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Learning, localization, and control of hydrobatic micro underwater robots for autonomous field exploration in confined environments
Citation Link: https://doi.org/10.15480/882.4602
Publikationstyp
Doctoral Thesis
Date Issued
2022
Sprache
English
Author(s)
Dücker, Daniel-André
Herausgeber*innen
Advisor
Title Granting Institution
Technische Universität Hamburg
Place of Title Granting Institution
Hamburg
Examination Date
2022-07-11
Institut
TORE-DOI
First published in
Number in series
6
Citation
MuM Notes in Mechanics and Dynamics: 6 (2022)
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.
Subjects
Autonomous robotic systems
Localization
Environmental monitoring
Robot learning
micro autonomous underwater vehicles
MLE@TUHH
DDC Class
600: Technik
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Dissertation_DanielAndreDuecker.pdf
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