Learning Dynamic Spatial Relations
The Case of a Knowledge-based Endoscopic Camera Guidance Robot
(Sprache: Englisch)
Andreas Bihlmaier describes a novel method to model dynamic spatial relations by machine learning techniques. The method is applied to the task of representing the tacit knowledge of a trained camera assistant in minimally-invasive surgery. The model is...
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Klappentext zu „Learning Dynamic Spatial Relations “
Andreas Bihlmaier describes a novel method to model dynamic spatial relations by machine learning techniques. The method is applied to the task of representing the tacit knowledge of a trained camera assistant in minimally-invasive surgery. The model is then used for intraoperative control of a robot that autonomously positions the endoscope. Furthermore, a modular robotics platform is described, which forms the basis for this knowledge-based assistance system. Promising results from a complex phantom study are presented.Inhaltsverzeichnis zu „Learning Dynamic Spatial Relations “
Endoscope Robots and Automated Camera Guidance.- Knowledge-based Cognitive Systems.- Modular Research Platform for Robot-Assisted Surgery based on ROS.- Learning of Surgical Know-how by Models of Spatial Relations.- Intraoperative Camera Assistance.- Evaluation Study: TME in the Open Source Heidelberg Laparoscopic Phantom (OpenHELP).Autoren-Porträt von Andreas Bihlmaier
Andreas Bihlmaier is leader of the Cognitive Medical Technologies group in the Institute for Anthropomatics and Robotics - Intelligent Process Control and Robotics Lab (IAR-IPR) at the Karlsruhe Institute of Technology (KIT). His research focuses on cognitive surgical robotics for minimally-invasive surgery, as part of the SFB/Transregio 125 "Cognition-Guided Surgery".
Bibliographische Angaben
- Autor: Andreas Bihlmaier
- 2016, 1st ed. 2016, XV, 267 Seiten, Maße: 14,8 x 21 cm, Kartoniert (TB), Englisch
- Verlag: Springer, Berlin
- ISBN-10: 3658149132
- ISBN-13: 9783658149130
- Erscheinungsdatum: 18.08.2016
Sprache:
Englisch
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