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Updated: Jan 30, 2026

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使用机器人过程自动化框架,自动化基于PINN的机器人关节动力学分辨率
Parth Agrawal1, Pavithra Sekar1, Kush Kumar Kushwaha1
1School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, India.
Frontiers in robotics and AI
|January 29, 2026
概括
这项研究将物理信息神经网络 (PINNs) 与机器人过程自动化 (RPA) 集成,以改进机器人关节运动控制和自动化. 结合的方法提高了复杂的机器人系统的精度和效率.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
- 计算科学 计算科学
背景情况:
- 物理信息神经网络 (PINNs) 通过将物理定律集成到神经网络训练中,为解决机器人学中复杂问题提供了一种新的方法.
- 机器人过程自动化 (RPA) 工具可以简化和自动化机器人任务,但它们与先进的建模技术的集成需要进一步探索.
- 机器人运动控制的现有挑战包括高的培训成本和传统方法的缓慢融合率.
研究的目的:
- 探索PINNs和RPA工具的协同集成,用于建模和控制刚性机器人关节运动.
- 研究先进的PINN技术 (扩展PINN,混合PINN,最小化损失) 以克服培训效率低下的问题.
- 展示PDE启发PINNs与RPA的应用,用于机器人导航,操纵和现实世界的过程自动化.
主要方法:
- 实现各种PINN架构,包括扩展PINN,混合PINN和最小化损失技术.
- 将PINN模型与RPA工具集成,用于自动化机器人控制和运动规划过程.
- 使用机器人操作系统 (ROS) 与RPA一起用于机器人系统中协调关节运动和角度控制.
主要成果:
- 该研究表明,将先进的PINN与RPA工具相结合,可以显著提高机器人控制的精度和效率.
- 先进的PINN技术有效地解决了高培训成本和缓慢的融合率的问题.
- 当与RPA和ROS集成时,PDE启发的PINN显示出在导航和操纵任务中复杂的运动规划的前景.
结论:
- 整合PINNs和RPA为先进的机器人建模和控制提供了一个强大的框架.
- 这种混合方法促进了复杂的机器人操作的更高效和精确的自动化,特别是在非线性和动态场景中.
- 这项研究为复杂的人工智能驱动的机器人系统的实际应用铺平了道路.
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