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Service Robotics (5 cr)

Code: TX00DT05-3004

General information


Enrollment
01.05.2023 - 08.06.2023
Registration for the implementation has ended.
Timing
21.08.2023 - 13.10.2023
Implementation has ended.
Number of ECTS credits allocated
5 cr
Mode of delivery
On-campus
Unit
(2019-2024) School of Smart and Clean Solutions
Campus
Leiritie 1
Teaching languages
Finnish
Degree programmes
Electrical and Automation Engineering
Teachers
Timo Tuominen
Kristian Junno
Teacher in charge
Raisa Kallio
Course
TX00DT05

Implementation has 8 reservations. Total duration of reservations is 28 h 15 min.

Time Topic Location
Thu 31.08.2023 time 17:00 - 20:15
(3 h 15 min)
Teollisuusrobotiikka/Palvelurobotiikka
Wed 06.09.2023 time 17:00 - 20:15
(3 h 15 min)
Palvelurobotiikka TX00DT05-3004
MMB349 IT-Tila
Wed 13.09.2023 time 17:00 - 20:15
(3 h 15 min)
Palvelurobotiikka TX00DT05-3004
MMB349 IT-Tila
Wed 20.09.2023 time 17:00 - 20:15
(3 h 15 min)
Palvelurobotiikka TX00DT05-3004
MMB349 IT-Tila
Wed 04.10.2023 time 17:00 - 20:15
(3 h 15 min)
Palvelurobotiikka TX00DT05-3004
MMC376.3 Automaatiolaboratorio 3, Robotiikka
Mon 09.10.2023 time 17:00 - 21:00
(4 h 0 min)
Palvelurobotiikka TX00DT05-3004
MMC376.3 Automaatiolaboratorio 3, Robotiikka
Wed 11.10.2023 time 17:00 - 21:00
(4 h 0 min)
Palvelurobotiikka TX00DT05-3004
MMC376.3 Automaatiolaboratorio 3, Robotiikka
Thu 12.10.2023 time 17:00 - 21:00
(4 h 0 min)
Palvelurobotiikka TX00DT05-3004
MMC376.3 Automaatiolaboratorio 3, Robotiikka
Changes to reservations may be possible.

Objective

Student knows most common structures of service robots.
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.

Content

1. Cobot and servicerobot use in industry
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. Methods used in Pattern Recognition, Object Recognition with Segmentation, Feature Detection and Classification
7. Industrial Machine Vision Applications
8. Coordinate systems and coordinate transforms using matrices

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.

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