The reliable execution of high-level missions in multi-robot systems with heterogeneous agents, requires robust methods for detecting spurious behaviors. In this paper, we address the challenge of identifying spurious executions of plans specified as a Linear Temporal Logic (LTL) formula, as incorrect task sequences, violations of spatial constraints, timing inconsis- tencies, or deviations from intended mission semantics. To tackle this, we introduce a structured data generation framework based on the Nets-within-Nets (NWN) paradigm, which coordinates robot actions with LTL-derived global mission specifications. We further propose a Transformer-based anomaly detection pipeline that classifies robot trajectories as normal or anomalous. Experi- mental evaluations show that our method achieves high accuracy (91.3%) in identifying execution inefficiencies, and demonstrates robust detection capabilities for core mission violations (88.3%) and constraint-based adaptive anomalies (66.8%). An ablation experiment of the embedding and architecture was carried out, obtaining successful results where our novel proposition performs better than simpler representations.

翻译：在具有异构智能体的多机器人系统中，高层级任务的可信执行需要鲁棒的异常行为检测方法。本文致力于解决识别以线性时序逻辑（LTL）公式规约的计划的异常执行问题，这些异常表现为不正确的任务序列、空间约束违反、时序不一致或偏离预期任务语义。为此，我们引入了一个基于网中网（NWN）范式的结构化数据生成框架，该框架将机器人动作与LTL衍生的全局任务规约进行协调。我们进一步提出了一种基于Transformer的异常检测流程，用于将机器人轨迹分类为正常或异常。实验评估表明，我们的方法在执行效率低下识别方面达到了高准确率（91.3%），并在核心任务违反（88.3%）和基于约束的自适应异常（66.8%）检测方面表现出鲁棒的检测能力。对嵌入和架构进行了消融实验，获得了成功的结果，其中我们提出的新方案优于更简单的表示方法。