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Prof. Dr.-Ing. Dr. h.c. Heinz Wörn

Professor im Ruhestand
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Professor Wörn studierte Elektrotechnik an der Universität Stuttgart und promovierte dort am Institut für Werkzeugmaschinen mit seiner Arbeit zu dem Thema "Mehrprozessorsteuerungssystem für Werkzeugmaschinen mit standartisierten Schnittstellen". Im Anschluss arbeitete er bei KUKA Schweißanlagen und Roboter GmbH, wo er eine leitende Stellung in Forschung und Entwicklung inne hatte. Professor Wörn ist ein international anerkannter Experte für Roboter und Automation. Seine Erfahrung umfasst Roboteranwendungen, Robotersteuerungen und Sensoren für Roboter, sowie deren Programmmierung und Simulation. Seit 1997 leitet er das Institut für Prozessrechentechnik, Automation und Robotik der Universität Karlsruhe als Professor für "Komplexe Systeme in Automation und Robotik".


  • Planung, Programmierung, Steuerung, Diagnose und Sensorsysteme für Industrieroboter
  • Autonome, mobile Roboter, Mikroroboter, Serviceroboter, Teleroboter, Autonome Fahrzeuge
  • Planung und Simulation von Anlagen und Fabriken
  • Roboter- und sensorgestützte Chirurgie
  • Mikromontage
  • Modellierung komplexer Systeme in Produktion und Medizin

Operation Planning and its Automation in Cranio Maxillofacial Surgery

AutorO. Schorr, G. Eggers, C. Haag, S. Haßfeld, H. Wörn
Veröffentlicht inProceedings der 2. Jahrestagung der Deutschen Gesellschaft für Computer- und Roboterassistierte Chirurgie e.V.
KurzfassungThe intraoperative application of complex computer assisted surgical tools like our surgical robot system RobaCKa require preoperative planning and treatment simulation. Therefore, we developed within the frame of the collaborative research centre SFB 414 a planning environment called KasOp. It is a planning tool for three dimensional visualization, landmark definition for intraoperative registration, definition of trajectories for osteotomy, and simulation of bone relocations. However, the evaluation by our surgical collaborators at the University of Heidelberg showed that there is the need of simplifying and reducing the interaction of the surgeon with the complex planning environment. In order to achieve this we started developing several automation methods for less-interactive surgical planning presented in this paper. Our first automation method is based on three requirements: the use of reference data, the alignment of the reference and predefined trajectories. Reference data in this context means models of physiologically shaped data sets of persons of same age and sex like the patient. Using reference data enables the surgeon to easier compare the patients malformed skull with a typical physiological shape. Differences between both models are calculated and represented colour coded on the patient surface model within the three dimensional display (image 1). The registration of the reference data to the patient is accomplished by three corresponding landmarks on both data sets. In order to increase accuracy we introduced as well an iterative closest point method after the preliminary registration by landmarks. The corresponding trajectory can be selected out of a set of trajectory types which have been predefined by surgical experts. Selection support is given by the colour coded distance deviations of patient and reference. After selection and registration of a reference and the selection of the trajectory, our newly developed method is able to map the trajectory from the reference onto the patient, so that the surgeon only needs to modify the trajectory rather than planning it completely new (image 2). This principle is achieved by using an elastic deformation method. The second automation approach is the automated surveillance of medical restrictions which have to be taken into account during planning. Until now, the surgeon intraoperatively needs to take care that bore holes for the fixation of screws have a minimum distance to the trajectory. As we are planning to perform the drilling of the holes by our surgical robot, we need to preoperatively define their positions and orientations. In order to support the surgeon during planning we integrated surveillance methods for the detection of distances too bow between holes and trajectories. Moreover, the detection of volume collisions during relocations of bone structures are integrated. With the newly developed methods presented in this paper, we are able to do automated planning of trajectories on patient models and to do automated examination of the planning data properties (collisions, minimum distances etc.). The results of our two automation approaches are promising. The surface based registration method for automated trajectory planning with the iterative closest point method turned out to produce inadequate results if the reference and the patient surface have large differences. However, landmark based alignment seems to be sufficient for the mapping process.
Bibtex@article{ ipr_1079077378, author = "{O. Schorr and G. Eggers and C. Haag and S. Ha{\ss}feld and H. W{{\"o}}rn}", title = "{Operation Planning and its Automation in Cranio Maxillofacial Surgery}", year = "2003", journal = "{Proceedings der 2. Jahrestagung der Deutschen Gesellschaft f{{\"u}}r Computer- und Roboterassistierte Chirurgie e.V.}", }
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