Lecturer: Tim Tiedemann
Fields: Robotics, Sensor Data Processing, Edge Computing, Machine Learning
Content
(this course description is under construction)
In this practical course, we will touch hardware — robots and microcontrollers — and we will see: there is no brain inside! But as robots and lone small edge hardware out in the woods would really benefit from something like a brain, this course combines hardware and cognitive sciences. And as we are nice guys and gals, we will do it on our own: Put it on ’em! Put the brain on ’em!
As it is currently planned, different hardware will be on site and/or accessible:
– mobile wheel-based robot
– autonomous underwater vehicle (AUV)
– multiple microcontroller boards with different sensors
– (and as there need to be disappointments: systems in simulation)
We will try to implement different findings from the cognitive sciences (ie. Biology and Cognitive Psychology) or Data Science on the (small!) systems (some were already implemented by the instructor in research projects, some are brand-new and unrevealed).
The course is planned as BYOD and detailed descriptions of what notebook/installation should be brought to the course to participate hands-on, will follow.
Literature
- (will follow soon)
Lecturer
Tim Tiedemann studied computer science with a focus on robotics and neural networks at Bielefeld University, Bielefeld, Germany. After receiving his Diploma in computer science (i.e. Master degree level), he worked as research assistant at the Cognitive Psychology Group and at the Computer Engineering Group (both at Bielefeld University). In his Ph.D. studies in biorobotics he focused on the transfer of (neuro-) biological concepts to the robotic domain. From 2010 till 2016 he worked as postdoc in the area of space and underwater robotics at the German Research Center for Artificial Intelligence (DFKI) in Bremen, Germany. Since 2016 he is professor of intelligent sensing at the University of Applied Sciences Hamburg (HAW Hamburg, Hamburg, Germany). His main research interests are sensors and sensor data processing (including machine learning methods) and robotics.
Affiliation: HAW Hamburg