An artificial potential field path planning method and an apparatus based on obstacle classification solve the problem of path and motion uncertainty in steering a flexible needle in soft tissue. The apparatus includes an image sensing system, a control module, an execution system and an upper PC. Using the apparatus, the method includes: the image sensing system obtains real-time images of the puncture environment, identifies a target and obstacles from the real-time images, classifies the obstacles, and calculates total potential energy of points in the current environment based on artificial potential field. With a curvature constraint and an optimization index for the flexible needle, the path planning module carries out static path planning to obtain an initial path and the needle entry point, then conducts dynamic path planning to determine the path for steering the flexible needle in the soft tissue accordingly.

Methods and systems for filtering a trigger signal from an encoder for triggering an image capturing device. In embodiments, a trigger signal may be received as an input signal from an encoder including a sequence of pulses for triggering the image capturing device. In embodiments, an average pulse frequency of the trigger signal over a period of time may be determined based on sampling a number of pulses in the trigger signal over the period of time, and frequency restrictions may be applied to the average pulse frequency to generate a trigger frequency. In embodiments, an output frequency may be determined based on the trigger frequency, and a pulse width modulation (PWM) signal having a frequency based on the output frequency may be determined for triggering the image capturing device.

One variation of a method for autonomously scanning and processing a part includes: collecting a set of images depicting a part positioned within a work zone adjacent a robotic system; assembling the set of images into a part model representing the part. The method includes segmenting areas of the part model—delineated by local radii of curvature, edges, or color boundaries—into target zones for processing by the robotic system and exclusion zones avoided by the robotic system. The method includes: projecting a set of keypoints onto the target zone of part model defining positions, orientations, and target forces of a sanding head applied at locations on the part model; assembling the set of keypoints into a toolpath and projecting the toolpath onto the target zone of the part model; and transmitting the toolpath to a robotic system to execute the toolpath on the part within the work zone.

A method may be provided to operate a medical system. First data may be provided for a first 3-dimensional (3D) image scan of an anatomical volume, with the first data identifying a blood vessel node in a first coordinate system for the first 3D image scan. Second data may be provided for a second 3D image scan of the anatomical volume, with the second data identifying the blood vessel node in a second coordinate system for the second 3D image scan. The first and second coordinate systems for the first and second 3D image scans of the anatomical volume may be co-registered using the blood vessel node identified in the first data and in the second data as a fiducial.

Machine vision systems and methods for automatically generating machine vision job(s) based on region of interests (ROls) of digital images are disclosed herein. The systems and methods comprise configuring a machine vision tool for capturing an image ID depicted in training images and labeling each training image to indicate a success or failure status of an object depicted by the training images. Candidate image feature(s) are extracted from the training images for generation of candidate ROI(s). A training set of ROls are selected from the candidate ROI(s) and are designated as an included or excluded ROls. The training set of ROls and the training images are used to train a vision learning model configured to output a machine vision job deployable to an imaging device that implements the machine vision job to detect the success or failure statuses of additional image(s) depicting the object.

A method for generating reconstruction a reconstructed image is adapted to an input image having a target object. The method comprises converting the input image into a feature map with vectors by an encoder; performing a training procedure according to training images of reference objects to generate feature prototypes associated with the training images and store the feature prototypes to a memory; selecting a part of feature prototypes from the feature prototypes stored in the memory according to similarities between the feature prototypes and the feature vectors; generating a similar feature map according the part of feature prototypes and weights, wherein the weights represents similarities between the part of feature prototypes and the feature vectors; and converting the similar feature map into the reconstructed image by a decoder; wherein the encoder, the decoder and the memory form an auto-encoder.

One variation of a method for autonomously scanning and processing a part includes: collecting a set of images depicting a part positioned within a work zone adjacent a robotic system; assembling the set of images into a part model representing the part. The method includes segmenting areas of the part model—delineated by local radii of curvature, edges, or color boundaries—into target zones for processing by the robotic system and exclusion zones avoided by the robotic system. The method includes: projecting a set of keypoints onto the target zone of part model defining positions, orientations, and target forces of a sanding head applied at locations on the part model; assembling the set of keypoints into a toolpath and projecting the toolpath onto the target zone of the part model; and transmitting the toolpath to a robotic system to execute the toolpath on the part within the work zone.

A method for inspecting and post-processing a workpiece having a laser-cut, closed inner contour, in which method a gripper moves a workpiece picked up in a defined manner, between a previously stored pre-defined pick-up position AP and a pre-defined first gripper position GP1, delivering the workpiece to an inspection unit, and if post-processing is required, a pre-defined second gripper position GP2, delivering the workpiece to an ejector unit.

A non-transitory computer-readable medium comprising computer-executable instructions that, when executed, are configured to cause a processor to perform operations that include receiving image data after an operation is performed by an industrial automation device on a product; analyzing the image data based an object-based image analysis (OBIA) model to classify the product as one of a plurality of conditions related to manufacturing quality and the OBIA model includes property layers associated with features related to a manufacturing of the product; determining whether the one of the conditions indicates an anomaly being present in the product; sending a notification indicative of the one of the plurality of conditions is presently associated with the product; identifying a property layer associated with classifying the one of the plurality of conditions; and updating the OBIA model based on the property layer and the input indicative of the anomaly being incorrectly associated with the product.

An industrial Ethernet configuration tool with preview capabilities is disclosed herein. An example implementation includes a computing device for executing an application, the application operable to configure a machine vision job, wherein configuring the machine vision job includes: (1) configuring at least one tool to be executed by the imaging device during an execution of the job; (2) configuring an output data stream based on the at least one tool, the output data stream being formatted for communication to the a third-party computing device; and (3) displaying a representation of an output message, the output message being formed based on: (i) the configuring the output data stream, and (ii) previously acquired job run data, the output message being a representation of a transmission of a message from the imaging device to the third-party computing device.