High-Level Multi-Robot Trajectory Planning And Spurious Behavior Detection
This addresses the need for reliable anomaly detection in multi-robot systems with heterogeneous agents, representing an incremental improvement through a novel hybrid method.
The paper tackled the problem of detecting spurious behaviors in multi-robot systems executing high-level missions specified by Linear Temporal Logic, achieving high accuracy (91.3%) in identifying execution inefficiencies and robust detection for mission violations (88.3%) and adaptive anomalies (66.8%).
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.