{"refrec":{"BRefID":362330,"RR":"<b>Ponguillo-Intriago, R.; Ochoa, D.; Lopez, A.J.; Semanjski, I.; Gautama, S.</b> (2021). Genetic algorithm for solving coverage issues in undersea mining, <b><i>in</i></b>: <i>2021 International Conference on Engineering and Emerging Technologies (ICEET).</i> pp. 838-842. <a href=\"https://dx.doi.org/10.1109/ICEET53442.2021.9659708\" target=\"_blank\">https://dx.doi.org/10.1109/ICEET53442.2021.9659708</a>","BEntID":360047,"PublicFlag":1,"CheckedFlag":1,"wosflag":0,"vabbflag":0,"RefStringPartII":", <b><i>in</i></b>: <i>2021 International Conference on Engineering and Emerging Technologies (ICEET).</i> pp. 838-842. <a href=\"https://dx.doi.org/10.1109/ICEET53442.2021.9659708\" target=\"_blank\">https://dx.doi.org/10.1109/ICEET53442.2021.9659708</a>","DocTypID":17,"DocType":"Book chapters","MarineFlag":1,"FreshFlag":0,"BrackishFlag":0,"TerrestrialFlag":0,"Authorstring":"Ponguillo-Intriago, R.; Ochoa, D.; Lopez, A.J.; Semanjski, I.; Gautama, S.","OrigTitleTranslFlag":0,"Authorstringtrunc":"Ponguillo-Intriago, R. <i>et al.</i>","Englishabstract":"To be cost-effective, robot-based undersea mining must comply several operational constraints. Among the main constraints are the time and energy required to extract the mineral from the seabed. It is also important to reduce the wear of the joints that connect the ship on the surface with the robot crawler that does the mining on the seabed, since this not only reduces operating costs, but also lengthens the useful life of these parts which increases system security. For this reason, the least amount of twisting in these pieces is preferable, so it is advisable to reduce the number of turns or changes of direction in the trajectory of the robot that extracts the mineral. In this article, we present an algorithm to optimize Coverage Path Planning using Genetic Algorithm to produce paths with longer segments, which can be used in underwater mining and reduce the effects the mentioned turning problem. The resulting paths have on average 55% less changes of directions in the trajectory than a GA with standard cost function. In addition, in tests made by placing small obstacles in a random way, 76% of useful paths were obtained and up to 59% of useful path when the obstacles were grouped into a single larger obstacle.","AbstractOtherLang":null,"BibLvlCode":"AM","StandardTitle":"Genetic algorithm for solving coverage issues in undersea mining","OrigTitleLangCode":"en","OrigTitleLangCodeExtended":"eng","OrigTitleLangID":15,"DateLastModified":{"date":"2024-12-10 01:33:17.368041","timezone_type":1,"timezone":"+01:00"},"UserAccessRight":null,"UserAccID":null,"AuthorKeywords":"genetic algorithms; coverage path planning; deep sea mining; autonomous systems","OtherDescriptors":null,"Notes":null,"AnaPub":2021,"MonPub":null,"DateUpdate":"2023-03-20","DateCreate":"2023-03-20","SecASFANote":null,"ConfID":null,"PeerRev":0,"VlizCoreFlag":1,"WoScode":"WOS:000828108100149","VABBcode":null,"OpenAcc":0,"DOI":"10.1109/ICEET53442.2021.9659708"},"refs":null,"anarec":{"AnaID":362330,"PubliDate":2021,"Pagination":"838-842","XtraPublOfAnaID":null,"ISBN":"978-1-6654-2715-9","Volume":null,"Issue":null,"BRefMon":362349,"BRefMonRR":"(2021). 2021 International Conference on Engineering and Emerging Technologies (ICEET). 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