Machine Learning and Dynamic Whole Body Control for Underwater Manipulation

Publikation

José de Gea Fernández, Frank Kirchner, Christian Ott, Sirko Straube, Bilal Wehbe, Daniel Kuehn, Nina Hoyer

In: Frank Kirchner, Sirko Straube, Daniel Kuehn, Nina Hoyer. AI Technology for Underwater Robots. Seiten 107-115 ISBN 978-3-030-30683-0 Springer International Publishing 10/2020.

Abstrakt

Autonomous underwater manipulation is nowadays still an open research challenge. This paper describes the approaches to tackle some of the open challenges. On the one side, the use of machine learning techniques for the online identification and adaption of vehicle dynamics (dealing with drift compensation, mass changes, etc.) as well as the use of high-level context-based configuration of controllers to adapt to changes in system morphology, hardware, and/or tasks. On the other side, a robust control of underwater manipulators based on an extension of whole-body control techniques is envisaged which takes into account the heterogeneous actuation (thrusters on the base, actuators on the arm joints) as well as the uncertain underwater vehicle dynamics. The result is a highly-reconfigurable system that can automatically adapt its behavior to cope with changes in the environment, in its own morphology and/or in the task goals. The outcomes are planned to be validated in two different scenarios: a floating-base dynamics testbed originating from space applications and aerial robots at DLR and an underwater pool at DFKI.

Weitere Links

https://doi.org/10.1007/978-3-030-30683-0_9

preprint-chapter.pdf (pdf, 5 MB)

Machine Learning and Dynamic Whole Body Control for Underwater Manipulation

Abstrakt

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