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Path Planning for Multi-AGV Systems based on Two-Stage Scheduling

Volume 13, Number 8, December 2017, pp. 1347-1357
DOI: 10.23940/ijpe.17.08.p16.13471357

Wan Xu, Qi Wang, Mingjin Yu, Daxing Zhao

School of Mechanical Engineering, Hubei University of Technology, Wuhan 430068, China

(Submitted on October 11, 2017; Revised on November 12, 2017; Accepted on November 23, 2017)


This paper proposes an optimal path planning method for the multiple automated guided vehicle (AGV) system based on two-staged scheduling; at the offline scheduling stage, high degree of genetic algorithm is used for the optimal obstacle avoidance path planning of AGV under the static environment, which cannot only solve the premature convergence of genetic algorithm, but also the obstacle avoidance of AGV path planning. Online scheduling stage mainly refers to test the node conflict, opposite conflict and pursuit conflict between AGV and these conflicts are solved to achieve online collision avoidance scheduling for AGV. Finally, the paper uses the secondary developed openTCS for algorithm simulation. The processing methods when all kinds of conflicts occur are simulated in the multi-AGV systems, and the results show that the method is effective and reliable for the path planning of multi-AGV systems.


References: 15

      1. V. F. Caridá, O. Morandin, and C. C. M. Tuma, “Approaches of fuzzy systems applied to an AGV dispatching system in a FMS,” The International Journal of Advanced Manufacturing Technology, vol. 79, no. 1-4, pp. 615-625, July 2015
      2. I. Draganjac, D. Miklić, Z. Kovačić, G. Vasiljević, and S. Bogdan, “Decentralized Control of Multi-AGV Systems in Autonomous Warehousing Applications,” IEEE Transactions on Automation Science & Engineering, vol. 13, no.4, pp. 1433-1447, 2016
      3. H. Fazlollahtabar, M. Saidi-Mehrabad, and E. Masehian, “Mathematical model for deadlock resolution in multiple AGV scheduling and routing network: a case study,” Industrial Robot, vol. 42, no. 3, pp. 252-263, 2015
      4. Z. Lin, X. M. Fan, and Q. C. He, “Scheduling optimization for multi-AGVs in batching area of flexible production system,” Computer Integrated Manufacturing Systems, vol. 18, no. 6, pp. 1168-1175, June 2012
      5. G. D. Liu, D. K. Qu, and L. Zhang, “Two-stage Dynamic Path Planning for Multiple AGV Scheduling Systems,” Robot, vol. 27, no.3, pp. 210-214, 2005
      6. J. Liu, Z. Wang, Q. Xu, and Q. Huang, “Path scheduling for multi-AGV system based on two-staged traffic scheduling scheme and genetic algorithm,” Journal of Computational Methods in Sciences and Engineering, vol. 15, no. 2, pp. 163-169, 2015
      7. X. C. Lu, “Research on Multi-AGV Scheduling Based on Neuro-endocrine Coordination Mechanism,” Nanjing University of Aeronautics and Astronautics, 2014
      8. X. Lu, P. H. Lou, X. M. Qian, and X. Wu, “Scheduling of Automated Guided Vehicles for Material Distribution based on Improved Roved Genetic Algorithm,” Machine Design & Manufacturing Engineering, vol. 44, no. 3, pp. 16-21, March 2015
      9. T. Nishi, and R. Maeno, “Petri Net Decomposition Approach to Optimization of Route Planning Problems for AGV Systems,” IEEE Transactions on Automation Science & Engineering, vol. 7, no. 3, pp. 523-537, July 2010
      10. Q. Sun, “The Research on Path Planning of AGV System,” Zhejiang University, 2012
      11. J. R. Wang, “Dynamic Path Planning and Scheduling for Multiple AGV System Based on Improved Two-stage Traffic Control Scheme,” Nanjing University of Aeronautics and Astronautics, 2008
      12. R. X. Wang, “Genetic algorithm and its application in logistics path optimization,” Jiangnan University, 2009
      13. T. Xia, and N. Wang, “Application of Improved Ant Colony Algorithm in Multiple AGV Scheduling ,” Logistics Technology, vol.34, no.23, pp. 87-89, 2015
      14. H. Y. Zhang, “Research on distributed multi AGV path planning and collision avoidance based on Petri net,” Northwestern Polytechnical University, 2002
      15. D. X. Zhao, M. J. Yu, and W. Xu, “The Optimal Path Planning for AGV based on High Fitness Value of Genetic Algorithm AGV,” Computer Engineering and Design, vol. 38, no.6, pp. 1635-1641, June 2017


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