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JCR Impact Factor: 0.595
JCR 5-Year IF: 0.661
Issues per year: 4
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Next issue: Nov 2017
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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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LATEST NEWS

2017-Jun-14
Thomson Reuters published the Journal Citations Report for 2016. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.595, and the JCR 5-Year Impact Factor is 0.661.

2017-Apr-04
We have the confirmation Advances in Electrical and Computer Engineering will be included in the EBSCO database.

2017-Feb-16
With new technologies, such as mobile communications, internet of things, and wide applications of social media, organizations generate a huge volume of data, much faster than several years ago. Big data, characterized by high volume, diversity and velocity, increasingly drives decision making and is changing the landscape of business intelligence, from governments to private organizations, from communities to individuals. Big data analytics that discover insights from evidences has a high demand for computing efficiency, knowledge discovery, problem solving, and event prediction. We dedicate a special section of Issue 4/2017 to Big Data. Prospective authors are asked to make the submissions for this section no later than the 31st of May 2017, placing "BigData - " before the paper title in OpenConf.

2017-Jan-30
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2016-Dec-17
IoT is a new emerging technology domain which will be used to connect all objects through the Internet for remote sensing and control. IoT uses a combination of WSN (Wireless Sensor Network), M2M (Machine to Machine), robotics, wireless networking, Internet technologies, and Smart Devices. We dedicate a special section of Issue 2/2017 to IoT. Prospective authors are asked to make the submissions for this section no later than the 31st of March 2017, placing "IoT - " before the paper title in OpenConf.

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  1/2012 - 10

A Motion Planning System for Mobile Robots

TUNCER, A. See more information about TUNCER, A. on SCOPUS See more information about TUNCER, A. on IEEExplore See more information about TUNCER, A. on Web of Science, YILDIRIM, M. See more information about  YILDIRIM, M. on SCOPUS See more information about  YILDIRIM, M. on SCOPUS See more information about YILDIRIM, M. on Web of Science, ERKAN, K. See more information about ERKAN, K. on SCOPUS See more information about ERKAN, K. on SCOPUS See more information about ERKAN, K. on Web of Science
 
Click to see author's profile on See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (855 KB) | Citation | Downloads: 1,009 | Views: 2,700

Author keywords
genetic algorithm, mobile robot, motion planning

References keywords
robot(12), genetic(12), planning(10), path(9), mobile(8), algorithm(8), algorithms(6), robotics(5), autonomous(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2012-02-28
Volume 12, Issue 1, Year 2012, On page(s): 57 - 62
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2012.01010
Web of Science Accession Number: 000301075000010
SCOPUS ID: 84860754571

Abstract
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In this paper, a motion planning system for a mobile robot is proposed. Path planning tries to find a feasible path for mobile robots to move from a starting node to a target node in an environment with obstacles. A genetic algorithm is used to generate an optimal path by taking the advantage of its strong optimization ability. Mobile robot, obstacle and target localizations are realized by means of camera and image processing. A graphical user interface (GUI) is designed for the motion planning system that allows the user to interact with the robot system and to observe the robot environment. All the software components of the system are written in MATLAB that provides to use non-predefined accessories rather than the robot firmware has, to avoid confusing in C++ libraries of robot's proprietary software, to control the robot in detail and not to re-compile the programs frequently in real-time dynamic operations.


References | Cited By  «-- Click to see who has cited this paper

[1] Y. Hu and S. X. Yang, "A knowledge based genetic algorithm for path planning of a mobile robot," Proceedings of the 2004 IEEE, Int. Conf. on Robotics & Automation, Vol. 5, pp. 4350-4355, 2004.
[CrossRef]


[2] J. Tu and S. X. Yang, "Genetic algorithm based path planning for a mobile robot," Robotics and Automation. Proceedings. ICRA '03. IEEE Int Conf. on, Vol. 1, pp. 1221-1226, 2003.
[CrossRef]


[3] I. Al-Taharwa, A. Sheta and M. Al-Weshah, "A mobile robot path planning using genetic algorithm in static environment," J of Computer Science, Vol. 4, pp. 341-344, 2008.
[CrossRef]


[4] A. Elshamli, H. A. Abdullah and S. Areibi, "Genetic algorithm for dynamic path planning," Electrical and Computer Engineering, Canadian Conference on, Vol. 2, pp. 677-680, 2004.
[CrossRef]


[5] S. M. H. Nabavi, A. Kazemi and M. A. S. Masoum, "Social welfare improvement by TCSC using real code based genetic algorithm in double-sided auction market," Advances in Electrical and Computer Engineering, Vol. 11, pp. 99-106, 2011.
[CrossRef] [Full Text] [Web of Science Times Cited 6] [SCOPUS Times Cited 10]


