一个基于物理的神经网络方法来增强动力学多旋转器的视觉服务
IEEE transactions on cybernetics
|July 8, 2024
概括
本研究介绍了一种新的视觉服务策略,用于使用物理信息的神经网络 (PINNs) 估计系统不确定性. 这种方法提高了稳定性,并减少了对精确机器人控制的数据需求.
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:
- 视觉伺服使机器人能够使用相机反来精确控制运动.
- 由于复杂的控制输入和潜在的不确定性,多轮机动态带来了挑战.
- 基于物理学的神经网络 (PINNs) 提供了一个强大的工具,用于模拟具有有限数据的复杂系统.
研究的目的:
- 通过将PINN与动态中心控制集成,为多旋转机开发出强大的视觉伺服器战略.
- 为了消除在多旋转机运动控制中需要反向雅可比式计算的需要.
- 为了提高视觉伺服器的稳定性,应对摄像头和多旋转器参数的不确定性.
主要方法:
- 使用物理信息神经网络 (PINN) 来估计系统的不确定性和不准确性.
- PINN模型与以动力为中心的视觉伺服技术集成,直接将像素变化映射到扭矩和推力输入.
- 一个具有适应性视界的非线性模型预测控制器 (NMPC) 用于实时实现.
主要成果:
- 与现有的数据驱动方法相比,拟议的方法减少了65%的标记数据的需求.
- 集成系统在摄像机参数不确定性高达70%的情况下表现出稳健性.
- 通过NMPC,控制力度的处理速度比传统的MPC策略快10倍.
结论:
- 结合PINN和以动力为中心的视觉伺服策略,为多轮控制提供了强大且数据效率高的解决方案.
- 这种方法有效地处理系统不确定性和建模不准确性,这对于现实应用至关重要.
- NMPC的实时功能确保了动态轨迹跟踪的实际实施.
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