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Modular Robotics - CV2D

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Building a Modular Robot

  • Develop a robust detection and localization solution based on supplied demo parts
  • Develop a web-based interface that integrates into the robot's existing UI
  • Include Mapping from camera to robot coordinates

Steps to startup

1. Dependencies

2. Calibrate the camera and test the CV2D code locally

  • calibration docs

    • run the scripts available under ./cam_calibration/
  • camera configurations

  • when the camera is successfully calibrated, the calibration data is stored under ./camera_data/

  • To capture images from the Basler camera, run $ python camera_control.py. The script uses the data in config-a2A3840-13gmPRO_40137700.pfs as camera configurations.

  • To test the object recognition

    • We need 2 images: image of the scene with no objects i.e background image and an image with objects in the scene i.e sample image.
    • change the background image param(img_bg) and sample image param(img_example) in the script to desired values.
    • run $ python object_recognition.py
  • Assuming that the camera is calibrated by running all the calibration scriprts, test the CV2D code by running $ python test.py.

3. To Run the algorithm as a service

  • run $ python server.py

NodeRed Integration

  • import flows.json into the NodeRed interface.
  • open the imported flow named CV2D
  • change the param in the http node to the url obtained from running server.py script.

CAD Models

  • The CAD models of the Robot end of Arm, camera along with its assembly can be found here
Resources

Additionally, We also tried different state of the art object detection and matching algorithms and the results are documented here.

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