Grasping by parallel shape matching

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Vydáno v:arXiv.org (Dec 11, 2024), p. n/a
Hlavní autor: Zhang, Wenzheng
Další autoři: Fahira Afzal Maken, Lai, Tin, Ramos, Fabio
Vydáno:
Cornell University Library, arXiv.org
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022 |a 2331-8422 
035 |a 3143451564 
045 0 |b d20241211 
100 1 |a Zhang, Wenzheng 
245 1 |a Grasping by parallel shape matching 
260 |b Cornell University Library, arXiv.org  |c Dec 11, 2024 
513 |a Working Paper 
520 3 |a 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. 
653 |a Parallel processing 
653 |a Matching 
653 |a Cost function 
653 |a Grasping (robotics) 
653 |a Software 
653 |a Computing time 
700 1 |a Fahira Afzal Maken 
700 1 |a Lai, Tin 
700 1 |a Ramos, Fabio 
773 0 |t arXiv.org  |g (Dec 11, 2024), p. n/a 
786 0 |d ProQuest  |t Engineering Database 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/3143451564/abstract/embedded/ZKJTFFSVAI7CB62C?source=fedsrch 
856 4 0 |3 Full text outside of ProQuest  |u http://arxiv.org/abs/2412.08346