Skip to main content
SpringerLink
Log in

Application of Improved Ant Colony Algorithm in Path Planning

  • Conference paper
  • First Online:
Book cover Complex, Intelligent, and Software Intensive Systems (CISIS 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 993))

Included in the following conference series:

  • 1709 Accesses

Abstract

Aiming at the problem of path planning in topography, this paper studies the influence of relevant factors on speed and path planning, and proposes an intelligent algorithm for terrain path planning based on slope factor and ant colony algorithm. Firstly, the slope factors affecting walking speed are analyzed, then the terrain data are pretreated, and the ant colony algorithm is improved according to the walking requirements. Finally, the optimal walking path is obtained. The simulation results show that the algorithm can achieve better terrain path planning according to walking requirements.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Xu, L., Zhang, S.: Study of path planning in obstacle environment based on an improved ant algorithm. Mach. Electron. 7, 61–64 (2013)

    Google Scholar 

  2. Zhu, S., Xu, F., Teng, Z.: Application of improvement ants algorithm in solving shortest path. Comput. Technol. Dev. 21(7), 202–205 (2011)

    Google Scholar 

  3. Wang, Y., Ye, Q.: Improved strategies of ant colony algorithm for solving shortest path problem. Comput. Eng. Appl. 48(13), 35–38 (2012)

    Google Scholar 

  4. Zhang, Y., Chen, X.: General ant colony algorithm and its applications in robot formation. Pattern Recognit. Artif. Intell. 19, 20(3), 3–8 (2007)

    Google Scholar 

  5. Clornei, I., Kyriakides, E.: Hybrid ant colony—genetic algorithm (GAAPI) for global continuous optimization. IEEE Trans. Syst. Man Cybern. Part B, Cybern. 42(1), 234–245 (2012)

    Article  Google Scholar 

  6. Shah, S., Kothari, R., Chandra, S.: Debugging ants: how ants find the shortest routs. In: 8th International Conference on Information, Communications and Signal Processing, pp. 1–5. IEEE (2011)

    Google Scholar 

  7. Colomi, A., Dorigo, M., Maniezzo, V.: Distributed optimization by ant colonies. In: Proceeding of the First European Conference of Artificial Life. Elsevier Publishing, Paris (1991)

    Google Scholar 

  8. Dorigo, M., Ganbardella, L.M.: Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans. Evol. Comput. 1(1), 53–66 (1997)

    Article  Google Scholar 

  9. Jackson, D.E., Holcombe, M., Ratnieks, F.L.W.: Trail geometry gives polarity to ant foraging networks. Nature 432(7019), 907–909 (2004)

    Article  Google Scholar 

  10. Jia, Z., Siqing, B., Wang, H.: Path planning based on heuristic algorithm. Comput. Simul. 29(1), 135–138 (2012)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Air Force Engineering University, Xi’an, 710051, China

    Zhe Li & Baoxiang Ren

  2. College of Information Engineering, Armed Police Engineering University, Xi’an, 710086, China

    Ruilian Tan

Authors
  1. Zhe Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ruilian Tan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Baoxiang Ren
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhe Li .

Editor information

Editors and Affiliations

  1. Department of Information and Communication Engineering, Fukuoka Institute of Technology, Faculty of Information Engineering, Fukuoka, Japan

    Leonard Barolli

  2. School of Software, University of Technology Sydney (UTS), Ultimo, NSW, Australia

    Farookh Khadeer Hussain

  3. Department of Information and Communication Engineering, Fukuoka Institute of Technology, Faculty of Information Engineering, Fukuoka, Japan

    Makoto Ikeda

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Li, Z., Tan, R., Ren, B. (2020). Application of Improved Ant Colony Algorithm in Path Planning. In: Barolli, L., Hussain, F., Ikeda, M. (eds) Complex, Intelligent, and Software Intensive Systems. CISIS 2019. Advances in Intelligent Systems and Computing, vol 993. Springer, Cham. https://doi.org/10.1007/978-3-030-22354-0_53

Download citation

Publish with us

Policies and ethics

Search

Navigation