基于单一平面约束的线结构光机器人视觉系统手眼校准方法

IF 9.1 1区 计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Robotics and Computer-integrated Manufacturing Pub Date : 2024-07-26 DOI:10.1016/j.rcim.2024.102825
Kaifan Zhong , Jingxin Lin , Tao Gong , Xianmin Zhang , Nianfeng Wang
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引用次数: 0

摘要

手眼校准是机器人视觉系统应用的先决条件。然而,由于缺乏图像特征,使用线结构光传感器进行手眼校准受到复杂程序和特殊参考对象的限制。本文旨在利用二维激光传感器开发一种稳定且要求不高的手眼校准方法,该方法可适用于大多数常见场景中的随机测量策略。除了专门的校准对象外,所提出的方法可以使用加工工件上容易获得的平面,这有利于工业机器人的自动化。此外,在该方法中,两步迭代法与基于单一平面约束的快速模拟退火相结合,克服了较大的初始偏差和传感器误差。为了评估该方法的性能,我们进行了模拟和校准实验,证明了所提出的手眼校准方法的可行性和准确性。
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Hand-eye calibration method for a line structured light robot vision system based on a single planar constraint

Hand-eye calibration is a prerequisite for robot vision system applications. However, due to the lack of image features, implementing hand-eye calibration with a line-structured light sensor is limited by complex procedures and special reference objects. The aim of this paper is to develop a stable and undemanding eye-in-hand calibration method with a 2D laser sensor that can be adapted to random measurement strategies in most common scenes. The proposed method can use an easily accessible plane from machined workpieces, except for specialized calibration objects, which facilitates the automation of industrial robots. Moreover, in this method, a two-step iterative method is combined with fast simulated annealing based on a single planar constraint to overcome large initial deviations and sensor errors. Simulations and calibration experiments are conducted to assess the method performance and demonstrate the feasibility and accuracy of the proposed eye-in-hand calibration 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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