Journal of Robotics Spectrum

Navigation Techniques and Algorithms for Mobile Robots and Automated Guided Vehicles

Journal of Robotics Spectrum

Received On : 10 January 2024

Revised On : 14 March 2024

Accepted On : 29 March 2024

Published On : 11 May 2024

Volume 02, 2024

Pages : 056-065


Mobile robots are self-contained devices that can navigate and move around their surroundings. They observe their environment and create maps using different sensors such as lidar, Global Positioning System (GPS), and cameras. Automated Guided Vehicles (AGVs) are vehicles that can move independently utilizing different approaches such as underground cables, laser scanners, or GPS systems. This research investigates several navigation methodologies and algorithms used in AGVs and mobile robots. It focusses on the advantages of integrating lidar technology with other sensors as well as how it is used in local navigation techniques. We look at two motion planning techniques for obstacle avoidance: the Artificial Potential Field (APF) approach and the Vector Field Histogram (VFH) algorithm. Two effective techniques for finding the best routes are discussed: the A* algorithm and Dijkstra's algorithm. The various AGV types and their navigation systems (such as wired, guide, laser, vision-based, and gyro-based) are also examined in the study. Neural networks and fuzzy logic are investigated as AGV control techniques, with line following and obstacle avoidance as examples of their use. The study emphasizes how crucial precise and dependable navigation systems are to the effective and secure operation of mobile robots and AGVs.


Artificial Potential Field (APF), Automated Guided Vehicles (AGVs), Vector Field Histogram (VFH), Light Detection and Ranging (LIDAR).

