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AGV navigation analysis based on multi-sensor data fusion

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Abstract

For the navigation problem of differential AGV, its motion model was established, and the inertial guidance method based on multi-sensor was adopted. The encoder, gyro, acceleration sensor, ultrasonic sensor and infrared sensor were selected to establish the Kalman filter multi-sensor. And the models and algorithms of the data fusion navigation and the obstacle avoidance were proposed. Further, the simulation calculation was conducted. The research results show that the navigation accuracy of AGV and navigation performance were improved by the method discussed in the paper.

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References

  1. Almasri M, Elleithy K, Alajlan A (2015) Sensor fusion based model for collision free mobile robot navigation. Sensors 16(1):1–24

    Article  Google Scholar 

  2. Benxian X, Haixia L, Cancan Z et al (2005) Navigation research of AGV based-on multi-sensors behaviors fusion. Journal of System Simulation 17(8):1939–1943

    Google Scholar 

  3. Bo Z, Hua Z, Jun J (2014) Multi-sensor information fusion’s improved extended Kalman filter attitude determination. Application Research of Computers 31(04):1035–1038+1042

    Google Scholar 

  4. Cardarelli E, Sabattini L, Secchi C, et al. (2014) Multisensor data fusion for obstacle detection in automated factory logistics IEEE International Conference on Intelligent Computer Communication and Processing. IEEE:221–226

  5. Cecco MD (2002) Self-calibration of AGV inertial-odometric navigation using absolute-reference measurements. IEEE Instrumentation & Measurement Technology Conference 2(2):1513–1518

    Google Scholar 

  6. Congmin Z, Yumei H, Guanwangyi S et al (2008) AGV navigation system with multiple sensors. Chinese Journal of Scientific Instrument 29(11):2419–2423

    Google Scholar 

  7. Congmin Z, Meiyu H, BInliang M et al (2009) Path iterative learning of inertial guided AGV with magnets correction. Transactions of the Chinese Society for Agricultural Machinery 40(7):40–44

    Google Scholar 

  8. Demesure G, Defoort M, Bekrar A et al (2016) Navigation scheme with priority-based scheduling of mobile agents: application to AGV-based flexible manufacturing system. Journal of Intelligent & Robotic Systems 82(3–4):495–512

    Article  Google Scholar 

  9. Jinghua Y, Di L, Feng Y (2013) Robust guidance control of nonholonomic AGV with localization based on multi-source information fusion. Electric Machines and Control 17(9):111–118

    Google Scholar 

  10. Jung K, Kim J, Kim J et al (2014) Positioning accuracy improvement of laser navigation using UKF and FIS. Robotics & Autonomous Systems 62(9):1241–1247

    Article  Google Scholar 

  11. Kim J, Cho H, Kim S (2013) Positioning and driving control of fork-type automatic guided vehicle with laser navigation. International Journal of Fuzzy Logic & Intelligent Systems 13(4):307–314

    Article  Google Scholar 

  12. Kim J, Jung K, Kim J et al (2013) Positioning accuracy improvement of laser navigation using unscented Kalman filter. Robotics & Autonomous Systems 62(9):1241–1247

    Google Scholar 

  13. Lee SY, Yang HW (2012) Navigation of automated guided vehicles using magnet spot guidance method. Robot Comput Integr Manuf 28(3):425–436

    Article  Google Scholar 

  14. Lee IS, Kim JY, Lee JH et al (2012) Kalman filter-based sensor fusion for improving localization of AGV. Adv Mater Res 488-489:1818–1822

    Article  Google Scholar 

  15. Pei L, Xinde L (2015) Obstacle avoidance algorithm of AGV based on multi-sensors information fusion. Journal of Huazhong University of Science and Technology (Nature Science Edition), (s1):224–227

  16. Sang WY, Park SB, Kim JS (2015) Kalman filter sensor fusion for Mecanum wheeled automated guided vehicle localization. Journal of Sensors 2015(8):1–7

    Article  Google Scholar 

  17. Yiqiang W, Shiliu W, Rui F (2011) The multi-sensor fusion based automated guided vehicle system design and control strategy. Computer Applications and Software 28(7):49–52

    Google Scholar 

  18. Yuan P, Chen D, Wang T, et al. (2014) AGV System Based on Multi-sensor Information Fusion International Symposium on Computer, Consumer and Control. IEEE Computer Society, 900–905

  19. Yudanto RG, Petré F. (2015) Sensor fusion for indoor navigation and tracking of automated guided vehicles International Conference on Indoor Positioning and Indoor Navigation. IEEE:1–8

  20. Zaifang Z, Yang X, Haifeng W (2005) Study on navigation system based on multi-sensors information fusion technique. Mechanical Engineer 1:43–44

    Google Scholar 

Download references

Acknowledgments

This research was supported by the National Natural Science Foundation of China (No.51775272); The Fundamental Research Funds for the Central Universities, China (No. NS2014050).

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Authors and Affiliations

  1. College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China

    Ti-chun Wang, Chang-sheng Tong & Ben-ling Xu

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  1. Ti-chun Wang
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  2. Chang-sheng Tong
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  3. Ben-ling Xu
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Correspondence to Ti-chun Wang.

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Wang, Tc., Tong, Cs. & Xu, Bl. AGV navigation analysis based on multi-sensor data fusion. Multimed Tools Appl 79, 5109–5124 (2020). https://doi.org/10.1007/s11042-018-6336-3

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  • DOI: https://doi.org/10.1007/s11042-018-6336-3

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