burgard [AT] informatik.uni-freiburg.de.Profile
Biography:
research lab for Autonomous Intelligent Systems. His areas of interest lie in the fields of artificial intelligence and mobile robots. His research mainly focuses on the development of robust and adaptive techniques for state estimation and control. Over the past years he has 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 his previous position from 1996 to 1999 at the University of Bonn he was head of the research lab for Autonomous Mobile Systems. He has published over 250 papers and articles in robotic and artificial intelligence conferences and journals. In 2005, he 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. In 2008, he became a Fellow of the European Coordinating Committee for Artificial Intelligence (ECCAI), and in 2009, a Fellow of the Association for the Advancement of Artificial Intelligence (AAAI). In 2009, he received the Gottfried Wilhelm Leibniz Prize, the most prestigious German research award. In 2010, he received an Advanced Grant of the European Research Council. Since 2012, he is the coordinator of the Cluster of Excellence BrainLinks-BrainTools funded by the German Research Foundation. | |
Speech : Probabilistic Techniques for Mobile Robot Navigation
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 including autonomous cars, logistics applications, and robots navigating in city environments. |