DD2425 Robotics and Autonomous Systems 9.0 credits
Robotik och autonoma system
Advanced course in computer science focusing on the broad, inter-disciplinary field of robotics. Students in groups design, build and program their own autonomous robots for a contest between the groups.
Education cycle
Second cycleMain field of study
Computer Science and Engineering
Grading scale
A, B, C, D, E, FX, F
Course offerings
Autumn 18 Robots 18 for programme students FULL
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Periods
Autumn 18 P1 (3.5 credits), P2 (5.5 credits)
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Application code
50973
Start date
27/08/2018
End date
14/01/2019
Language of instruction
English
Campus
KTH Campus
Tutoring time
Daytime
Form of study
Normal
-
Number of places *
Max. 25
*) If there are more applicants than number of places selection will be made.
Schedule
Planned timeslots
P1: H1, I1, A2, I2. P2: I1, I2. more info
Course responsible
Patric Jensfelt <patric@kth.se>
Teacher
Patric Jensfelt <patric@kth.se>
Target group
Searchable for students from year 3 and for students admitted to a master programme.
Meritis done in the following way: Credits in the program. Someone who only follows a master's program at KTH will count 180 credits plus credits in the master's program. Anyone who attended at leastone course in electronics, computer vision, control engineering, mechanics, computer engineering or C/C ++-programmingwill add 5 credits per subject to the taken credts.
Part of programme
- Master's Programme, Computer Science, 120 credits, year 1, CSDA, Recommended
- Master's Programme, Computer Science, 120 credits, year 2, CSCS, Conditionally Elective
- Master's Programme, Computer Science, 120 credits, year 2, CSDA, Recommended
- Master's Programme, Embedded Systems, 120 credits, year 2, INEL, Conditionally Elective
- Master's Programme, Embedded Systems, 120 credits, year 2, INMV, Conditionally Elective
- Master's Programme, Embedded Systems, 120 credits, year 2, INPF, Conditionally Elective
- Master's Programme, Embedded Systems, 120 credits, year 2, INSR, Conditionally Elective
- Master's Programme, Interactive Media Technology, 120 credits, year 2, FID, Recommended
- Master's Programme, Machine Learning, 120 credits, year 1, Conditionally Elective
- Master's Programme, Machine Learning, 120 credits, year 2, Conditionally Elective
- Master's Programme, Medical Engineering, 120 credits, year 1, Conditionally Elective
- Master's Programme, Medical Engineering, 120 credits, year 2, Conditionally Elective
- Master's Programme, Systems, Control and Robotics, 120 credits, year 1, Recommended
- Master's Programme, Systems, Control and Robotics, 120 credits, year 1, RASM, Conditionally Elective
- Master's Programme, Systems, Control and Robotics, 120 credits, year 2, Recommended
- Master's Programme, Systems, Control and Robotics, 120 credits, year 2, RASM, Conditionally Elective
Intended learning outcomes
After completing the course the student should be able to
- recall basic concepts in robotics
- implement and integrate software components for robots
- solve a robotics task with limited resources
- identify and discuss ethical and societal aspects of robot technology
in order to
- be able to work with autonomous and other complex systems in research and/or development
- become better at planning, executing and developing work in project groups.
Course main content
During the course a small, mobile, autonomous robot for performing certain tasks is built. This work is carried out in groups as a project. At the end of the course there is a contest between the robots that the participants have constructed.
The lecture part of the course deals with fundamental concepts in robotics.
The practical part of the course adds hands on experience with sensors, actuators, programming of systems and building of robots.
Eligibility
Single course students: Bachelor of Science degree or equivalent including 45 university credits in Mathematics or Information Technology. Furthermore: English B, or equivalent.
Recommended prerequisites
Knowledge in mathematics and programming.
Electronics, computer vision, control engineering, mechanics and computer technology is an advantage.
A very large part of the project consists of programming in C/C++ why experience of this is a big plus and knowledge of a language at least similar to C (as Java or C#) is a requirement.
Literature
Meddelas senast 4 veckor före kursstart på kursens hemsida.
Examination
- LAB1 - Laboratory Works, 0.5, grading scale: P, F
- PRO1 - Project, 5.5, grading scale: P, F
- TEN1 - Exam, 3.0, grading scale: P, F
In this course all the regulations of the code of honor at the School of Computer science and Communication apply, see: http://www.kth.se/csc/student/hederskodex/1.17237?l=en_UK.
Offered by
EECS/ ?
Contact
Patric Jensfelt, tel: 790 6731, e-post: patric@nada.kth.se
Examiner
Patric Jensfelt <patric@kth.se>
Supplementary information
The number of participants is limited!
Version
Course syllabus valid from: Autumn 2016.
Examination information valid from: Spring 2019.