Grasping Unknown Objects Based on Gripper Workspace Spheres

Sorour, Mohamed, Elgeneidy, Khaled, Srinivasan, Aravinda and Hanheide, Marc (2019) Grasping Unknown Objects Based on Gripper Workspace Spheres. In: 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

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Grasping Unknown Objects Based on Gripper Workspace Spheres
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Item Type:Conference or Workshop contribution (Paper)
Item Status:Live Archive


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).

Keywords:grasping, Robotics
Subjects:G Mathematical and Computer Sciences > G160 Engineering/Industrial Mathematics
H Engineering > H671 Robotics
Divisions:College of Science
ID Code:36370
Deposited On:24 Jul 2019 15:36

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