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.
Lernziel
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.
Inhalt
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.
Skript
Course notes and other education material will be provided for free in an electronic form.
Literatur
There is no required textbook, but an excellent reference is Steve Lavalle's book on "Planning Algorithms."
Voraussetzungen / Besonderes
Students should have taken basic courses in optimization, control systems, probability theory, and should be familiar with basic programming (e.g., Python, and/or C/C++). Previous exposure to robotic systems is a definite advantage.
Kompetenzen
Fachspezifische Kompetenzen
Konzepte und Theorien
geprüft
Verfahren und Technologien
geprüft
Leistungskontrolle
Information zur Leistungskontrolle (gültig bis die Lerneinheit neu gelesen wird)
Die Leistungskontrolle wird in jeder Session angeboten. Die Repetition ist ohne erneute Belegung der Lerneinheit möglich.
Prüfungsmodus
schriftlich 150 Minuten
Zusatzinformation zum Prüfungsmodus
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.
Hilfsmittel schriftlich
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.)
Diese Angaben können noch zu Semesterbeginn aktualisiert werden; verbindlich sind die Angaben auf dem Prüfungsplan.
Lernmaterialien
Keine öffentlichen Lernmaterialien verfügbar.
Es werden nur die öffentlichen Lernmaterialien aufgeführt.
Gruppen
Keine Informationen zu Gruppen vorhanden.
Einschränkungen
Keine zusätzlichen Belegungseinschränkungen vorhanden.