基于深度神经网络的机器人视觉服务器用于卫星目标跟踪
Shayan Ghiasvand1, Wen-Fang Xie1, Abolfazl Mohebbi2
1Department of Mechanical, Industrial and Aerospace Engineering, Concordia University, Montréal, QC, Canada.
Frontiers in robotics and AI
|October 23, 2024
概括
这项研究引入了用于国际空间站自动卫星跟踪的深度神经网络 (DNN),从而减少错误和成本. 基于DNN的机器人视觉伺服显著提高了跟踪精度和效率.
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
- 航空航天工程 航空航天工程
背景情况:
- 国际空间站 (ISS) 上的手动卫星跟踪是昂贵的,容易出错.
- 目前的视觉伺服方法在运动脱方面遇到了困难,限制了跟踪准确度.
研究的目的:
- 开发基于深度神经网络 (DNN) 的机器人视觉伺服解决方案,用于自动化卫星跟踪.
- 解决和减轻视觉服务中的运动脱问题.
- 为了提高卫星操作的控制性能和跟踪精度.
主要方法:
- 利用深度神经网络 (DNN) 来估计操纵者的姿势.
- 实现了一个带有6-DOF Denso操纵器和RGB摄像头的机器人视觉伺服系统.
- 进行实时实验测试,使用准针作为模拟卫星.
主要成果:
- 与传统方法相比,实现了32.04%的姿势错误减少.
- 在速度准确度上表现出了21.67%的改进.
- 通过基于DNN的姿势估计,成功地减少了合效应.
结论:
- 基于DNN的机器人视觉伺服方法显著提高了卫星跟踪的准确性和效率.
- 这种方法为视觉伺服器中运动脱的挑战提供了可行的解决方案.
- 这些发现表明,有潜力改善卫星目标跟踪和捕获操作.
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