Path planning for mobile robots based on improved ant colony optimization

Research output: Chapter in Book/Report/Conference proceedingConference contribution

15 Citations (Scopus)

Abstract

Although traditional ant colony system (ACS) has the ability of fast convergence, it tends to find local optima. To solve this problem, this paper proposes an improved ant colony system algorithm for path planning of mobile robots by considering two main aspects, including continuous tuning of a setting parameter and the establishment of new mechanisms for pheromone updating. As a result, the ability of global searching of the improved ACS can be significantly enhanced in comparison to the traditional ACS algorithms in deriving an optimal path for mobile robots. Simulation results show the proposed approach has a better performance in terms of shortest distance, mean distance, and successful rate of the optimal paths than those obtained by the traditional ACS algorithms.

Original languageEnglish
Title of host publicationProceedings - 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013
Pages2777-2782
Number of pages6
DOIs
Publication statusPublished - 2013 Dec 1
Event2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013 - Manchester, United Kingdom
Duration: 2013 Oct 132013 Oct 16

Publication series

NameProceedings - 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013

Other

Other2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013
CountryUnited Kingdom
CityManchester
Period13/10/1313/10/16

Keywords

  • Ant colony optimization
  • Mobile robot
  • Navigation
  • Path planning

ASJC Scopus subject areas

  • Human-Computer Interaction

Fingerprint Dive into the research topics of 'Path planning for mobile robots based on improved ant colony optimization'. Together they form a unique fingerprint.

  • Cite this

    Hsu, C-C. J., Hou, R. Y., & Wang, W-Y. (2013). Path planning for mobile robots based on improved ant colony optimization. In Proceedings - 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013 (pp. 2777-2782). [6722227] (Proceedings - 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013). https://doi.org/10.1109/SMC.2013.474