Robotics and Machine Vision Systems (15 ECTS)

Objective

Student knows most common structures of industrial robots. Student is acquainted with realizing robotic projects and knows various types of robotic solutions. Student knows possibilities of robots in product handling.
Student can prepare on-line programs for robots in automation laboratory. Student is able to simulate robot cells and plan robot programs with simulation systems in automation laboratory.
Student knows the structure of Machine Vision Systems and Principles of Lighting. Student understands principles of most common Image Processing and Analyzing Methods.
Student can use Machine Vision Systems in Automation Laboratory and their Image Processing Algorithms to Object Recognition, Classification and Quality Control applications.
Student can utilize mathematical methods to handle various coordinates needed in robotics and machine vision.
Student knows principles of automation safety.

Content

1. Robots in agile industrial automation
2. Robot structures, safety systems, sensors and end-of-arm tooling
3. Robot programming and simulation
4. Robot kinematics and control
5. Principles and essential Components of Machine Vision Systems
6. Digital Images and Image Processing
7. Most Important Image Processing Methods for Gray Scale and Color Images
8. Methods used in Pattern Recognition, Object Recognition with Segmentation, Feature Detection and Classification
9. Industrial Machine Vision Applications
10. Coordinate systems and coordinate transforms using matrices
11. Safety automation

Evaluation scale

0-5

Assessment criteria, satisfactory (1)

The student has achieved the course objectives fairly. The student will be able to identify, define and use the course subject area’s concepts and models. The student understands the criteria and principles of the expertise development. The student has completed the required learning exercises in minimum requirement level. His/her competences have developed in a way that he/she may complete the remaining studies in electrical engineering and automation technology and finally work in a suitable job position related to this field.

Assessment criteria, good (3)

The student has achieved the course objectives well, even though the knowledge and skills need improvement on some areas. The student has completed the required learning exercises in good or satisfactory level. The student is able to define the course concepts and models and is able to justify the analysis. The student is able to apply their knowledge in study and work situations. The student understands the importance of expertise in the field of electrical engineering and automation technology and is able to analyze his/her own expertise.

Assessment criteria, excellent (5)

The student has achieved the objectives of the course with excellent marks. The student master commendably the course subject area’s concepts and models. The student has completed the required learning exercises in good or excellent level. The student is able to make justified and fluent analysis and to present concrete development measures. The student is well prepared to apply their knowledge study and work situations. Students are able to analyze the expertise in electrical engineering and automation technology and the evolvement of their own expertise.

Assessment criteria, approved/failed

The student has achieved the course objectives fairly. The student will be able to identify, define and use the course subject area’s concepts and models. The student understands the criteria and principles of the expertise development. The student has completed the required learning exercises in minimum requirement level. His/her competences have developed in a way that he/she may complete the remaining studies in electrical engineering and automation technology and finally work in a suitable job position related to this field.