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Model Predictive Control for Positioning and Navigation of Mobile Robot with Cooperation of UAV

Moustafa M. Kurdi, Imad A. Elzein, Alex K. Dadykin. Published in Control Systems.

Communications on Applied Electronics
Year of Publication: 2017
Publisher: Foundation of Computer Science (FCS), NY, USA
Authors: Moustafa M. Kurdi, Imad A. Elzein, Alex K. Dadykin
10.5120/cae2017652506

Moustafa M Kurdi, Imad A Elzein and Alex K Dadykin. Model Predictive Control for Positioning and Navigation of Mobile Robot with Cooperation of UAV. Communications on Applied Electronics 6(7):17-25, February 2017. BibTeX

@article{10.5120/cae2017652506,
	author = {Moustafa M. Kurdi and Imad A. Elzein and Alex K. Dadykin},
	title = {Model Predictive Control for Positioning and Navigation of Mobile Robot with Cooperation of UAV},
	journal = {Communications on Applied Electronics},
	issue_date = {February 2017},
	volume = {6},
	number = {7},
	month = {Feb},
	year = {2017},
	issn = {2394-4714},
	pages = {17-25},
	numpages = {9},
	url = {http://www.caeaccess.org/archives/volume6/number7/703-2017652506},
	doi = {10.5120/cae2017652506},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

The purpose of navigation system is to help mobile robot in order to select an optimal and short path to reach the target. In most of these systems, GPS are used to determine the robot position. There are errors in positioning using GPS. This paper considers the problem of navigating a Mobile Robot in an unknown environment while maintaining visibility with a (movable or non-movable) target by means of Fuzzy Model Predictive Control (FMPC). The approach combines input variables from different resources such as: GPS, RVS (Robot Vision System), and QVS (Quad-copter Vision System). In this paper, a new approach based on Fuzzy Model Predictive Control (FMPC) is proposed to solve the positioning and navigation problems for mobile robot.

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Keywords

Navigation; Fuzzy; MPC; Mobile Robot; UAV