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Trajectory tracking and fault detection algorithm for automatic guided vehicle based on multiple positioning modules

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  • Control Theory and Applications
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Abstract

This paper presents an implementation and experimental validation of trajectory tracking and fault detection algorithm for sensors and actuators of Automatic Guided Vehicle (AGV) system based on multiple positioning modules. Firstly, the system description and the mathematical modeling of the differential drive AGV system are described. Secondly, a trajectory tracking controller based on the backstepping method is proposed to track the given trajectory. Thirdly, a fault detection algorithm based on the multiple positioning modules is proposed. The AGV uses encoders, laser scanner, and laser navigation system to obtain the position information. To understand the characteristics of each positioning module, their modeling are explained. The fault detection method uses two or more positioning systems and compares them using Extended Kalman Filter (EKF) to detect an unexpected deviation effected by fault. The pairwise differences between the estimated positions obtained from the sensors are called as residue. When the faults occur, the residue value is greater than the threshold value. Fault isolation is obtained by examining the biggest residue. Finally, to demonstrate the capability of the proposed algorithm, it is applied to the differential drive AGV system. The simulation and experimental results show that the proposed algorithm successfully detects the faults when the faults occur.

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Abbreviations

(X C ;Y C ;q C ):

AGV pose in global coordinates frame

v cx , v cy :

linear velocities in local coordinate frame

ω c :

angular velocity of AGV

T :

sampling time

(X R ;Y R ;θ R ):

reference position and orientation

v R , θ R :

reference linear and angular velocities of AGV

e 1, e 2, e 3 :

tracking error in local coordinate of AGV

k 1, k 2, k 3 :

control gains

u c =[v xc ω c ]T :

output controller

r :

wheel radius

b :

distance between the wheels and the geometric center of AGV

(X E , Y E , θ E ):

AGV position and orientation obtained from the encoder position

u E =[Δϕ1 Δϕ2]T :

input for encoder model

Δϕ1 Δϕ2 :

changes of right and left wheel rotation angles

Q E :

process noise covariance for encoder

k r , k l :

error constants related to encoders

(X L , Y L , θ L ):

AGV position and orientation obtained from the laser position module

u L = [Δx L Δy L Δθ L ]T :

input for laser positioning model

Δx L Δy L Δθ L :

changes in x and y direction and orientation

Q L :

process noise covariance for laser positioning

k x , k y , k θ :

error constants related to laser positioning

(X N ;Y N ;θN):

AGV position and orientation obtained from the NAV position module

n x , n y , n θ :

error constants related to NAV positioning

x i k :

state vector

w i k−1 :

process noise at previous time k−1

v i k :

observation noises at current time k

f i (·):

process nonlinear vector function

h i (·):

observation nonlinear vector function

z i k :

output vector at current time k

\(\tilde y_{ik}\) :

measurement innovation at current time k

s i :

residue

Th :

threshold value

References

  1. F. Baghernezhad and K. Khorasani, “A robust fault detection scheme with an application to mobile robots by using adaptive thresholds generated with locally linear models,” Proc. of IEEE Conf. on Computational Intelligence in Control and Automation (CICA), pp. 9–16, 2013. [click]

    Google Scholar 

  2. E. N. Skundrianos and S. G. Tzafestas, “Fault diagnosis on the wheels of a mobile robot using local model neural networks,” IEEE Robotics and Automation Magazine, pp. 83–90, September 2004. [click]

    Google Scholar 

  3. W. Y. Jeong, P. S. Pratama, B. H. Jun, and S. B. Kim, “Fault detection for underwater hexapod robot based on PI observer,” Proc. of Conf. Korea Marine Science and Technology, pp. 1772–1777, 2013.

    Google Scholar 

  4. J. H. Min, W. Y. Jeong, P. S. Pratama, Y. D. Setiawan, H. K. Kim, and S. B. Kim, “Fault diagnosis system of caterpillar wheel type pipeline inspection robot,” Proc. of International symposium on Mechatronics and Robotics, pp.74–78, 2013.

    Google Scholar 

  5. X. J. Li and G. H. Yang, “Adaptive fault detection and isolation approach for actuator stuck faults in closed-loop system,” International Journal of Control, Automation and Systems, vol. 10, no. 4, pp. 830–834, August 2012. [click]

    Article  Google Scholar 

  6. T. Li, L. Y. Wu, and X. J. Wei, “Robust fault detection filter design for uncertain LTI systems based on new bounded real lemma,” International Journal of Control, Automation and Systems, vol. 7, no. 4, pp. 644–650, August 2009. [click]

    Article  Google Scholar 

  7. S. C. Jee, H. J. Lee, and Y. H. Joo, “H —/H ∞ sensor fault detection and isolation in linear time-invariant systems,” International Journal of Control, Automation and Systems, vol. 10, no. 4, pp. 830–834, August 2012.

