Objective: In some surgical specialties (eg, orthopedics), robots are already used in the operating room for bony milling work. Otological surgery and otoneurosurgery may also greatly benefit from the enhanced precision of robotics. Study Design: Experimental study on robotic milling of oak wood and human temporal bone specimen. Methods: A standard industrial robot with a six-degrees-of-freedom serial kinematics was used, with force feedback to proportionally control the robot speed. Different milling modes and characteristic path parameters were evaluated to generate milling paths based on computeraided design (CAD) geometry data of a cochlear implant and an implantable hearing system. Results: The best-suited strategy proved to be the spiral horizontal milling mode with the burr held perpendicular to the temporal bone surface. To reduce groove height, the distance between paths should equal half the radius of the cutting burr head. Because of the vibration of the robot's own motors, a high oscillation of the SD of forces was encountered. This oscillation dropped drastically to nearly 0 Newton (N) when the burr head made contact with the dura mater, because of its damping characteristics. The cutting burr could be kept in contact with the dura mater for an extended period without damaging it, because of the burr's blunt head form. The robot moved the burr smoothly according to the encountered resistances. Conclusion: The study reports the first development of a functional robotic milling procedure for otoneurosurgery with force-based speed control. Future plans include implementation of ultrasound-based local navigation and performance of robotic mastoidectomy.

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We present a method for quickly determining the minimum distance between multiple known and multiple unkown objects within a camera image. Known objects are objects with known geometry, position, orientation, and configuration. Unkown objects are objects which have to be detected by a vision sensor but with unkown geometry, position, orientation and configuration. The known objects are modeled and expanded in 3D and then projected into a camera image. The camera image is classified into object areas including known and unknown objects and into non-object areas. The distance is conservatively estimated by searching for the largest expansion radius where the projected model does not intersect the object areas classified as unknown in the camera image. The method requires only minimal computation times and can be used for surveillance and safety applications.