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3D Modeling, Distance and Gradient Computation for Motion Planning: A Direct GPGPU Approach

Rene Wagner, Udo Frese, Berthold Bauml
DLR Institute of Robotics and Mechatronics, 82234 Wessling, Germany
IEEE International Conference on Robotics and Automation (ICRA’13), 2013

@article{wagner3d,

   title={3D Modeling, Distance and Gradient Computation for Motion Planning: A Direct GPGPU Approach},

   author={Wagner, Ren{‘e} and Frese, Udo and B{"a}uml, Berthold},

   year={2013}

}

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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.
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