Grasping by parallel shape matching
保存先:
| 出版年: | arXiv.org (Dec 11, 2024), p. n/a |
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| 第一著者: | |
| その他の著者: | , , |
| 出版事項: |
Cornell University Library, arXiv.org
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| 主題: | |
| オンライン･アクセス: | Citation/Abstract Full text outside of ProQuest |
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| 抄録: | Grasping is essential in robotic manipulation, yet challenging due to object and gripper diversity and real-world complexities. Traditional analytic approaches often have long optimization times, while data-driven methods struggle with unseen objects. This paper formulates the problem as a rigid shape matching between gripper and object, which optimizes with Annealed Stein Iterative Closest Point (AS-ICP) and leverages GPU-based parallelization. By incorporating the gripper's tool center point and the object's center of mass into the cost function and using a signed distance field of the gripper for collision checking, our method achieves robust grasps with low computational time. Experiments with the Kinova KG3 gripper show an 87.3% success rate and 0.926 s computation time across various objects and settings, highlighting its potential for real-world applications. |
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| ISSN: | 2331-8422 |
| ソース: | Engineering Database |