Please use this identifier to cite or link to this item: http://artemis.cslab.ece.ntua.gr:8080/jspui/handle/123456789/18434
Full metadata record
DC FieldValueLanguage
dc.contributor.authorΒλάχος, Χρίστος-
dc.date.accessioned2022-09-08T06:42:08Z-
dc.date.available2022-09-08T06:42:08Z-
dc.date.issued2022-09-05-
dc.identifier.urihttp://artemis.cslab.ece.ntua.gr:8080/jspui/handle/123456789/18434-
dc.description.abstractIn this thesis, a novel solution is presented for optimal motion planning in static, fully-known environments. Our approach allows a robot to safely navigate towards any destination within a subset of its workspace by using a parametric controller based on Artificial Potential Field (APF) theory. Optimization is achieved through the application of Reinforcement Learning (RL) techniques. More specifically, the parameters of the underlying potential field are adjusted appropriately with a policy gradient algorithm in order to minimize a cost function. The proposed method is not without its limitations, but can still be a valuable addition to the arsenal of established motion planning approaches.en_US
dc.languageenen_US
dc.subjectOptimal Motion Planningen_US
dc.subjectParametric Controlleren_US
dc.subjectArtificial Potential Fielden_US
dc.subjectReinforcement Learningen_US
dc.subjectPolicy Gradient Algorithmen_US
dc.titleFrom All To A Subset Harmonic-Based Optimal Motion Planning in Constrained Workspaces using Reinforcement Learningen_US
dc.description.pages62en_US
dc.contributor.supervisorΤζαφέστας Κωνσταντίνοςen_US
dc.departmentΤομέας Σημάτων, Ελέγχου και Ρομποτικήςen_US
Appears in Collections:Διπλωματικές Εργασίες - Theses

Files in This Item:
File Description SizeFormat 
Thesis_Vlachos.pdf3.26 MBAdobe PDFView/Open


Items in Artemis are protected by copyright, with all rights reserved, unless otherwise indicated.