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arXiv · 2510.17261

High-Level Multi-Robot Trajectory Planning And Spurious Behavior Detection

Fernando Salanova·Jesús Roche·Cristian Mahulea·Eduardo Montijano
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Abstract

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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Fernando Salanova, Jesús Roche, Cristian Mahulea, Eduardo Montijano. 2025-10-20. High-Level Multi-Robot Trajectory Planning And Spurious Behavior Detection. https://arxiv.org/abs/2510.17261

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