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

TL;DR

This paper presents a novel framework combining structured data generation and Transformer-based anomaly detection to identify spurious behaviors in multi-robot high-level mission execution, ensuring robustness and accuracy.

Contribution

It introduces a structured data generation method using Nets-within-Nets and a Transformer-based pipeline for anomaly detection in multi-robot systems, improving detection accuracy.

Findings

Achieved 91.3% accuracy in identifying execution inefficiencies.

Demonstrated 88.3% detection rate for core mission violations.

66.8% success in detecting constraint-based anomalies.

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.