700.204 (21W) Mobile Robot Navigation with Artificial Intelligence
Überblick
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- Lehrende/r
- LV-Titel englisch Mobile Robot Navigation with Artificial Intelligence
- LV-Art Vorlesung-Kurs (prüfungsimmanente LV )
- LV-Modell Präsenzlehrveranstaltung
- Semesterstunde/n 2.0
- ECTS-Anrechnungspunkte 4.0
- Anmeldungen 21 (14 max.)
- Organisationseinheit
- Unterrichtssprache Englisch
- LV-Beginn 08.10.2021
- eLearning zum Moodle-Kurs
Zeit und Ort
LV-Beschreibung
Intendierte Lernergebnisse
After completing this course, the student will
- have a broad overview of existing AI-based methods for mobile robot navigation and control
- know advantages and disadvantages of various methods depending on the task at hand
- have gained hands-on experience implementing state-of-the art AI methods for solving problems in mobile robot navigation
Lehrmethodik
The following teaching methods will be used:
- presentation of novel content with examples
- group excercises during class and discussion
- home excercises / mini-projects
The mini-projects are realistic use cases in mobile robot navigation that shall be solved using the AI methods discussed in the course and presented to the class during dedicated sessions in the second half of the semester.
Inhalt/e
- Overview of AI methods with focus on robotics
- AI methods for state estimation
- Path planning with AI methods
- AI-based control of mobile robots
- Depending on time and interest, other topics may be included such as simulation frameworks
Prüfungsinformationen
Prüfungsmethode/n
Grading is based on the active participation in class (presentation of group excercises, active participation in group excercises, active contributions to discussions etc.), and the successful conclusion and presentation of mini-projects assigned during class.
Prüfungsinhalt/e
All contents covered in the course.
Beurteilungskriterien/-maßstäbe
The grades will be determined by
- active participation during class (50%)
- successful conclusion and presentation of mini-projects (50%)
The final grade will be the weighted average of the two grades.
Please note that this course requires regular attendance. An unexcused absence from more than 5 classes will automatically lead to a failing grade for the entire course. Dropping out is possible until the end of November.
Beurteilungsschema
Note BenotungsschemaPosition im Curriculum
- Masterstudium Information and Communications Engineering (ICE)
(SKZ: 488, Version: 15W.1)
-
Fach: Information and Communications Engineering: Supplements (NC, ASR)
(Wahlfach)
-
Wahl aus dem LV-Katalog (Anhang 4) (
0.0h VK, VO, KU / 14.0 ECTS)
- 700.204 Mobile Robot Navigation with Artificial Intelligence (2.0h VC / 4.0 ECTS)
-
Wahl aus dem LV-Katalog (Anhang 4) (
0.0h VK, VO, KU / 14.0 ECTS)
-
Fach: Information and Communications Engineering: Supplements (NC, ASR)
(Wahlfach)
- Masterstudium Information and Communications Engineering (ICE)
(SKZ: 488, Version: 15W.1)
-
Fach: Information and Communications Engineering: Supplements (NC, ASR)
(Wahlfach)
-
Wahl aus dem LV-Katalog (Anhang 4) (
0.0h VK, VO, KU / 14.0 ECTS)
- 700.204 Mobile Robot Navigation with Artificial Intelligence (2.0h VC / 4.0 ECTS)
-
Wahl aus dem LV-Katalog (Anhang 4) (
0.0h VK, VO, KU / 14.0 ECTS)
-
Fach: Information and Communications Engineering: Supplements (NC, ASR)
(Wahlfach)
- Masterstudium Information and Communications Engineering (ICE)
(SKZ: 488, Version: 15W.1)
-
Fach: Autonomous Systems and Robotics: Advanced (ASR)
(Wahlfach)
-
Wahl aus dem LV-Katalog (siehe Anhang 3) (
0.0h VK, VO / 30.0 ECTS)
- 700.204 Mobile Robot Navigation with Artificial Intelligence (2.0h VC / 4.0 ECTS)
-
Wahl aus dem LV-Katalog (siehe Anhang 3) (
0.0h VK, VO / 30.0 ECTS)
-
Fach: Autonomous Systems and Robotics: Advanced (ASR)
(Wahlfach)