TDDE05 · 6 credits · Advanced level (A1N) · Datateknik, Datavetenskap
AI Robotics
Active · next start VT 2027
100%
passed the latest regular sitting
PRA1 · 15 Jun 2026 · 33 students
100%
passed the regular sittings, all sittings over the last 5 years
PRA1 · 107 students
This module only reports passing results, so the pass rate does not show how many did not pass.
Part of 4 programmes
Hand-in or lab
PRA1 Project assignment
- 5
- 4
- 3
Main rounds
100% passed · 107 students
- 100%33
- 100%30
- 100%24
- 100%20
Later rounds
100% passed · 7 students
- 100%7
- Pass a later round: 100% (7 students), compared with 100% (107 students) on the ordinary exam
- Get 4 or 5: 77% (114 students)
Show small sittings (7)
Sittings with fewer than 5 students, often credit transfers or special sittings. They are not part of any figure on this page.
- 100%3
- 100%3
- 100%1
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- 100%3
Other modules
Partial exams, hand-ins and labs. They are not part of the figures at the top.
Hand-in or lab
LAB1 Laboratory work
- G
Main rounds
100% passed · 33 students
- 100%13
- 100%20
Later rounds
100% passed · 67 students
- 100%27
- 100%9
- 100%31
- Pass a later round: 100% (67 students), compared with 100% (33 students) on the ordinary exam
Show small sittings (19)
Sittings with fewer than 5 students, often credit transfers or special sittings. They are not part of any figure on this page.
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- 100%3
- 100%1
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- 100%1
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About the course
Aim
The aim of this course is to give an overview of the use of Artificial Intelligence (AI) techniques for robotic systems, through the use of simulated robots and widely used software packages, such as the Robot Operating System (ROS). The main focus of the course is for students to learn how the different components that constitute a robot -- perception, control and deliberation – interact with each other to form an autonomous system. The course will have an emphasis on how such a system makes decisions to accomplish its goals.
After the course, the student will be able to:
- to list and explain important problems and techniques in the area of AI robotics,
- to use existing frameworks to develop an autonomous robot in simulation, and
- to design, implement and evaluate algorithms needed to provide autonomous functionality to a robot in a simulated environment
Content
Perception and Scene Interpretation. Navigation: Localisation and path planning Autonomy and Levels of autonomy. Control and Decision-Making. Behavior-based robotics. Robotic Programming. Reactive, Deliberative and Hybrid robot architectures. Human-Robot Interraction
- Examiner
- Mariusz Wzorek
- Scheduled hours
- 0 h
- Main field of study
- Datateknik, Datavetenskap