3D Modeling, Distance and Gradient Computation for Motion Planning: A Direct GPGPU Approach
| Type of publication: | Inproceedings |
| Citation: | WagnerICRA13 |
| Booktitle: | Proceedings of the IEEE International Conference on Robotics and Automation (ICRA 2013), Karlsruhe, Germany |
| Year: | 2013 |
| Abstract: | The Kinect sensor and KinectFusion algorithm have revolutionized environment modeling. We bring these advances to optimization-based motion planning by computing the obstacle and self-collision avoidance objective functions and their gradients directly from the KinectFusion model on the GPU without ever transferring any model to the CPU. Based on this, we implement a proof-of-concept motion planner which we validate in an experiment with a 19-DOF humanoid robot using real data from a tabletop work space. The summed-up time from taking the first look at the scene until the planned path avoiding an obstacle on the table is executed is only three seconds. |
| Userfields: | pdfurl={http://www.informatik.uni-bremen.de/agebv/downloads/published/wagner_icra_13.pdf}, project={SFBTR8}, status={Reviewed}, |
| Keywords: | kinect motionplanning optimization humanoid robot justin gpgpu gpu cuda |
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