Learning Dynamic Spatial Relations [electronic resource] : The Case of a Knowledge-based Endoscopic Camera Guidance Robot /
Bihlmaier, Andreas.author.
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) Target Groups 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 About the Author 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”.
"Research."
Includes bibliographical references.
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).
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) Target Groups 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 About the Author 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”.
Artificial intelligence.
Minimally invasive surgery.
Pattern recognition.
Artificial Intelligence.
Minimally Invasive Surgery.
Pattern Recognition.