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FDD3416 Safe Robot Planning and Control 7.5 credits

The course explores techniques for robot planning and control with specific focus on rigorous treatment of safety. The participants will get an overview of techniques including reachability analysis, control barrier functions, sampling-based motion planning, trajectory optimization, and formal synthesis from temporal logic specifications. They will also get an experience implementing a subset of the techniques.

Information per course offering

Termin

Information for Autumn 2025 Start 27 Oct 2025 programme students

Course location

KTH Campus

Duration
27 Oct 2025 - 12 Jan 2026
Periods

Autumn 2025: P2 (7.5 hp)

Pace of study

50%

Application code

10647

Form of study

Normal Daytime

Language of instruction

English

Course memo
Course memo is not published
Number of places

Places are not limited

Target group
No information inserted
Planned modular schedule
[object Object]
Schedule
Schedule is not published
Part of programme
No information inserted

Contact

Examiner
No information inserted
Course coordinator
No information inserted
Teachers
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Course syllabus as PDF

Please note: all information from the Course syllabus is available on this page in an accessible format.

Course syllabus FDD3416 (Autumn 2025–)
Headings with content from the Course syllabus FDD3416 (Autumn 2025–) are denoted with an asterisk ( )

Content and learning outcomes

Course disposition

The course involves lectures and optional tutorial sessions, online quizzes and own work on project.

Course contents

  • Introduction to safety of robotic systems, techniques and approaches.
  • Safety/reachability analysis, safe set representation and reachability analysis for dynamical systems.
  • Safe robot control, invariant sets, potential fields and control barrier functions.
  • Fail-safe and risk-aware planning.
  • Advanced motion planning algorithms, feedback motion planning, sampling-based motion planning under differential constraints, trajectory optimization.
  • Task planning and integrated task and motion planning.
  • Formal methods for robot planning and control. Discrete- and continuous-time temporal logics for goal and constraint specification. Correct-by-design planning and control.
  • Reinforcement learning for robot control, reinforcement learning for planning under uncertainty, safe reinforcement learning.
  • Multi-agent planning and control.

Intended learning outcomes

After passing the course, the student shall be able to

  • Account for and apply different principles of robot planning and control.
  • Formulate a planning and control problem for a given robotic application.
  • Select and motivate appropriate techniques for robot planning and control for various contexts and domains.
  • Analyze and evaluate safety of a given robotic system.

Literature and preparations

Specific prerequisites

Knowledge in introduction to robotics, 7.5 higher education credits equivalent to completed course DD2410.

Recommended prerequisites

Basic knowledge of robotics and robot control.

Literature

You can find information about course literature either in the course memo for the course offering or in the course room in Canvas.

Examination and completion

Grading scale

P, F

Examination

  • TEN1 - Written examen, 1.5 credits, grading scale: P, F
  • LAB1 - Laboratory work, 6.0 credits, grading scale: P, F

Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.

The examiner may apply another examination format when re-examining individual students.

If the course is discontinued, students may request to be examined during the following two academic years.

The examination will be via quizzes and a project focused on implementation of a technique related to safety analysis, safe planning, safe control, or similar.

Examiner

No information inserted

Ethical approach

  • All members of a group are responsible for the group's work.
  • In any assessment, every student shall honestly disclose any help received and sources used.
  • In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.

Further information

Course room in Canvas

Registered students find further information about the implementation of the course in the course room in Canvas. A link to the course room can be found under the tab Studies in the Personal menu at the start of the course.

Offered by

Education cycle

Third cycle

Postgraduate course

Postgraduate courses at EECS/Robotics, Perception and Learning