  1. Y. Zeng, R. Zhang, and T. J. Lim, “Wireless communications with unmanned aerial vehicles: opportunities and challenges,” IEEE Communications Magazine, vol. 54, no. 5, pp. 36–42, May 2016, doi: 10.1109/mcom.2016.7470933.
  2. C. C. Eriksen et al., “Seaglider: a long-range autonomous underwater vehicle for oceanographic research,” IEEE Journal of Oceanic Engineering, vol. 26, no. 4, pp. 424–436, Jan. 2001, doi: 10.1109/48.972073.
  3. A. M. Howard, “Real-time stereo visual odometry for autonomous ground vehicles,” 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, Sep. 2008, doi: 10.1109/iros.2008.4651147.
  4. A. Babinec, F. Duchoň, M. Dekan, Z. Mikulová, and L. Jurišica, “Vector Field Histogram* with look-ahead tree extension dependent on time variable environment,” Transactions of the Institute of Measurement and Control, vol. 40, no. 4, pp. 1250–1264, Nov. 2016, doi: 10.1177/0142331216678062.
  5. D. E. Knuth, “A generalization of Dijkstra’s algorithm,” Information Processing Letters, vol. 6, no. 1, pp. 1–5, Feb. 1977, doi: 10.1016/00200190(77)90002-3.
  6. Y. Chen, G.-C. Luo, Y. Mei, J. Yu, and X. Su, “UAV path planning using artificial potential field method updated by optimal control theory,” International Journal of Systems Science, vol. 47, no. 6, pp. 1407–1420, Jun. 2014, doi: 10.1080/00207721.2014.929191.
  7. D. Foead, A. Ghifari, M. B. Kusuma, N. Hanafiah, and E. Gunawan, “A Systematic Literature review of A* pathfinding,” Procedia Computer Science, vol. 179, pp. 507–514, Jan. 2021, doi: 10.1016/j.procs.2021.01.034.
  8. S. Kavitha, S. Varuna, and R. Ramya, “A comparative analysis on linear regression and support vector regression,” 2016 Online International Conference on Green Engineering and Technologies (IC-GET), Nov. 2016, doi: 10.1109/get.2016.7916627.
  9. R. M. P, S. Ponnan, S. Shelly, Md. Z. Hussain, M. Ashraf, and A. Haldorai, “Autonomous navigation system based on a dynamic access control architecture for the internet of vehicles,” Computers and Electrical Engineering, vol. 101, p. 108037, Jul. 2022, doi: 10.1016/j.compeleceng.2022.108037.
  10. D. Díaz and L. Marín, “VFH+D: an improvement on the VFH+ algorithm for dynamic obstacle avoidance and local planning,” IFACPapersOnLine, vol. 53, no. 2, pp. 9590–9595, Jan. 2020, doi: 10.1016/j.ifacol.2020.12.2450.
  11. I. Ulrich and J. Borenstein, “VFH+: reliable obstacle avoidance for fast mobile robots,” Proceedings. 1998 IEEE International Conference on Robotics and Automation (Cat. No.98CH36146), Nov. 2002, doi: 10.1109/robot.1998.677362.
  12. S. Clain, D. A. R. Lopes, and R. M. S. Pereira, “Very high-order Cartesian-grid finite difference method on arbitrary geometries,” Journal of Computational Physics, vol. 434, p. 110217, Jun. 2021, doi: 10.1016/
  13. J. Borenstein and Y. Koren, “The vector field histogram-fast obstacle avoidance for mobile robots,” IEEE Transactions on Robotics and Automation, vol. 7, no. 3, pp. 278–288, Jun. 1991, doi: 10.1109/70.88137.
  14. Y. H. Hwang and N. Ahuja, “A potential field approach to path planning,” IEEE Transactions on Robotics and Automation, vol. 8, no. 1, pp. 23– 32, Jan. 1992, doi: 10.1109/70.127236.
  15. E. Burgos and S. Bhandari, “Potential flow field navigation with virtual force field for UAS collision avoidance,” 2016 International Conference on Unmanned Aircraft Systems (ICUAS), Jun. 2016, doi: 10.1109/icuas.2016.7502641.
  16. O. Amine and M. Mestari, “Predicting A* search algorithm heuristics using neural networks,” 2021 International Conference on Electrical, Computer and Energy Technologies (ICECET), Dec. 2021, doi: 10.1109/icecet52533.2021.9698700.
  17. V. N. Nguyen, R. Jenssen, and D. Roverso, “Automatic autonomous vision-based power line inspection: A review of current status and the potential role of deep learning,” International Journal of Electrical Power & Energy Systems, vol. 99, pp. 107–120, Jul. 2018, doi: 10.1016/j.ijepes.2017.12.016.
  18. N. S. Manikandan, G. Kaliyaperumal, S. Hakak, and T. R. Gadekallu, “Curve-Aware Model Predictive Control (C-MPC) Trajectory Tracking for Automated Guided Vehicle (AGV) over On-Road, In-Door, and Agricultural-Land,” Sustainability, vol. 14, no. 19, p. 12021, Sep. 2022, doi: 10.3390/su141912021.
  19. R. Stahn, G. Heiserich, and A. Stopp, “Laser Scanner-Based navigation for commercial vehicles,” IEEE Intelligent Vehicles Symposium, Jun. 2007, doi: 10.1109/ivs.2007.4290242.
  20. A. K. Bourke and G. M. Lyons, “A threshold-based fall-detection algorithm using a bi-axial gyroscope sensor,” Medical Engineering & Physics, vol. 30, no. 1, pp. 84–90, Jan. 2008, doi: 10.1016/j.medengphy.2006.12.001.
  21. Z. Zheng and Y. Lu, “Research on AGV trackless guidance technology based on the global vision,” Science Progress, vol. 105, no. 3, p. 003685042211037, Jul. 2022, doi: 10.1177/00368504221103766.
  22. A. Hamam and N. D. Georganas, “A comparison of Mamdani and Sugeno fuzzy inference systems for evaluating the quality of experience of Hapto-Audio-Visual applications,” 2008 IEEE International Workshop on Haptic Audio-Visual Environments and Games, Oct. 2008, doi: 10.1109/have.2008.4685304.
  23. A. Pandey, R. K. Sonkar, K. K. Pandey, and D. R. Parhi, “Path planning navigation of mobile robot with obstacles avoidance using fuzzy logic controller,” 2014 IEEE 8th International Conference on Intelligent Systems and Control (ISCO), Jan. 2014, doi: 10.1109/isco.2014.7103914.
  24. G. Antonelli, S. Chiaverini, and G. Fusco, “A Fuzzy-Logic-Based approach for mobile robot path tracking,” IEEE Transactions on Fuzzy Systems, vol. 15, no. 2, pp. 211–221, Apr. 2007, doi: 10.1109/tfuzz.2006.879998.
  25. A. Saffiotti, “The uses of fuzzy logic in autonomous robot navigation,” Soft Computing, vol. 1, no. 4, pp. 180–197, Dec. 1997, doi: 10.1007/s005000050020.
  26. O. Linda and M. Manic, “Uncertainty-Robust design of interval Type-2 Fuzzy Logic Controller for Delta Parallel robot,” IEEE Transactions on Industrial Informatics, vol. 7, no. 4, pp. 661–670, Nov. 2011, doi: 10.1109/tii.2011.2166786.
  27. J. Yi, X. Zhang, Z. Ning, and Q. Huang, “Intelligent Robot Obstacle Avoidance System Based on Fuzzy Control,” 2009 First International Conference on Information Science and Engineering, Dec. 2009, doi: 10.1109/icise.2009.688.
  28. H. Fazlollahtabar and M. Saidi‐Mehrabad, “RETRACTED ARTICLE: Methodologies to Optimize Automated guided vehicle scheduling and routing Problems: A review study,” Journal of Intelligent and Robotic Systems, vol. 77, no. 3–4, pp. 525–545, Dec. 2013, doi: 10.1007/s10846013-0003-8.
  29. A. Haldorai, A. Ramu, and S. Murugan, “Cognitive Radio Communication and Applications for Urban Spaces,” Computing and Communication Systems in Urban Development, pp. 161–183, 2019, doi: 10.1007/978-3-030-26013-2_8.
  30. D. Scaramuzza et al., “Vision-Controlled Micro Flying Robots: From system design to autonomous navigation and mapping in GPS-Denied environments,” IEEE Robotics & Automation Magazine, vol. 21, no. 3, pp. 26–40, Sep. 2014, doi: 10.1109/mra.2014.2322295.


Author(s) thanks to Tokyo Institute of Technology for research lab and equipment support.


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Cite this article

Andrzej Wajda, “Navigation Techniques and Algorithms for Mobile Robots and Automated Guided Vehicles”, Journal of Robotics Spectrum, vol.2, pp. 056-065, 2024. doi: 10.53759/9852/JRS202402006.


© 2024 Andrzej Wajda. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.