Cover
Vol. 15 No. 2 (2019)

Published: December 31, 2019

Pages: 61-70

Original Article

An Efficient Mathematical Approach for an Indoor Robot Localization System

Israa Sabri A. AL-Forati 1 Abdulmuttalib Rashid Fatemah Al-Assfor 2
1Electrical Engineering Department University of Basrah Basrah Iraq
2Computer Engineering Department University of Basrah Basrah Iraq
History
  • Received: October 31, 2019
  • Available Online: December 31, 2019
DOI

https://doi.org/10.37917/ijeee.15.2.7

Abstract

In a counterfeit clever control procedure, another productive methodology for an indoor robot localization framework is arranged. In this paper, a new mathematic calculation for the robot confinement framework utilizing light sensors is proposed. This procedure takes care of the issue of localization (position recognizing) when utilizing a grid of LEDs distributed uniformly in the environment, and a multi- portable robot outfitted with a multi-LDRs sensor and just two of them activate the visibility robot. The proposed method is utilized to assess the robot's situation by drawing two virtual circles for each two LDR sensors; one of them is valid and the other is disregarded according to several suggested equations. The midpoint of this circle is assumed to be the robot focus. The new framework is simulated on a domain with (n*n) LEDs exhibit. The simulation impact of this framework shows great execution in the localization procedure.

Keywords

References

  1. R. Tron, J. Thomas, G. Loianno, K. Daniilidis and V. Kumar (2016). A Distributed Optimization Framework for Localization and Formation Control: Applications to Vision-Based Measurements. IEEE Contr. Sys. Mag., 36(4), 22-44.
  2. I. Borg, P. Groenen, Modern Multidimensional Scaling: Theory and Applications, In: Springer Series in Statistics, Springer, 2005.
  3. I. Loevsky, I. Shimshoni, Reliable and efficient landmark-based localization for mobile robots, Robotics and Autonomous Systems 58 (2010) 520_528, www.elsevier.com/locate/robot.
  4. D. Belter and M. Nowicki, Optimization-based legged audiometry and Sensor fusion for legged robot continuous localization, Elsevier, Robotics and Autonomous Systems (2018) 1–20, https://doi.org/10.1016/j.robot.2018.10.013.
  5. E. Cole and S. Galerne, Mobile robot localization by multiangulation using set inversion, Elsevier, Robotics and Autonomous Systems (2013) 39–48, www.elsevier.com/locate/robot.
  6. Carmelo DI Franco, Mauro Marinoni, Enrico Bini and Scaling with anchors and height constraints for indoor localization of mobile nodes, Elsevier, Robotics and Autonomous Systems (2018) 28–37, www.elsevier.com/locate/robot.
  7. H. Liu, J. Yang, S. Sidhom, Y. Wang, Y. Chen, F. Yeah, Accurate Wi-Fi based localization for smartphones using peer assistance, Transactions on Mobile Computing 13 (10) (2014) 2199–2214.
  8. N. B. Priyantha, H. Balakrishnan, E. Demaine, and S. Teller (2003). “Anchor Free Distributed Localization in Sensor Networks “. Tech Report #892.
  9. Abdulmuttalib T. Rashid, M. Frasca, A, A, Ali, A. Rizzo and L. Foruna (2015) Multi-robot localization and orientation estimation using robotic cluster matching algorithm. Robotics and Autonomous Systems, 63, 108– 121.
  10. Abdulmuttalib T. Rashid, W. H. Zayer and M. T. Rashid (2018). “Design and Implementation of Locations Matching Algorithm for Multi-Object Recognition and Localization “. Iraqi Journal of Electrical and Electronic Engineering, 14(1), 10-21.
  11. T. Arampatzis, J. Lygeros and S. Manesis (2005). “A Survey of Applications of Wireless Sensors and Wireless Sensor Networks,” Intelligent control. Proceedings of the 2005 Symposium on, Mediterranean Conference on Control and Automation, 719 – 724.
  12. I. S. Alfurati , Abdulmuttalib T. Rashid and Alaa Alusing K means clustering algorithm “. International Journal of Simulation Systems, Science & Technology, 12.1-12.6.
  13. O. A. Hasan, Abdulmuttalib T. Rashid and R. S. Ali (2016). Centralized approach for multi-node localization and identification. Iraq J. Electrical and Electronic Engineering, 12 (2), 178-187.
  14. O. A. Hasan, Abdulmuttalib T. Rashid and R. S. Ali (2016). “A Hybrid approach for multi-node localization and Identification “. Basrah Journal for Engineering Sciences, 16(2), 11- 20.
  15. H. Liu, H. Darabi, P. Banerjee, and J. Liu (2007). “Survey of Wireless Indoor Positioning Techniques and Systems “. IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART C: APPLICATIONS AND REVIEWS, 37(6), 1067 – 1080.
  16. I. S. Alfurati and Abdulmuttalib T. Rashid (2018). “Performance Comparison of Three Types of Sensor Matrices for Indoor Multi-Robot Localization “. 8887), 181 (26), 22-29.
  17. Y. Gu, A. Lo, and I. Niemegeers, “A survey of indoor positioning systems for wireless personal networks,” Communications Surveys & Tutorials, IEEE, vol. no. 1, pp. 13-32, 2009.
  18. C. Lee, Y. Chang, G. Park, J. Ryu, S.-G. Jeong, S. Park, J. W.Park, “H. C. Lees and K-s. Hong,” Indoor Positioning System Based on Incident Angles of Infrared Emitters”, The 30th Annular Conference of the IEEE
  19. X. N. Fernando, S. Krishnan, H. Sun and K. K-Moud,” Adaptive denoising at Infrared wireless receivers”, The September 2003.
  20. I. S. Alfurati and Abdulmuttalib T. Rashid (2019),” Design and an Robot Localization System Using Minimum Bounded Circle Algorithm”, in press.