Pick and Place Conditioning with a Conveyor Belt

V.1.1

Pick and place conditionning with a Conveyor

Difficulty: medium

Time: ~30 min

Note

This tutorial is working from:
The version v3.0.0 of the ned_ros_stack
The version v3.0.0 of Niryo Studio
The version v3.1.0 of PyNiryo

Objectives

  • Show multiple examples of applications you can do with the Vision Set and Ned/Ned2

  • Have a first approach with industrial processes which can be realized with Ned/Ned2

Requirements

  • Basic knowledge of Python

  • Being able to use Ned

  • Having studied the Vision Set’s User Manual

  • Having studied the Conveyor Belt’s User Manual

  • Having looked at the PyNiryo library documentation

Note

Documentation of Ned: https://archive-docs.niryo.com/product/ned/index.html
Documentation of Ned2: https://archive-docs.niryo.com/product/ned2/index.html
The Vision Set’s User Manual: https://archive-docs.niryo.com/product/vision-set/index.html
PyNiryo documentation: https://archive-docs.niryo.com/dev/pyniryo/index.html
The Conveyor Belt’s User Manual: https://archive-docs.niryo.com/product/conveyor-belt/index.html

What you will need

  • A Vision Set

  • A Ned (or a Ned2)

  • A Conveyor Belt

  • The PyNiryo library installed on your computer

Conditioning with a Conveyor Belt and the Vision Set

This process consists in picking any type of object from a Conveyor Belt and conditioning them in a packing area.

You can find below a schematics of the application to help you visualizing it.

Ned

The script of the application can be found here: Vision conditioning conveyor

Explanation of the script

This script shows an example on how to use Ned/Ned2’s Vision Set with a Conveyor Belt.

At the beginning, the robot camera is placed over the Conveyor Belt. When it detects an object, it stops the Conveyor Belt, then, it realizes a pick and place operation as the one we have in the first example.

Note

The code is well commented so you can easily understand how this application is working.

Before running the application, you will have to change these variables below:

  • robot_ip_address

  • tool_used

  • workspace_name

You can change the variable below if you want to do your own environment:

  • grid_dimension

  • vision_process_on_robot

  • display_stream

Finally, you may change the variable below to adapt the application the perfect way to your environment:

  • observation_pose

  • center_conditioning_pose

  • sleep_pose