ICRA 2011 Paper Abstract


Paper WeP109.3

Lampariello, Roberto (German Aerospace Center (DLR)), Nguyen-Tuong, Duy (Max Planck Institute for Biological Cybernetics), Castellini, Claudio (DLR - German Aerospace Research Center), Hirzinger, Gerd (German Aerospace Center (DLR)), Peters, Jan (Max-Planck Inst. for Bio. Cybernetics)

Trajectory Planning for Optimal Robot Catching in Real-Time

Scheduled for presentation during the Regular Sessions "Motion and Path Planning III" (WeP109), Wednesday, May 11, 2011, 14:10−14:25, Room 5D

2011 IEEE International Conference on Robotics and Automation, May 9-13, 2011, Shanghai International Conference Center, Shanghai, China

This information is tentative and subject to change. Compiled on July 14, 2020

Keywords Motion and Path Planning, Learning and Adaptive Systems, Motion Control of Manipulators


Many real-world tasks require fast planning of highly dynamic movements for their execution in real-time. The success often hinges on quickly finding one of the few plans that can achieve the task at all. A further challenge is to quickly find a plan which optimizes a desired cost. In this paper, we will discuss this problem in the context of catching small flying targets efficiently. This can be formulated as a non-linear optimization problem where the desired trajectory is encoded by an adequate parametric representation. The optimizer generates an energy-optimal trajectory by efficiently using the robot kinematic redundancy while taking into account maximal joint motion, collision avoidance and local minima. To enable the resulting method to work in real-time, examples of the global planner are generalized using nearest neighbour approaches, Support Vector Machines and Gaussian process regression, which are compared in this context. Evaluations indicate that the presented method is highly efficient in complex tasks such as ball-catching.



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