Feedforward continuous positioning control of end-effectors

a technology of end-effectors and positioning controls, which is applied in the direction of programme control, image enhancement, instruments, etc., can solve the problems that the known techniques devised for positioning control of portions of interventional devices (e.g. end-effectors) have provided limited benefits, and achieve effective positioning control

Pending Publication Date: 2022-04-28
KONINKLJIJKE PHILIPS NV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Benefits of technology

The present patent text describes a need for improved techniques for positioning controls of interventional devices, particularly for the end-effectors of these devices. The text proposes a feed-forward positioning control, a feedback positioning control, and a data collection approach for positioning interventional devices. These techniques provide a more effective way to position these devices during interventional procedures. The technical effects of the patent text include improved accuracy and efficiency in positioning interventional devices, which can lead to better outcomes and reduced risks during procedures.

Problems solved by technology

Known techniques devised for positioning controls of portions of interventional devices (e.g. end-effectors) have provided limited benefits.

Method used

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  • Feedforward continuous positioning control of end-effectors
  • Feedforward continuous positioning control of end-effectors
  • Feedforward continuous positioning control of end-effectors

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Experimental program
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second embodiment

[0108]In a second embodiment for implementing a regression of joint variables Q to pose {circumflex over (T)}, neural network base 160a includes a set of N convolutional layers 164a followed by either a set of M fully connected layers 163a or a set of W recurrent layers 165a or a set of W long term short memory layers 166a.

third embodiment

[0109]In a third embodiment for implementing a regression of joint variables Q to pose {circumflex over (T)}, neural network base 160a includes a set of N convolutional layers 164a followed combination of a set of M fully connected layers 163a and a set of W recurrent layers 165a or a set W of long term short memory layers 166a.

[0110]In practice, a fully connected layer 163a may include K neurons, where N, M, W, K may be any positive integer, and values may vary depending on the embodiments. For example, N may be about 8, M may be about 2, W may be about 2, and K can be about 1000. Alson, a convolutional layer 164a may implement a non-linear transformation, which may be a composite function of operations (e.g., batch normalization, rectified linear units (ReLU), pooling, dropout and / or convolution), and a convolutional layer 164a may also include a non-linearity function (e.g. including rectified non-linear ReLU operations) configured to extract rectified feature maps.

[0111]Further...

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Abstract

A positioning controller (50) including a forward predictive model (60) and / or inverse control predictive model (70) for positioning control of an interventional device (30) including a portion (40) of an interventional device. In operation, the controller (50) may apply the forward predictive model (60) to a commanded positioning motion of the interventional device (30) to render a predicted navigated pose of the end-effector (40), and generate positioning data informative of a positioning by the interventional device (30) of said portion of interventional device (40) to a target pose based on the predicted navigated pose of said portion (40). Alternatively, antecedently or subsequently, the controller (50) may apply the control predictive model (70) to the target pose of the portion of interventional device (40) to render a predicted positioning motion of the interventional device (30), and generate positioning commands controlling a positioning by the interventional device (30) of said device portion (40) to the target pose based on the predicted positioning motion of the interventional device (30).

Description

FIELD OF THE INVENTION[0001]The present disclosure generally relates to a positioning control of portions of interventional devices (e.g. end-effectors of interventional devices) utilized in interventional procedures (e.g., minimally-invasive surgery, video-assisted thoracic surgery, vascular procedures, endoluminal procedures, orthopedic procedures). The present disclosure can specifically relate to incorporation of predictive models in the positioning control of such portions of interventional devices utilized in interventional procedures.BACKGROUND OF THE INVENTION[0002]Continuous (or non-continuous) control—positioning of said device portion (e.g. end-effector) within a certain workspace—is one of the most commonly attempted forms of control in conventional rigid link robots. By taking the advantage of a discrete rigid link structure of the robot, a precise positioning of said portion of interventional device (e.g. end-effector) can be achieved as desired in structured applicati...

Claims

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Application Information

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IPC IPC(8): A61B34/20A61B34/30G06N3/04G06N3/08B25J9/16A61B90/00
CPCA61B34/20A61B34/30G06N3/0454G06N3/0472G06N3/0445A61B2090/378B25J9/1607A61B90/37A61B2034/2061A61B2034/301G06N3/084G06N3/082A61B2090/064B25J9/1697A61B2034/107G05B19/423G05B2219/39286G06N3/047G06N3/048G06N3/044G06N3/045G06T7/246G06T7/70G16H20/40G16H30/20A61B8/12G06N3/08G06T2207/20084G06T2207/30244A61B2034/2065
InventorTOPOREK, GRZEGORZ ANDRZEJPOPOVIC, ALEKSANDRA
OwnerKONINKLJIJKE PHILIPS NV