One of the most recent research areas over the last two decades is the navigation of mobile robots in unknown environments. In this paper, real time navigation for Wheeled Mobile Robot (WMR) using fuzzy logic technique, wireless communication and MATLAB is investigated. Two fuzzy logic controllers (FLCs) with two inputs and two outputs are used to navigate WMR in obstacle ridden environment. Our work combines the behaviors of reaching the target and obstacle avoidance. Goal Seeking Fuzzy Logic Controller (GSFLC) and Fuzzy Logic for Obstacles Avoiding (FLOA) work simultaneously to navigate the robot to its target. The target of this work is to use the WMR in many applications, such as a construction sites or warehouse with dynamic environment. The proposed methods are applied using simulation and experimentation to show the success of the suggested methods.

 

Published In : IJCSN Journal Volume 2, Issue 6

Date of Publication : 01 December 2013

Pages : 25 - 31

Figures : 25

Tables : --

Publication Link : ijcsn.org/IJCSN-2013/2-6/IJCSN-2013-2-6-119.pdf

 

 

 

Ramdane Hedjar : received the M.S degree in Electronics from Electronic Institute - BLIDA University, ALGERIA, and the Ph.D. degree in Automatic control from the Université des Sciences et Technologie Houari Boumediène ( USTHB), Algeria. His current research interests include the control of nonlinear systems, Asynchronous machines and Synchronous motors, intelligent controller like: Neural network controller, and Fuzzy logic controller. Currently my research is oriented to Networked control systems.

Mohammed Faisal : received the M.S degree in security of wireless sensor network from University of King Saud, and the Ph.D. student in intelligent mobile robot navigation from University of King Saud. His current research interests include the Fuzzy logic controller, mobile Robot navigation, and security of wireless sensor network.

Mohammed Algabri : received the B.S. degree in Computer Science from Umm Al-Qura University, and he is a M.S. student in Computer Science in King Saud University. His current research interests include the Soft Computing techniques, Fuzzy logic controller, mobile Robot navigation, and Speech Processing.

Khalid Al-Mutib : received the Ph.D from Univ. of Reading, UK, 1997. His current research interests include the Bio-inspired robotics control, Fuzzy logic controller, Biometric Control Algorithm, and Intelligent mobile robot.

 

 

 

 

 

 

 

Robotics

Navigation

Energy Efficient Protocol

Wheeled Mobile Robot

Wireless Communication

Matlab

Fuzzy Logic

 

EACP provides better lifetime for nodes compared to SEP and CBRP. In addition to reducing energy dissipation, EACP successfully distributes energy-usage among the nodes in the network such that the nodes die randomly and at essentially the same rate. We have used only residual energy for head selection procedure. Presently, EACP consumes higher computational power due to reporting and cluster head selection. The second limitation is that the performances have been compared with standard SEP and CBRP algorithm. Performance of other sensor network head selection like PEGASIS, EEHC, TEEN etc. have not been considered. We only considered energy heterogeneity in future; we take computation heterogeneity as well as link heterogeneity and test the result how it improves the lifetime of system.

 

 

 

 

 

 

 

 

 

[1] G. Oriolo, A. De Luca, and M. Vendittelli, “WMR control via dynamic feedback linearization: design, implementation, and experimental validation,” IEEE Transactions on Control Systems Technology, vol. 10, no. 6, pp. 835–852, Nov. 2002.

[2] L. A. Zadeh, “ Fuzzy Sets, Information and Control”, vol.8, (1965), pp 29-44.

[3] L. A. Zadeh, "Outline of a new approach to the analysis of complex systems and decision process," IEEE, Trans Syst, Man, Cybern., vol.3, pp. 28-44, 1973.

[4] D. Driankov, H. Hellendourn and M. Reinfrank, “ An Introduction to Fuzzy Control”, Springer-Verlag, Berlin Heidelberg, New York, 1993.

[5] B. Kosko, “ Neural Network and Fuzzy Systems: A Dynamical Systems Approach to Machine Intelligence”, Prentice Hall International, Inc., 1992.

[6] R. Carelli, E. F. Camacho, and D. Patiño, “A neural network based feed forwardadaptive controller for robot,” IEEE Trans. Syst., Man, Cybern,vol. 25, no. 9, pp. 1281–1288, Sep. 1995.

[7] F. L. Lewis, S. Jagannathan, and A. Yesildirek, Neurl Network Controlof Robot Manipulators and Nonlinear Systems, London, U.K.: Taylor &Francis, 1999.

[8] S. K. Pradhan, D. R. Parhi, and A. K. Panda, “Fuzzy logic techniques for navigation of several mobile robots,” Applied soft computing, vol. 9, no. 1, pp. 290–304, 2009.

[9] R. Rashid, I. Elamvazuthi, M. Begam, and M. Arrofiq, “Differential Drive Wheeled Mobile Robot (WMR) Control Using Fuzzy Logic Techniques,” 2010, pp. 51–55.

[10] M.K. Singh, “Intelligent Controller for Mobile Robot: Fuzzy Logic Approach,” International Association for Computer Methods and Advances in Geomechanics (IACMAG), pp. 1–6, 2008.

[11] Wireless Networked Autonomies Mobile Robot with high Resolution Pan-Tilt-Zoom CCD Camera, i90 “Dr Robot Quick-start Guide”. [Online] Available: http://www.drrobot.com/products/item_downloads/Scout2_1.pdf. Accessed 2012 April 5.

[12] D. Tse and P. Viswanath, Fundamentals of wireless communication. Cambridge: Cambridge University Press, 2005.

[13] V. Raudonis and R. Maskeliunas, “Trajectory based fuzzy controller for indoor navigation,” in Computational Intelligence and Informatics (CINTI), 2011 IEEE 12th International Symposium on, 2011, pp. 69–72.

[14] “KiKS is a Khepera Simulator “, April 29, 2010, [Online]. Available: http://www.theodorstorm.se/index/2866.html [Accessed: Jan 2013].

[15] Dr Robot web site [Online] Available: http://drrobot.com/

[16] MATLAB web site [Online] Available http://www.mathworks.com/index.html.

[17] M. Faisal, R. Hedjar, M. Alsulaiman and K. Al-Mutib , “Fuzzy Logic Navigation and Obstacle Avoidance of Mobile Robot in Unknown Dynamic Environment,” International Journal of Advanced Robotic Systems, vol. 10, 2013.

[18] M. Faisal, K. Al-Mutib, R. Hedjar, H. Mathkour, M. Alsulaiman, and E. Mattar, "Multi Modules Fuzzy Logic for Mobile Robots Navigation and Obstacle Avoidance in Unknown Indoor Dynamic Environment." International Conference on Systems, Control and Informatics, Venice, 2013.