Cover
Vol. 16 No. Special Issue (2020)

Published: June 30, 2020

Pages: 44-52

Conference Article

Robotics Path Planning Algorithms using Low-Cost IR Sensor

Abstract

A robot is a smart machine that can help people in their daily lives and keep everyone safe. the three general sequences to accomplish any robot task is mapping the environment, the localization, and the navigation (path planning with obstacle avoidance). Since the goal of the robot is to reach its target without colliding, the most important and challenging task of the mobile robot is the navigation. In this paper, the robot navigation problem is solved by proposed two algorithms using low-cost IR receiver sensors arranged as an array, and a robot has been equipped with one IR transmitter. Firstly, the shortest orientation algorithm is proposed, the robot direction is corrected at each step of movement depending on the angle calculation. secondly, an Active orientation algorithm is presented to solve the weakness in the preceding algorithm. A chain of the active sensors in the environment within the sensing range of the virtual path is activated to be scan through the robot movement. In each algorithm, the initial position of the robot is detected using the modified binary search algorithm, various stages are used to avoid obstacles through suitable equations focusing on finding the shortest and the safer path of the robot. Simulation results with multi-resolution environment explained the efficiency of the algorithms, they are compatible with the designed environment, it provides safe movements (without hitting obstacles) and a good system control performance. A Comparison table is also provided.

References

  1. Siegwart and I.R. Nourbakhsh, “Introduction to Autonomous Mobile Robot,” Massachusetts Institute of Technology Press, Cambridge, USA, Vol. 1, pp. 13-36, 2004.
  2. Y. GU, A .Lo, S. Member, and I. Niemegeers, “A Survey of Indoor Positioning Systems for Wireless Personal Networks,” IEEE Communications Surveys & Tutorials, Vol. 11, No. 1, pp. 13 – 32, 2009.
  3. Y.Q. Qin, D.B. Sun, N. Li, Y.G. Cen, “Path planning for mobile robot using the particle swarm optimization with mutation operator,” In Proceedings of the international conference on machine learning and cybernetics (IEEE Cat. No. 04EX826), Shanghai, China, Vol. 4, pp. 2473- 2478, 2004.
  4. I. S. Alfurati and Abdulmuttalib T. Rashid, “Shortest Distance Orientation Algorithm for Robot Path Planning using Low-Cost IR Sensor System,” . Proc. of the 2nd International Conference on Electrical, Communication and Computer Engineering (ICECCE) 14-15 April 2020, Istanbul, Turkey, inpress.
  5. H. Zhang, J. Butzke, and M. Likhachev, “Combining global and local planning with guarantees on the completeness,” International Conference on Robotics and Automation, USA, pp. 4500–4506, 2012.
  6. I. S. Alfurati and Abdulmuttalib T. Rashid, “An Efficient Mathematical Approach for an Indoor Robot Localization System,” Iraqi Journal of Electrical and Electronic Engineering, vol. 15, Issue 2, pp. 61-70, 2019.
  7. Z. Bi, Y. Yimin and Y. Wei, “Hierarchical path planning approach for mobile robot navigation under the dynamic environment,” 6th International Conference on Industrial Informatics, Korea, pp. 372–376, 2008.
  8. F. Wu, and J. Williams, “Design and implementation of a multi-sensor based object detecting and removing autonomous robot exploration system,” Journal of Computer and Communications, vol. 2, pp. 8-16, 2014.
  9. S. Rathod, V. Bansal, and K. T. Patil, “Real-time speed based obstacle detection with path planning,” International Journal of Advance Foundation and Research in Science & Engineering, vol. 2, no.10, pp. 32- 41, 2016.
  10. J. I. Bangash, A. Abdullah, and A. Khan, “Issues and challenges in localization of wireless sensor networks,” Sci.Int (Lahore), pp. 595-603, 2014.
  11. I. S. Alfurati and Abdulmuttalib T. Rashid, “Performance Comparison of Three Types of Sensor Matrices for Indoor Multi-Robot Localization,” International Journal of Computer Applications (0975 – 8887), vol.181, Issue 26, pp. 22-29, 2018.
  12. O. A. Hasan, R. S. Ali, A. T. Rashid, “Centralized approach for multi-node localization and identification,” Iraq J. Electrical and Electronic Engineering, vol.12, no 2, pp. 178-187, 2016.
  13. I. S. Alfurati and Abdulmuttalib T. Rashid, “Practical Implementation of an Indoor Robot Localization and Identification System using an Array of Anchor Nodes,” Iraqi Journal of Electrical and Electronic Engineering, DOI: 10.37917/ijeee.16.1.2.
  14. J. Guivant, E. Nebot, and S. Baiker, “Localization and map building using laser range sensors in outdoor applications,” Journal of Robotic Systems, vol.17, no.10, pp. 565-583, 2000.
  15. R. J. Guivant, F. Masson, and E. Nebot, “Simultaneous localization and map building using natural features and absolute information,” Robotics and Autonomous Systems, vol.40, pp. 79–90, 2002.
  16. I. S. Alfurati and Abdulmuttalib T. Rashid, “Multi- Robot localization system using an array of LEDs and LDR sensors,” International Journal of Computer AL-Forati & Rashid Applications (0975 – 8887), Vol. 176, no. 10, April 2020.
  17. V. Jungnickel, V. Pohl, S. Nönnig, and C. V. Helmolt, “A Physical Model of the Wireless Infrared Communication Channel,” IEEE Journal On Selected Areas In Communications, vol. 20, no. 3, pp.159-209, 2002.
  18. C. Galvan, I. G. Tejada, and R. Brena, “Wifi Bluetooth based combined positioning algorithm,” Procedia Engineering 35, pp.101–108, 2012.
  19. Raghavan AN, Ananthapadmanaban H, Sivamurugan MS, Ravindran B, “Accurate mobile robot localization in indoor environments using Bluetooth,” International conference on robotics and automation, pp 4391–4396, 2010.
  20. G. Jekabsons, and V. Kairish, “An analysis of wi-fi based indoor positioning accuracy,” Appl Comput Syst., vol. 44, pp.131–137, 2011.
  21. G. Cho SH and Hong S, “Map-based indoor robot navigation and localization using a laser range finder,” 11th international conference on control automation robotics vision, pp 1559–1564, 2010.
  22. Yayan U, Yucel H, and Yazıcı A, “A low cost ultrasonic based positioning system for the indoor navigation of mobile robots,” J Intell Robot Syst., vol. 78, no. 3, pp. 541–552, 2015.
  23. G. Mi J and Takahashi Y, “Performance analysis of mobile robot self-localization based on different configurations of the RFID system,” International conference on advanced intelligent mechatronics (AIM), Busan, Korea, pp 1591–1596, July 2015.
  24. I. S. Alfurati and Abdulmuttalib T. Rashid, “Design and Implementation an Indoor Robot Localization System Using Minimum Bounded Circle Algorithm,” The 8th International Conference on Modeling, Simulation and Applied Optimization (ICMSAO'2019), 2019.