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151-0325-00L 4 Credits MSC D-ITET , D-INFK , D-MATH , D-MAVT , D-ERDW , D-PHYS

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Planning and Decision Making for Autonomous Robots

Lecturers & Examiners: Prof. Dr. Emilio Frazzoli

VVZ CR 3.0

Last Updated: 2026-06-01 11:30:53

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 ethical/regulatory 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. Steering methods. Configuration space and collision checking. Potential and Navigation functions. Grids, lattices, visibility graphs. Mathematical Programming. Sampling-based methods. 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: graded semester performance

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

Maschineningenieurwissenschaften Master
- Kernfächer
Rechnergestützte Wissenschaften Master
- Vertiefungsgebiete
  - Robotik (Höchstens eine der beiden Lerneinheiten 263-5902-00L Computer Vision bzw. 227-0447-00L Image Analysis and Computer Vision darf an das gesamte Studium (RW BSc und MSc) angerechnet werden. Höchstens eine der beiden Lerneinheiten 263-5210-00L Probabilistic Artificial Intelligence bzw. 252-0535-00L Advanced Machine Learning darf im Vertiefungsgebiet "Robotik" an das gesamte Studium (RW BSc und MSc) angerechnet werden. Eine Anrechnung der anderen Lerneinheit in einer anderen Kategorie ist jedoch zulässig.)
Robotics, Systems and Control Master
- Kernfächer
Data Science Master
- Master-Studium (Studienreglement 2023)
  - Wahlfächer
    - Interdisziplinäre Wahlfächer
Space Systems Master
- Fachspezifische Vertiefung (Es müssen mindestens 20 KP aus den Deep Track Lerneinheiten absolviert werden. Überzählige KP können für Wahlfächer angerechnet werden.)
  - Robotics
    - Vertiefungsfächer Robotics (Diese LE's können sowohl als Vertiefungsfach als auch als Wahlfach angerechnet werden.)