In this paper, a robot system for hanging and removing grounding rods is designed by integrating 3D recognition technology, hand-eye self-calibration technology, and automatic operation technology. Specifically, a novel hand-eye self-calibration algorithm is proposed that only uses common objects in the actual scene, which differs from traditional robot hand-eye calibration in that it requires a dedicated calibration board to assist with offline completion. Addressing the problem that existing self-calibration methods cannot be optimized as a whole, resulting in low accuracy and instability of the solution, a multi-stage objective function optimization self-calibration algorithm is proposed. An optimization method based on the minimization of re-projection error is designed to compensate for the results, which uses an efficient Oriented fast and rotated brief (ORB) feature extraction algorithm and introduces a scoring mechanism to retain more correct matching points in the feature matching stage. Experimental verifications are conducted using both public dataset and practical robot system. In the dataset experiment, our method demonstrates superior accuracy and robustness compared to existing self-calibration methods. Furthermore, the practical robot platform experiment confirms the feasibility and efficacy of our approach across various wind speeds and lighting conditions.

本文集成3D识别技术、手眼自校准技术和自动操作技术，设计了一种用于悬挂和拆除接地棒的机器人系统。具体来说，提出了一种新颖的手眼自标定算法，仅使用实际场景中的常见物体，与传统机器人手眼标定的不同之处在于，它需要专用的标定板来辅助离线完成。针对现有自标定方法无法整体优化，导致解精度低且不稳定的问题，提出一种多阶段目标函数优化自标定算法。设计了一种基于重投影误差最小化的优化方法来补偿结果，该方法使用高效的定向快速和旋转简短（ORB）特征提取算法，并引入评分机制以在特征匹配中保留更多正确的匹配点阶段。使用公共数据集和实际机器人系统进行实验验证。在数据集实验中，与现有的自校准方法相比，我们的方法表现出卓越的准确性和鲁棒性。此外，实际的机器人平台实验证实了我们的方法在各种风速和照明条件下的可行性和有效性。