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Volume 5 Issue 1
Jan.  2018

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2018  >  5(1): 155-168
Danfeng Wu, Guangping Zeng, Lingguo Meng, Weijian Zhou and Linmin Li, "Gini Coefficient-based Task Allocation for Multi-robot Systems With Limited Energy Resources," IEEE/CAA J. Autom. Sinica, vol. 5, no. 1, pp. 155-168, Jan. 2018. doi: 10.1109/JAS.2017.7510385
Citation: Danfeng Wu, Guangping Zeng, Lingguo Meng, Weijian Zhou and Linmin Li, "Gini Coefficient-based Task Allocation for Multi-robot Systems With Limited Energy Resources," IEEE/CAA J. Autom. Sinica, vol. 5, no. 1, pp. 155-168, Jan. 2018. doi: 10.1109/JAS.2017.7510385
shu

Gini Coefficient-based Task Allocation for Multi-robot Systems With Limited Energy Resources

doi: 10.1109/JAS.2017.7510385
  • 1.

    School of Software, Liaoning Technical University, Huludao 125105, China

  • 2.

    School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China

Funds:

the National High Technology Research and Development Program of China (863 Program) 2015AA015403

the National Natural Science Foundation of China 61404069

the National Natural Science Foundation of China 61401185

the Project of Education Department of Liaoning Province LJYL052

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    Abstract
  • Nowadays, robots generally have a variety of capabilities, which often form a coalition replacing human to work in dangerous environment, such as rescue, exploration, etc. In these operating conditions, the energy supply of robots usually cannot be guaranteed. If the energy resources of some robots are consumed too fast, the number of the future tasks of the coalition will be affected. This paper will develop a novel task allocation method based on Gini coefficient to make full use of limited energy resources of multi-robot system to maximize the number of tasks. At the same time, considering resources consumption, we incorporate the market-based allocation mechanism into our Gini coefficient-based method and propose a hybrid method, which can flexibly optimize the task completion number and the resource consumption according to the application contexts. Experiments show that the multi-robot system with limited energy resources can accomplish more tasks by the proposed Gini coefficient-based method, and the hybrid method can be dynamically adaptive to changes of the work environment and realize the dual optimization goals.

     

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