Title An evolutionary approach to constrained path planning of an autonomous surface vehicle for maximizing the covered area of Ypacarai Lake
Authors Arzamendia M., Gregor D., GUTIÉRREZ REINA, DANIEL, Toral S.L., GUTIÉRREZ REINA, DANIEL
External publication No
Means Soft Comput.
Scope Article
Nature Científica
JCR Quartile 2
SJR Quartile 2
JCR Impact 3.05000
Area International
Web https://www.scopus.com/inward/record.uri?eid=2-s2.0-85032820586&doi=10.1007%2fs00500-017-2895-x&partnerID=40&md5=0d00342d77a9b24085214d6b93e7e426
Publication date 01/01/2019
ISI 000459315900019
Scopus Id 2-s2.0-85032820586
DOI 10.1007/s00500-017-2895-x
Abstract The path planning of an autonomous surface vehicle in a lake for environmental monitoring has been modeled as a constrained case of the traveling salesman problem, in which the vehicle should visit a ring of beacons deployed at the shore of the lake for data exchange. The problem is solved using a genetic algorithm. The algorithm searches for the maximization of the area covered by the vehicle. Three types of fitness functions are studied related to each of the following cases: the unconstrained case, the constrained case with death penalty and the constrained case with a penalty factor. Moreover, the proposed approach is compared with other algorithms, such as brute force algorithms, the randomized algorithm and the greedy algorithm. The results show that genetic algorithm outperforms the other approaches by 15 and 3%, respectively. © 2017, Springer-Verlag GmbH Germany.
Keywords Electronic data interchange; Genetic algorithms; Lakes; Motion planning; Traveling salesman problem; Unmanned surface vehicles; Vehicles; Autonomous surface vehicles; Brute force algorithms; Constrain
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