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Dynamic analysis of AGV control under dead-reckoning algorithm

Published online by Cambridge University Press:  01 September 2008

Abílio Azenha*
Affiliation:
Institute for Systems and Robotics, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias s/n, 4200–465 Porto, Portugal
Adriano Carvalho
Affiliation:
Institute for Systems and Robotics, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias s/n, 4200–465 Porto, Portugal
*
*Corresponding author. E-mail: [email protected]

Summary

In this paper, dead-reckoning localization errors for automated guided vehicles (AGVs) in indoors quasi-structured environments are studied. Based on error ellipse localization analysis, dead-reckoning errors arising from mismatched AGV wheels radius are investigated. The dynamic model for a differential-drive AGV type with mismatched wheels radius is also derived. The developed rationale shows that sensor fusion is a key feature for minimizing AGV localization errors. Two simulation scenarios and one experiment are presented to show system performance under AGV proportional-derivative (PD)-type control.

Type
Article
Copyright
Copyright © Cambridge University Press 2008

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References

1.Coelho, P. and Nunes, U., “Lie algebra application to mobile robot control: A tutorial,” Robotica 21, 483493 (2003).CrossRefGoogle Scholar
2.Choi, J.-S. and Kim, B. K., “Near minimum-time direct voltage control algorithms for wheeled mobile robots with current and voltage constraints,” Robotica 19, 2939 (2001).CrossRefGoogle Scholar
3.Borenstein, J., “The CLAPPER: A Dual-drive Mobile Robot with Internal Correction of Dead-reckoning Errors,” Proceedings of the IEEE International Conference on Robotics and Automation San Diego, CA, USA, (1994) pp. 3085–3090.Google Scholar
4.Azenha, A. and Carvalho, A., “Indoor Localization Systematical Errors Analysis for AGVs,” 13th Saint Petersburg International Conference on Integrated Navigation Systems, Saint Petersburg, Russia (2006) pp. 199–204.Google Scholar
5.Tur, J. M. M., Gordillo, J. L. and Borja, C. A., “A closed-form expression for the uncertainty in odometric position estimate of an autonomous vehicle,” IEEE Trans. Robot. 21, 10171022 (2005).CrossRefGoogle Scholar
6.Yi, S.-Y. and Choi, B.-W., “Autonomous navigation of indoor mobile robots using a global ultrasonic system,” Robotica 22, 369374 (2004).CrossRefGoogle Scholar
7.Borenstein, J., Everett, H. R. and Feng, L., Navigating Mobile Robots: Systems and Techniques (A. K. Peters, Wellesley, MA, USA, 1996).Google Scholar
8.Bevly, D. M. and Parkinson, B., “Cascaded Kalman filters for accurate estimation of multiple biases, dead-reckoning navigation, and full state feedback control of ground vehicles,” IEEE Trans. Control Syst. Techonol. 15, 199208 (2007).CrossRefGoogle Scholar
9.Leavitt, J., Sideris, A. and Bobrow, J. E., “High bandwidth tilt measurement using low-cost sensors,” IEEE/ASME Trans. Mechatronics 11, 320327 (2006).CrossRefGoogle Scholar
10.Piyabongkarn, D., Rajamani, R. and Greminger, M., “The development of a MEMS gyroscope for absolute angle measurement,” IEEE Trans. Control Syst. Technol. 13, 185195 (2005).CrossRefGoogle Scholar
11.Geen, J. and Krakauer, D., “New iMEMS angular rate-sensing gyroscope,” Analog Dialogue 37, 1215 (2003).Google Scholar
12.Azenha, A. and Carvalho, A., “Instrumentation and Localization in Quasi-structured Environments for AGV Positioning,” Preprints of 5th IFAC International Symposium on Intelligent Components and Instruments for Control Applications Aveiro, Portugal (2003) pp. 73–80.Google Scholar
13.Azenha, A., Fernandes, G. and Carvalho, A., “AGV Positioning in Quasi-structured Environments,” 11th IEEE International Conference on Methods and Models in Automation and Robotics Miedzyzdroje, Poland, (2005) pp. 543–548.Google Scholar
14.Zhou, Y. and Chirikjian, G. S., “Probabilistic Models of Dead-reckoning Error in Nonholonomic Mobile Robots,” Proceedings of the IEEE International Conference on Robotics and Automation, Taipei, Taiwan (2003) pp. 1594–1599.Google Scholar
15.Yun, X. and Yamamoto, Y., “Internal Dynamics of a Wheeled Mobile Robot,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Yokohama, Japan (1993) pp. 1288–1294.Google Scholar
16.de Carvalho, J. L. M., Dynamical Systems and Automatic Control (Prentice-Hall International, London, 1993).Google Scholar
17.Ojeda, L., Cruz, D., Reina, G. and Borenstein, J., “Current-based slippage detection and odometry correction for mobile robots and planetary rovers,” IEEE Trans. Robot. 22, 366378 (2006).CrossRefGoogle Scholar