Learning Dynamic Spatial Relations

Beschreibung

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.
Contents

• 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)
  

• Scientists and students in the field of robotics, surgical assistance systems, cognitive and knowledge-based systems
  
• Practitioners in companies selling manually controlled robots or motorized endoscope holders
  

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.

Autor*in

Andreas Bihlmaier

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machine learning techniques trained camera assistant minimally-invasive surgery endoscope modular robotics platform knowledge-based assistance system

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