eprintid: 3001
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/30/01
datestamp: 2023-11-09 15:51:15
lastmod: 2023-11-09 15:51:15
status_changed: 2023-11-09 15:44:46
type: article
metadata_visibility: show
creators_name: Pierre, D.M.
creators_name: Zakaria, N.
creators_name: Pal, A.J.
title: Quantitative and qualitative analysis of unmanned aerial vehicle's path planning using master-slave parallel vector-evaluated genetic algorithm
ispublished: pub
keywords: Computational capability; Contrasting Objectives; Experimental setup; Hazardous environment; Master-slave; Multi objective; Natural disasters; Optimal results; Path planning problems; Path-planning; Qualitative analysis; UAV accidents, Genetic algorithms; Motion planning; Problem solving; Soft computing, Unmanned aerial vehicles (UAV)
note: cited By 0; Conference of International Conference on Soft Computing for Problem Solving, SocProS 2011 ; Conference Date: 20 December 2011 Through 22 December 2011; Conference Code:89817
abstract: The demand of Unmanned Aerial Vehicle (UAV) to monitor natural disasters extends its use to multiple civil missions. While the use of remotely control UAV reduces the human casualties' rates in hazardous environments, it is reported that most of UAV accidents are caused by human factor errors. In order to automate UAVs, several approaches to path planning have been proposed. However, none of the proposed paradigms optimally solve the path planning problem with contrasting objectives. We are proposing a Master-Slave Parallel Vector-Evaluated Genetic Algorithm (MSPVEGA) to solve the path planning problem. MSPVEGA takes advantage of the advanced computational capabilities to process multiple GAs concurrently. In our present experimental set-up, the MSPVEGA gives optimal results for UAV. © 2012 Springer India Pvt. Ltd.
date: 2012
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84861150369&doi=10.1007%2f978-81-322-0487-9_55&partnerID=40&md5=46254732471a2b6f94d29b250356b2c7
id_number: 10.1007/978-81-322-0487-9₅₅
full_text_status: none
publication: Advances in Intelligent and Soft Computing
volume: 130 AI
number: VOL. 1
place_of_pub: Roorkee
pagerange: 567-577
refereed: TRUE
isbn: 9788132204862
issn: 18675662
citation:   Pierre, D.M. and Zakaria, N. and Pal, A.J.  (2012) Quantitative and qualitative analysis of unmanned aerial vehicle's path planning using master-slave parallel vector-evaluated genetic algorithm.  Advances in Intelligent and Soft Computing, 130 AI (VOL. 1).  pp. 567-577.  ISSN 18675662