Dr.-Ing. Tim Beyl

  • group: Medizin-Gruppe am IPR (MeGI)

Zur Person

Berufliche Laufbahn seit 05/2011

Wissenschaftlicher Mitarbeiter am Institut für Prozessrechentechnik, Automation und Robotik



„Robotergestützte Ultraschalltomographie“ Institut für Prozessrechentechnik, Automation und Robotik


10/2006 - 12/2011

Studium der Medizinischen Informatik an der Universität Heidelberg und der Hochschule Heilbronn


Forschungsprojekte ACTIVE - Active constraint technologies for ill-defined or volatile environments

The ACTIVE project exploits ICT and other engineering methods and technologies for the design and development of an integrated redundant robotic platform for neurosurgery. A light and agile redundant roboticcell with 20 degrees-of-freedom (DoFs) and an advanced processing unit for pre- and intra-operative control willoperate both autonomously and cooperatively with surgical staff on the brain. As the patient will not be considered rigidly fixed to the operating table and/or to the robot, the system will
push the boundaries of the state of the art in the fields of robotics and control for the accuracy and bandwidth requiredby the challenging and complex surgical scenario.
Two cooperating robots will interact with the brain that will deform for the tool contact, blood pressure, breathing and deliquoration. Human factors are considered by allowing easy interaction with the users through a novel haptic interface for tele-manipulation and by a collaborative control mode ("hands-on"). Active constraints will limit and direct tool tip position, force and speedpreventing damage to eloquent areas, defined on realistic tissue models updated on-the-field through sensorsinformation. The active constraints will be updated (displaced) in real time in response to the feedback fromtool-tissue interactions and any additional constraints arising from a complex shared workspace. The overarching control architecture of ACTIVE will negotiate the requirements and references of the two slave robots. The operative room represents the epitome of a dynamic and unstructured volatile environment, crowded withpeople and instruments. The workspace will thus be monitored by environmental cameras, and machine learning techniques will be used for the safe workspace sharing. Cognitive skills will help to identify the target location in the brain and constrain robotic motionsby means of on-field observations.