一个5PUS-RPUR平行机器人的动力错误分析和自主校准
Zesheng Wang1,2, Yanbiao Li3, Bo Chen3
1School of Intelligent Manufacturing, Hangzhou Polytechnic, Hangzhou, China.
PloS one
|September 2, 2025
概括
这项研究引入了一种用于并行机器人动力校准的全新优化方法,显著提高了位置精度. 这种方法克服了传统方法的局限性,有效地处理复杂的错误参数以提高机器人的性能.
科学领域:
- 机器人技术
- 机械工程
- 控制系统
背景情况:
- 需要精确的动力校准才能达到绝对准确度.
- 传统的方法在复杂的误差参数合中扎,导致不理想的校准.
- 现有的技术可能会趋于局部最佳,从而限制校准的有效性.
研究的目的:
- 通过全局优化策略,为并行机器人提出一种新的自我校准方法.
- 提高并行机器人的绝对准确性和性能.
- 解决传统运动校准方法的局限性.
主要方法:
- 使用螺丝理论为5PUS-RPUR平行机器人建立反向动力学.
- 通过有限差异进行灵敏度分析以确定关键错误来源.
- 使用最远点采样来选择统一的测量点.
- 构建了一个基因算法 (GA) 目标函数集成执行器和平台构成错误.
- 对非线性约束应用了惩罚函数方法.
主要成果:
- 提出的全局优化方法显著提高了机器人的工作空间的位置精度.
- 敏感性分析有效选和消除可以忽略的错误来源.
- 最远点采样确保了测量工作空间的全面覆盖.
- 为了进行可靠的校准,GA成功集成了多个错误来源.
结论:
- 与现有最先进的方法相比,新型的自我校准方法提供了更高的性能.
- 全球优化策略有效地克服了并行机器人校准中的本地最佳问题.
- 这种方法为提高并行机器人的运动精度提供了强大而准确的解决方案.
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