基于视觉伺服的机械臂跟踪控制研究
摘 要
随着工业自动化和机器人技术的发展，机械臂在复杂环境下的精确跟踪控制成为研究热点。基于视觉伺服的机械臂跟踪控制旨在利用视觉信息实现对目标物体的实时跟踪与操作，具有非接触、适应性强等优势。本研究针对传统机械臂控制方法中存在的模型依赖性强、鲁棒性差等问题，提出了一种基于视觉伺服的机械臂跟踪控制策略。该策略融合了图像雅可比矩阵与深度学习算法，通过构建视觉反馈回路，实现了对机械臂末端执行器的精准定位与跟踪。实验中采用双目视觉系统获取目标物体的空间位置信息，并将其转化为机械臂关节角度指令，以完成对动态目标的实时跟踪任务。结果表明，所提出的控制方法能够有效提高机械臂的跟踪精度，在不同光照条件和背景干扰下均表现出良好的鲁棒性和稳定性。此外，该方法无需精确建立机械臂的动力学模型，降低了系统设计难度，提高了实际应用中的灵活性。
关键词：视觉伺服控制 机械臂跟踪 深度学习
Abstract
With the development of industrial automation and robotics, the precise tracking and control of robotic arms in complex environments has become a research hotspot. The robotic arm tracking control based on visual servo aims to realize the real-time tracking and operation of the target ob ject by using visual information, which has the advantages of non-contact and strong adaptability. In this study, a visual servo-based robotic arm tracking control strategy is proposed to address the strong model dependence and poor robustness in traditional robotic arm control methods. This strategy integrates the image Jacobian matrix and the deep learning algorithm, and realizes the accurate positioning and tracking of the end actuator of the mechanical arm by constructing the visual feedback loop. In the experiment, the binocular vision system was used to obtain the spatial position information of the target ob ject and convert it into the mechanical arm joint angle instruction to complete the real-time tracking task of the dynamic target. The results show that the proposed control method can effectively improve the tracking accuracy of the robotic arm and show good robustness and stability under different light conditions and background interference. In addition, this method does not need to accurately establish the dynamic model of the mechanical arm, which reduces the difficulty of the system design and improves the flexibility in practical application.
Keyword:Visual Servo Control Mechanical Arm Tracking Deep Learning
目 录
1绪论 1
1.1研究背景与意义 1
1.2国内外研究现状 1
1.3本文研究方法 2
2视觉伺服系统建模 2
2.1视觉传感器选择与配置 2
2.2机械臂运动学建模 3
2.3视觉反馈模型构建 3
3控制算法设计 4
3.1基于图像的控制策略 4
3.2基于位置的控制方法 5
3.3混合控制方案分析 5
4实验验证与结果分析 6
4.1实验平台搭建 6
4.2跟踪性能测试 7
4.3结果对比与讨论 7
结论 8
参考文献 10
致谢 11
摘 要
随着工业自动化和机器人技术的发展，机械臂在复杂环境下的精确跟踪控制成为研究热点。基于视觉伺服的机械臂跟踪控制旨在利用视觉信息实现对目标物体的实时跟踪与操作，具有非接触、适应性强等优势。本研究针对传统机械臂控制方法中存在的模型依赖性强、鲁棒性差等问题，提出了一种基于视觉伺服的机械臂跟踪控制策略。该策略融合了图像雅可比矩阵与深度学习算法，通过构建视觉反馈回路，实现了对机械臂末端执行器的精准定位与跟踪。实验中采用双目视觉系统获取目标物体的空间位置信息，并将其转化为机械臂关节角度指令，以完成对动态目标的实时跟踪任务。结果表明，所提出的控制方法能够有效提高机械臂的跟踪精度，在不同光照条件和背景干扰下均表现出良好的鲁棒性和稳定性。此外，该方法无需精确建立机械臂的动力学模型，降低了系统设计难度，提高了实际应用中的灵活性。
关键词：视觉伺服控制 机械臂跟踪 深度学习
Abstract
With the development of industrial automation and robotics, the precise tracking and control of robotic arms in complex environments has become a research hotspot. The robotic arm tracking control based on visual servo aims to realize the real-time tracking and operation of the target ob ject by using visual information, which has the advantages of non-contact and strong adaptability. In this study, a visual servo-based robotic arm tracking control strategy is proposed to address the strong model dependence and poor robustness in traditional robotic arm control methods. This strategy integrates the image Jacobian matrix and the deep learning algorithm, and realizes the accurate positioning and tracking of the end actuator of the mechanical arm by constructing the visual feedback loop. In the experiment, the binocular vision system was used to obtain the spatial position information of the target ob ject and convert it into the mechanical arm joint angle instruction to complete the real-time tracking task of the dynamic target. The results show that the proposed control method can effectively improve the tracking accuracy of the robotic arm and show good robustness and stability under different light conditions and background interference. In addition, this method does not need to accurately establish the dynamic model of the mechanical arm, which reduces the difficulty of the system design and improves the flexibility in practical application.
Keyword:Visual Servo Control Mechanical Arm Tracking Deep Learning
目 录
1绪论 1
1.1研究背景与意义 1
1.2国内外研究现状 1
1.3本文研究方法 2
2视觉伺服系统建模 2
2.1视觉传感器选择与配置 2
2.2机械臂运动学建模 3
2.3视觉反馈模型构建 3
3控制算法设计 4
3.1基于图像的控制策略 4
3.2基于位置的控制方法 5
3.3混合控制方案分析 5
4实验验证与结果分析 6
4.1实验平台搭建 6
4.2跟踪性能测试 7
4.3结果对比与讨论 7
结论 8
参考文献 10
致谢 11
