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

  • Jieke Shi
  • Zhou Yang
  • Junwu ZhuEmail author


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.


Multi-robot system Task allocation Auction Optimization 



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.


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

Authors and Affiliations

  1. 1.College of Information EngineeringYangzhou UniversityYangzhouChina

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