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One-Shot Multi-Path Planning Using Fully Convolutional Networks in a Comparison to Other Algorithms

dc.contributor.authorKulvicius, Tomas
dc.contributor.authorHerzog, Sebastian
dc.contributor.authorLüddecke, Timo
dc.contributor.authorTamosiunaite, Minija
dc.contributor.authorWörgötter, Florentin
dc.date.accessioned2021-05-17T16:28:40Z
dc.date.available2021-05-17T16:28:40Z
dc.date.issued2021de
dc.identifier.urihttp://resolver.sub.uni-goettingen.de/purl?gs-1/17791
dc.description.abstractPath planning plays a crucial role in many applications in robotics for example for planning an arm movement or for navigation. Most of the existing approaches to solve this problem are iterative, where a path is generated by prediction of the next state from the current state. Moreover, in case of multi-agent systems, paths are usually planned for each agent separately (decentralized approach). In case of centralized approaches, paths are computed for each agent simultaneously by solving a complex optimization problem, which does not scale well when the number of agents increases. In contrast to this, we propose a novel method, using a homogeneous, convolutional neural network, which allows generation of complete paths, even for more than one agent, in one-shot, i.e., with a single prediction step. First we consider single path planning in 2D and 3D mazes. Here, we show that our method is able to successfully generate optimal or close to optimal (in most of the cases <10% longer) paths in more than 99.5% of the cases. Next we analyze multi-paths either from a single source to multiple end-points or vice versa. Although the model has never been trained on multiple paths, it is also able to generate optimal or near-optimal (<22% longer) paths in 96.4 and 83.9% of the cases when generating two and three paths, respectively. Performance is then also compared to several state of the art algorithms.de
dc.description.sponsorshipOpen-Access-Publikationsfonds 2020
dc.language.isoengde
dc.rightsopenAccess
dc.rightsNamensnennung 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subject.ddc530
dc.titleOne-Shot Multi-Path Planning Using Fully Convolutional Networks in a Comparison to Other Algorithmsde
dc.typejournalArticlede
dc.identifier.doi10.3389/fnbot.2020.600984
dc.type.versionpublishedVersionde
dc.relation.eISSN1662-5218
dc.bibliographicCitation.volume14de
dc.type.subtypejournalArticle
dc.description.statuspeerReviewedde
dc.bibliographicCitation.journalFrontiers in Neuroroboticsde


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