Skip to main content
SpringerLink
Log in

Sensors applied to automated guided vehicle position control: a systematic literature review

  • Survey Paper
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

The position or movement control of an automated guided vehicle (AGV) is crucial for its operation. However, choosing the AGV position sensor is not a trivial task. This paper investigates the sensors and sensing techniques in machine vision applied in AGV position control in the past 5 years of published academic research. Using a systematic literature review method, we seek to answer the main research question: which sensors and sensing techniques are used in indoor AGV positioning control problems according to the past 5 years of published research and their technological impact. In doing so, we address three sub-question: (i) is the sensor/sensing technique related to the AGV application area; (ii) is the sensor/sensing technique applied to the problem related to the control strategy and/or the AGV guide; (iii) is the sensor/sensing technique related to the required AGV accuracy/sensitivity level. The paper contributions are the application of a systematic method of literature review in AGV position sensors, the research area overview from the selected 31 articles of the past 5 years, and a research agenda proposal.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Data availability

All the data and material are available upon request to the corresponding author.

Notes

  1. Further information and software download available at: http://lapes.dc.ufscar.br/tools/start_tool

  2. A control group is a group of PSs that we have identified before the SLR execution. The group has relevant studies and irrelevant ones. We used it to refine the used search strings by identifying the keywords that return works related to the research questions.

References

  1. Ullrich G (2015) Automated guided vehicle systems. Inf Syst Front 17

  2. Stetter R, Witczak M, Pazera M (2018) Virtual diagnostic sensors design for an automated guided vehicle. Appl Sci 8(5) https://doi.org/10.3390/app8050702, https://www.mdpi.com/2076-3417/8/5/702

  3. Kitchenham B (2004) Procedures for performing systematic reviews. Keele, UK Keele University 33(2004):1–26

    Google Scholar 

  4. Kitchenham BA, Budgen D, Brereton P (2015) Evidence-based software engineering and systematic reviews, vol 4. CRC Press, Boca Raton

    Book  Google Scholar 

  5. Matarić MJ, Maja J et al (2007) The robotics primer. MIT Press, Cambridge

    Google Scholar 

  6. Fabbri S, Silva C, Hernandes E, Octaviano F, Di Thommazo A, Belgamo A (2016) Improvements in the start tool to better support the systematic review process. In: Proceedings of the 20th international conference on evaluation and assessment in software engineering, pp 1–5

  7. Fornari G, de Santiago Júnior VA (2019) Dynamically reconfigurable systems: a systematic literature review. J Intell Robot Syst 95(3-4):829–849

    Article  Google Scholar 

  8. Cawood G, Gorlach I (2015) Navigation and locomotion of a low-cost automated guided cart. In: Proceedings of the 2015 Pattern Recognition Association of South Africa and Robotics and Mechatronics International Conference, PRASA-RobMech 2015 pp 83–88, https://doi.org/10.1109/RoboMech.2015.7359503, (to appear in print)

  9. Pratama P, Nguyen T, Kim H, Kim D, Kim S (2016) Positioning and obstacle avoidance of automatic guided vehicle in partially known environment. Int J Control Automat Syst 14(6):1572–1581. https://doi.org/10.1007/s12555-014-0553-y

    Article  Google Scholar 

  10. Lee JH, Uk-Jin J, Hong YS (2016) Indoor navigation for an automatic guided vehicle with beacon based relative distance estimation. International Conference on Ubiquitous and Future Networks ICUFN 2016-August 424–429 https://doi.org/10.1109/ICUFN.2016.7537063

  11. Pratama P, Gulakari A, Setiawan Y, Kim D, Kim H, Kim S (2016) Trajectory tracking and fault detection algorithm for automatic guided vehicle based on multiple positioning modules. Int J Control Autom Syst 14(2):400–410. https://doi.org/10.1007/s12555-014-0294-y

    Article  Google Scholar 

  12. Zou W, Le D, Berger U, Andulkar M, Ampatzopoulos A (2016) Integration of a PID control system for a mobile system for manufacturing task on continuous conveyor. IFAC-PapersOnLine 49(12):162–167. https://doi.org/10.1016/j.ifacol.2016.07.568

    Article  MathSciNet  Google Scholar 

  13. Song Z, Wu X, Xu T, Sun J, Gao Q, He Y (2016) A new method of AGV navigation based on Kalman filter and a magnetic nail localization. In: 2016 IEEE international conference on robotics and biomimetics, ROBIO 2016, pp 952–957, https://doi.org/10.1109/ROBIO.2016.7866447, (to appear in print)

  14. Bui T (2016) Decentralized motion control for omnidirectional mobile platform–tracking a trajectory using PD fuzzy controller. Lecture Notes Elect Eng 371:803–819. https://doi.org/10.1007/978-3-319-27247-4_67

