用于机器人批量精密装配弹性吸附钉的力视融合模糊控制

IF 9.1 1区 计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
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引用次数: 0

摘要

本文的重点是提高由吸盘灵活吸附的小型挂件批量精密装配的顺应性、效率和稳健性。其主要贡献在于提出了一种力-视觉融合模糊控制方法(FVFFC),以实现未知间隙或过盈配合的精密装配。设计了视觉特征和力特征来描述吸盘变形时钉和孔的状态。然后,提出了力-视觉融合控制框架,其中视觉特征动态地修改了导纳控制的参考位置,并指导对钉角度进行顺应性调整。此外,在理论分析的基础上,还开发了两个模糊逻辑推理模块,用于估计接触状态以及挂件和孔之间的间隙或干涉量,从而自适应地调整控制参数。最后,通过充分的实验证明了 FVFFC 方法的优越性和鲁棒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Force–vision fusion fuzzy control for robotic batch precision assembly of flexibly absorbed pegs

This article focuses on improving the compliance, efficiency, and robustness of batch precision assembly of small-scale pegs flexibly absorbed by a suction cup. The main contribution is that a force–vision fusion fuzzy control method (FVFFC) is proposed to achieve precision assembly with unknown clearance or interference fit. Both visual and force features are designed to describe the state of the peg and hole with the deformation of the suction cup. Then, a force–vision fusion control framework is proposed, where the visual features dynamically modify the reference position of admittance control and guide compliant adjustment of the peg angles. Furthermore, based on theoretical analysis, two fuzzy logic inference modules are developed to estimate the contact state as well as either the clearance or interference amount between the peg and hole in order to adaptively tune the control parameters. Finally, sufficient experiments are conducted to demonstrate the superiority and robustness of the FVFFC method.

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来源期刊
Robotics and Computer-integrated Manufacturing
Robotics and Computer-integrated Manufacturing 工程技术-工程:制造
CiteScore
24.10
自引率
13.50%
发文量
160
审稿时长
50 days
期刊介绍: The journal, Robotics and Computer-Integrated Manufacturing, focuses on sharing research applications that contribute to the development of new or enhanced robotics, manufacturing technologies, and innovative manufacturing strategies that are relevant to industry. Papers that combine theory and experimental validation are preferred, while review papers on current robotics and manufacturing issues are also considered. However, papers on traditional machining processes, modeling and simulation, supply chain management, and resource optimization are generally not within the scope of the journal, as there are more appropriate journals for these topics. Similarly, papers that are overly theoretical or mathematical will be directed to other suitable journals. The journal welcomes original papers in areas such as industrial robotics, human-robot collaboration in manufacturing, cloud-based manufacturing, cyber-physical production systems, big data analytics in manufacturing, smart mechatronics, machine learning, adaptive and sustainable manufacturing, and other fields involving unique manufacturing technologies.
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