Inhalt des Dokuments
Motion Generation is concerned with the planning and execution of motion tasks while avoiding collisions. We are especially interested in moving manipulators in real world environments.
Mobile manipulation presents several challenges for motion generation. First mobile manipulators have a high number of degrees of freedom, leading to challenging search problems in configuration space. Furthermore, motion skills for manipulation are often subject to secondary constraints e.g. maintaining the orientation of a tray during motion. Second, mobile manipulation takes place in unstructured environments. Detailed and reliable models of these environments are usually not available. The robot has to use its on-board sensors to obtain the information needed for collision-free motion and successful execution of manipulation tasks. To do so efficiently, it needs to reason about the uncertainty present in the environment.
Motion Generation under Uncertainty
We want to find methods for motion generation that are more robust to changes in the environment. To achieve this, we shift the boundary between planning and control. Giving more responsibility to control allows us to leverage the inherent capability of controllers; dealing with dynamic situations. Feedback Motion Planning is a motion generation method that takes into consideration information acquired while the robot performs a task. So, instead of calculating explicitly paths in the configuration space of the robot and let the responsibility of execution to the controllers, we sequence different controllers that carry out the task.
A mobile manipulator in unstructured environments needs to account for uncertainty due to noisy sensors with limited range and partially unknown, dynamic environments. General algorithms that plan under uncertainty are infeasible to use in real applications due to the high complexity. Feedback motion planning already provides many desirable qualities for planning under uncertainty. Milestones use controllers to continuously integrate new sensor data. These local controllers reduce uncertainty about the robots state in the environment implicitly, so much that we do not consider it during planning. A source of uncertainty we cannot ignore is the environment itself. We propose a time-dependent probabilistical model for the uncertain state of the world. In the case of an environment composed of static and moving obstacles, we can solve planning problems in polynomial time by solving an Expected Shortest Path problem. The solution to this problem is a motion policy for the robot that incorporates goal-directed sensing actions and task-consistent motion.
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. Exploitation tries to find a valid path to a specific goal, using the available information.
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.
Contact: Arne Sieverling
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