TY  - JOUR
Y1  - 2012///
AV  - none
SP  - 567
A1  - Pierre, D.M.
A1  - Zakaria, N.
A1  - Pal, A.J.
KW  - 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
KW  -  Genetic algorithms; Motion planning; Problem solving; Soft computing
KW  -  Unmanned aerial vehicles (UAV)
CY  - Roorkee
VL  - 130 AI
TI  - Quantitative and qualitative analysis of unmanned aerial vehicle's path planning using master-slave parallel vector-evaluated genetic algorithm
N1  - 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
SN  - 18675662
IS  - VOL. 1
JF  - Advances in Intelligent and Soft Computing
EP  - 577
UR  - 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  - scholars3001
N2  - 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.
ER  -