Event Location: NSH 1305
Bio: I am a professor for computer science at the University of Freiburg and head of the research lab for Autonomous Intelligent Systems. My areas of interest lie in artificial intelligence and mobile robots. My research mainly focuses on the development of robust and adaptive techniques for state estimation and control. Over the past years my group and I have developed a series of innovative probabilistic techniques for robot navigation and control. They cover different aspects such as localization, map-building, SLAM, path-planning, exploration, and several other aspects.

In my previous position from 1996 to 1999 at the University of Bonn I was head of the research lab for Autonomous Mobile Systems. In 1997 we deployed Rhino as the first interactive mobile tour-guide robot in the Deutsches Museum Bonn in Germany (see corresponding overview article). In 1998 my group and I went to Washington, DC, to install the mobile robot Minerva in the Smithsonian Museum of American History. Afterwards we produced several robots that autonomously operated in trade shows and Museums. In 2008, we developed an approach that allowed a car to autonomously navigate through a complex parking garage and park itself. In 2012, we developed the robot Obelix that autonomously navigated like a pedestrian from the campus of the Faculty of Engineering to the city center of Freiburg. I have published over 250 papers and articles in robotic and artificial intelligence conferences and journals. In 2005, I co-authored two books. Whereas the first one, entitled Principles of Robot Motion – Theory, Algorithms, and Implementations, is about sensor-based planning, stochastic planning, localization, mapping, and motion planning, the second one, entitled Probabilistic Robotics, covers robot perception and control in the face of uncertainty.

Abstract: Probabilistic approaches have been discovered as one of the most powerful approaches to highly relevant problems in mobile robotics including perception and robot state estimation. Major challenges in the context of probabilistic algorithms for mobile robot navigation lie in the questions of how to deal with highly complex state estimation problems and how to control the robot so that it efficiently carries out its task. In this talk, I will present recently developed techniques for efficiently learning a map of an unknown environment with a mobile robot. I will also describe how this state estimation problem can be solved more effectively by actively controlling the robot. For all algorithms I will present experimental results that have been obtained with mobile robots in real-world environments.