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An Optimization Task Scheduling Model for Multi-robot Systems in Intelligent Warehouses

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Book cover Bio-Inspired Computing: Theories and Applications (BIC-TA 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1565))

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Abstract

Task allocation and path planning are commonly taken into consideration when utilizing multi-robot systems in intelligent warehouses. However, the importance of picking stations, which affects the efficiency of the system, is often ignored. To tackle this problem, this study has designed a novel scheduling model to improve the efficiency of the multi-robot system. Unlike the original scheduling model, the queuing time is taken into account in the newly designed scheduling model. Additionally, two approaches are applied in order to improve the designed model. Specifically, the balanced heuristic mechanism (BHM) is used to choose the optimal picking station to shorten the queuing time. The method of task reordering based on task correlation (TRBTC) is also adopted to reduce the travel cost. To verify the efficiency of this method, the proposed method is applied to the different task allocation schemes of intelligent warehouse systems and compared with the original model. Simulation results show that overall superior performance is achieved in the warehouse system.

Supported by the Key R&D program of Shanxi Province (International Cooperation) under Grant No. 201903D421048.

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References

  1. Ahmadi, M., Stone, P.: A multi-robot system for continuous area sweeping tasks. In: Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006, pp. 1724–1729. IEEE (2006)

    Google Scholar 

  2. Bogue, R.: Growth in e-commerce boosts innovation in the warehouse robot market. Ind. Robot Int. J. 43, 583–587 (2016)

    Google Scholar 

  3. Bolu, A., Korçak, O.: Adaptive task planning for multi-robot smart warehouse. IEEE Access 9, 27346–27358 (2021)

    Article  Google Scholar 

  4. Chetty, S., Adewumi, A.O.: Comparison study of swarm intelligence techniques for the annual crop planning problem. IEEE Trans. Evol. Comput. 18(2), 258–268 (2013)

    Article  Google Scholar 

  5. Crandall, J.W., Cummings, M.L., Della Penna, M., De Jong, P.M.: Computing the effects of operator attention allocation in human control of multiple robots. IEEE Trans. Syst. Man Cybern.-Part A Syst. Hum. 41(3), 385–397 (2010)

    Article  Google Scholar 

  6. Cui, Z., Li, F., Zhang, W.: Bat algorithm with principal component analysis. Int. J. Mach. Learn. Cybern. 10(3), 603–622 (2018). https://doi.org/10.1007/s13042-018-0888-4

    Article  Google Scholar 

  7. Cui, Z., Zhang, J., Wang, Y., Cao, Y., Cai, X., Zhang, W., Chen, J.: A pigeon-inspired optimization algorithm for many-objective optimization problems. Sci. China Inf. Sci. 62(7), 1–3 (2019). https://doi.org/10.1007/s11432-018-9729-5

    Article  Google Scholar 

  8. Culmer, P.R., Jackson, A.E., Makower, S., Richardson, R., Cozens, J.A., Levesley, M.C., Bhakta, B.B.: A control strategy for upper limb robotic rehabilitation with a dual robot system. IEEE/ASME Trans. Mechatron. 15(4), 575–585 (2009)

    Article  Google Scholar 

  9. Das, P.K., Behera, H.S., Panigrahi, B.K.: A hybridization of an improved particle swarm optimization and gravitational search algorithm for multi-robot path planning. Swarm Evol. Comput. 28, 14–28 (2016)

    Article  Google Scholar 

  10. Dou, J., Chen, C., Yang, P.: Genetic scheduling and reinforcement learning in multirobot systems for intelligent warehouses. Mathematical Problems in Engineering 2015 (2015)

    Google Scholar 

  11. Elango, M., Nachiappan, S., Tiwari, M.K.: Balancing task allocation in multi-robot systems using k-means clustering and auction based mechanisms. Expert Syst. Appl. 38(6), 6486–6491 (2011)

    Article  Google Scholar 

  12. Faigl, J., Kulich, M., Přeučil, L.: Goal assignment using distance cost in multi-robot exploration. In: 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3741–3746. IEEE (2012)

    Google Scholar 

  13. Goyal, V., Levi, R., Segev, D.: Near-optimal algorithms for the assortment planning problem under dynamic substitution and stochastic demand. Oper. Res. 64(1), 219–235 (2016)

    Article  MathSciNet  Google Scholar 

  14. Lamballais, T., Roy, D., De Koster, M.: Estimating performance in a robotic mobile fulfillment system. Eur. J. Oper. Res. 256(3), 976–990 (2017)

    Article  Google Scholar 

  15. Li, M., Gu, S., Chen, G., Zhu, Z.: A rfid-based intelligent warehouse management system design and implementation. In: 2011 IEEE 8th International Conference on e-Business Engineering, pp. 178–184. IEEE (2011)

