Name
Abstract | ||
|---|---|---|
Editorial NotesThe authors have requested minor, non-substantive changes to the Version of Record and, in accordance with ACM policies, a Corrected Version of Record was published on August 1, 2022. For reference purposes, the VoR may still be accessed via the Supplemental Material section on this page.AbstractThis work proposes a novel generative design tool for passive grippers---robot end effectors that have no additional actuation and instead leverage the existing degrees of freedom in a robotic arm to perform grasping tasks. Passive grippers are used because they offer interesting trade-offs between cost and capabilities. However, existing designs are limited in the types of shapes that can be grasped. This work proposes to use rapid-manufacturing and design optimization to expand the space of shapes that can be passively grasped. Our novel generative design algorithm takes in an object and its positioning with respect to a robotic arm and generates a 3D printable passive gripper that can stably pick the object up. To achieve this, we address the key challenge of jointly optimizing the shape and the insert trajectory to ensure a passively stable grasp. We evaluate our method on a testing suite of 22 objects (23 experiments), all of which were evaluated with physical experiments to bridge the virtual-to-real gap. Code and data are at https://homes.cs.washington.edu/~milink/passive-gripper/ |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1145/3528223.3530162 | ACM Transactions on Graphics |
Keywords | DocType | Volume |
passive gripper, generative design, additive manufacturing, fabrication | Journal | 41 |
Issue | ISSN | Citations |
4 | 0730-0301 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Milin Kodnongbua | 1 | 0 | 0.34 |
| Ian Good | 2 | 0 | 1.01 |
| Yu Lou | 3 | 0 | 0.34 |
| Jeffrey Lipton | 4 | 0 | 0.34 |
| Adriana Schulz | 5 | 56 | 8.91 |