    Article  Google Scholar 

  15. Wu CF, Xiao-Long W, Qing-Xie C, Xiao-Wei C, Guo-Dong L (2017) Research on visual navigation algorithm of AGV used in the small agile warehouse. In: Proceedings - 2017 Chinese automation congress CAC 2017 2017-January, pp 217–222, https://doi.org/10.1109/CAC.2017.8242766, (to appear in print)

  16. Cho H, Kim E, Jang E, Kim S (2017) Improved positioning method for magnetic encoder type AGV using extended Kalman filter and encoder compensation method. Int J Control Autom Syst 15(4):1844–1856. https://doi.org/10.1007/s12555-016-0544-2

    Article  Google Scholar 

  17. Henebrey J, Gorlach I (2017) Enhancement of an automated guided cart. In: 2016 pattern recognition association of South Africa and robotics and mechatronics international conference PRASA-RobMech 2016, https://doi.org/10.1109/RoboMech.2016.7813177, (to appear in print)

  18. Wu X, Zhang Y, Zou T, Zhao L, Lou P, Yin Z (2018) Coordinated path tracking of two vision-guided tractors for heavy-duty robotic vehicles. Robot Comput Integr Manuf 53:93–107. https://doi.org/10.1016/j.rcim.2018.03.012

    Article  Google Scholar 

  19. Li B, Qi M, Zhang K, Chen B, Zhang Y, Zhou J, Miao L (2018) Landmark-based visual positioning system for automatic guided vehicle. In: CICTP 2018: Intelligence connectivity, and mobility - Proceedings of the 18th COTA international conference of transportation professionals, pp 438–447, https://doi.org/10.1061/9780784481523.044, (to appear in print)

  20. Ye H, Zhou C (2018) A new EFK slam algorithm of lidar-based AGV fused with bearing information. TechConnect Briefs 2018 - Advanced Materials 4:32–39

    Google Scholar 

  21. de Oliveira D, Dos Reis W, Morandin Junior O (2019) A qualitative analysis of a USB camera for AGV control. Sensors (Switzerland) 19(19). https://doi.org/10.3390/s19194111

  22. Yan Q, Yan Hu H, Hang T, Si F u Y (2019) Path tracking of ins AGV corrected by double magnetic nails based on fuzzy controller. In: Proceedings of 2019 IEEE 3rd advanced information management, communicates, electronic and automation control conference IMCEC 2019, pp 1732–1735, https://doi.org/10.1109/IMCEC46724.2019.8984131, (to appear in print)

  23. Chen R, Hao F, Fei Z (2019) Design of magnetic navigation automatic guided vehicle system. J Phys Conf Series 1311(1). https://doi.org/10.1088/1742-6596/1311/1/012040

  24. Kar A, Dhar N, Mishra P, Verma N (2019) Relative vehicle displacement approach for path tracking adaptive controller with multisampling data transmission. IEEE Trans Emerg Topics Comput Intell 3 (4):322–336. https://doi.org/10.1109/TETCI.2018.2865205

    Article  Google Scholar 

  25. Xu H, Xia J, Yuan Z, Cao P (2019) Design and implementation of differential drive AGV based on laser guidance. In: Proceedings of 2019 3rd IEEE international conference on robotics and automation sciences ICRAS, vol 2019, pp 112–117, https://doi.org/10.1109/ICRAS.2019.8808992

  26. Li R, Zhang K, Qi M, Dong Y (2019) Landmark assisted stereo visual odometry. In: International conference on transportation and development 2019: Innovation and sustainability in smart mobility and smart cities - selected papers from the international conference on transportation and development 2019, pp 46–53, https://doi.org/10.1061/9780784482582.005, (to appear in print)

  27. Song W, Luo Z, Tang Y (2019) Research on correction method in AGV motion using inertial guidance and qr code. In: Proceedings of SPIE - The international society for optical engineering, p 11343, https://doi.org/10.1117/12.2548720, (to appear in print)

  28. Dobrzanska M, Dobrzanski P (2020) An application of the Kalman filter in automated guided vehicles. IOP Conf Series Mater Sci Eng 776(1). https://doi.org/10.1088/1757-899X/776/1/012109

  29. Kim D, Kim S (2020) Path following control of automated guide vehicle using camera sensor. Lecture Notes Elect Eng 554:932–938. https://doi.org/10.1007/978-3-030-14907-9_90

    Article  Google Scholar 

  30. Li Y, Huang D, Feng D, Zhang L, Wu X, Huang S, Huang S (2020) Tracking control algorithm based on fuzzy logic for batch-feeding AGV. Lecture Notes Elect Eng 588:564–573. https://doi.org/10.1007/978-981-32-9437-0_59

