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基于ECOA-BP的工业机器人手臂的终端效应器位置错误补偿研究
Wenping Xiang1,2, Junhua Chen1, Hao Li1,2
1College of Science and Technology, Ningbo University, Ningbo 315300, China.
Sensors (Basel, Switzerland)
|January 25, 2025
概括
由于灵活的变形,工业机器人手臂的经验存在偏差. 使用增强的鱼优化算法 (ECOA) 和BP神经网络的新方法显著提高了定位准确率75.77%.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 机械工程 机械工程
- 控制系统 控制系统
背景情况:
- 工业机器人手臂因因链接灵活性和联合合规性等非线性变形引起的终端效应体位偏差而受到影响.
- 这些错误会影响高精度任务的精度.
研究的目的:
- 分析和补偿六度自由度 (6-DOF) 机器人手臂的终端位置错误.
- 通过解决灵活的错误来提高机器人操纵器的定位精度.
主要方法:
- 使用Denavit-Hartenberg (DH) 参数方法建立了运动模型.
- 开发了一种使用ANSYS和ADAMS进行动态模拟的刚性-灵活合虚拟原型模型.
- 建议使用增强的鱼优化算法 (ECOA) 来优化BP神经网络的错误补偿方法.
主要成果:
- 动力学模拟分析了在刚性-柔性合下端效应器位置错误.
- 实验验证证了拟议的补偿方法的有效性.
- 在补偿后,定位精度提高了75.77%.
结论:
- 开发的虚拟原型模型准确地代表了机器人手臂在灵活条件下的行为.
- 经过ECOA优化的BP神经网络有效地弥补了终端效应器位置错误.
- 拟议的方法为提高机器人手臂精度提供了可靠的解决方案.
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