Path Planning and Control for Multiple AGVs Based on Improved Two-Stage  Traffic Scheduling

Lou Peihuang; Wu Xing; Wang Jiarong

doi:10.20965/ijat.2009.p0157

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IJAT Vol.3 No.2 pp. 157-164

doi: 10.20965/ijat.2009.p0157

(2009)

Paper:

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Path Planning and Control for Multiple AGVs Based on Improved Two-Stage Traffic Scheduling

Lou Peihuang, Wu Xing, and Wang Jiarong

Nanjing University of Aeronautics and Astronautics
29 YuDao Street, Nanjing, PR China

Received:

December 1, 2008

Accepted:

January 30, 2009

Published:

March 5, 2009

Keywords:

AGV, traffic scheduling, conflicts avoidance, motion control, genetic algorithm

Abstract

An improved two-stage traffic scheduling algorithm for path planning and conflict avoidance of multiple AGVs (Automated Guided Vehicle) is combined with an adaptive motion control algorithm for path following of a single AGV in this paper, in order to implement an integrated planning and control system. A genetic algorithm (GA) is used for feasible path planning both offline and online. Multiple objectives and constraints are added to the online GA when some digital map routes cannot be used due to unavoidable conflict. The conflict-free policy we propose changes the speed or route of the AGV with a lower priority to make the conflict settled. Adaptive motion control enables individual AGV to follow the planned paths at any given speed. The planned paths and given speed are the information links connecting traffic scheduling and motion control. Numerical simulation confirms the effectiveness of our traffic scheduling algorithm and the adaptability of the motion control algorithm.

Cite this article as:

L. Peihuang, W. Xing, and W. Jiarong, “Path Planning and Control for Multiple AGVs Based on Improved Two-Stage Traffic Scheduling,” Int. J. Automation Technol., Vol.3 No.2, pp. 157-164, 2009.

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References

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[10] L. Lin et al., “Network model and effective evolutionary approach for AGV dispatching in manufacturing system,” Journal of IntelligentManufacturing, Vol.17, No.4, pp. 465-477, 2006.
[11] W. Chen, B. Li, and H. Sun et al., “Study on Fuzzy-optimal Controlof Vision Navigation for an AGV,” Journal of China Mechanical Engineering, Vol.17, No.24, pp. 2546-2550, 2006.
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[13] D. Gu, H. Hu, and M. Brady, “Motion Predictive Control for Mobile Robots,” Journal of Scientific Instrument, Vol.21, No.2, pp. 155-158, 2000.

This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

[1] [1] J. Barraquand, B. Langlois, and J. C. Latombe, “Numerical PotentialTechniques for Robot Path Planning,” IEEE Trans on Syst.,Man, and Cyb., Vol.22, No.2, pp. 224-241, 1992.

[2] [2] S. M. LaValle and S. A. Hutchinson, “Optimal Motion Planning forMultiple Mobile Robots having Independent Goals,” IEEE Trans.on Robotics and Automation, Vol.14, pp. 912-925, 1998.

[3] [3] R. Alami, F. Ingrand, and S. Qutub, “A Scheme for CoordinatingMulti-robot Planning Activities and Plans Execution,” Proc. of 13thEuropean Conference on AI, 1998.

[4] [4] S. Carpin and E. Pagello, “A Distributed Algorithm for Multi-robotMotion Planning,” Proc. of the fourth European Conference on AdvancedMobile Robotics, 2001.

[5] [5] S. P. Walker et al., “Free-Ranging AGV and Scheduling System,”R. H. Hollier (Ed.), London, U.K.: IFS Ltd., pp. 301-309, 1987.

[6] [6] C. W. Kim and J. M. A. Tanchoco, “Collision-free shortest bidirectionalAGV routing,” Int. J. Prod. Res., Vol.29, No.12, pp. 2377-2391, 1991.

[7] [7] J. Huang, U. S. Palekar, and S. G. Kapoor, “A labeling algorithm forthe navigation of automated guided vehicles,” Trans. ASMEJ. Eng.Ind., Vol.115, pp. 315-321, 1993.

[8] [8] J. H. Lee and B. H. Lee, “Real time traffic control scheme of multipleAGV systems for collision free minimum time motion: a routingtable approach,” IEEE Trans. on Syst., Man, and Cyb., 1998.

[9] [9] E. Roszkowska, “Undirected colored Petri net for modeling and supervisorycontrol of AGV systems,” Proc. of the 6th InternationalWorkshop on Discrete Event Systems, pp. 135-142, 2002.

[10] [10] L. Lin et al., “Network model and effective evolutionary approach for AGV dispatching in manufacturing system,” Journal of IntelligentManufacturing, Vol.17, No.4, pp. 465-477, 2006.

[11] [11] W. Chen, B. Li, and H. Sun et al., “Study on Fuzzy-optimal Controlof Vision Navigation for an AGV,” Journal of China Mechanical Engineering, Vol.17, No.24, pp. 2546-2550, 2006.

[12] [12] Z. Wang and Hong Yue et al., “Optimal Control of Wheeled Robot Trajectory Tracking,” Journal of Mechanical Science and Technology, Vol.25, No.1, pp. 21-23, 2006.

[13] [13] D. Gu, H. Hu, and M. Brady, “Motion Predictive Control for Mobile Robots,” Journal of Scientific Instrument, Vol.21, No.2, pp. 155-158, 2000.