[6] H. J. Chang, C. S. G. Lee, Yung-Hsiang Lu, Y. C. Hu, "P-SLAM: simultaneous localization and mapping with environmental-structure prediction," IEEE Transactions on Robotics, Vol. 23, No. 2, pp.281-293,2007.
[CrossRef] [Web of Science Times Cited 24] [SCOPUS Times Cited 34]


[7] Y. Zhuang, M. Gu, W. Wang and H. Yu, "Multi-robot cooperative localization based on autonomous motion state estimation and laser data interaction," Science China Information Sciences, Vol. 53, No. 11, pp. 2240-2250, 2010.
[CrossRef] [Web of Science Times Cited 12] [SCOPUS Times Cited 13]


[8] H. Teimoori and A. V. Savkin, "Equiangular navigation and guidance of a wheeled mobile robot based on range-only measurements," Robotics and Autonomous Systems, Vol. 58, pp. 203-215, 2010.
[CrossRef] [Web of Science Times Cited 41] [SCOPUS Times Cited 53]


[9] A. Yufka, O. Parlaktuna, "Performance comparison of bug algorithms for mobile robots," 5th International Advanced Technologies Symposium (IATSÂ’09), Karabuk, Turkey, May 13-15, 2009.

[10] [Online] Available: Temporary on-line reference link removed - see the PDF document

[11] [Online] Available: Temporary on-line reference link removed - see the PDF document

[12] [Online] Available: Temporary on-line reference link removed - see the PDF document

[13] Pioneer 3 Operations Manual, Version 6, MobileRobots Inc., September 2010.

[14] T. W. Manikas, K. Ashenayi and R. L. Wainwright, "Genetic algorithms for autonomous robot navigation," Instrumentation & Measurement Magazine, IEEE, Vol. 10, pp. 26-31, 2007.
[CrossRef] [Web of Science Times Cited 55] [SCOPUS Times Cited 74]


[15] M. Naderan-Tahan and M. T. Manzuri-Shalmani, "Efficient and safe path planning for a mobile robot using genetic algorithm," Evolutionary Computation, CEC '09. IEEE Congress on, pp. 2091-2097, 2009.
[CrossRef] [Web of Science Times Cited 9] [SCOPUS Times Cited 12]


[16] Z. Yao and L. Ma, "A static environment-based path planning method by using genetic algorithm," Computing, Control and Industrial Engineering (CCIE), 2010 International Conference on, Vol. 2, pp. 405-407, 2010.
[CrossRef] [SCOPUS Times Cited 14]


[17] Q. Li, W. Zhang, Y. Yin, Z. Wang and G. Liu, "An improved genetic algorithm of optimum path planning for mobile robots," Intelligent Systems Design and Applications, ISDA '06. Sixth International Conference on, Vol. 2, pp. 637-642, 2006.
[CrossRef] [SCOPUS Times Cited 35]


[18] E. Gelenbe, P. Liu and J. Laine, "Genetic algorithms for route discovery," Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, Vol. 36, pp. 1247-1254, 2006.
[CrossRef] [Web of Science Times Cited 26] [SCOPUS Times Cited 35]


[19] K. Sugihara and J. Smith, "Genetic algorithms for adaptive motion planning of an autonomous mobil robot," Computational Intelligence in Robotics and Automation, CIRA'97, Proceedings, IEEE International Symposium on, pp. 138-143, 1997.
[CrossRef] [Web of Science Times Cited 64]


[20] G. Nagib and W. Gharieb, "Path planning for a mobile robot using genetic algorithms," Electrical, Electronic and Computer Engineering, ICEEC '04, International Conference on, pp. 185-189, 2004.
[CrossRef] [Web of Science Times Cited 11]


[21] E. Masehian and D. Sedighizadeh, "Multi-objective PSO- and NPSO-based algorithms for robot path planning," Advances in Electrical and Computer Engineering, Vol. 10, pp. 69-76, 2010.
[CrossRef] [Full Text] [Web of Science Times Cited 22] [SCOPUS Times Cited 30]


References Weight

Web of Science® Citations for all references: 270 TCR
SCOPUS® Citations for all references: 310 TCR

Web of Science® Average Citations per reference: 13 ACR
SCOPUS® Average Citations per reference: 15 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2017-09-26 12:56 in 109 seconds.




Note1: Web of Science® is a registered trademark of Thomson Reuters.
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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania


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