3D modeling, distance and gradient computation for motion planning: A direct GPGPU approach
3D modeling, distance and gradient computation for motion planning: A direct GPGPU approach
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用于运动规划的 3D 建模、距离和梯度计算：直接 GPGPU 方法
DOI：
10.1109/icra.2013.6631080
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发表时间：
2013
期刊：
影响因子：
--
通讯作者：
B. Bäuml
中科院分区：
文献类型：
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作者：
R. Wagner;U. Frese;B. Bäuml
Kinect传感器和KinectFusion算法彻底改变了环境建模。我们通过直接从GPU上的KinectFusion模型计算障碍物和自碰撞避免目标函数及其梯度，而无需将任何模型传输到CPU，从而将这些进步带到基于优化的运动规划中。在此基础上，我们实现了一个概念验证运动规划器，我们验证了在实验中与19自由度人形机器人使用真实的数据从桌面工作空间。从第一次看到场景到执行避开桌面上障碍物的规划路径的总时间仅为3秒。
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.