发表机构
University of Connecticut; U.S. Army DEVCOM Ground Vehicle Systems Center (GVSC)（康涅狄格大学; 美国陆军DEVCOM地面车辆系统中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对高混合低批量制造中的检测适应性问题，提出FRAME分层双臂框架，将指令转化为可追溯计量证据，通过确定性门控提升检测可靠性并减少误接受。
AI 中文摘要
高混合低批量（HMLV）制造要求检测系统能够适应不断变化的零件、规格和工作订单，而无需重复的任务特定编程。现有检测自动化通常假设预定义的传感序列，而通用机器人智能体则优化任务完成，而非计量证据的完整性和有效性。我们提出了任务指定的主动计量检测问题，并提出了从需求到可接受的计量证据（FRAME）框架，这是一个分层双臂框架，将检测指令和结构化规格转换为可追溯的一致性证据。FRAME协调学习操控与校准激光轮廓测量：任务管理器对需求进行落地和调度，主动表面对应验证物理到规格的定位，证据记忆跟踪测量来源、可接受性和覆盖率。学习组件可提出检测目标和物理访问动作，但确定性基准测量、可接受性检查、覆盖审计和一致性评估防止不完整或未验证的证据授权通过（PASS）。一系列物理实验表明，FRAME实现了更高的端到端检测可靠性、更少的误接受和更短的任务完成时间。
英文摘要
High-mix low-volume (HMLV) manufacturing requires inspection systems to adapt to changing parts, specifications, and work orders without repeated task-specific programming. Existing inspection automation typically assumes predefined sensing sequences, while general purpose robot agents optimize task completion rather than the completeness and validity of metrological evidence. We formulate task-specified active metrological inspection and propose From Requirements to Admissible Metrological Evidence (FRAME), a hierarchical dual-arm framework that converts an inspection instruction and structured specification into traceable conformance evidence. FRAME coordinates learned manipulation with calibrated laser profilometry: a task manager grounds and schedules requirements, active surface correspondence verifies physical-to-specification localization, and evidence memory tracks measurement provenance, admissibility, and coverage. Learned components may propose inspection targets and physical access actions, but deterministic datum-grounded measurement, admissibility checks, coverage auditing, and conformance evaluation prevent incomplete or unverified evidence from authorizing PASS. A series of physical experiments shows that FRAME achieves higher end-to-end inspection reliability, fewer false accepts, and shorter task completion time.
Comments19 pages, 13 figures
