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Improved positioning method for Magnetic Encoder type AGV using Extended Kalman Filter and Encoder Compensation Method

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  • Robot and Applications
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Abstract

This paper presents an improved positioning method for a Magnetic Encoder type Guided Vehicle (MEGV) using the Extended Kalman Filter and Encoder Compensation Method. The magnetic encoder system is one of several available guidance systems for autonomous guided vehicles using magnetic sticks that are buried at regular intervals (such as near landmarks, turning points, and work places) on designated paths. The system guides MEGVs on a pre-defined path using either of two types of devices: encoders or magnetic positioning devices. The encoder information is used in a range of positions between the magnetic sticks, and the magnetic positioning device is used to correct positioning of MEGV using global positioning of a magnetic stick. However, calculating the exact position of a MEGV is challenging because of errors (cumulative error of the encoder and disturbances in the general magnetic field). Therefore, this study proposes a method, which is a combination of EKF and ECM, for positioning MEGVs. In the proposed method, EKF first estimates the position of the MEGV; then, ECM corrects the error of the encoders. To analyze the performance of the proposed method, a MEGV was designed and developed. The proposed method was compared with three other positioning methods (that use encoders, magnetic encoders, or EKF), and experiments were performed under similar working conditions. The experimental results demonstrated that the proposed method is superior to the other methods.

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Authors and Affiliations

  1. Department of Interdisciplinary Cooperative Course: Robot, Pusan National University, 2, Busandaehak-ro 63beongil, Geumjeong-gu, Busan, 46241, Korea

    Hyunhak Cho

  2. Department of Electrical and Computer Engineering, Pusan National University, 2, Busandaehak-ro 63beon-gil, Geumjeong-gu, Busan, 46241, Korea

    Eun Kyeong Kim, Eunseok Jang & Sungshin Kim

Authors
  1. Hyunhak Cho
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  2. Eun Kyeong Kim
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  3. Eunseok Jang
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  4. Sungshin Kim
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Corresponding author

Correspondence to Sungshin Kim.

Additional information

Recommended by Associate Editor Do Wan Kim under the direction of Editor Euntai Kim. This work was supported by BK21PLUS, Creative Human Resource Development Program for IT Convergence and by the Ministry of Trade, Industry and Energy, Korea under the Industry Convergence Liaison Robotics Creative Graduates Education Program supervised by the KIAT (N0001126).

Hyunhak Cho received the M.S. degree in Interdisciplinary Cooperative Course: Robot from Pusan National University, Busan, Korea, in 2013. He is currently a Ph.D. candidate at Interdisciplinary Cooperative Course: Robot, Pusan National University, Busan, in Korea. His research interests include sensor fusion, mobile robot and localization.

Eun Kyeong Kim received the B.S. and M.S. degree in Electrical and Computer Engineering from Pusan National University, Busan, Korea, in 2014 and 2016. She is currently a Ph.D. candidate at Department of Electrical and Computer Engineering, Pusan National University, Busan, in Korea. Hers research interests include vision camera and 3D recognition.

Eunseok Jang received the B.S. degree in Electronic Engineering from Inje University, Busan, Korea, in 2015. He is currently a M.S. candidate at Department of Electrical and Computer Engineering, Pusan National University, Busan, in Korea. His research interests include control theory, mobile robot and localization.

Sungshin Kim received the Ph.D. degree in electrical and computer engineering from Georgia Institute of Technology, Atlanta, in 1996. He is currently a Professor in the Department of Electrical Engineering, Pusan National University, Busan, Korea. His research interests include intelligent system, control, hierarchical learning structures and data mining.

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Cho, H., Kim, E.K., Jang, E. et al. Improved positioning method for Magnetic Encoder type AGV using Extended Kalman Filter and Encoder Compensation Method. Int. J. Control Autom. Syst. 15, 1844–1856 (2017). https://doi.org/10.1007/s12555-016-0544-2

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  • DOI: https://doi.org/10.1007/s12555-016-0544-2

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