一种用于工业机器人的精度维护的连续动力学校准方法,基于递归最小平方算法
Guifang Qiao1,2, Xinyi Jiang3, Mingyu Zhang3
1School of Automation, Nanjing Institute of Technology, Nanjing, 211167, Jiangsu, China. zdhxqgf@njit.edu.cn.
Scientific reports
|April 3, 2025
概括
本研究介绍了工业机器人的连续动力学校准方法,以解决精度下降的问题. 递归最小平方 (RLS) 算法显著提高了机器人的准确性,并减少了校准时间.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 制造业 工程 制造工程
- 控制系统 控制系统
背景情况:
- 工业机器人在先进制造中至关重要,但在校准后受到精度降低的影响.
- 保持机器人的精度对于在生产环境中保持一致的性能至关重要.
研究的目的:
- 提出和验证一种连续动力学校准方法,以保持工业机器人的精度.
- 评估用于连续校准的递归最小方程 (RLS) 算法的效率和稳定性.
主要方法:
- 已建立的工业机器人的修改DH (MDH) 和动力错误模型.
- 通过测量机器人姿势来证明精度降低.
- 引入了一种使用递归最小平方 (RLS) 算法的连续动力学校准方法.
主要成果:
- 与Levenberg-Marquardt (LM) 算法相比,RLS算法显示出更高的效率和稳定性.
- 连续校准15个更新的姿势使机器人的精度提高了84.31%.
- 基于RLS的方法减少了69.52%的计算时间和70%的测量时间.
结论:
- 提出的连续动力学校准方法有效地保持了工业机器人的精度.
- RLS算法为实时机器人校准提供了计算效率高,稳定的解决方案.
- 这种方法显著提高了性能,并降低了先进制造业的运营成本.
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