Data processing: generic control systems or specific application – Specific application – apparatus or process – Product assembly or manufacturing
Reexamination Certificate
2003-04-30
2004-06-29
Paladini, Albert W. (Department: 2125)
Data processing: generic control systems or specific application
Specific application, apparatus or process
Product assembly or manufacturing
C083S768000, C606S128000
Reexamination Certificate
active
06757582
ABSTRACT:
FIELD
This disclosure relates generally to controls, and more specifically to controlling a tool for shaping a workpiece.
BACKGROUND
A wide variety of applications call for objects to be formed from general workpieces, where the object formation can include cutting the object from the workpiece. The precision required of the cut may depend on the particular application.
One application that can call for high precision is the shaping of bone during surgery. For example, in a surgery such as a total knee replacement (TKR), the bones to which a prosthetic knee may be attached, typically the femur and the tibia, can be shaped to facilitate stable and effective implantation of the prosthesis.
Some cutting systems achieve increased accuracy by fixating the workpiece, such as bone. Bone fixation can be accomplished using a screw(s) and/or a clamp(s) to secure the bone to a secure positioning brace. Fixation can be used for many robotic orthopedic surgery systems because such systems depend on a fixed target and cannot or do not track the target. Fixation may be used in robotic systems despite the risks to the patient that can include pain, infection, and increased recovery and rehabilitation periods caused by the invasive nature of fixation.
Robotic and other surgical systems may be susceptible to failure that can occur suddenly and can cause damage or injury to the subject. Controllable or detectible failure may be detected before harm is done. Undetectable failure may cause damage when the system appears to be functioning normally. If a robot provides power drive or power assistance to an operator, failure may result when the robot malfunctions because the operator may be unable to react in time or with sufficient force to prevent the robot components from injuring the subject. Robotic systems may also be undetectable failures, since the operator may not be in full or even partial physical control of the robot.
SUMMARY
The disclosed methods and systems include a control method that includes providing a workpiece that includes a target shape, providing a cutting tool, providing a 3-D image associated with the workpiece, identifying the target shape within the workpiece image, providing a 3-D image associated with the cutting tool, registering the workpiece with the workpiece image, registering the cutting tool with the cutting tool image, tracking the workpiece and/or the cutting tool, transforming the tracking data based on image coordinates to determine a relationship between the workpiece and the cutting tool, and based on the relationship, providing a control to the cutting tool. The control can include an analog signal, a digital signal, a control to at least partially retract a cutting element associated with the cutting tool, a control to reduce the speed of a cutting element associated with the cutting tool, a control to stop a cutting element associated with a cutting tool, or another control.
The methods and systems can include representing the workpiece image using volume pixels (voxels), and classifying the workpiece image voxels based on the target shape. Accordingly, based on the relationship between the cutting tool and the workpiece, the methods and systems can include re-classifying the voxels based on the relationship.
The methods and systems can include providing an image based on CT scan data, X-ray data, MRI data, fluoroscopy data, and/or ultrasound data. The methods and systems can also include classifying such image data, represented as three dimensional volume pixels or “voxels,” where classifying the image voxels based on the target shape includes distinguishing target shape voxels and workpiece voxels. In an embodiment, distinguishing target and workpiece voxels includes associating target shape voxels with the target shape and associating non-target shape voxels as waste. Color-coding voxels, such as target shape voxels associated with the target shape, can also be performed to distinguish voxels. The images and/or voxels can be displayed to a user to enable a user to view relative positions of the cutting tool and workpiece and/or target shape. In one embodiment, the methods and systems can include re-classifying the voxels based on the relationship.
Classifying and/or re-classifying voxels can include identifying mixture voxels that include part workpiece and part target shape, subdividing the mixture voxels, and iteratively identifying and subdividing mixture voxels to a predetermined voxel resolution. In one embodiment, mixture voxels can be understood to be voxels that can be associated with more than one classification, where exemplary voxel classifications can include target, workpiece, waste, empty, cutting tool, cutting element, or other classifications. Subdividing the mixture voxels can be performed based on an octree data structure. Further, the methods and systems can include recombining voxels having the same classification, where such recombining can generally be performed based on neighboring voxels of the same classification.
The methods and systems can also include a probe that can be calibrated and employed to register the workpiece and/or the cutting tool to the workpiece image and the cutting tool image, respectively. The disclosed tracker can include a tracking method and system based on providing one or more markers on or otherwise associated with the workpiece and/or the cutting tool. The tracker can measure and/or determine at least one position and at least one angle associated with the workpiece and/or the cutting tool, where in one embodiment, the tracker can track in three positions and three angles to provide six degrees of freedom. The tracked data can thus be transformed to an image coordinate system to allow an updating of the respective image positions, angles, etc.
The image updating can also include (re)classifying voxels associated with the workpiece, where the reclassification can be based on the tracking data associated with the workpiece and/or the cutting tool. Such classifying and/or reclassifying can include identifying voxels associated with the workpiece that are eliminated by the cutting tool. The classifying and/or reclassifying can also include identifying mixture voxels, subdividing the mixture voxels, and, iteratively identifying and subdividing mixture voxels until reaching a predetermined voxel resolution. As provided previously, identifying mixture voxels includes identifying voxels having more than one classification. The subdividing can be based on an octree data structure. Voxel recombination of voxels having the same classification can also be performed.
Accordingly, the methods and systems include providing a control based on determining a distance between the cutting tool image and the voxels classified based on the target shape. In one embodiment, the control can be based on increasing the size of the cutting tool image to determine whether the increased size cutting tool intersects with the target image. The cutting tool image can be increased by a fixed amount, and/or based on tracking data associated with the cutting tool. The control provided to the cutting tool can thus be based on the relationship between a cutting element associated with the cutting tool image, and voxels classified based on the target shape.
In an embodiment, the control provided to the cutting tool can be based on the relationship between the cutting tool image and the voxels classified and/or associated with the target shape, where the relationship can be based on collision detection and/or intersection detection between at least part of the cutting tool and voxels associated with the target shape.
In one embodiment, the workpiece image can be understood to be associated with voxels that can be further associated with a three-dimensional grid of voxels, where an image associated with the workpiece can be incorporated into the grid, and grid voxels can be identified as being associated with the workpiece. Some of the workpiece voxels can thus further be associated with the target shape.
The methods and systems include pr
Brisson Gabriel
DiGioia, III Anthony
Jaramaz Branislav
Kanade Takeo
Carnegie Mellon University
Foley & Hoag LLP
Oliver Kevin A.
Paladini Albert W.
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