the classical approach to control a mobile robot. Firstly, a mathematical model was presented describing the kinematics of a two-wheel mobile robot, which was later used for the simulation of the proposed algorithm. Secondly, a method for designing an ANFIS controller was described with the use of reverse learning. A flowchart of learning was presented together with a final control layout. The next part was devoted to a detailed description of the proposed algorithm for controlling a mobile robot. It was divided into three parts due to the complexity of the algorithm. Each subsection contains a block diagram and the description of the learning process for each of the employed networks. The last part features tests of the proposed solutions. At the beginning, a comparison between the ANFIS controller and conventional controllers was presented. Next, a simulation environment, in which the proposed algorithm had been tested, was discussed. Finally, the paper presents the performance of the mobile robot control in an unfamiliar environment with the help of ANFIS.
