Generalized Grasping for Mechanical Grippers for Unknown Objects with Partial Point Cloud Representations

• URL: http://arxiv.org/abs/2006.12676v1
• Date: Tue, 23 Jun 2020 00:34:05 GMT
• Title: Generalized Grasping for Mechanical Grippers for Unknown Objects with Partial Point Cloud Representations
• Authors: Michael Hegedus, Kamal Gupta, Mehran Mehrandezh
• Abstract summary: We use point clouds to discover grasp pose solutions for multiple grasp types, executed by a mechanical gripper, in near real-time. 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.
• Score: 4.196869541965447
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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