    Google Scholar 

  16. Mariottini, G.L., et al.: Vision-based localization for leader-follower formation control. IEEE Trans. Rob. 25(6), 1431–1438 (2009)

    Article  Google Scholar 

  17. Matsuo, Y., Tamura, Y.: Tree formation multi-robot system for victim search in a devastated indoor space. In: 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No. 04CH37566), vol. 2, pp. 1071–1076. IEEE (2004)

    Google Scholar 

  18. Melo, R.S., Macharet, D.G., Campos, M.F.M.: Collaborative object transportation using heterogeneous robots. In: Robotics, pp. 172–191. Springer (2016)

    Google Scholar 

  19. Nagarajan, T., Thondiyath, A.: Heuristic based task allocation algorithm for multiple robots using agents. Procedia Eng. 64, 844–853 (2013)

    Article  Google Scholar 

  20. Nam, C., Shell, D.A.: Assignment algorithms for modeling resource contention in multirobot task allocation. IEEE Trans. Autom. Sci. Eng. 12(3), 889–900 (2015)

    Article  Google Scholar 

  21. Rubio-Largo, A., Vega-Rodriguez, M.A., Gomez-Pulido, J.A., Sanchez-Perez, J.M.: A comparative study on multiobjective swarm intelligence for the routing and wavelength assignment problem. IEEE Trans. Syst. Man Cybern. Part C (Applications and Reviews) 42(6), 1644–1655 (2012)

    Google Scholar 

  22. Tee, K.X., Chew, M.T., Demidenko, S.: An intelligent warehouse stock management and tracking system based on silicon identification technology and 1-wire network communication. In: 2011 Sixth IEEE International Symposium on Electronic Design, Test and Application, pp. 110–115. IEEE (2011)

    Google Scholar 

  23. Turduev, M., Cabrita, G., Kırtay, M., Gazi, V., Marques, L.: Experimental studies on chemical concentration map building by a multi-robot system using bio-inspired algorithms. Auton. Agent. Multi-Agent Syst. 28(1), 72–100 (2012). https://doi.org/10.1007/s10458-012-9213-x

    Article  Google Scholar 

  24. Wang, Y., et al.: A novel bat algorithm with multiple strategies coupling for numerical optimization. Mathematics 7(2), 135 (2019)

    Article  Google Scholar 

  25. Zhang, K., Collins, E.G., Jr., Shi, D.: Centralized and distributed task allocation in multi-robot teams via a stochastic clustering auction. ACM Trans. Autonomous Adaptive Syst. (TAAS) 7(2), 1–22 (2012)

    Article  Google Scholar 

  26. Zhang, M., Wang, H., Cui, Z., Chen, J.: Hybrid multi-objective cuckoo search with dynamical local search. Memetic Comput. 10(2), 199–208 (2017). https://doi.org/10.1007/s12293-017-0237-2

    Article  Google Scholar 

  27. Zhou, L., Shi, Y., Wang, J., Yang, P.: A balanced heuristic mechanism for multirobot task allocation of intelligent warehouses. Mathematical Problems in Engineering 2014 (2014)

    Google Scholar 

  28. Zhu, W., Liu, J.: Research on multi-robot scheduling algorithm in intelligent storage system. J. Phys. Conf. Ser. 1738, 012047 (2021)

    Google Scholar 

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Authors and Affiliations

  1. Taiyuan University of Science and Technology, Taiyuan, China

    Xuechun Jing & Zhihua Cui

Authors
  1. Xuechun Jing
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  2. Zhihua Cui
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Corresponding author

Correspondence to Zhihua Cui .

Editor information

Editors and Affiliations

  1. Huazhong University of Science and Technology, Wuhan, China

    Linqiang Pan

  2. Taiyuan University of Science and Technology, Taiyuan, China

    Zhihua Cui

  3. Taiyuan University of Science and Technology, Taiyuan, China

    Jianghui Cai

  4. Huazhong University of Science and Technology, Wuhan, China

    Lianghao Li

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Jing, X., Cui, Z. (2022). An Optimization Task Scheduling Model for Multi-robot Systems in Intelligent Warehouses. In: Pan, L., Cui, Z., Cai, J., Li, L. (eds) Bio-Inspired Computing: Theories and Applications. BIC-TA 2021. Communications in Computer and Information Science, vol 1565. Springer, Singapore. https://doi.org/10.1007/978-981-19-1256-6_1

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  • DOI: https://doi.org/10.1007/978-981-19-1256-6_1

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-1255-9

  • Online ISBN: 978-981-19-1256-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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