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

2025-10-20

AI Summary

Detecting anomalous behaviors—such as erroneous task sequences, violations of spatial/temporal constraints, or semantic deviations—in high-level task execution for heterogeneous multi-robot systems remains challenging. To address this, we propose a trajectory planning and anomaly monitoring framework integrating Linear Temporal Logic (LTL) and Nets-within-Nets (NWN). Our approach innovatively couples LTL-guided global task specifications with NWN-based collaborative modeling, and introduces a Transformer-driven trajectory embedding and anomaly classification pipeline. This enables structured trajectory representation and multi-dimensional anomaly identification. Experiments demonstrate detection accuracies of 91.3% for inefficient execution, 88.3% for critical task violations, and 66.8% for constraint-adaptive anomalies. Ablation studies confirm the essential contributions of both the LTL–NWN co-modeling mechanism and the Transformer architecture to overall performance.

Technology Category

• Intelligent Robots: Multi-Robot Systems
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Multiagent Systems: Multiagent Planning

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs

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 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.

Problem

• Detecting spurious behaviors in multi-robot mission executions
• Identifying anomalies in robot trajectories and LTL plan violations
• Classifying incorrect task sequences and spatial constraint deviations

Innovation

• Uses Nets-within-Nets paradigm for robot coordination
• Proposes Transformer-based pipeline for anomaly detection
• Classifies robot trajectories as normal or anomalous