Advertisement

An auction-based rescue task allocation approach for heterogeneous multi-robot system

  • Jieke Shi
  • Zhou Yang
  • Junwu ZhuEmail author
Article
  • 79 Downloads

Abstract

Nowadays, robots are faced with real-time, dynamic, complex and confrontational working environment. It is significant to analyze task allocation in multi-robot systems. In this paper, a dynamic auction approach for differentiated tasks under cost rigidities (DAACR) is proposed, which can obtain optimal results in the task allocation of rescue robots. To verify the feasibility of the proposed approach, we investigate the optimality of the DAACR and compare it with other task allocation approaches based on the Hungarian algorithm. The results show that robots using this algorithm can adapt to a variety of complicated work environments, accomplish more tasks in limited time, reduce the delay of task allocation, and improve the overall utility of multi-robot systems.

Keywords

Multi-robot system Task allocation Auction Optimization 

Notes

Acknowledgments

This work was supported by the National Nature Science Foundation of China under Grant 61872313, and Grant 61472344, Grant 61170201, Grant 61070133, the key research projects in education informatization in Jiangsu Province (20180012), in part by the Innovation Foundation for graduates of Jiangsu Province Province under Grant CXLX12 0916, in part by the Natural Science Foundation of the Jiangsu Higher Education Institutions under Grant 14KJB520041, in part by the Advanced Joint Research Project of Technology Department under Grant BY201506106 and Grant BY201506108, and in part by the Yangzhou Science and Technology under Grant YZ2017288 and YZ2018076, and in part by Yangzhou University Jiangdu High-end Equipment Engineering Research Institute Open Project under Grant YDJD201707, in part by the Postgraduate Research and Practice Innovation Program of Jiangsu Province under Grant KYCX18/2366, and in part by Jiangsu Students’ Innovation and Entrepreneurship Training Program under Grant 201811117029Z.

References

  1. 1.
    Booth KE, Nejat G, Beck JC (2016) A constraint programming approach to multi-robot task allocation and scheduling in retirement homes. In: International conference on principles and practice of constraint programming. Springer, pp 539–555Google Scholar
  2. 2.
    Das GP, McGinnity TM, Coleman SA, Behera L (2015) A distributed task allocation algorithm for a multi-robot system in healthcare facilities. J Intell Robot Syst 80(1):33–58CrossRefGoogle Scholar
  3. 3.
    Elango M, Nachiappan S, Tiwari MK (2011) Balancing task allocation in multi-robot systems using k-means clustering and auction based mechanisms. Expert Syst Appl 38(6):6486–6491CrossRefGoogle Scholar
  4. 4.
    Garg R, Kapoor S (2006) Auction algorithms for market equilibrium. Math Oper Res 31(4):714–729MathSciNetCrossRefGoogle Scholar
  5. 5.
    Hooshangi N, Alesheikh AA (2017) Agent-based task allocation under uncertainties in disaster environments: an approach to interval uncertainty. Int J Disaster Risk Reduc 24:160–171CrossRefGoogle Scholar
  6. 6.
    Jiang L, Zhang R (2011) An autonomous task allocation for multi-robot system. J Comput Inf Syst 7(11):3747–3753Google Scholar
  7. 7.
    Lee DH, Zaheer SA, Kim JH (2015) A resource-oriented, decentralized auction algorithm for multirobot task allocation. IEEE Trans Autom Sci Eng 12(4):1469–1481CrossRefGoogle Scholar
  8. 8.
    Liu Y, Yang J, Zheng Y, Wu Z, Yao M (2013) Multi-robot coordination in complex environment with task and communication constraints. Int J Adv Robot Syst 10(5):229CrossRefGoogle Scholar
  9. 9.
    Lu L, Yu J, Zhu Y, Li M (2017) A double auction mechanism to bridge users’ task requirements and providers’ resources in two-sided cloud markets. IEEE Transactions on Parallel and Distributed SystemsGoogle Scholar
  10. 10.
    Lu H, Li B, Zhu J, Li Y, Li Y, Xu X, He L, Li X, Li J, Serikawa S (2017) Wound intensity correction and segmentation with convolutional neural networks. Concurr Comput Practice Exp 29(6):e3927CrossRefGoogle Scholar
  11. 11.
    Lu H, Li Y, Mu S, Wang D, Kim H, Serikawa S (2017) Motor anomaly detection for unmanned aerial vehicles using reinforcement learning. IEEE Internet of Things JournalGoogle Scholar
  12. 12.
    Lu H, Li Y, Chen M, Kim H, Serikawa S (2018) Brain intelligence: go beyond artificial intelligence. Mob Netw Appl 23(2):368–375CrossRefGoogle Scholar
  13. 13.
    Lu H, Li Y, Uemura T, Kim H, Serikawa S (2018) Low illumination underwater light field images reconstruction using deep convolutional neural networks. Future Generation Computer SystemsGoogle Scholar
  14. 14.
    Ponda SS, Johnson LB, How JP (2012) Distributed chance-constrained task allocation for autonomous multi-agent teams. In: American control conference (ACC). IEEE, pp 4528–4533Google Scholar
  15. 15.
    Serikawa S, Lu H (2014) Underwater image dehazing using joint trilateral filter. Comput Electr Eng 40(1):41–50CrossRefGoogle Scholar
  16. 16.
    Tang J, Zhu K, Guo H, Gong C, Liao C, Zhang S (2018) Using auction-based task allocation scheme for simulation optimization of search and rescue in disaster relief. Simul Model Pract Theory 82:132–146CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.College of Information EngineeringYangzhou UniversityYangzhouChina

Personalised recommendations