DSP715 Autonomous systems and robots

Code DSP715
Name Autonomous systems and robots
Status Compulsory/Courses of Limited Choice
Level and type Post-graduate Studies, Academic
Field of study Computer Science
Faculty
Academic staff Ilze Andersone
Credit points 3.0 (4.5 ECTS)
Parts 1
Annotation The study course provides knowledge about autonomous systems and robots. The study course consists of lectures and five practical tasks that enable the application of the most essential methods for autonomous robot control..
Contents
Content Full- and part-time intramural studies Part time extramural studies
Contact hours Independent work Contact hours Independent work
Introduction, terms of autonomous systems and robots, autonomous mobile robots. 4 0 0 0
Sensors, sensor merging. 10 6 0 0
Mapping and path planning. 22 16 0 0
Decision making in autonomous systems. 10 16 0 0
Autonomous robot teams. 14 16 0 0
Examples and applications of autonomous systems. 6 0 0 0
Total: 66 54 0 0
Goals and objectives
of the course in terms
of competences and skills
The aim of the study course is to provide theoretical knowledge about autonomous systems and robots, practical use of their mathematical models, as well as to provide practical skills in the development of autonomous robot control algorithms. The tasks of the study course are to provide knowledge and skills: - to develop and apply mathematical models of robot dynamics; - to use robot models and control functions in navigation tasks; - to use multi-robot control methods and algorithms; - to use card merging techniques to create a global map.
Learning outcomes
and assessment
Is able to recognize and describe autonomous systems. - Corresponding exam questions.
Is able to describe the sensors of autonomous systems and their applications. - Corresponding exam questions.
Is able to describe and apply the main mapping approaches and path planning algorithms. - Corresponding exam questions, practical tasks.
Is able to describe and apply decision making methods in autonomous systems. - Corresponding exam questions, practical task.
Is able to describe and apply the main robot team mapping approaches. - Corresponding exam questions, practical task.
Evaluation criteria of study results
Practical work - 75%
Exam - 25%
 
Course prerequisites Mathematics
Course planning
Part CP ECTS Hours Tests
Lectures Practical Lab. Test Exam Work
1 3.0 4.5 2.0 1.0 0.0 *

[Extended course information PDF]