用LTL逻辑与Transformer检测多机器人任务中的异常行为
High-Level Multi-Robot Trajectory Planning And Spurious Behavior Detection
- 基于LTL逻辑和NWN框架生成结构化数据，协调异构机器人的任务
- 91.3%准确率识别执行低效，88.3%检测核心任务违规
- 适合复杂多机器人系统中保障任务可靠性的人工智能研究者
在异构多机器人系统中可靠执行高层任务，需具备检测异常行为的能力。本文针对以线性时序逻辑（LTL）公式定义的任务计划中出现的错误任务序列、空间约束违反、时间不一致或任务语义偏离等问题，提出一种基于嵌套网络（Nets-within-Nets, NWN）范式的结构化数据生成框架，用于协调机器人动作与全局任务规范。进一步设计基于Transformer的异常检测流水线，对机器人轨迹进行正常/异常分类。实验表明，该方法在识别执行低效方面达到91.3%准确率，对核心任务违规的检测率达88.3%，对基于约束的自适应异常检测率达66.8%。消融实验验证了所提嵌入与架构优于简单表示。
原文摘要 · Abstract (English)
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 inconsistencies, 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. Experimental 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.
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