Sensor-aided Milling with a Surgical Robot System

Size: px

Start display at page:

Download "Sensor-aided Milling with a Surgical Robot System"

Elisabeth Terry
6 years ago
Views:

1 1 Sensor-aided Milling with a Surgical Robot System Dirk Engel, Joerg Raczkowsky, Heinz Woern Institute for Process Control and Robotics (IPR), Universität Karlsruhe (TH) Engler-Bunte-Ring 8, Karlsruhe dengel@ira.uka.de, rkowsky@ira.uka.de, woern@ira.uka.de Abstract In this paper sources of errors are pointed out that occur using a robot system in order to cut bones. It is shown that the deflection of the robot tool must not be neglected if an accuracy in the range of one millimeter (from planning to execution) is required, since the tool deflection is up to 1.5 millimeters depending on the affecting torques. Therefore, special focus is set on methods which cope with this problem. A calibration approach considering the deflection of the robot tool is presented. After this calibration, it is possible to consider the tool deflection during the registration procedure (ball-in-cone strategy) as well as during the intervention. Furthermore, a discrete feed control algorithm is outlined. Keywords: robot-assisted surgery, sensor control 1. Introduction The main purposes of a surgical robot system are on the one hand the support for the surgeon and on the other hand the improvement of the surgical result. Thereby, the safety of the patient, the surgeon, and the medical staff are presumed [1, 2]. Unlike the surgeon, the robot controller has direct access to the planning data. Further, the robot arm provides high repeat accuracy and high precision independent of the progress of operation time. Hence, the greatest benefit of a robot system in the operating theater is the gain in quality. The presented methods and implementations are part of the surgical robot system RobaCKa for craniofacial surgery, which has been developed at our institute. The robot assistance in craniofacial surgery is reasonable because of the vicinity to vital parts and the great impact of bone repositionings at the human skull to the later appearance of the patient. In the following sections, sources of errors, which have to be considered developing an accurate surgical robot system as well as our methods of solution are discussed. For the measurement of process data and for system surveillance the robot system is equipped with several sensors (Fig. 1). The robot tool consists of: pneumatic collision protection (CP), force-/torque sensor (FTS), rigid body equipped with infrared LEDs to be tracked by an optical navigation system, and surgical milling cutter. The CP is a fail safe electrical/mechanical system without any data measurement. Additionally, the FTS acquires the exact forces and torques which are transmitted to a sensor data processing computer. This serial arrangement, CP followed by FTS, and the resulting elasticity are the main sources of the occurring tool deflection. A CP with a trigger sensibility of 0.2

2 2 causes an excursion of up to 1 mm at the tool tip (given tool length 300 mm), for example. Since the patient position is determined during the registration procedure using the robot as measuring device, the deflection error impairs not only the quality of the trajectory execution, but also the quality of registration. Fig.1: RobaCKa system set-up, robot and sensors 2. Determination of tool deflection The determination of the tool deflection is performed at the same time when the physical tool dimension is calibrated. In order to compute the tool length, the robot has to be guided above a calibration body (e.g. a metal cube), from where the robot moves down until a contact state was reached. This procedure is conducted in three steps: 1. Tool is in perpendicular configuration 2. Tool is -45 angled around x-axis 3. Tool is +45 angled around x-axis The tool length can be computed as: l = ( + ) h h = h h 45 + h. h is + 45 the difference of height in the different contact states, measured along the robot z-axis (Fig. 2, left). The moment of contact of the perpendicular configuration is detected by the increase of the total force. To avoid a bending of the tool in the angled configurations the contact state is assumed when the torque around the x-axis is equal zero, T x = , ( ) For the calibration of the tool tip offset along the x- and y-axis, the robot tool is guided to the left side of the calibration body (Fig. 2, right). In the following, the robot moves its tool to contact the calibration body with the tip, detaches after contact and turns its tool 90 around the z-axis. This procedure is repeated four times until the calibration body was contacted in positive and negative x-/y-direction. Afterwards, the physical tool dimension is known. For the determination of the tool deflection d x and d y, the robot continues moving its tool after a detected x-/y-contact in the same direction until a previously adjusted maximal torque is reached. In this way, to each measured torque a corresponding deflection can be assigned. The change of the tool length d z can be neglected because of d x,y << l. Fig. 3 depicts the correlation of the measured torques T x,y and the corresponding

3 3 tool deflection d y,x. The graph shows also that the correlation can be linear approximated by d x = c xy T y and d y = c yx T x (c xy, c yx : constant factors). Fig.2: Calibration of tool dimension and simultaneous determination of tool deflection. First step: calculation of tool length (left). Second step: determination of x-/y-offset and tool deflection (right) Fig. 3: Correlation of affecting torque and tool deflection. The correlation can be linear approximated by d x = c y T y and d y = c x T x (c x,y : constant factors) 3. Registration The role of registration is to establish the spatial relationship (via a transformation matrix) registration) between the planning data and the real patient [3]. Therefore, four titanium screws are implanted preopratively into the patient skull before the CT scans are taken. The positions of these four fiducial landmarks are preoperatively determined in the 3Dpatient-model based on CT slices; and also intraoperatively by guiding the robot tool to the screw positions (ball-in-cone strategy) [4]. The calculation of the transformation matrix is done by Least-Squares Fitting [5]. After the registration, the intraoperative robot pathways of the planned cut trajectories are known, because the planning was performed in relation to the same coordinate system which was used for the preoperative screw position determination.

