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151-0325-00L 4 Credits MSC D-ITET , D-MAVT , D-INFK
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Planning and Decision Making for Autonomous Robots

Lecturers & Examiners: Prof. Dr. Emilio Frazzoli
VVZ CR 3.0

Last Updated: 2026-02-05 15:48:07

Abstract

Planning safe and efficient motions for robots in complex environments, often shared with humans and other robots, is a difficult problem combining discrete and continuous mathematics, as well as probabilistic, game-theoretic, and learning aspects. This course will cover the algorithmic foundations of motion planning, with an eye to real-world implementation issues.

Objective

The students will learn how to design and implement state-of-the-art algorithms for planning the motion of robots executing challenging tasks in complex environments.

Content

Discrete planning, shortest path problems. Planning under uncertainty. Game-theoretic planning. Geometric Representations. Configuration space. Grids, lattices, visibility graphs. Sampling-based methods. Potential and Navigation functions. Mathematical Programming. Local and global optimization, convex relaxations. Planning with limited information. Multi-agent Planning.

Resources

Lecture Notes

Course notes and other education material will be provided for free in an electronic form.

Literature

There is no required textbook, but an excellent reference is Steve Lavalle's book on "Planning Algorithms."

General Information

Language
English
Levels
MSC
Frequency
Yearly recurring

Examination

Type
session examination
Mode
written 150 minutes
Aids
One sheet of A4 paper, front and back. Only handwritten material by the individual student is allowed --- no computer printouts or photocopies. (Preparing such a sheet would be an important part of the learning process.)
There is a written final exam during the examination session, which covers all material taught during the course, i.e. the material presented during the lectures and corresponding problem sets, programming exercises, and recitations.Additionally, there will be programming assignments, which are an optional learning task during the semester, requiring the students to understand and apply the lecture material. These contribute a maximum of 0.25 grade points to the final grade, but only if it helps to improve the final grade.

Course Components

Type Title Time & Place Hours
lecture Planning and Decision Making for Autonomous Robots
  • Wed 10:15-12:00 (HG E 3)
2 h weekly
exercise Planning and Decision Making for Autonomous Robots
  • Wed 12:15-13:00 (HG F 1)
1 h weekly

Offered In