Modular Robotics - CV2D

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

Recommended to work with Anaconda - manages the env's better
- ref : https://docs.anaconda.com/anaconda/install/index.html
OpenCV - $ sudo apt-get install python3-opencv
- ref : https://docs.opencv.org/4.x/d2/de6/tutorial_py_setup_in_ubuntu.html
Flask - $ pip install flask

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.

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.

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

Object Detection Ref: https://github.com/pacogarcia3/hta0-horizontal-robot-arm/blob/master/README.md
NodeRED
- graphical programming : https://docs.robco.de/sections/node_programming.html
- test based programming : https://docs.robco.de/sections/text_based_programming.html

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