Graduiertenseminar/ Brown Bag Talks

Die AG Robotik von der Universität Bremen bietet in Kooperation mit dem Robotics Innovation Center von der DFKI GmbH eine ganze Reihe von Seminaren und Vorträgen an. In dieser Reihe sind die PhD Vorträge, externe Vorträge und interne Projektberichte enthalten. Die Termine werden hier regelmäßig aktualisiert. Externe können in vielen Fällen nach Rücksprache mit dem Sekretariat an den Vorträgen teilnehmen.

Vorträge

 -  15:00 Uhr
DFKI Vortrag
RH5 117

Learning the Structure of Continuous Markov Decision Processes

von: Jan Hendrik Metzen  (Uni Bremen AG Robotik)
Kolloquium
 -  13:00 Uhr
DFKI Vortrag
RH1 A 1.03

Robust, Fault-Tolerant Multi-Hardware Communication Protocol for Robotics

von: Steffen Planthaber  (DFKI GmbH)
Communication methods in modern robotic systems typically connect multiple sensors, actuators, micro-controllers, and embedded PCs. There are several ways in hard- and software to communicate inside and between robots. In many cases a communication middlewares are used introducing extra protocols.Al…
 -  11:00 Uhr
DFKI Vortrag
RH5 117

Embodied Localisation and Mapping

von: Jakob Schwendner  (DFKI GmbH)
Mobile autonomous robots have finally emerged from the confined spaces of structured and controlled indoor environments. To fulfill the promises of ubiquitous robotics in unstructured outdoor environments, robust navigation is a key requirement. The research in the Simultaneous Localisation and Mapp…
 -  13:00 Uhr
DFKI Vortrag
RH1 A 1.03

Underwater Multi-Modal 3D Reconstruction

von: Alexander Duda  (DFKI GmbH)
Structure from motion and structure from light are usually regarded separately. To combine both methods, using the same images to retrieve three dimensional information about the environment, both methods must work side by side without interfering each other. This research work describes the first s…
 -  13:00 Uhr
DFKI Vortrag
RH1 A 1.03

Sensor Fusion Framework for Localization of Planetary Rovers

von: Javier Hidalgo Carrió  (DFKI GmbH)
Planetary rover exploration requires three fundamental tasks: control, localization and mapping. The performance of autonomous navigation depends significantly on the robustness and reliability of the localization subsystem, which is responsible for closing the navigation control loop while driving.…
 -  12:30 Uhr
DFKI Vortrag
RH5 117

Marine Vessel Inspection as a Novel Field for Service Robotics: A Contribution to Systems, Control Methods and Semantic Perception Algorithms

von: Markus Eich  (DFKI GmbH)
Kolloquium
 -  13:00 Uhr
DFKI Vortrag
RH5 117

The Use of Evolutionary Computation in Complex Real World Application of Legged Robots

von: Malte Langosz  (DFKI GmbH)
Developing control algorithms for kinematically complex robots is a challenging task. Model based approaches can be used to simplify the control problem but the performance of these approaches is limited to the quality of the model. Machine learning is often an attractive method to create optimal co…
 -  12:00 Uhr
DFKI Vortrag
RH5 117

Team ARTEMIS at the DLR SpaceBot cup

von: Jakob Schwendner  (DFKI GmbH)
The DLR SpaceBot competition was held from the 10.11.-12.11. this year. The aim was to navigate an autonomous robot through difficult terrain, and perform various manipulation tasks on the way. The system had to be operated under difficult communication conditions. The DFKI took part at the competit…
 -  13:00 Uhr
DFKI Vortrag
RH5 117

Online Movement Prediction for Brain Reading – Application of Source Localization to EEG Single Trials

von: Anett Seeland  (DFKI GmbH)
The electroencephalogram (EEG) is the sum of electrical activation of thousands of neurons distorted by the head volume conductor. Hence, it lacks spatial resolution and thereby complicates brain state predictions. Source localization (SL) methods aim to restore spatial resolution by reconstructing …
 -  13:00 Uhr
DFKI Vortrag
RH5 117

Generalizing and Decoding SVM Classification of Spatiotemporal Data

von: Mario Krell  (Uni Bremen AG Robotik)
This presentation of my dissertation progress consists of two main parts. After giving a general overview, I will present new insights into Support Vector Machine (SVM) Classification. The Balanced Relative Margin Machine (BRMM) will be introduced as a more general model, which connects among others…
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zuletzt geändert am 06.09.2024