Autonomy in Check: Governor-Mediated Adaptive Security at the Edge
本文针对边缘自适应安全中因观察不全或被操纵导致语义错误的问题,提出一种由监管器检查安全性的分控架构方法。
本文针对边缘自适应安全中因观察不全或被操纵导致语义错误的问题,提出一种由监管器检查安全性的分控架构方法。
本文提出一个多步骤框架,通过自训练和双阈值机制提高NER数据集标注质量,解决低资源语言中NER标注噪声问题。
本文通过使用多个微调的视觉-语言模型作为独立注释器,并结合字符级多数投票和激活探针方法,解决了视觉-语言模型输出中幻觉字符跨度检测与分类的问题。
为应对多样化生产带来的挑战,本文提出一种基于CAD模型的编程和执行系统,通过动态参数化的行为树控制结构实现人-机器人-起重机协同任务。
This study addresses the challenge of lateral vibration suppression in cooperative transportation of large flexible payloads by heterogeneous robots. A passive force-control collaborative framework is proposed, employing a leader-follower architecture that integrates velocity command shaping with admittance control to establish an equivalent mass-spring-damper model. The system’s energy dissipation stability is rigorously proven through passivity analysis. Both simulations and experimental results demonstrate that this strategy effectively achieves passive vibration damping and stable cooperative transport for heavy flexible loads. Consequently, the proposed method successfully resolves critical issues regarding compliant control and stability assurance in heterogeneous robotic collaboration, offering a robust solution for manipulating large-scale flexible objects without active feedback complexity.
本文针对边缘自适应安全中因观察不全或被操纵导致语义错误的问题,提出一种由监管器检查安全性的分控架构方法。
本文提出一个多步骤框架,通过自训练和双阈值机制提高NER数据集标注质量,解决低资源语言中NER标注噪声问题。
本文通过使用多个微调的视觉-语言模型作为独立注释器,并结合字符级多数投票和激活探针方法,解决了视觉-语言模型输出中幻觉字符跨度检测与分类的问题。
为应对多样化生产带来的挑战,本文提出一种基于CAD模型的编程和执行系统,通过动态参数化的行为树控制结构实现人-机器人-起重机协同任务。
This study addresses the challenge of lateral vibration suppression in cooperative transportation of large flexible payloads by heterogeneous robots. A passive force-control collaborative framework is proposed, employing a leader-follower architecture that integrates velocity command shaping with admittance control to establish an equivalent mass-spring-damper model. The system’s energy dissipation stability is rigorously proven through passivity analysis. Both simulations and experimental results demonstrate that this strategy effectively achieves passive vibration damping and stable cooperative transport for heavy flexible loads. Consequently, the proposed method successfully resolves critical issues regarding compliant control and stability assurance in heterogeneous robotic collaboration, offering a robust solution for manipulating large-scale flexible objects without active feedback complexity.