论文标题
对于具有部分点云表示的未知物体的机械握把的概括性握把
Generalized Grasping for Mechanical Grippers for Unknown Objects with Partial Point Cloud Representations
论文作者
论文摘要
我们提出了一种广义的抓地算法，该算法使用点云（即一组点及其各自的表面正常），以在几乎实时实时地发现由机械抓手执行的多种抓地力类型的grasp姿势溶液。该算法介绍了两个想法：1）用手指接触正态的直方图表示掌握“形状”，以指导物体表面正常的直方图中的握把方向搜索，以及2）抓地力和物体的体素网格表示，以互相功能以匹配手指的接触点，即抓握'grasp's grasp's grasp's grasp sode grasp osse grasp sods grasp osse grasp osse grasp osse grasp osse grasp osse grasp sode grasp osse grasp osse grasp sose grasp sose grasp。约束（例如与相邻对象的碰撞）可选地合并到互相关计算中。我们通过模拟和实验表明，1）可以在几乎实时地找到三种抓地力类型的掌握姿势，2）抓握姿势溶液在部分和完整的点云扫描方面与素的分辨率变化是一致的，而3）计划的抓握是用机械抓手执行的。
We present a generalized grasping algorithm that uses point clouds (i.e. a group of points and their respective surface normals) to discover grasp pose solutions for multiple grasp types, executed by a mechanical gripper, in near real-time. The algorithm introduces two ideas: 1) a histogram of finger contact normals is used to represent a grasp 'shape' to guide a gripper orientation search in a histogram of object(s) surface normals, and 2) voxel grid representations of gripper and object(s) are cross-correlated to match finger contact points, i.e. grasp 'size', to discover a grasp pose. Constraints, such as collisions with neighbouring objects, are optionally incorporated in the cross-correlation computation. We show via simulations and experiments that 1) grasp poses for three grasp types can be found in near real-time, 2) grasp pose solutions are consistent with respect to voxel resolution changes for both partial and complete point cloud scans, and 3) a planned grasp is executed with a mechanical gripper.
