In the field of robotic automation, achieving high position accuracy in robotic vision systems (RVSs) is a pivotal challenge that directly impacts the efficiency and effectiveness of industrial applications. This study introduces a comprehensive modeling approach that integrates kinematic and joint compliance factors to significantly enhance the position accuracy of a system. In the first place, we develop a unified kinematic model that effectively reduces the complexity and error accumulation associated with the calibration of robotic systems. At the heart of our approach is the formulation of a joint compliance model that meticulously accounts for the intricacies of the joint connector, the external load, and the self-weight of robotic links. By employing a novel 3D rotary laser sensor for precise error measurement and model calibration, our method offers a streamlined and efficient solution for the accurate integration of vision systems into robotic operations. The efficacy of our proposed models is validated through experiments conducted on a FANUC LR Mate 200iD robot, showcasing notable improvements in the position accuracy of robotic vision system. Our findings contribute a framework for the calibration and error compensation of RVS, holding significant potential for advancements in automated tasks requiring high precision.

中文翻译：

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提高机器人视觉系统位置精度的运动学和关节柔顺建模方法

在机器人自动化领域，实现机器人视觉系统（RVS）的高位置精度是一个关键挑战，直接影响工业应用的效率和有效性。本研究引入了一种综合建模方法，该方法集成了运动学和关节柔量因素，以显着提高系统的位置精度。首先，我们开发了一个统一的运动学模型，可以有效降低与机器人系统校准相关的复杂性和误差累积。我们方法的核心是制定关节顺应模型，该模型仔细考虑了关节连接器的复杂性、外部负载和机器人连杆的自重。通过采用新型 3D 旋转激光传感器进行精确误差测量和模型校准，我们的方法为将视觉系统准确集成到机器人操作中提供了简化且高效的解决方案。我们提出的模型的有效性通过在 FANUC LR Mate 200iD 机器人上进行的实验得到验证，展示了机器人视觉系统的位置精度的显着改进。我们的研究结果为 RVS 的校准和误差补偿提供了一个框架，为需要高精度的自动化任务的进步提供了巨大的潜力。