Autonomous Robotics
The practical course gives an introduction to mobile robotics with a focus on dynamical systems approaches. In the exercises, the computing environment Matlab is used to control e-puck miniature mobile robots, equipped with a differential drive, combined infrared/proximity sensors and a video camera. The course covers elementary problems in robot odometry, use of sensors and motor control. It then teaches basic dynamic methods for robot navigation, in which the robot's sensors are used for obstacle avoidance and approach to a target location.
Lecturers
Dr.-Ing. Mathis RichterLecturer |
mathis.richter@ini.rub.de |
Dr.-Ing. Jan TekülveTeaching Assistant |
(+49) 234-32-27976 jan.tekuelve@ini.rub.de NB 02/75 |
Details
- Course type
- Lab courses
- Credits
- 3 CP
- Term
- Winter Term 2017/2018
Dates
- Lab course
-
Takes place
every day from 10:00 to 18:00 in room NB 02/77.
First appointment is on 19.02.2018
Last appointment is on 23.02.2018 - Preliminary meeting
- Takes place on 01.02.2018 from 10:15 to 11:15 in room NB 3/57.
Requirements
Interested students who do not have experience in Matlab should attend the Matlab introduction of the lab exercise Introduction to Deep Learning for Computer Vision.
The practical part of the lab course consists of a week of full-time work in which students solve programming tasks with mobile robots. The students then write reports in which they describe and analyze the work they have done. The grade for the lab course is based on the reports alone. Students will get support during programming and will receive elaborate feedback on their writing.
Documents
| Document | General information |
| Document | Background material |
| Exercises | Exercise sheets |
| Document | Report guidelines |
| Latex template | Report template |
The Institut für Neuroinformatik (INI) is a central research unit of the Ruhr-Universität Bochum. We aim to understand the fundamental principles through which organisms generate behavior and cognition while linked to their environments through sensory systems and while acting in those environments through effector systems. Inspired by our insights into such natural cognitive systems, we seek new solutions to problems of information processing in artificial cognitive systems. We draw from a variety of disciplines that include experimental approaches from psychology and neurophysiology as well as theoretical approaches from physics, mathematics, electrical engineering and applied computer science, in particular machine learning, artificial intelligence, and computer vision.
Universitätsstr. 150, Building NB, Room 3/32
D-44801 Bochum, Germany
Tel: (+49) 234 32-28967
Fax: (+49) 234 32-14210