机器人动力参数误差校准基于莱文伯格-马奎特和人工子优化算法
Mengyao Fan1, Huining Zhao1, Fei Liu2
1School of Instrument Science and Opto-Electronics Engineering, Hefei University of Technology, Hefei 230009, China.
The Review of scientific instruments
|October 27, 2025
概括
这项研究引入了一种新的机器人校准方法,将Levenberg-Marquardt算法与人工子优化相结合. 改进的技术大大减少了机器人定位错误,提高了整体准确性.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 控制系统 控制系统
- 优化算法 优化算法
背景情况:
- 机器人定位准确性对于工业应用至关重要.
- 传统的校准方法,如Levenberg-Marquardt可以通过圆形错误来限制.
- 需要改进的校准技术来提高机器人的精度.
研究的目的:
- 提出一种新的机器人校准方法,整合莱文伯格-马奎特和人工子的优化.
- 为了提高机器人运动参数校准的准确性.
- 通过模拟和实验验证拟议方法的有效性.
主要方法:
- 使用修改后的德纳维特-哈顿伯格模型建立机器人运动误差模型.
- 使用莱文伯格-马奎特算法进行初始校准.
- 应用人工子优化算法来准确校准动力参数错误.
主要成果:
- 组合方法显著减少了模拟 (1.6348毫米到0.0244毫米) 和验证实验 (0.8303毫米到0.1636毫米) 中的平均定位误差.
- 在标准环尺应用中,测量误差从0.1239mm降至0.0623mm.
- 与现有技术相比,拟议的方法显示出更高的准确性.
结论:
- 这种新的校准方法有效地提高了机器人的定位准确性.
- 将Levenberg-Marquardt与人工子优化相结合,可以克服传统算法的局限性.
- 这种方法为各种应用程序的精确机器人校准提供了显著的进步.
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