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Mobile Robot Path Planning Based on Angle Guided Ant Colony Algorithm

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Advances in Swarm Intelligence (ICSI 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13344))

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Abstract

Ant colony algorithm is easy to fall into local optimum and its convergent speed is slow when solving mobile robot path planning. Therefore, an ant colony algorithm based on angle guided is proposed in this paper to solve the problems. In the choice of nodes, integrate the angle factor into the heuristic information of the ant colony algorithm to guide the ants’ search direction and improve the search efficiency. The pheromone differential updating is carried out for different quality paths and the pheromone chaotic disturbance updating mechanism is introduced, then the algorithm can make full use of the better path information and maintain a better global search ability. According to simulations, its global search is strong and it can range out of local optimum and it is fast convergence to the global optimum. The improved algorithm is feasible and effective.

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Acknowledgement

This work was supported by National Natural Science Foundation of China (No. 61866003).

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Authors and Affiliations

  1. School of Artificial Intelligence, Guangxi University for Nationalities, Nanning, 530006, Guangxi, China

    Yongsheng Li, Yinjuan Huang, Shibin Xuan & Xi Li

  2. College of Electronic Information, Guangxi University for Nationalities, Nanning, 530006, Guangxi, China

    Yuang Wu

Authors
  1. Yongsheng Li
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  2. Yinjuan Huang
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  3. Shibin Xuan
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  4. Xi Li
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  5. Yuang Wu
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Corresponding author

Correspondence to Yinjuan Huang .

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Editors and Affiliations

  1. Peking University, Beijing, China

    Ying Tan

  2. Southern University of Science and Technology, Shenzhen, China

    Yuhui Shi

  3. Shenzhen University, Shenzhen, China

    Ben Niu

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Li, Y., Huang, Y., Xuan, S., Li, X., Wu, Y. (2022). Mobile Robot Path Planning Based on Angle Guided Ant Colony Algorithm. In: Tan, Y., Shi, Y., Niu, B. (eds) Advances in Swarm Intelligence. ICSI 2022. Lecture Notes in Computer Science, vol 13344. Springer, Cham. https://doi.org/10.1007/978-3-031-09677-8_19

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  • DOI: https://doi.org/10.1007/978-3-031-09677-8_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-09676-1

  • Online ISBN: 978-3-031-09677-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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