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Ant Colony Algorithm Based on Upper Bound of Nodes for Robot Path Planning Problems

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Bio-inspired Computing: Theories and Applications (BIC-TA 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1159))

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Abstract

In order to solve the problems of ant colony algorithm such as low search efficiency, slow convergence speed and easy to fall into local optimum, an improved ant colony algorithm is proposed in this paper. In the proposed algorithm, the upper bound of path nodes is adjusted and the grid map mode is integrated. The upper bound of nodes are adjusted for driving the optimization process of algorithm. Experiment simulation is carried out based on two specific robot path-planning examples. Results show that the proposed ant colony algorithm can guarantees the global optimization capability, and it also can improve the efficiency of path planning.

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Authors and Affiliations

  1. School of Mechanical and Electric Engineering, Guangzhou University, Guangzhou, 510006, China

    Sizhi Qiu, Bo Hu, Yongbin Quan, Xiaoxin Jian & Haibin Ouyang

Authors
  1. Sizhi Qiu
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  2. Bo Hu
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  3. Yongbin Quan
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  4. Xiaoxin Jian
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  5. Haibin Ouyang
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Corresponding author

Correspondence to Haibin Ouyang .

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Editors and Affiliations

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

    Linqiang Pan

  2. Zhengzhou University, Zhengzhou, China

    Jing Liang

  3. Zhongyuan University of Technology, Zhengzhou, China

    Boyang Qu

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Qiu, S., Hu, B., Quan, Y., Jian, X., Ouyang, H. (2020). Ant Colony Algorithm Based on Upper Bound of Nodes for Robot Path Planning Problems. In: Pan, L., Liang, J., Qu, B. (eds) Bio-inspired Computing: Theories and Applications. BIC-TA 2019. Communications in Computer and Information Science, vol 1159. Springer, Singapore. https://doi.org/10.1007/978-981-15-3425-6_8

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  • DOI: https://doi.org/10.1007/978-981-15-3425-6_8

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-3424-9

  • Online ISBN: 978-981-15-3425-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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