Implementation of Fuzzy Controller for Mobile Robot Navigation on NI’s Embedded- FPGA Robotic Platform

Yıl 2019, Cilt: 4 Sayı: 2, 80 - 87, 01.12.2019

Mahmut Dirik , Adnan Fatih Kocamaz Emrah Donmez

Öz

Autonomous mobile
robots in the field of modern automation technology have gained interest in the
last decades. A navigation system of an autonomous mobile robot in a cluttered
(with static or dynamic obstacle) environment is one of the interest areas.
This paper presents Fuzzy logic controller based obstacle avoidance approach
for National Instruments (NI)’s embedded robotic platform which to host the
SBRIO (Single-board Reconfigurable Input-Output) that includes a powerful
real-time controller, and a field programmable gate array (FPGA). The robot
platform used here has an ultrasonic sensor located at the front of the robot
and rotatable from -65 to 65 degrees. To construct the proposed system, it has
used twelve (12) sensors information as input parameters. The design and
software were implemented using LabVIEW modules. In order to provide better
insight into the experiment’s objectives, the proposed methods compared with
the VFH algorithm. The experimental results verified in simulation modes which
are simplicity and quicker reacting to sudden changes in sharp-edged shapes. It
is cleared that the fuzzy logic approach was successfully applied to the DaNI
mobile robot to navigate in the safest direction.   

Anahtar Kelimeler

Mobile Robot, Obstacle Avoidance, DaNI, LabVIEW, , Fuzzy Logic

Kaynakça

• [1] M. Mysorewala, K. Alshehri, E. Alkhayat, A. Al-Ghusain, and O. Al-Yagoub, “Design and implementation of fuzzy-logic based obstacle-avoidance and target-reaching algorithms on NI’s embedded-FPGA robotic platform,” in Proceedings of the 2013 IEEE Symposium on Computational Intelligence in Control and Automation, CICA 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013, 2013.
• [2] J. Borenstein and Y. Koren, “The Vector Field Histogram—Fast Obstacle Avoidance for Mobile Robots,” IEEE Trans. Robot. Autom., 1991.
• [3] A. Shitsukane, W. Cheruiyot, C. Otieno, and M. Mvurya, “Fuzzy Logic Sensor Fusion for Obstacle Avoidance Mobile Robot,” 2018 IST-Africa Week Conf., no. May, p. Page 1 of 8-Page 8 of 8, 2018.
• [4] F. Ali, E. K. Kim, and Y. G. Kim, “Type-2 fuzzy ontology-based semantic knowledge for collision avoidance of autonomous underwater vehicles,” Inf. Sci. (Ny)., 2015.
• [5] M. Ider, “Type-2 fuzzy logic control for a mobile robot tracking a moving target,” vol. 03, pp. 57–65, 2015.
• [6] K. Al-mutib and F. Abdessemed, “Indoor Mobile Robot Navigation in Unknown Environment Using Fuzzy Logic Based Behaviors,” vol. 2, no. 3, pp. 327–337, 2017.
• [7] M. Dirik, A. F. Kocamaz, and E. Donmez, “Static path planning based on visual servoing via fuzzy logic,” in 2017 25th Signal Processing and Communications Applications Conference, SIU 2017, 2017.
• [8] M. Dirik, A. F. Kocamaz, and E. Dönmez, “Visual servoing based path planning for wheeled mobile robot in obstacle environments,” in IDAP 2017 - International Artificial Intelligence and Data Processing Symposium, 2017.
• [9] S. P. Shrivas, “Fuzzy Controller Technique in Navigation of a Mobile Robot,” no. 4, pp. 165–168, 2014.
• [10] E. Dönmez, A. F. Kocamaz, and M. Dirik, “A Vision-Based Real-Time Mobile Robot Controller Design Based on Gaussian Function for Indoor Environment,” Arab. J. Sci. Eng., pp. 1–16, 2017.
• [11] T. Weerakoon, K. Ishii, and A. A. F. Nassiraei, “An Artificial Potential Field Based Mobile Robot Navigation Method To Prevent From Deadlock,” J. Artif. Intell. Soft Comput. Res., 2015.
• [12] T. W. Manikas, K. Ashenayi, and R. L. Wainwright, “Genetic algorithms for autonomous robot navigation,” IEEE Instrum. Meas. Mag., 2007.
• [13] L. Moreno, J. M. Armingol, S. Garrido, A. De La Escalera, and M. A. Salichs, “A genetic algorithm for mobile robot localization using ultrasonic sensors,” J. Intell. Robot. Syst. Theory Appl., 2002.
• [14] R. Fierro and F. L. Lewis, “Control of a nonholonomic mobile robot using neural networks,” IEEE Trans. Neural Networks, 1998.
• [15] L. Astudillo, O. Castillo, P. Melin, A. Alanis, J. Soria, and L. T. Aguilar, “Intelligent Control of an Autonomous Mobile Robot using Type-2 Fuzzy Logic,” p. 5.
• [16] M. M. M. Dr. Bahaa I.Kazem, Ali H. Hamad, “Modified Vector Field Histogram with a Neural Network Learning Model for Mobile Robot Path Planning and Obstacle Avoidance,” Int. J. Adv. Comput. Technol., 2010.
• [17] J. J. Rodríguez-andina, S. Member, M. D. Valdés-peña, and M. J. Moure, “Advanced Features and Industrial Applications of FPGAs — A Review,” vol. 11, no. 4, pp. 853–864, 2015.
• [18] S. Dubey and T. S. Savithri, “Fuzzy Control for Person Follower FPGA based Robotic System,” no. December 2016, 2015.
• [19] J. J. Rodriguez-andina, S. Member, M. J. Moure, and M. D. Valdes, “Features , Design Tools , and Application Domains of FPGAs,” vol. 54, no. 4, pp. 1810–1823, 2007.
• [20] “NI FPGA -National Instruments.” [Online]. Available: http://www.ni.com/fpga/. [Accessed: 25-Nov-2018].
• [21] “Creating a Smart Grid Monitoring and Control System Using LabVIEW and NI Single-Board RIO - Solutions - National Instruments.” [Online]. Available: http://sine.ni.com/cs/app/doc/p/id/cs-13487. [Accessed: 25-Nov-2018].
• [22] P. E-mail, “A LabVIEW-based Autonomous Vehicle Navigation System using Robot Vision and Fuzzy Control,” vol. XII, pp. 129–136, 2011.
• [23] D. Oswald et al., “Implementation of Fuzzy Color Extractor on NI myRIO Embedded Device,” 2013.
• [24] M. Mysorewala, K. Alshehri, E. Alkhayat, A. Al-ghusain, and O. Al-yagoub, “Design and Implementation of Fuzzy-Logic based Obstacle-Avoidance and Target-Reaching Algorithms on NI ’ s Embedded-FPGA Robotic Platform,” no. April, 2013.
• [25] P. Shakouri, O. Duran, A. Ordys, and G. Collier, Teaching Fuzzy Logic Control Based on a Robotic Implementation, vol. 46, no. 17. IFAC, 2013.
• [26] C. K. Hui, R. Tyasnurita, P. Alexandra, M. Fan, and R. Lee, “Localization and Obstacle Avoidance Using Fuzzy Logic and Neural Network,” 2009.
• [27] R. King, Mobile Robotics Experiments with DaNI.
• [28] “Mobile Robotics Experiments with DaNI COLORADO SCHOOL OF MINES.” [Accessed: 25-Nov-2018].