Enhancing Grasp Pose Computation in Gripper Workspace Spheres

Sorour, Mohamed, Elgeneidy, Khaled, Hanheide, Marc and Srinivasan, Aravinda (2020) Enhancing Grasp Pose Computation in Gripper Workspace Spheres. In: ICRA 2020, 31st May - 4th June 2020, Paris, France.

Enhancing Grasp Pose Computation in Gripper Workspace Spheres

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In this paper, enhancement to the novel grasp planning algorithm based on gripper workspace spheres is presented. Our development requires a registered point cloud of the target from different views, assuming no prior knowledge of the object, nor any of its properties. This work features
a new set of metrics for grasp pose candidates evaluation, as well as exploring the impact of high object sampling on grasp success rates. In addition to gripper position sampling, we now perform orientation sampling about the x, y, and z-axes, hence the grasping algorithm no longer require object orientation estimation. 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) as a proof of its versatility. Higher grasp success rates of 76% and 85:5% respectively has been reported by real world experiments.

Keywords:grasping, manipulation
Subjects:H Engineering > H671 Robotics
Divisions:College of Science
ID Code:39957
Deposited On:05 Feb 2020 16:08

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