Grasping Unknown Objects Based on Gripper Workspace Spheres

In this paper, we present a novel grasp planning algorithm for unknown objects given a registered point cloud of the target from different views. The proposed methodology requires no prior knowledge of the object, nor offline learning. In our approach, the gripper kinematic model is used to generate a point cloud of each finger workspace, which is then filled with spheres. At run-time, first the object is segmented, its major axis is computed, in a plane perpendicular to which, the main grasping action is constrained. The object is then uniformly sampled and scanned for various gripper poses that assure at least one object point is located in the workspace of each finger. In addition, collision checks with the object or the table are performed using computationally inexpensive gripper shape approximation. Our methodology is both time efficient (consumes less than 1. 5 seconds in average) and versatile. Successful experiments have been conducted on a simple jaw gripper (Franka Panda gripper) as well as a complex, high Degree of Freedom (DoF) hand (Allegro hand).

Authors

• Mohamed Sorour
• Khaled Elgeneidy
• Aravinda Srinivasan
• Marc Hanheide
• Gerhard Neumann

Keywords

• Hands
• Point cloud compression
• Shape
• Grasping
• Kinematics
• Distance measurement
• Planning
• Complexity theory
• Grippers
• Intelligent robots
• Workspace
• Unknown Objects
• Degrees Of Freedom
• Collision
• Point Cloud
• Major Axis
• Successful Experiences
• High Degree Of Freedom
• Object Point
• Point Cloud Registration
• Offline Learning
• Right-hand
• Computation Time
• Bounding Box
• Transformation Matrix
• Rotation Axis
• Blue Dots
• Object Shape
• Depth Camera
• Offset Settings
• Number Of Spheres
• End-effector
• Random Sample Consensus
• Object Point Cloud
• Semi-axis
• Object In Frame
• Joint Space
• Coordinate Frame
• Computation Time Of Algorithm
• manipulation