Underwater Robot Task Planning Using Multi-Objective Meta-Heuristics

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dc.contributor.authorLanda-Torres, Itziar
dc.contributor.authorManjarres, Diana
dc.contributor.authorBilbao, Sonia
dc.contributor.authorDel Ser, Javier
dc.contributor.institutionTecnalia Research & Innovation
dc.contributor.institutionIA
dc.contributor.institutionBIGDATA
dc.date.issued2017-04-04
dc.descriptionPublisher Copyright: © 2017 by the authors. Licensee MDPI, Basel, Switzerland.
dc.description.abstractRobotics deployed in the underwater medium are subject to stringent operational conditions that impose a high degree of criticality on the allocation of resources and the schedule of operations in mission planning. In this context the so-called cost of a mission must be considered as an additional criterion when designing optimal task schedules within the mission at hand. Such a cost can be conceived as the impact of the mission on the robotic resources themselves, which range from the consumption of battery to other negative effects such as mechanic erosion. This manuscript focuses on this issue by devising three heuristic solvers aimed at efficiently scheduling tasks in robotic swarms, which collaborate together to accomplish a mission, and by presenting experimental results obtained over realistic scenarios in the underwater environment. The heuristic techniques resort to a Random-Keys encoding strategy to represent the allocation of robots to tasks and the relative execution order of such tasks within the schedule of certain robots. The obtained results reveal interesting differences in terms of Pareto optimality and spread between the algorithms considered in the benchmark, which are insightful for the selection of a proper task scheduler in real underwater campaigns.en
dc.description.statusPeer reviewed
dc.format.extent1
dc.format.extent903330
dc.identifier.citationLanda-Torres , I , Manjarres , D , Bilbao , S & Del Ser , J 2017 , ' Underwater Robot Task Planning Using Multi-Objective Meta-Heuristics ' , Sensors , vol. 17 , no. 4 , 762 , pp. 762 . https://doi.org/10.3390/s17040762
dc.identifier.doi10.3390/s17040762
dc.identifier.issn1424-3210
dc.identifier.otherresearchoutputwizard: 11556/390
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85017228868&partnerID=8YFLogxK
dc.language.isoeng
dc.relation.ispartofSensors
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subject.keywordsScheduling
dc.subject.keywordsHeuristic
dc.subject.keywordsMulti-objective optimization
dc.subject.keywordsRandom keys encoding
dc.subject.keywordsUnderwater robots
dc.subject.keywordsHarmony search
dc.subject.keywordsScheduling
dc.subject.keywordsHeuristic
dc.subject.keywordsMulti-objective optimization
dc.subject.keywordsRandom keys encoding
dc.subject.keywordsUnderwater robots
dc.subject.keywordsHarmony search
dc.subject.keywordsAnalytical Chemistry
dc.subject.keywordsInformation Systems
dc.subject.keywordsAtomic and Molecular Physics, and Optics
dc.subject.keywordsBiochemistry
dc.subject.keywordsInstrumentation
dc.subject.keywordsElectrical and Electronic Engineering
dc.subject.keywordsProject ID
dc.subject.keywordsinfo:eu-repo/grantAgreement/EC/H2020/662107/EU/Smart and Networking UnderWAter Robots in Cooperation Meshes/SWARMs
dc.subject.keywordsinfo:eu-repo/grantAgreement/EC/H2020/662107/EU/Smart and Networking UnderWAter Robots in Cooperation Meshes/SWARMs
dc.titleUnderwater Robot Task Planning Using Multi-Objective Meta-Heuristicsen
dc.typejournal article
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