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2020
Book Article
Title

Intelligent Propulsion

Abstract
Free-floating underwater robotic vehicles are free to move in all six degrees of freedom. While active pitch and roll is typically limited by design, i.e. hydrostatic stability, the robots attitude, position and speed control is based on thrusters possibly in combination with control surfaces, moving masses or variable buoyancy systems. Current systems often lack self-diagnostic capabilities and redundancy, leaving the high level mission control ""in the dark"" about the state of the thruster. This lack of information can lead to uncertain binary decisions about aborting or continuing missions. Better information possibly taking system redundancy into account will make it possible for the high level mission controller to scale the fault or system performance response accordingly, increasing the likelihood of at least partial mission success including system and data recovery compared to loss of data and possibly total system loss. In this chapter we propose to approach the topic of propulsion from different perspectives like motor design and control, systems engineering as well as optimization through machine learning and adaptive identification and control. The driving motivation is the research towards a propulsion solution, that suffices the requirements for a long-term autonomous underwater robot with respect to high system efficiency, reliability, and self-diagnostic capabilities. This will be achieved through an integrated systems approach between the electric machine, the propeller and possibly a nozzle. Furthermore research is going to focus on the real-time system performance using machine learning techniques in combination with more deterministic model based approaches for performance prediction and monitoring for failure detection of soft and hard errors.
Author(s)
Bachmayer, Ralf
Marum, Universität Bremen
Kampmann, Peter
DFKI GmbH, Robotics Innovation Center, University Bremen
Pleteit, Hermann  
Fraunhofer-Institut für Fertigungstechnik und Angewandte Materialforschung IFAM  
Busse, Matthias  
Fraunhofer-Institut für Fertigungstechnik und Angewandte Materialforschung IFAM  
Kirchner, Frank
DFKI GmbH & Robotic Group University Bremen, Robotics Innovation Center
Mainwork
AI Technology for Underwater Robots  
DOI
10.1007/978-3-030-30683-0_6
Language
English
Fraunhofer-Institut für Fertigungstechnik und Angewandte Materialforschung IFAM  
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