.png){kind=icon}
返回
Finger-position optimization by using caging qualities
.png){kind=icon}
PRE
AI
DOI：10.1016/j.sigpro.2015.04.012.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Caging aims at constraining objects like a bird cage. It does not need force closure and contacts, and therefore is robust to perception noises. This paper considers these merits of caging and proposes an efficient approach to find the optimized finger positions for robust grasping under perception noises by measuring the quality of caging. It initially uses two quality models (the quality model of translational constraint and the quality model of rotational constraint) to combinatorically represent the quality of caging and finds the optimized finger positions (finger positions that have large robustness to perception noises) by maximizing the margins to caging breaking. Meanwhile, it employs Genetic Algorithm (GA) to accelerate the combinatorial optimization of the two quality models. The encoding rules, the initial procedure, and the stopping criterion of the GA are designed carefully towards the quality models to improve its computational efficiency. Experiments with various object shapes show that with the help of multi-model Optimization and GA acceleration, the proposed approach is not only robustness to perception noises but also efficient in computation. (C) 2015 Elsevier B.V. All rights reserved.
Keyword:
Caging
Quality of caging
Robot grasping
Multi-quality model
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.6
论文数:
10.0K
被引数:
1.7W
机构
引用论文
.png){kind=icon}
Powder flow studies IV. Tensile strength and orifice flow rate relationships of binary mixtures
PREAI
分享收藏分享收藏
查看更多内容