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Gocator Ring Layout Application

    Ardent Automation


Raw mode Surface scan from the Gocator interface

The application allows scanning of shapes for dimensional measurement with a Gocator ring mounted system generating a 360-degree point cloud, which may also include intensity image acquisition as well for defect detection using traditional machine vision tools. The system uses 4x LMI Gocator sensors with encoder feedback for triggering.

Collected measurements can be displayed as well as sent to any PLC via an industrial fieldbus. The number and type of measurements can be customized based on the application needs and may use the intensity data along with the point cloud. All the measurements collected can be sent to the PLC to be recorded, displayed, or manipulated in the PLC and transmitted to other supervisory systems. In cases, where there is a large quantity of data required, the data can be transmitted over multiple messages to the PLC.

Two way communication with the PLC is possible for HMI interaction, program settings changes, and PLC decision feedback to allow for central control over the application. Adaptive Vision software was selected for data processing as it can handle the Raw Mode 3D point cloud from the Gocator as well as the intensity image collected with the scans. This combination of data allows for coordination of measurements between the two data types.


The hardware solution includes a GoMax accelerator, 4x Gocator 2XXX series scanners, a Master810, an industrial PC with the Adaptive Vision Runtime license and a fieldbus gateway for communications with the PLC. A combination of custom algorithms by Ardent Automation and standard filters from the Adaptive Vision software allows for fast data processing and manipulation while maintaining responsive user and PLC interfaces.

The vision program allows for inputs from either the PLC or the user to define the location of the measurements sent to the PLC as well as the adjustments for noise suppression and point cloud or image smoothing. The user can select which set of values to use and adjust them as needed. The total number of measurements must be defined prior to the deployment to generate consistent data map for the PLC communication. Once changed, all user defined settings are also automatically saved to a file.

Program Inputs and Settings
History Viewer Screen

The solution running on a PC allows for the use of a History viewer of the application, with a user-definable number of scans to choose from. This also allows the user to define how many scans to archive if required. The archive size is limited either by user settings or based on the hard disk capacity of the PC. Typical scans are can be as small as 0.5MB and up to 50MB+. The history data saves multiple versions of the scan as well as all results for efficiency in the visualization.

Main Operator Interface Visualization
Main Operator Interface Results View

The vision program follows the following sequence:

  1. Gocator data acquisition.
  2. Point cloud combination.
  3. Point cloud filtering.
  4. Optional point cloud alignment to a known feature to remove vibration or unintentional part movement.
  5. Calculate measurements as needed.
  6. Filter calculated values as necessary, based on program settings.
  7. Output to the PLC and wait for response data, defining what measurements should be shown to the user.
  8. Show current and last scan for the operator to view.
  9. Wait for next scan from the Gocator.

The wait and timeout allow the user interface to remain active and responsive throughout the program running time and allow the user to interact with the visualization to pan, zoom, and rotate the point cloud and change the point size.

Practical use

This type of application can easily be adapted to a variety of uses, including stitching of multiple scans of a rotating object with a single scanner, or using standard vision tools along with the surface data and/or the intensity data from the scanner to detect contrast changes along with surface changes.

Another possibility would be to use the Adaptive Vision Deep Learning add-on, allowing for the use of modified surface data for deep learning applications where defects may be more difficult to define.

Author: Nate Hinkle (