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Multi-Robot Task Allocation Based on Cloud Ant Colony Algorithm

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Neural Information Processing (ICONIP 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10637))

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Abstract

In this paper, an improved ant colony algorithm based on cloud model is proposed to study the multi-robot task allocation problem. The improvement of the proposed algorithm mainly includes the construction of adaptive control mechanism, pheromone updating mechanism and task point selection mechanism. Some important optimization operators are designed such as evaluation of pheromone distribution, determination of suboptimal solution and selection of task point. Simulation results show that the proposed algorithm can obtain high-quality solution and fast convergence, the effect is significant.

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Acknowledgment

This work is partially supported by National Natural Science Foundation of China (No. 61673117) and two other research grants (Nos. rcxm201713 and 2017FSKJ11).

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Authors and Affiliations

  1. School of Computer and Information Engineering, Fuyang Teachers College, Fuyang, Anhui Province, China

    Xu Li, Zhengyan Liu & Fuxiao Tan

Authors
  1. Xu Li
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  2. Zhengyan Liu
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  3. Fuxiao Tan
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Corresponding author

Correspondence to Xu Li .

Editor information

Editors and Affiliations

  1. Guangdong University of Technology, Guangzhou, China

    Derong Liu

  2. Guangdong University of Technology, Guangzhou, China

    Shengli Xie

  3. South China University of Technology, Guangzhou, China

    Yuanqing Li

  4. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Dongbin Zhao

  5. King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia

    El-Sayed M. El-Alfy

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Li, X., Liu, Z., Tan, F. (2017). Multi-Robot Task Allocation Based on Cloud Ant Colony Algorithm. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10637. Springer, Cham. https://doi.org/10.1007/978-3-319-70093-9_1

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  • DOI: https://doi.org/10.1007/978-3-319-70093-9_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-70092-2

  • Online ISBN: 978-3-319-70093-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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