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Reseach Article

Article:Collaborative Evolutionary Planning Framework (EPF) for Route Planning

by Ayesha Khan, Abdul Rauf Baig, Kashif Zafar
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 4 - Number 9
Year of Publication: 2010
Authors: Ayesha Khan, Abdul Rauf Baig, Kashif Zafar
10.5120/853-1194

Ayesha Khan, Abdul Rauf Baig, Kashif Zafar . Article:Collaborative Evolutionary Planning Framework (EPF) for Route Planning. International Journal of Computer Applications. 4, 9 ( August 2010), 33-38. DOI=10.5120/853-1194

@article{ 10.5120/853-1194,
author = { Ayesha Khan, Abdul Rauf Baig, Kashif Zafar },
title = { Article:Collaborative Evolutionary Planning Framework (EPF) for Route Planning },
journal = { International Journal of Computer Applications },
issue_date = { August 2010 },
volume = { 4 },
number = { 9 },
month = { August },
year = { 2010 },
issn = { 0975-8887 },
pages = { 33-38 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume4/number9/853-1194/ },
doi = { 10.5120/853-1194 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T19:53:02.655630+05:30
%A Ayesha Khan
%A Abdul Rauf Baig
%A Kashif Zafar
%T Article:Collaborative Evolutionary Planning Framework (EPF) for Route Planning
%J International Journal of Computer Applications
%@ 0975-8887
%V 4
%N 9
%P 33-38
%D 2010
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This research presents a collaborative evolutionary planning framework for large scale grid exploration and planning problems. It caters for both dynamic and unknown environments using evolutionary techniques. In addition, we integrate the exploration and planning process in a unified framework using multi agent system. As a proof of success, we have developed extensive simulations with realistic obstacles and targets. Our algorithm addresses the issues involved during such exploration and post exploration route planning. It acts as a controller and navigator for multiple agents and demonstrates the applicability for two different domains, Field Exploration and Route Planning. The EPF uses an optimized search algorithm for exploration phase and genetic algorithm for optimization of route in dynamic environments. The EPF can be used in different exploration and route planning problems but this paper focuses on obstacle detection and avoidance for its implementation.

References
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Index Terms

Computer Science
Information Sciences

Keywords

autonomous agents route planning genetic algorithm