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研究基于狗腿和PSONN算法的机器人定位错误补偿算法
1School of Instrument Science and Opto-electronic Engineering, Hefei University of Technology, Hefei, China.
PloS one
|September 16, 2025
概括
本研究介绍了一种工业机器人错误补偿算法,它结合了动力学校准和先进的预测方法. 这种新的方法显著提高了机器人的定位精度,并减少了定位错误.
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
- 机器学习 机器学习
背景情况:
- 与重复任务相比,工业机器人具有较低的绝对定位精度.
- 现有的算法经常受到低准确度或局部最佳问题的影响.
研究的目的:
- 为工业机器人提出一个先进的错误补偿算法.
- 为了提高工业机器人的绝对定位精度.
主要方法:
- 使用增强的狗腿算法对动力参数进行校准.
- 使用粒子集群优化神经网络 (PSONN) 进行奇点错误预测.
- 通过空间网格多点插值 (SGMI) 计算定位错误.
主要成果:
- 动力学校准将定位误差从3.158毫米减少到0.406毫米.
- SGMI补偿进一步降低了定位误差到0.0685毫米.
- 该算法有效校准了动力学参数并降低了不确定性.
结论:
- 拟议的算法将传统的解释性与神经网络非线性相结合.
- 它成功地解决了传统方法和神经网络的局限性.
- SGMI算法显著提高了工业机器人的定位准确度.
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