Autonomous Robotics

Limited number of participants!

Please register by email to mathis.richter@ini.rub.de, stating your name, student ID number, study program - Studiengang, and semester.

Please do not forget to register with your Prüfungsamt as well.

Registration period: from December 1, 2019 to January 10, 2020.

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

Details

Course type
Lab courses
Credits
3 CP
Term
Winter Term 2019/2020

Dates

Lab course
Takes place every day from 10:00 to 18:00 in room NB 02/77.
First appointment is on 03.02.2020
Last appointment is on 07.02.2020
Preliminary meeting
Takes place from 10:15 to 11:00 in room NB 3/57.
First appointment is on 30.01.2020
Last appointment is on 30.01.2020

Requirements

Interested students who do not have experience in Matlab should attend the Matlab introduction of the lab exercise "Computer Vision: Deep Learning Lab Course".


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 both the practical work and the report. Students will get support during programming.

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