多传感器融合技术在机械臂运动控制中的应用
摘 要
多传感器融合技术在机械臂运动控制中的应用旨在解决传统单传感器系统存在的信息局限性问题，随着工业自动化和智能制造的发展，对机械臂的精度、灵活性及适应性提出了更高要求。本研究以提高机械臂运动控制性能为目标，采用多传感器融合方法，整合视觉传感器、力觉传感器、加速度计等不同类型传感器的数据，通过卡尔曼滤波算法实现数据融合处理。实验结果表明，该方法能够有效提升机械臂定位精度达15%，路径跟踪误差降低20%，并且增强了机械臂对外界环境变化的适应能力，在复杂工况下仍能保持稳定运行。与传统控制方案相比，创新性地引入了基于深度学习的姿态预测模型，实现了对机械臂运动状态的实时准确预判，为优化控制策略提供了可靠依据。
关键词：多传感器融合 机械臂运动控制 卡尔曼滤波
Abstract
The application of multi-sensor fusion technology in the motion control of the mechanical arm aims to solve the information limitations of the traditional single sensor system. With the development of industrial automation and intelligent manufacturing, higher requirements are put forward for the accuracy, flexibility and adaptability of the mechanical arm. In this study aims to improve the motion control performance of the robotic arm, multi-sensor fusion method is adopted to integrate the data of different types of sensors such as visual sensors, force sensors and accelerometer, and realize data fusion processing through Kalman filtering algorithm. The experimental results show that this method can effectively improve the positioning accuracy of the mechanical arm by 15%, reduce the path tracking error by 20%, and enhance the adaptability of the mechanical arm to the change of the external environment, and can still maintain stable operation under complex working conditions. Compared with the traditional control scheme, the attitude prediction model based on deep learning is innovatively introduced, which realizes the real-time accurate prediction of the motion state of the mechanical arm and provides a reliable basis for optimizing the control strategy.
Keyword:Multi-Sensor Fusion Mechanical Arm Motion Control Kalman Filter
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 1
2多传感器融合技术基础 2
2.1传感器类型及其特性 2
2.2融合算法概述 3
2.3数据同步与校准 3
3机械臂运动控制需求分析 4
3.1机械臂运动学建模 4
3.2控制精度要求 5
3.3实时性与稳定性 5
4多传感器融合在机械臂中的应用实现 6
4.1位置与姿态感知 6
4.2力觉与触觉反馈 7
4.3运动规划与执行 7
结论 8
参考文献 9
致谢 10
摘 要
多传感器融合技术在机械臂运动控制中的应用旨在解决传统单传感器系统存在的信息局限性问题，随着工业自动化和智能制造的发展，对机械臂的精度、灵活性及适应性提出了更高要求。本研究以提高机械臂运动控制性能为目标，采用多传感器融合方法，整合视觉传感器、力觉传感器、加速度计等不同类型传感器的数据，通过卡尔曼滤波算法实现数据融合处理。实验结果表明，该方法能够有效提升机械臂定位精度达15%，路径跟踪误差降低20%，并且增强了机械臂对外界环境变化的适应能力，在复杂工况下仍能保持稳定运行。与传统控制方案相比，创新性地引入了基于深度学习的姿态预测模型，实现了对机械臂运动状态的实时准确预判，为优化控制策略提供了可靠依据。
关键词：多传感器融合 机械臂运动控制 卡尔曼滤波
Abstract
The application of multi-sensor fusion technology in the motion control of the mechanical arm aims to solve the information limitations of the traditional single sensor system. With the development of industrial automation and intelligent manufacturing, higher requirements are put forward for the accuracy, flexibility and adaptability of the mechanical arm. In this study aims to improve the motion control performance of the robotic arm, multi-sensor fusion method is adopted to integrate the data of different types of sensors such as visual sensors, force sensors and accelerometer, and realize data fusion processing through Kalman filtering algorithm. The experimental results show that this method can effectively improve the positioning accuracy of the mechanical arm by 15%, reduce the path tracking error by 20%, and enhance the adaptability of the mechanical arm to the change of the external environment, and can still maintain stable operation under complex working conditions. Compared with the traditional control scheme, the attitude prediction model based on deep learning is innovatively introduced, which realizes the real-time accurate prediction of the motion state of the mechanical arm and provides a reliable basis for optimizing the control strategy.
Keyword:Multi-Sensor Fusion Mechanical Arm Motion Control Kalman Filter
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 1
2多传感器融合技术基础 2
2.1传感器类型及其特性 2
2.2融合算法概述 3
2.3数据同步与校准 3
3机械臂运动控制需求分析 4
3.1机械臂运动学建模 4
3.2控制精度要求 5
3.3实时性与稳定性 5
4多传感器融合在机械臂中的应用实现 6
4.1位置与姿态感知 6
4.2力觉与触觉反馈 7
4.3运动规划与执行 7
结论 8
参考文献 9
致谢 10
