Autonomous SystemsLaajuus (3 cr)
Code: ELA18RE03
Credits
3 op
Objective
Is familiar with the basic concepts of intelligent systems and neural networks.
Understand the principles of self-learning systems and master relevant concepts regarding autonomous robots.
Content
Robot construction (hardware)
Implementation of autonomous software
Selection of sensors
Optimization of implementation (software and sensors)
Functionality tests
Demonstrations and final tests
Documentation of the implementation and selected algorithms
Qualifications
Microprocessor technology
Applied electronics
Assessment criteria, satisfactory (1)
Is familiar with the basic concepts of intelligent systems
Understands nonlinear systems and neural networks
Understands the principles of self-learning systems
Master relevant concepts in autonomous robots
Assessment criteria, good (3)
Is well versed in the basic concepts of intelligent systems
Can utilize neural networks for technical modeling
Is familiar with several different methods for implementing self-learning systems
Can formulate, structure and report a relevant problem regarding autonomous systems or robots
Assessment criteria, excellent (5)
Owns a deep insight into the basic concepts of intelligent systems and can speculate on future challenges and opportunities
Can perform demanding technical modeling using neural networks
Can program and simulate self-learning systems
Can successfully complete, report and structure a project on autonomous systems or autonomous robots
Materials
System documentation and datasheets
Enrollment
15.06.2024 - 20.10.2024
Timing
21.10.2024 - 15.12.2024
Number of ECTS credits allocated
3 op
Mode of delivery
Contact teaching
Unit
Faculty of Technology and Seafaring
Campus
Vasa, Wolffskavägen 33
Teaching languages
- Svenska
Degree programmes
- Degree Programme in Electrical Engineering and Automation
Teachers
- Hans Lindén
Teacher in charge
Ronnie Sundsten
Scheduling groups
- ELA21-A (Size: 40. Open UAS: 0.)
Groups
-
ELA21D-VIngenjör (YH), el- och automationsteknik, 2021, dagstudier
Small groups
- ELA21-A
Objective
Is familiar with the basic concepts of intelligent systems and neural networks.
Understand the principles of self-learning systems and master relevant concepts regarding autonomous robots.
Content
Robot construction (hardware)
Implementation of autonomous software
Selection of sensors
Optimization of implementation (software and sensors)
Functionality tests
Demonstrations and final tests
Documentation of the implementation and selected algorithms
Location and time
w. 43-50
Materials
Hardware Specific Documentation (Propeller Tutorials)
Teaching methods
Group work and demonstrations
Documentation of algorithms
Documentation of implementation
Exam schedules
Course examination 3 weeks after completion of the course.
Test of autonomous functions.
Submission of documentation according to given deadlines.
Completion alternatives
No alternative methods of performance. Requires laboratory equipment.
Student workload
The laboratory task and its documentation is carried out mainly as group work, also outside lecture hours.
Further information
w. 43-50: Two test occasions and a final test.
Evaluation scale
H-5
Assessment criteria, satisfactory (1)
Is familiar with the basic concepts of intelligent systems
Understands nonlinear systems and neural networks
Understands the principles of self-learning systems
Master relevant concepts in autonomous robots
Assessment criteria, good (3)
Is well versed in the basic concepts of intelligent systems
Can utilize neural networks for technical modeling
Is familiar with several different methods for implementing self-learning systems
Can formulate, structure and report a relevant problem regarding autonomous systems or robots
Assessment criteria, excellent (5)
Owns a deep insight into the basic concepts of intelligent systems and can speculate on future challenges and opportunities
Can perform demanding technical modeling using neural networks
Can program and simulate self-learning systems
Can successfully complete, report and structure a project on autonomous systems or autonomous robots
Assessment criteria, fail (0)
The assessment is made on the basis of submitted documentation and the result of the final test occasion.
Qualifications
Microprocessor technology
Applied electronics
Enrollment
15.06.2023 - 22.10.2023
Timing
23.10.2023 - 17.12.2023
Number of ECTS credits allocated
3 op
Mode of delivery
Contact teaching
Unit
Faculty of Technology and Seafaring
Campus
Vasa, Wolffskavägen 33
Teaching languages
- Svenska
Degree programmes
- Degree Programme in Electrical Engineering and Automation
Teachers
- Roger Mäntylä
Teacher in charge
Ronnie Sundsten
Scheduling groups
- ELA20-A (Size: 30. Open UAS: 0.)
Groups
-
ELA20D-VIngenjör (YH), el- och automationsteknik, h20, dagstudier
Small groups
- ELA20-A
Objective
Is familiar with the basic concepts of intelligent systems and neural networks.
Understand the principles of self-learning systems and master relevant concepts regarding autonomous robots.
Content
Robot construction (hardware)
Implementation of autonomous software
Selection of sensors
Optimization of implementation (software and sensors)
Functionality tests
Demonstrations and final tests
Documentation of the implementation and selected algorithms
Location and time
w. 43-50
Materials
Hardware Specific Documentation (Propeller Tutorials)
Teaching methods
Group work and demonstrations
Documentation of algorithms
Documentation of implementation
Exam schedules
Course examination 3 weeks after completion of the course.
Test of autonomous functions.
Submission of documentation according to given deadlines.
Completion alternatives
No alternative methods of performance. Requires laboratory equipment.
Student workload
The laboratory task and its documentation is carried out mainly as group work, also outside lecture hours.
Further information
w. 43-50: Two test occasions and a final test.
Evaluation scale
H-5
Assessment criteria, satisfactory (1)
Is familiar with the basic concepts of intelligent systems
Understands nonlinear systems and neural networks
Understands the principles of self-learning systems
Master relevant concepts in autonomous robots
Assessment criteria, good (3)
Is well versed in the basic concepts of intelligent systems
Can utilize neural networks for technical modeling
Is familiar with several different methods for implementing self-learning systems
Can formulate, structure and report a relevant problem regarding autonomous systems or robots
Assessment criteria, excellent (5)
Owns a deep insight into the basic concepts of intelligent systems and can speculate on future challenges and opportunities
Can perform demanding technical modeling using neural networks
Can program and simulate self-learning systems
Can successfully complete, report and structure a project on autonomous systems or autonomous robots
Assessment criteria, fail (0)
The assessment is made on the basis of submitted documentation and the result of the final test occasion.
Qualifications
Microprocessor technology
Applied electronics