具有弹性关节的机器人反向动态模型的双向循环学习:实时现实世界的实时实现
Brayan Valencia-Vidal1,2, Eduardo Ros1, Ignacio Abadía1
1Department of Computer Engineering, Automation and Robotics, Research Centre for Information and Communication Technologies, University of Granada, Granada, Spain.
Frontiers in neurorobotics
|July 3, 2023
概括
使用双向循环神经网络的机器学习模型准确地预测了cobot动态,改善了扭矩控制,以实现更安全的人机交互. 这些数据驱动的方法优于弹性执行器协同机器人的传统方法.
科学领域:
- 机器人和机器学习 机器人和机器学习
- 人与机器人的交互 (HRI)
- 控制系统工程 控制系统工程
背景情况:
- 协作机器人 (cobots) 需要精确的动态模型来实现安全的人机交互 (HRI).
- 传统的分析建模与具有弹性执行器的协作机器人的复杂,非线性动态作斗争.
- 数据驱动的方法对于准确的 cobot 动态建模是必要的.
研究的目的:
- 提出和评估三种机器学习 (ML) 方法,使用双向循环神经网络 (BRNNs) 来学习弹性执行器cobot的反向动态模型.
- 将基于ML的模型的性能与制造商的固体动态模型和默认位置控制器进行比较.
主要方法:
- 开发了基于BRNN的三个ML模型:一个非参数和两个半参数配置.
- 通过使用 cobot 的关节位置,速度和扭矩的数据集来训练模型.
- 将表现最好的非参数模型集成到前控制循环中进行验证.
主要成果:
- 所有三种ML方法都显示出与制造商的固体动态模型相比,更高的扭矩精度.
- 设计用于未知动态的非参数ML配置,在最坏的情况下显示出更好的准确性.
- 学习的反向动态模型,特别是非参数版本,在现实世界 cobot 性能方面超过了工厂位置控制器.
结论:
- 数据驱动的ML模型,特别是BRNN,对于学习弹性执行器协同机器人的反向动力学是有效的.
- 拟议的非参数 BRNN 方法为在具有挑战性的 HRI 场景中准确控制扭矩提供了强大的解决方案.
- 这项研究通过改进动态建模,提高了协作机器人的安全性和效率.
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