Fast Planning for 3D Any-Pose-Reorienting using Pivoting

Yifan Hou, Zhenzhong Jia and Matthew T. Mason
Conference Paper, International Conference on Robotics and Automation (ICRA) 2018, May, 2018

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In this paper, we consider reorienting 3D objects on a table using a two-finger pinch gripper. Given the 3D mesh model of the object, our algorithm solves for the gripper motions that are required to transit between arbitrary object poses, grasping positions and gripper poses. The two motion primitives we used, pivoting and compliant rolling, enable us to decompose the planning problem and solve it more efficiently. Our algorithm can work with approximated (simplified) mesh models while being robust to approximation errors, thereby allowing us to efficiently handle object shapes with originally thousands of facets. We show the effectiveness of the proposed method by testing on objects with non-trivial geometry in both simulations and experiments. Results show that our algorithm can solve a larger range of reorienting problems with less
number of making and breaking contacts when compared to traditional pick-and-place based methods, especially when the
gripper workspace is highly constrained.

Associated Lab - Manipulation Lab

author = {Yifan Hou, Zhenzhong Jia and Matthew T. Mason},
title = {Fast Planning for 3D Any-Pose-Reorienting using Pivoting},
booktitle = {International Conference on Robotics and Automation (ICRA) 2018},
year = {2018},
month = {May},
publisher = {IEEE Robotics and Automation Society (RAS)},
} 2018-03-12T11:21:17-04:00