Generalized Grasping for Mechanical Grippers for Unknown Objects with Partial Point Cloud Representations
This addresses the challenge of robotic grasping in unstructured environments, though it appears incremental as it builds on existing point cloud and voxel-based methods.
The paper tackles the problem of enabling mechanical grippers to grasp unknown objects using partial point clouds, achieving near real-time discovery of grasp poses for multiple grasp types with consistency across voxel resolutions.
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.