relation: https://khub.utp.edu.my/scholars/3001/
title: Quantitative and qualitative analysis of unmanned aerial vehicle's path planning using master-slave parallel vector-evaluated genetic algorithm
creator: Pierre, D.M.
creator: Zakaria, N.
creator: Pal, A.J.
description: 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
type: Article
type: PeerReviewed
identifier:   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     
relation: 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
relation: 10.1007/978-81-322-0487-9₅₅
identifier: 10.1007/978-81-322-0487-9₅₅