Skip to main content
SpringerLink
Log in

Robot Path Planning Based on A Hybrid Approach

  • Conference paper
  • First Online:
Neural Information Processing (ICONIP 2017)

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

Included in the following conference series:

Abstract

In this paper, an optimal method based on combination of improved genetic algorithm (IGA) and improved artificial potential field (IAPF) for path planning of mobile robot is proposed. This method consists of two steps. Firstly, free space model of mobile robot is established by using grid-based method and IGA is employed to find a global optimal collision-free path which is usually the shortest through known static environment. Secondly, according to the path obtained by IGA, IAPF is utilized to generate a real-time path to avoid dynamic obstacles. This ensures that robot can avoid obstacles as well as move along the optimal path. Simulation experiments are carried out to verify the superiority of the proposed algorithm.

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 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
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. Ge, S.S., Cui, Y.J.: Dynamic motion planning for mobile robots using potential field method. Auton. Robots 13, 207–222 (2000)

    Article  MATH  Google Scholar 

  2. Alexopoulos, C., Griffin, P.M.: Path planning for a mobile robot. IEEE Trans. Syst. Man. Cybern. 22, 318–322 (1992)

    Article  Google Scholar 

  3. Zhang, Y., Gong, D.W., Zhang, J.H.: Robot path planning in uncertain environment using multi-objective particle swarm optimization. Neurocomputing 103, 172–185 (2013)

    Article  Google Scholar 

  4. Byerly, A., Uskov, A.: A new parameter adaptation method for genetic algorithms and ant colony optimization algorithms. In: IEEE International Conference on Electro Information Technology, Grand Forks, pp. 668–763 (2016)

    Google Scholar 

  5. Zeng, X.P., Li, Y.M., Q, J.: A dynamic chain-like agent genetic algorithm for global numerical optimization and feature selection. Neurocomputing 74, 1214–1228 (2013)

    Google Scholar 

  6. Mathias, H.D., Ragusa, V.R.: An empirical study of crossover and mass extinction in a genetic algorithm for pathfinding in a continuous environment. In: IEEE Congress on Evolutionary Computation, Vancouver, pp. 4111–4118 (2016)

    Google Scholar 

  7. Bao, Y.Q., Wu, H.Y., Chen, Y.: The multi-robot task planning based on improved GA with elite set strategy. In: IEEE International Conference on Robotics and Biomimetics, QingDao, pp. 1367–1371 (2016)

    Google Scholar 

  8. Lu, N.N., Gong, Y.L., Pan, J.: Path planning of mobile robot with path rule mining based on GA. In: Chinese Control and Decision Conference, YinChuan, pp. 1600–1604 (2016)

    Google Scholar 

  9. Shehata, H.H., Schlattmann, J.: Non-dominated sorting genetic algorithm for smooth path planning in unknown environments. In: IEEE International Conference on Autonomous Robot Systems and Competitions, Espinho, pp. 14–21 (2014)

    Google Scholar 

  10. Hu, Y., Yang, S.X.: A knowledge based genetic algorithm for path planning of a mobile robot. In: IEEE International Conference on Robotics and Automation, New Orleans, pp. 4350–4355 (2004)

    Google Scholar 

  11. Tsai, C.C., Huang, H.C., Chan, C.K.: Parallel elite genetic algorithm and its application to global path planning for autonomous robot navigation. IEEE Trans. Ind. Electron. 58, 4813–4821 (2011)

    Article  Google Scholar 

  12. Alajlan, M., Koubâa, A., Châari, I., Bennaceur, H., Ammar, A.: Global path planning for mobile robots in large-scale grid environments using genetic algorithms. In: International Conference on Individual and Collective Behaviors in Robotics, Sousse, pp. 1–8 (2013)

    Google Scholar 

Download references

Acknowledgments

The work was supported by the Natural Science Foundation of China under Grants 61673188 and 61761130081, the National Key Research and Development Program of China under Grant 2016YFB0800402, the Foundation for Innovative Research Groups of Hubei Province of China under Grant 2017CFA005.

Author information

Authors and Affiliations

  1. School of Automation, Huazhong University of Science and Technology, Wuhan, 430074, China

    Zhou Jiang & Zhigang Zeng

Authors
  1. Zhou Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhigang Zeng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhigang Zeng .

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

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Jiang, Z., Zeng, Z. (2017). Robot Path Planning Based on A Hybrid Approach. 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_30

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-70093-9_30

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-70092-2

  • Online ISBN: 978-3-319-70093-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation