Inhalt des Dokuments
Motion Planning is the task of finding a way to move a robot from one position to another while avoiding obstacles. To do this, we need to find a trajectory in the space of all possible robot configurations. For a robot with several joints, this search space is high dimensional and very complex. Even for simple two-dimensional spaces, general motion planning is NP-hard.
We think that a successful and fast motion planning algorithm has to balance between two general planning strategies: Exploration and exploitation. Exploration tries to obtain information about the connectivity of the space without considering any specific goal. This is typically done by sampling the space and connecting sampled configurations as a graph. This approach theoretically solves the motion planning problem but results in unacceptably long run times. Exploitation tries to find a valid path to a specific goal, using the available information. This can be done by constructing artificial forces that drag the robot towards the goal and away from obstacles. This is a very efficient approach, but unfortunately it always fails if the collected information is insufficient.
In our motion planners, we use several sources of information to balance exploration and exploitation. Utility-guided sampling apply guided exploration by choosing samples with maximal expected utility. This approach gives us a good understanding of connectivity using much less samples than other approaches. Disassembly planning uses the 3D-workspace connectivity to identify the regions of the configuration space where a detailed search is needed. This connectivity can be obtained by expanding the workspace with a tree of bubbles. The same workspace information is used in our Exploring/Exploiting Tree .In easy regions we use the workspace information as a navigation function to drag the robot along the spheres. If this approach fails in hard regions, we gradually shift to exploration.
 S. M. LaValle. Planning Algorithms. Cambridge University Press, Cambridge, U.K., 2006, Available at http://planning.cs.uiuc.edu.
Flexible Skill Acquisitionen and Intuitive Robot Tasking for Mobile Manipulation in the Real World (First MM) - funded by European Commision, in the program Cognitive Systems and Robotics,
award number FP7-ICT-248258,
February 2010 - July 2013