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Titel: Neural Network Control of the Mobile Robotic Platform’s Adhesion Force
Autoren: Kondratenko, Y.
Wang, K.
Kozlov, O.
Shevchenko, A.
Denysenko, A.
Stichwörter: computer simulation
intelligent adhesion control
Mobile robotics
NARMA-L2 controller
neural network control system
universal robotic platform
Erscheinungsdatum: 2023
Zusammenfassung: This paper focuses on the advancements in mobile robotics, specifically on universal robotic platforms that have versatile applications in different technological environments and settings within industrial facilities. These platforms exhibit the capability to navigate complex terrains on horizontal surfaces and even ascend vertically on walls and ceilings, making them autonomous and adaptable tools for performing intricate operations in challenging and hazardous areas. The primary challenge addressed in this research pertains to the precise control of adhesion when these robotic platforms traverse inclined planes. To tackle this issue, the authors have developed and analyzed an intelligent adhesion control system. This system harnesses the principles of neural network control to stabilize the required adhesion force for safe and efficient platform movement across varying surface inclinations. The obtained computer simulations results confirm the high effectiveness of the proposed intelligent system.
Beschreibung: Kondratenko, Y., Wang, K., Kozlov, O., Shevchenko, A., & Denysenko, A. (2023). Neural Network Control of the Mobile Robotic Platform’s Adhesion Force. In CEUR Workshop Proceedings, 3538 (pp. 65-77). CEUR-WS. http://surl.li/ojpzy
URI: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85178269562&partnerID=40&md5=211ce790e9ae40e1568fdc8bb9ebf6d
https://ceur-ws.org/Vol-3538/Paper_7.pdf
https://dspace.chmnu.edu.ua/jspui/handle/123456789/1430
ISSN: 16130073
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