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https://doi.org/10.15480/882.4602
Title: | Learning, localization, and control of hydrobatic micro underwater robots for autonomous field exploration in confined environments | Language: | English | Authors: | Dücker, Daniel-André ![]() |
Editor: | Seifried, Robert ![]() |
Keywords: | Autonomous robotic systems; Localization; Environmental monitoring; Robot learning; micro autonomous underwater vehicles | Issue Date: | 2022 | Examination Date: | 11-Jul-2022 | Source: | MuM Notes in Mechanics and Dynamics: 6 (2022) | Abstract (german): | Diese Dissertation behandelt die Aufklärung und Überwachung von Umweltfeldern mit kleinen autonomen Unterwasserroboter (µAUVs). Trotz jüngster Fortschritte sind recheneffiziente Methoden für Planung, Lokalisierung und Regelung von µAUVs weitgehend unerforscht. Die im Rahmen dieser Arbeit entwickelte µAUV-Plattform HippoCampus und seine Regelarchitektur erlauben agiles Manövrieren auf engstem Raum. Zu diesem Zweck wird ein Selbstlokalisierungssystem entwickelt, das visuelle, elektromagnetische und akustische Signale zur räumlichen Lokalisierung verwendet. Basierend auf Methoden der informationstheoretischen Regelung wird abschließend wird eine recheneffiziente Architektur zur autonomen Feldaufklärung mit mehreren Robotern vorgeschlagen. Diese kombiniert Deep-Reinforcement Learning mit einer stochastischen Feldmodellierung. |
Abstract (english): | Exploration and monitoring of hazardous environmental fields are among the most promising tasks to be performed by micro autonomous underwater vehicles (µAUVs). Despite recent progress, computationally efficient solutions for guidance, navigation, and control are largely unsolved for agile µAUVs. First, the hydrobatic micro robot platform HippoCampus is presented along with a control system that allows agile maneuvering in confined spaces. Furthermore, an embedded self-localization system is developed which consists of modules using visual, electromagnetic, and acoustic ranging. Finally, an informative path planning framework for autonomous field exploration with multiple robot agents is proposed. It combines a deep reinforcement learning planner with a stochastic representation of the environmental field. |
URI: | http://hdl.handle.net/11420/13648 | DOI: | 10.15480/882.4602 | Institute: | Mechanik und Meerestechnik M-13 | Document Type: | Thesis | Thesis Type: | Doctoral Thesis | Advisor: | Kreuzer, Edwin | Referee: | Zhang, Jianwei Abdel-Maksoud, Moustafa ![]() |
License: | ![]() |
Part of Series: | MuM Notes in Mechanics and Dynamics | Volume number: | 6 |
Appears in Collections: | Publications with fulltext |
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File | Description | Size | Format | |
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Dissertation_DanielAndreDuecker.pdf | 10,21 MB | Adobe PDF | View/Open![]() |
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