触摸图:一种异步图形神经网络,用于使用神经形象视觉基于触摸感应的接触角度预测
Hussain Sajwani1,2, Abdulla Ayyad2, Yusra Alkendi3
1UAE National Service & Reserve Authority, Abu Dhabi, United Arab Emirates.
Sensors (Basel, Switzerland)
|July 29, 2023
概括
这项研究为机器人引入了一种新的基于神经形态视觉的触觉传感器 (N-VBTS). 它使用图形神经网络进行高效的接触角度预测,降低计算成本和仪器费用.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 计算机视觉 计算机视觉
- 传感器技术 传感器技术
背景情况:
- 基于视觉的触觉传感器 (VBTS) 为机器人提供高分辨率的触觉反.
- 传统的VBTS由于固定摄像头更新速率和冗余数据而遭受高计算开销.
- 现有的解决方案往往会在仪表成本或维护方面妥协.
研究的目的:
- 开发一种基于神经形态视觉的触觉传感器 (N-VBTS),用于高效的接触角度预测.
- 为了降低与触觉传感相关的计算开销和仪器仪表成本.
- 提高机器人技术中触觉反系统的速度和效率.
主要方法:
- 开发了一种新的基于神经形态视觉的触觉传感器 (N-VBTS),利用基于事件的摄像头.
- 设计了一个图形神经网络,TactiGraph,以异步处理原始N-VBTS数据.
- 利用传感器流中的时空相关性进行准确的预测.
主要成果:
- 在接触角度预测中,TactiGraph 实现了0.62°的平均绝对误差.
- 与传统的VBTS相比,N-VBTS表现出明显更快,更高效的性能.
- 该系统只需要传统VBTS的5.5%的计算时间.
- 随着内部照明源或没有内部照明源,N-VBTS有效地运行,降低成本.
结论:
- 拟议的N-VBTS和TactiGraph为机器人触觉传感提供了具有成本效益和高效的解决方案.
- 这种方法显著降低了计算负载,并提高了预测准确度.
- 这项技术有望提高机器人操纵和交互能力.
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