验证门控的智能工业多机器人系统任务状态治理
Verification-Gated Agentic Mission-State Governance for Intelligent Industrial Multi-Robot Systems
AI总结：
提出验证门控任务状态治理框架，通过同步任务森林与黑板状态，经确定性验证后原子提交，减少无效提交和冲突，提升工业多机器人系统安全与效率。
摘要：
代理人工智能越来越多地被用于分解工业任务、提出机器人动作以及在动态网络物理环境中调整执行计划。然而，自主提案生成本身并不能保证多机器人工业系统在长周期执行过程中保持任务依赖关系、资源所有权、安全约束或修复边界。本文提出了一种用于智能工业多机器人系统的验证门控代理任务状态治理框架。该框架维护两个同步的状态对象：一个用于持久层次结构、延迟接地和可修复子结构的演化任务森林；以及一个用于在线执行状态、机器人轨迹、资源锁、世界信念、提案、验证记录和场景临时约束的受控黑板。从每个森林-黑板快照中，导出的执行耦合拓扑揭示了用于提案验证、并行提交资格和有限修复的跨分支依赖关系。候选分配、修复、延迟和约束更新可以由启发式、优化或代理推理模块生成，但只有在确定性验证和原子提交后才能更新已提交的任务状态。我们在室内工厂多机器人场景、30种子远程构建压力基准、结构消融和可扩展性探测中评估了该框架。结果表明，在建模的任务谓词下，改进的验证和安全审计任务状态进展具有更少的无效提交、锁冲突、重复分配、废弃节点和破坏性修复。该研究将代理AI定位为受可检查任务状态验证治理的提案生成层，而非不受检查的执行权威。
英文摘要：
Agentic artificial intelligence is increasingly used to decompose industrial tasks, propose robot actions, and adapt execution plans in dynamic cyber-physical environments. However, autonomous proposal generation alone does not guarantee that multi-robot industrial systems preserve task dependencies, resource ownership, safety holds, or repair boundaries during long-horizon execution. This paper introduces a verification-gated agentic mission-state governance framework for intelligent industrial multi-robot systems. The framework maintains two synchronized state objects: an evolving task forest for persistent hierarchy, delayed grounding, and repairable substructures; and a governed blackboard for online execution state, robot traces, resource locks, world beliefs, proposals, verification records, and scene-temporary constraints. From each forest--blackboard snapshot, a derived execution coupling topology exposes cross-branch dependencies for proposal verification, parallel-commit eligibility, and bounded repair. Candidate assignments, repairs, deferrals, and constraint updates may be generated by heuristic, optimization, or agentic reasoning modules, but they can update the committed mission state only after deterministic verification and atomic commit. We evaluate the framework in an indoor factory multi-robot scenario, 30-seed remote-construction stress benchmarks, structural ablations, and scalability probes. The results show improved verified and safety-audited mission-state progress with fewer invalid commitments, lock conflicts, duplicate assignments, abandoned nodes, and disruptive repairs under modeled mission predicates. The study positions agentic AI as a proposal-generating layer governed by inspectable mission-state verification rather than as an unchecked execution authority.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
