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动力学和联合合规建模方法,以提高机器人视觉系统的位置精度
Fan Ye1, Guangpeng Jia2, Yukun Wang1
1School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
Sensors (Basel, Switzerland)
|April 27, 2024
概括
这项研究通过整合动态和联合合规模型来提高机器人视觉系统 (RVS) 的准确性. 实验表明,对自动化工业任务的精度有了显著的改善.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 自动化工程自动化工程
- 计算机视觉 计算机视觉
背景情况:
- 机器人视觉系统 (RVS) 的高位置精度对于工业自动化效率至关重要.
- 现有的方法在校准过程中面临复杂性和错误积累方面的挑战.
研究的目的:
- 开发一种全面的建模方法,以提高RVS的位置准确性.
- 整合动力学和联合合规因素,以提高机器人系统性能.
主要方法:
- 开发了一种统一的动力学模型,以减少校准复杂性和错误.
- 制定了一个共同的合规模型,考虑了联合连接器,外部负载和链接自重.
- 使用了一种新的3D旋转激光传感器,用于精确的误差测量和模型校准.
主要成果:
- 在机器人视觉系统的位置准确性方面取得了显著的改进.
- 在FANUC LR Mate 200iD机器人上的实验中验证了拟议的模型.
- 展示了用于RVS集成的简化和高效的解决方案.
结论:
- 集成的动力学和联合合规模型为RVS校准和错误补偿提供了强大的框架.
- 这种方法为高精度自动化任务的进步提供了潜力.
- 该研究有助于更有效和高效的工业机器人自动化.
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