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Positioning and obstacle avoidance of automatic guided vehicle in partially known environment

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Abstract

This paper presents positioning and obstacle avoidance of Automatic Guidance Vehicle (AGV) in partially known environment. To do this task, the followings are done. Firstly, the system configuration of AGV is described. Secondly, mathematical kinematic modeling of the AGV is presented to understand its characteristics and behavior. Thirdly, the Simultaneous Localization and Mapping (SLAM) algorithm based on the laser measurement system and encoders is proposed. The encoders are used for detecting the motion state of the AGV. In a slippery environment and a high speed AGV condition, encoder positioning method generates big error. Therefore, Extended Kalman Filter (EKF) is used to get the best position estimation of AGV by combining the encoder positioning result and landmark positions obtained from the laser scanner. Fourthly, to achieve the desired coordinate, D* Lite algorithm is used to generate a path from the start point to the goal point for AGV and to avoid unknown obstacles using information obtained from laser scanner. A backstepping controller based on Lyapunov stability is proposed for tracking the desired path generated by D* Lite algorithm. Finally, the effectiveness of the proposed algorithms and controller are verified by using experiment. The experimental results show that the AGV successfully reaches the goal point with an acceptable small error.

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References

  1. P. T. Doan, T. T. Nguyen, V. T. Dinh, H. K. Kim, and S. B. Kim, “Path tracking control of automatic guided vehicle using camera sensor,” Proceedings of the 1st International Symposium on Automotive and Convergence Engineering, pp. 20–26, 2011.

    Google Scholar 

  2. C. Chen, B. Wang, and Q. T. Ye, “Application of automated guided vehicle (AGV) based on inductive guidance for newsprint rolls transportation system,” Journal of Donghua University, vol. 21, no. 2, pp. 88–92, 2004.

    Google Scholar 

  3. S. Y. Lee and H.W. Yang “Navigation of automated guided vehicles using magnet spot guidance method,” Journal of Robotics and Computer-Integrated Manufacturing, vol. 28, no. 2, pp. 425–436, June 2012. [click]

    Article  Google Scholar 

  4. S. C. Yuan and L. Yao, “Robust type-2 fuzzy control of an automatic guided vehicle for wall-following,” Proceedings of International Conf. of Soft Computing and Pattern Recognition, pp. 172–177, 2009. [click]

    Google Scholar 

  5. P. S. Pratama, B. T. Luan, T. T. Tran, H. K. Kim, and S. B. Kim, “Trajectory tracking algorithm for automatic guided vehicle based on adaptive backstepping control method,” AETA 2013: Recent Advances in Electrical Engineering and Related Sciences/Lecture Notes in Electrical Engineering, vol. 282, pp. 535–544, 2014. [click]

    Google Scholar 

  6. J. Bruce and M. Veloso, “Real-time randomized path planning for robot navigation,” Proceedings of the 2002 IEEE/RSJ International Conference on Intelligent Robots and Systems EPFL, pp. 2383–2388, 2002.

    Google Scholar 

  7. D. H. Kim, “Escaping route method for a trap situation in local path planning,” International Journal of Control, Automation, and Systems, vol. 7, no. 3, pp. 495–500, 2009. [click]

    Article  Google Scholar 

  8. K. Konolige, “A gradient method for realtime robot control,” Proceedings of International Conf. on Intelligent Robots and Systems, pp. 639–646, 2000. [click]

    Google Scholar 

  9. R. Simmons, “The curvature-velocity method for local obstacle avoidance,” Proceedings of International Conference on Robotics and Automation, pp. 3375–3382, 1996. [click]

    Chapter  Google Scholar 

  10. D. Fox, W. Burgard, and S. Thrun, “The dynamic window approach to collision avoidance,” IEEE Robotics and Automation, vol. 4, no. 1, pp. 23–33, 1997. [click]

    Article  Google Scholar 

  11. P. S. Pratama, S. K. Jeong, S. S. Park, and S. B. Kim, “Moving object tracking and avoidance algorithm for differential driving AGV based on laser measurement technology,” International Journal of Science and Engineering, vol. 4, no. 1, pp. 11–15, January 2013.

