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Article:Cross-Country Path Finding using Hybrid approach of BBO and ACO

by Harish Kundra, Puja, Dr. V.K.Panchal
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 7 - Number 6
Year of Publication: 2010
Authors: Harish Kundra, Puja, Dr. V.K.Panchal
10.5120/1166-1369

Harish Kundra, Puja, Dr. V.K.Panchal . Article:Cross-Country Path Finding using Hybrid approach of BBO and ACO. International Journal of Computer Applications. 7, 6 ( September 2010), 20-24. DOI=10.5120/1166-1369

@article{ 10.5120/1166-1369,
author = { Harish Kundra, Puja, Dr. V.K.Panchal },
title = { Article:Cross-Country Path Finding using Hybrid approach of BBO and ACO },
journal = { International Journal of Computer Applications },
issue_date = { September 2010 },
volume = { 7 },
number = { 6 },
month = { September },
year = { 2010 },
issn = { 0975-8887 },
pages = { 20-24 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume7/number6/1166-1369/ },
doi = { 10.5120/1166-1369 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T19:55:40.794878+05:30
%A Harish Kundra
%A Puja
%A Dr. V.K.Panchal
%T Article:Cross-Country Path Finding using Hybrid approach of BBO and ACO
%J International Journal of Computer Applications
%@ 0975-8887
%V 7
%N 6
%P 20-24
%D 2010
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Biogeography based optimization (BBO) and ant colony optimization (ACO) to develop global optimization path. In natural scenario, there are no prior paths and we don't have any prior information about any geographical area. The key factor to achieve a task in such area is Path planning; therefore this research direction is very useful in recent years. This hybrid approach describes autonomous navigation for outdoor vehicles which includes terrain mapping, obstacle detection and avoidance, and goal seeking in cross-country using Swarm Intelligence. These approaches combine the strengths of both Biogeography Based Optimization (BBO) for natural and obstacle detection from the satellite image and Ant Colony Optimization (ACO) algorithm for obstacle avoidance and shortest path to the goal. In this paper this hybrid approach is to explore the improved swarm computing algorithms for the satellite image obstacle extraction and path planning which is safer, shorter, smoother and quickly optimized.

References
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  6. Macarthur, R. and Wilson, E. (1967), “The Theory of Biogeography”, First Edition, Princeton University Press, New Jersey, pp.19-67.
  7. T.M. Lillesand and R.W. kiefer “Remote Sensing & Image Interpretation”, 6th edition, Wiley, 2008.
  8. Marco Dorigo and Thomas Stutzle “Ant Colony Optimization”, The MIT Press Cambridge, Massachusetts, London, England, 2004.
  9. MATLAB (7.0), Mathworks, http://www.mathworks.com
Index Terms

Computer Science
Information Sciences

Keywords

Satellite image Path planning Terrain mapping Obstacle detection and avoidance Swarm Intelligence

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