    Article  Google Scholar 

  31. Pazderski D, Kozłowski K, Gawron T (2015) A unified motion control and low level planning algorithm for a wheeled skid-steering robot. In: IEEE international conference on emerging technologies and factory automation, ETFA 2015-October https://doi.org/10.1109/ETFA.2015.7301490

  32. Maruev I, Lebedko E, Nikulin A (2015) Control system of warehouse robots’ position. In: Proceedings of SPIE - The international society for optical engineering, p 9530, https://doi.org/10.1117/12.2184595, (to appear in print)

  33. Cho JH, Kim YT (2017) Design of autonomous logistics transportation robot system with fork-type lifter. Int J Fuzzy Logic Intell Syst 17(3):177–186. https://doi.org/10.5391/IJFIS.2017.17.3.177

    Article  Google Scholar 

  34. Yin P, Li W, Duan Y (2018) Combinatorial inertial guidance system for an automated guided vehicle. In: ICNSC 2018 - 15th IEEE international conference on networking sensing and control, pp 1–6, https://doi.org/10.1109/ICNSC.2018.8361286, (to appear in print)

  35. Zeng P, Wu F, Zhi T, Xiao L, Zhu S (2019) Research on automatic tool delivery for cnc workshop of aircraft equipment manufacturing. J Phys Conf Series 1215(1). https://doi.org/10.1088/1742-6596/1215/1/012007

  36. Wu X, Angeles J, Zou T, Sun C, Sun Q, Wang L (2020) Receding-horizon vision guidance with smooth trajectory blending in the field of view of mobile robots. Appl Sci (Switzerland) 10(2)

  37. Zhang X, Huo L (2017) A vision/inertia integrated positioning method using position and orientation matching. Math Probl Eng 2017 https://doi.org/10.1155/2017/6835456

  38. Wu X, Sun C, Zou T, Xiao H, Wang L, Zhai J (2019) Intelligent path recognition against image noises for vision guidance of automated guided vehicles in a complex workspace. Appl Sci (Switzerland) 9(19). https://doi.org/10.3390/app9194108

  39. Durant-Whyte H, Bailey T (2006) Simultaneous localization and mapping (SLAM): Part I. IEEE Robot Autom Mag 13(3):99– 110

    Article  Google Scholar 

  40. Boehler W, Vicent MB, Marbs A, et al. (2003) Investigating laser scanner accuracy. Int Archiv Photogrammet Remote Sensing Spatial Inform Sci 34(Part 5):696–701

    Google Scholar 

  41. Berman S, Schechtman E, Edan Y (2009) Evaluation of automatic guided vehicle systems. Robot Comput Integr Manuf 25(3):522–528

    Article  Google Scholar 

  42. Echelmeyer W, Kirchheim A, Lilienthal AJ, Akbiyik H, Bonini M (2011) Performance indicators for robotics systems in logistics applications. In: IROS workshop on metrics and methodologies for autonomous robot teams in Logistics (MMARTLOG), p 55

  43. Bostelman R, Hong T, Cheok G (2015) Navigation performance evaluation for automatic guided vehicles. In: 2015 IEEE international conference on technologies for practical robot applications (TePRA) IEEE, pp 1–6

  44. Ceballos NDM, Valencia JA, Ospina NL (2007) Performance metrics for robot navigation. In: Electronics, robotics and automotive mechanics conference (CERMA 2007). IEEE, pp 518–523

Download references

Funding

This study was financed in part by the Coordenaç ão de Aperfeiçoamento de Pessoal de Nível Superior—Brasil (CAPES)—Finance Code 001. W. dos Reis was financed in part by the Federal Institute of Rio de Janeiro—IFRJ, campus Volta Redonda.

Author information

Authors and Affiliations

  1. Department of Computing, Federal University of São Carlos, São Carlos, SP, Brazil

    Wallace Pereira Neves dos Reis & Orides Morandin Junior

  2. Federal Institute of Education, Science, and Technology of Rio de Janeiro, IFRJ campus Volta Redonda, Volta Redonda, RJ, Brazil

    Wallace Pereira Neves dos Reis

Authors
  1. Wallace Pereira Neves dos Reis
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Orides Morandin Junior
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed to the study conception and design. The first draft of the manuscript was written by Wallace dos Reis, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Wallace Pereira Neves dos Reis.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

dos Reis, W.P.N., Morandin Junior, O. Sensors applied to automated guided vehicle position control: a systematic literature review. Int J Adv Manuf Technol 113, 21–34 (2021). https://doi.org/10.1007/s00170-020-06577-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-020-06577-z

Keywords

Search

Navigation