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.
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. 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.
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 modern programming languages and practices (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)
Leistungskontrolle als Semesterkurs