    Article  MathSciNet  Google Scholar 

  8. Z. Q. Zheng, Y. X. Liu, H.W. Yuan, and Q. S. Wang, “Distributed fault detection and isolation scheme for abrupt and incipient faults in a class of nonlinear systems,” International Journal of Control, Automation and Systems, vol. 10, no. 3, pp. 623–631, June 2012. [click]

    Article  Google Scholar 

  9. M. Sami and R. J. Patton, “Active fault tolerant control for nonlinear systems with simultaneous actuator and sensor faults,” International Journal of Control, Automation and Systems, vol. 11, no. 6, pp. 1149–1161, December 2013. [click]

    Article  Google Scholar 

  10. Z. X. Cai, Z. H. Duan, J. F. Cai, X. B. Zou, and J. X. Yu, “A multiple particle filters method for fault diagnosis of mobile robot dead-reckoning system,” Proc. of IEEE Conf. on Intelligent Robots and Systems, pp. 481–486, 2005. [click]

    Google Scholar 

  11. Z. H. Duan, Z. X. Cai, and J. X. Yu, “Adaptive particle filter for unknown fault detection of wheeled mobile robots,” Proc. of IEEE Conf. on Intelligent Robot and System, pp. 1312–1315, 2006. [click]

    Google Scholar 

  12. A. Monteriu, P. Asthana, K. Valavanis, and S. Longhi, “Experimental validation of a real-time model-based sensor fault detection and isolation system for unmanned ground vehicles,” Proc. of IEEE Conf. on Control and Automation, pp. 1–8, 2006. [click]

    Google Scholar 

  13. P. S. Pratama, T. L. Bui, J. G. Kim, and S. B. Kim, “Trajectory tracking of automatic guided vehicle based on simultaneous localization and mapping method,” Proc. of Conf. Korean Society for Logistic Science and Technology, 2013.

    Google Scholar 

  14. H. H. Lin and C. C. Tsai, “Laser pose estimation and tracking using fuzzy extended information filtering for an autonomous mobile robot,” Journal of Intelligent Robotic System, vol. 53, no. 2, pp. 119–143, 2008. [click]

    Article  Google Scholar 

  15. A. J. Krener, “The convergence of the extended Kalman filter,” Lecture Notes in Control and Information Sciences, vol. 286, 2003, pp. 173–182. [click]

    Article  MathSciNet  MATH  Google Scholar 

  16. P. C. Mahalanobis, “On the generalised distance in statistics,” Proc. of the National Institute of Sciences of India, pp. 49–55, 1936.

    Google Scholar 

  17. C. Hajiyev and F. Caliska, Fault Diagnosis and Reconfiguration in Flight Control Systems, Kluwer Academic Publishers, Springer, 2003. [click]

    Book  MATH  Google Scholar 

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Author information

Authors and Affiliations

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

    Pandu Sandi Pratama, Amruta Vinod Gulakari, Yuhanes Dedy Setiawan, Dae Hwan Kim, Hak Kyeong Kim & Sang Bong Kim

Authors
  1. Pandu Sandi Pratama
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  2. Amruta Vinod Gulakari
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  3. Yuhanes Dedy Setiawan
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  4. Dae Hwan Kim
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  5. Hak Kyeong Kim
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  6. Sang Bong Kim
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Corresponding author

Correspondence to Sang Bong Kim.

Additional information

Recommended by Editor Hyouk Ryeol Choi. This work was supported by a Research Grant of Pukyong National University (2014 year).

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

Amruta Vinod Gulalkari was born in India on November, 1988. She received the B.S. degree from Dept. of Electronics and Telecommunication, SSGB Amravati University, Amravati, India in 2010. She then received the M.S. degree from the Dept. of Interdisciplinary Program of Mechatronics Engineering, Pukyong National University, Busan, Korea in 2015. Her research fields of interest are legged robots, mobile robot control and image processing.

Yuhanes Dedy Setiawan was born in Indonesia on December 28, 1989. He received the B.S. degree in Mechanical Engineering from Diponegoro University, Indonesia, in 2012. He is received his Master degree from the Dept. of Mechanical Design Engineering in Pukyong National University, Busan, Korea in 2014. His research fields of interest are nonlinear control, adaptive control, path planning algorithm, AGV control and SLAM.

Dae Hwan Kim was born in Korea on March, 1982. He received the B.S. degree in Electrical Engineering from Chosun University, Kwangju, Korea in 2008. He received the 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.

Hak Kyeong Kim was born in Korea on November 11, 1958. He received the B.S. and M.S. degrees from Dept. of Mechanical Engineering, 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.

Sang Bong Kim was born in Korea on August 6, 1955. He received the 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., Gulakari, A.V., Setiawan, Y.D. et al. Trajectory tracking and fault detection algorithm for automatic guided vehicle based on multiple positioning modules. Int. J. Control Autom. Syst. 14, 400–410 (2016). https://doi.org/10.1007/s12555-014-0294-y

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