    Google Scholar 

  12. Y. Dai and S. G. Lee, “Formation control of mobile robots with obstacle avoidance based on GOACM using onboard sensors,” International Journal of Control, Automation, and Systems, vol. 12, no. 5, pp. 1077–1089, August 2014. [click]

    Article  Google Scholar 

  13. N. Hung, J. S. Im, S. K. Jeong, H. K. Kim, and S. B. Kim, “Design of a sliding mode controller for an automatic guided vehicle and its implementation,” International Journal of Control, Automation, and Systems, vol. 8, no. 1, pp. 81–90, 2010.

    Article  Google Scholar 

  14. T. A. Tamba, B. H. Hong, and K. S. Hong, “A path following control of an unmanned autonomous forklift,” International Journal of Control, Automation, and Systems, vol. 7, no. 1, pp. 113–122, March 2009. [click]

    Article  Google Scholar 

  15. Y. D. Setiawan, P. S. Pratama, S. K. Jeong, V. H, Duy, and S. B. Kim, “Experimental comparison of A* and D* lite path planning algorithms for differential drive automated guided vehicle,” AETA 2013: Recent Advances in Electrical Engineering and Related Sciences/Lecture Notes in Electrical Engineering, vol. 282, pp. 555–564, 2014. [click]

    Google Scholar 

  16. N. M. Kwok, Q. P. Ha, and G. Fang, “Data association in bearing-only SLAM using a cost function-based approach,” Proceedings of IEEE International Conference on Robotics and Automation, pp. 4108–4113, 2007. [click]

    Google Scholar 

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Authors and Affiliations

  1. Department of Mechanical Design Engineering, Pukyong National University, Busan, 48547, Korea

    Pandu Sandi Pratama, Trong Hai Nguyen, Hak Kyeong Kim, Dae Hwan Kim & Sang Bong Kim

Authors
  1. Pandu Sandi Pratama
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  2. Trong Hai Nguyen
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  3. Hak Kyeong Kim
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  4. Dae Hwan Kim
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  5. Sang Bong Kim
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Corresponding author

Correspondence to Sang Bong Kim.

Additional information

Recommended by Associate Editor Dong-Joong Kang under the direction of Editor Fuchun Sun. This research was supported by a grant (11 Transportation System- Logistics 02) from Transportation System Efficiency Program funded by Ministry of Land, Infrastructure and Transport (MOLIT) of Korean government.

Pandu Sandi Pratama was born in Indonesia on November 1, 1986. He received his B.S. degree in Electrical Engineering Dept. of Diponegoro University, Indonesia in 2011. He then received the M.S degree in the Interdisciplinary Program of Mechatronics Engineering Dept., Pukyong National University, Busan, Korea in 2013. He then received a Ph.D. degree in the Dept. of Mechanical Engineering, Pukyong National University, Busan, Korea in 2015. His research fields of interest are computer science, robotic and mobile robot.

Trong Hai Nguyen was born in Vietnam on February 1, 1975. He received his B.S. and M.S. degree in Dept. of Electronics and Telecommunication, Hochiminh City University of Technology, Vietnam in 1999 and 2003. He is currently a student in doctor degree course at Pukyong National University, Busan, Korea. His research fields of interest are nonlinear control, robust control, path planning algorithm, conveyor control.

Hak Kyeong Kim was born in Korea on November 11, 1958. He received his B.S. and M.S. degrees in Dept. of Mechanical Engineering from Pusan National University, Korea in 1983 and 1985. He received his Ph.D. degree from the Dept. of Mechatronics Engineering, Pukyong National University, Busan, Korea in February, 2002. His fields of interest are robust control, biomechanical control, mobile robot control, and image processing control.

Dae Hwan Kim was born in Korea on March, 1982. He received his B.S. degree in Electrical Engineering from Chosun University, Kwangju, Korea in 2008. He then received his M.S and Ph.D degrees in Mechanical engineering from the Pukyong National University, Busan, Korea, in 2009 and 2015, respectively. His fields of interests are robust control, combustion engineering control, and mobile robot control.

Sang Bong Kim was born in Korea on August 6, 1955. He received his B.S. and M.S. degrees from National Fisheries University of Busan, Korea, in 1978 and 1980. He received his Ph.D. degree from Tokyo Institute of Technology, Japan in 1988. After then, he is a Professor of the Dept. of Mechanical Engineering, Pukyong National University, Busan, Korea. His research has been on robust control, biomechanical control, and mobile robot control.

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

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  • DOI: https://doi.org/10.1007/s12555-014-0553-y

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