在模糊的机器人网络中以突出度为导向的链接预测
D Joseph Jeyakumar1, V Rajkumar2, T P Dayana Peter3
1Department of Electronics and Communication Engineering, Sri Muthukumaran Institute of Technology, Mangadu, Kanchipuram, Tamilnadu, India.
Scientific reports
|December 19, 2025
概括
本研究引入了修改后的强度突出度 (SP) 指数,用于预测复杂的网络物理系统 (CPS) 中的相互作用. 增强的指数可以提高早期检测故障和机器人网络中的协作预测.
科学领域:
- 网络物理系统 (CPSs) 是指
- 机器人技术和智能自动化
- 网络科学 网络科学
背景情况:
- 网络物理系统 (CPS) 越来越复杂,需要强大的预测模型来实现可靠的操作.
- 现有的模糊链路预测方法在准确建模机器人网络内的不确定的交互方面面临挑战.
- 强度突出度 (SP) 指数最初是用于社交网络的,需要为CPS应用程序进行调整.
研究的目的:
- 为机器人和智能自动化系统中的模糊交互图重新制定强度突出度 (SP) 指数.
- 通过考虑连接性和突出性,提高网络物理系统 (CPS) 中链接概率的预测.
- 为了验证修改后的SP指数的表现与用于链接预测的传统模糊指数相比.
主要方法:
- 对模糊交互图的强度突出度 (SP) 指数的重构,其中节点代表机器人组件,边缘表示不确定的依赖关系.
- 使用连接强度和突出程度来评估链接概率,即使没有共同的邻居.
- 对称性,边界性和单调性等属性的理论演示.
主要成果:
- 经过修改的SP指数在现实世界和基于ROS的机器人数据集上,与传统的模糊指数 (CN,RSM,CAR) 相比,显示出更高的预测准确度.
- 实现了更高的精度,AUC (曲线下的面积) 和AUP (精度回忆曲线下的面积) 测量.
- 早期成功识别了交互失败,并改善了协作预测.
结论:
- 适应的SP指数为网络物理系统 (CPS) 中的模糊链接预测提供了一个新的跨学科工具.
- 这种方法有助于为自主系统开发耐故障设计和弹性网络结构.
- 能够改善实时机器人协作,并提高复杂的机器人网络的可靠性.
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