CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
SFLA-Based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production

Duc Hoang Nguyen
Random Articles
Reseach Article

Fuzzy Logic based Real Time Go to Goal Controller for Mobile Robot

by Nabeel Ali Abdullah
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 176 - Number 11
Year of Publication: 2020
Authors: Nabeel Ali Abdullah
10.5120/ijca2020920078

Nabeel Ali Abdullah . Fuzzy Logic based Real Time Go to Goal Controller for Mobile Robot. International Journal of Computer Applications. 176, 11 ( Apr 2020), 32-36. DOI=10.5120/ijca2020920078

@article{ 10.5120/ijca2020920078,
author = { Nabeel Ali Abdullah },
title = { Fuzzy Logic based Real Time Go to Goal Controller for Mobile Robot },
journal = { International Journal of Computer Applications },
issue_date = { Apr 2020 },
volume = { 176 },
number = { 11 },
month = { Apr },
year = { 2020 },
issn = { 0975-8887 },
pages = { 32-36 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume176/number11/31248-2020920078/ },
doi = { 10.5120/ijca2020920078 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:42:15.751401+05:30
%A Nabeel Ali Abdullah
%T Fuzzy Logic based Real Time Go to Goal Controller for Mobile Robot
%J International Journal of Computer Applications
%@ 0975-8887
%V 176
%N 11
%P 32-36
%D 2020
%I Foundation of Computer Science (FCS), NY, USA
Abstract

For any mobile device, the ability to move smoothly in its environment is of the ultimate importance, which rationalizes the persistent work of researchers to find new technologies to achieve this target. In this work, we briefly describe the tough work done for designing a fuzzy logic controller (FLC) for the reacting behaviour in a mobile robot, namely “go-to goal" problem. This new technology allows optimal planning of movement in terms of path length and travel time; it is intended to achieve the shortest path followed by a mobile robot. The efficiency of the proposed motion control unit is checked against the results of other smart methods; its features make it an effective alternative way to solve the go-to goal problem for the mobile robot.

References
  1. Yuan, G., Yang, S., & Mittal, G. (n.d.). Tracking control of a mobile robot using a neural dynamics-based approach. Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164). doi: 10.1109/robot.2001.932547
  2. Dierks, T., & Jagannathan, S. (2007). Control of Nonholonomic Mobile Robot Formations: Backstepping Kinematics into Dynamics. 2007 IEEE 22nd International Symposium on Intelligent Control. doi: 10.1109/isic.2007.4359798
  3. Benaoumeur, I., Laredj, B., Reda, H. E. A., & Zoubir, A.-F. (2016). Backstepping Approach for Autonomous Mobile Robot Trajectory Tracking. Indonesian Journal of Electrical Engineering and Computer Science, 2(3), 478. doi: 10.11591/ijeecs.v2.i3.pp478-485
  4. Kim, D.-H., & Oh, J.-H. (1999). Tracking control of a two-wheeled mobile robot using input–output linearization. Control Engineering Practice, 7(3), 369–373. doi: 10.1016/s0967-0661(98)00184-1
  5. Yang, J.-M., & Kim, J.-H. (1999). Sliding mode control for trajectory tracking of nonholonomic wheeled mobile robots. IEEE Transactions on Robotics and Automation, 15(3), 578–587. doi: 10.1109/70.768190
  6. Ollero, A., García-Cerezo, A., & Martínez, J. (1994). Fuzzy supervisory path tracking of mobile reports. Control Engineering Practice, 2(2), 313–319. doi: 10.1016/0967-0661(94)90213-5
  7. Pandey, A., & Parhi, D. R. (2017). Optimum path planning of mobile robot in unknown static and dynamic environments using Fuzzy-Wind Driven Optimization algorithm. Defence Technology, 13(1), 47–58. doi: 10.1016/j.dt.2017.01.001
  8. Velagic, J., Osmic, N., & Lacevic, B. (2010). Design of Neural Network Mobile Robot Motion Controller. New Trends in Technologies. doi: 10.5772/7584
  9. Singh, M. K., & Parhi, D. R. (2009). Intelligent neuro-controller for navigation of mobile robot. Proceedings of the International Conference on Advances in Computing, Communication and Control - ICAC3 09. doi: 10.1145/1523103.1523129
  10. Hatab, R. D. A. A. (2013). Dynamic Modelling of Differential-Drive Mobile Robots using Lagrange and Newton-Euler Methodologies: A Unified Framework. Advances in Robotics & Automation, 02(02). doi: 10.4172/2168-9695.1000107
  11. Leyden, M. & Toal, D. and Flanagan, C. (1999). A Fuzzy Logic Based Navigation System for a Mobile Robot, Department of Electronic & Computer Engineering, University of Limerick, Ireland
Index Terms

Computer Science
Information Sciences

Keywords

Nonholonomic robots Modeling differential drive Fuzzy logic mobile robot LabVIEW.

Powered by PhDFocusTM