Programming for robots and manipulators (VRM)

Requirements:

Software:

Robot Operating System (ROS)
RoboStudio ABB
Automation Studio B&R
Linux Ubuntu (16.04 or 18.04)
Unity3D and Vuforia

Programming Language:

Python and/or C/C++, C#

Other:

Algorithmization, Programming, Mathematics and Optimization

Description:

The VRM (Programming for Robots and Manipulators) course enables students to acquire skills and knowledge in programming industrial / mobile robots and manipulators. This course also expands skills in advanced system integration and deployment in real-world robotic applications. The aim of the VRM course is to introduce students with modern approaches to robotic technology with a focus on programming, kinematics / dynamics solutions, motion planning, Industry 4.0 and the use of machine learning (ML).

The main focus is on students practical skills in laboratory exercises, which include several blocks:

1. RobotStudio ABB
2. Forward/Inverse kinematics
3. Robotic operating system (ROS) extended by advanced industrial capabilities ROS-Industrial (ROS-I)
4. Virtual / digital twin using Unity3D extended by system integration with B&R Automation PLC via OPC UA
5. A simple demonstration of augmented reality based on robotics

These few blocks are extended by theoretical knowledge, which students acquire in the form of lectures.

Link: Detailed description of the Syllabus (Czech)

Link: Course descrition - FME, BUT

Detailed description of the Syllabus:

Part 1:

• Introduction to the course, main goals, methods and evaluation criteria, etc.
• Introduction to the issue, development and definition of robots, manipulators.
• Introduction of an advanced robotic production line called Industry 4.0 (i4C).

Link: Lecture 1

Part 2:

• Stationary industrial robots and single-purpose manipulators. Specific constructions of industrial robots, parallel structures. Programmable logic controllers (PLC) and use in robotics.
• Control and programming of industrial robots. Introduction of basic tools for creating robotic simulations.
• Assignment of seminar paper.

Link: Lecture 2

Part 3:

• End-effectors and their adaptability.
• ABB RobotStudio - Workshop (Part 1: Introduction, Create tool, Simple task with an industrial robot, etc.)
• Assignment of project.

Link: Lecture 3

Link: Laboratory 1

Part 4:

• ABB RobotStudio - Workshop (Part 2: Simple task with an collaborative robot, Conveyor control, Smart gripper, Sync., etc.)

Link: Laboratory 2

Part 5:

• Forward / Inverse Kinematics.
• Demonstration of Forward / Inverse kinematics on a two-link simple manipulator. Creation of a working envelope of a specified robotic construction.

Link: Lecture 4

Link: Laboratory 3

Part 6:

• Differential Kinematics and Robotic Dynamics.
• Demonstration of Differential Kinematics on a two-link simple manipulator. Example of dynamics calculation using Euler-Lagrange equation.

Link: Lecture 5

Link: Laboratory 4

Part 7:

• Motion planning in robotics (mobile, industrial robots) using classical Joint / Cartesian interpolation and other planning methods such as RRT (Rapidly-exploring random tree), PRM (Probabilistic roadmap) and Reinforcement / Deep-Reinforcement learning.
• Bezier curves (Linear, Quadratic, Cubic).
• Demonstration of simple motion planning using Joint / Cartesian interpolation on a two-link manipulator. Trajectory smoothing using Bézier curves. Animation of the resulting trajectory, check of reachable points, etc.

Link: Lecture 6

Link: Laboratory 5

Part 8:

• ROS (Robot Operating System), ROS-I (Industrial) Introduction.
• ROS installation (melodic distribution), package configuration, explanation of basic concepts (topics, services, messages, etc.)
• A simple example of TurtleSim motion control and working with a terminal.
• Creating a ROS workspace for simple control of TurtleSim motion using the Python programming language (catkin, rospy, launch file, etc.)

Link: Laboratory 6

Part 9:

• A lecture on an interesting topic related to robotics. It can be realized by a company from the South Moravian region (INTEMAC, B&R Automation, SMC Industrial Automation, etc.)

Part 10:

• Simple demonstration of robot motion control and trajectory planning via the ROS system using several simulation tools (RVIZ, gazebo, etc.)
• Controlling the movement of multiple industrial robots (ABB, Fanuc, Universal Robots, etc.) using the Python programming language (catkin, rospy, launch file, etc.)
• Presentation of students' Bachelor's / Master's theses (ROS, robotics, system integration, etc.)

Link: Laboratory 7

Part 11:

• Unity3D as a tool for creating digital / virtual twins, connection with B&R Automation Studio (follow-up project from the VPL course).
• Introduction to augmented reality and a simple demonstration of the application in the real world.

Link: Laboratory 8

Part 12:

• Introduction to the concept of Industry 4.0.
• Industry 5.0, 6.0 and automation a few years later.

Part 13:

• Presentation of team projects.

Practical Demonstration:

The practical form of the VRM course will be carried out in the I4C (Industry 4.0 Cell) laboratory on several tasks related to industrial robotics. Each task will be assigned by the course leader and the students will have to implement it in groups in a real robot. The practical exercise will end with a defense of the results of each task.

Assessment Methodology:

Description:

• Active participation in laboratory exercises and lectures: 10 points
• Seminar paper: 20 points [Link]
• Project no. 1: 30 points [Link]
• Project no. 2 (Team project): 40 points [Link]

The condition for writing a seminar paper is the use of LaTex (e.g., Overleaf -> Online LaTeX Editor). Projects are submitted via GitHub, which will contain a folder of all relevant files for each project and a short description in English.

The penalty equation for late submission of a project is defined as:

$\Large p_s = \lvert \frac{\Delta t}{24}e^{\frac{1}{2}} \rvert + \delta_p,$

where $\Delta t$ is defined as the difference between the date of deadline and the date of assignment of the project (in hours), and $\delta_p$ is the project error factor defined as $\frac{s_{max}}{10}$.

The maximum possible score is defined as:

$\Large s = s_{max} - p_s,$

where $s_{max}$ is the initial maximum score, and $p_s$ is a penalty.

The script for the calculation can be found at [Link].

Resources and Literature:

Textbooks:

1. Introduction to AI Robotics, Robin R. Murphy
2. Roboty a robotizované výrobní technologie, Zdeněk Kolíbal
3. Handbook of Robotics, Bruno Siciliano
4. Modern Robotics: Mechanics, Planning, and Control, Kevin M. Lynch and Frank C. Park
5. Robotics, Vision and Control, Peter Corke
6. Planning Algorithms, Steven M. LaValle
7. Industrial Robotics: Theory, Modelling and Control, Sam Cubero
8. Mathematics for Computer Graphics, John Vince

Others:

1. IEEE Xplore
2. Science Direct
3. Springer - International Publisher Science

Course Preview:

YouTube (2021): https://www.youtube.com/watch?v=YzlaLfuPLOY&t=1s

YouTube (2022): https://www.youtube.com/watch?v=pPqqe7-ma-Q&t=2s

YouTube (2023): https://www.youtube.com/watch?v=J0LXaKxu6bs&t=15s

YouTube (2024): https://www.youtube.com/watch?v=WGuWrrZOnNk

Contact Info:

Roman.Parak@vutbr.cz or Microsoft Teams (recommended)

## License
[MIT](https://choosealicense.com/licenses/mit/)