4 As aforementioned, the position of the landmarks are determined intraoperatively by guiding the robot force-controlled to the screw positions.

Because of this matter of fact, a remaining bending stress is possible between the FTS and the tool tip placed in the screw cone.

4 4 As aforementioned, the position of the landmarks are determined intraoperatively by guiding the robot force-controlled to the screw positions. However, the robot follows the user who pushes and pulls the robot tool - in order to be safe against tremor and unintended hitches - very slowly and only if the forces exceed lower limits. Because of this matter of fact, a remaining bending stress is possible between the FTS and the tool tip placed in the screw cone. Such a stress causes an error while measuring the landmark position using the internal robot resolvers. In order to compensate this error, an offset vector d = (d x, d y, 0) T corresponding to the remaining torques is added to the measured landmark position. An example for the compensation of a registration error caused by the tool deflection is depicted in Fig. 4. Fig. 4: Correction distances to compensate the tool deflection during registration procedure (light grey) and additional compensation of tool deflection caused by tool weight (dark grey). Data refer to cut trajectory depicted in Fig. 5 Fig. 5: 3D-model of a human skull (point cloud), a cut trajectory, and the positions of the titanium screws (spheres)

5 5 4. Execution of a cut trajectory After the registration, when the patient position in relation to the robot base is known, the robot tool orientation can be calculated in every point of the planned trajectory. Hence, it is possible to consider the deflection caused by the weight of the tool before the actual execution of the cut trajectory starts (Fig. 4, Fig. 5). During the cutting process the feed of the robot tool is controlled with a discrete controlling algorithm. The forces are kept in a controlling window with a lower and an upper limit. This controlling algorithm knows three states: 1. Torques less than lower limit, speed is increased. 2. Torques within the controlling window, speed is kept 3. Torques exceed upper limit, speed is set to minimum speed value Furthermore, the tool acceleration and the cutting depth has been investigated. An acceleration too high causes a sidewise tool deflection into the direction of the milling cutter rotation. The successive increasing speed takes this behavior into account. In order to reduce the maximal forces, a cut can be performed in several steps, whereby the milling cutter is lead along the planned trajectory in different depths. 6. Conclusions and Future Work In this paper sources of errors which occur using a surgical robot system in order to cut bones were explained as well as methods of solution were discussed. It was shown, that the deflection of the robot tool must not be neglected if an accuracy of about one millimeter (from planning to execution) is required. To cope with this problem the calibration of the robot tool, a discrete feed control and a method which takes the deflection of the robot tool into account were presented. The consideration of the tool deflection increases the accuracy of the registration as well as the execution of the planned trajectory. Additionally, the feed control of the milling cutter is an important factor to reduce the tool deflection and to act with consideration for the patient fixation. In a next step of development, a real-time compensation of the tool deflection is intended. Therefore, the robot pathway should be adapted respectively to the occurring torques by an online control mechanism. Furthermore, it is planned to counterbalance micromovements of the patient by simultaneous consideration of the tracking data yielded by the optical navigation system. The overall system was tested with dummies, in animal experiments, and also in a preliminary test with a human test subject using a pen instead of the milling cutter. This subject test took place in an operating theater of our research partner, the Department of Oral and Maxillofacial Surgery - University of Heidelberg, and proved the applicability of the robot system. First surgical interventions will be performed later this year.

6 6 Acknowledgements This research has been performed at the Institute for Process Control and Robotics headed by Prof. Dr.-Ing. H. Woern at the University of Karlsruhe, Germany. The work has been funded by the German Research Foundation (Deutsche Forschungsgemeinschaft), as it is part of the collaborative research center SFB 414: Information Technology in Medicine Computer and Sensor Aided Surgery. References 1. Davies, B. L.: "A Discussion of Safety Issues for Medical Robots", Computer-Integrated Surgery Technology and Clinical Applications, pp , The MIT Press, Engel, D., Raczkowsky, J., Woern, H.: A Safe Robot System for Craniofacial Surgery, IEEE International Conference on Robotics and Automation, pp , Seoul, Korea, Simon, D.: What is Registration and Why is it so Important in CAOS?, Proceedings of the First Joint CVRMed / MRCAS Conference, pp , June, Kazanzides, P., Zuhars, J., Mittelstadt, B., Taylor, R. H.: "Force Sensing and Control for a Surgical Robot, IEEE International Conference on Robotics and Automation, pp , Nice, France, Arun, K. S., Huang, T. S., Blostein, S. D.: Least-Squares Fitting of Two 3-D Point Sets, IEEE Transactions on Pattern Analysis and Machine Intelligence, pp , Vol. PAMI-9, No. 5, 1997.

Increasing the accuracy with a rich sensor system for robotic laser osteotomy

Increasing the accuracy with a rich sensor system for robotic laser osteotomy Holger Mönnich moennich@ira.uka.de Daniel Stein dstein@ira.uka.de Jörg Raczkowsky rkowsky@ira.uka.de Heinz Wörn woern@ira.uka.de