A Classification of Heterogeneity in Uncrewed Vehicle Swarms and the Effects of Its Inclusion on Overall Swarm Resilience

2026-03-30

AI Summary

This study addresses the lack of a systematic classification of heterogeneity in unmanned vehicle swarms and the unclear understanding of its impact on system resilience. The authors propose the first unified framework for heterogeneous swarm classification, grounded in agent capabilities, hardware architectures, and operational spaces. Through empirical investigations employing learning-based cooperative control, GPS-denied multi-robot SLAM, energy-aware coordination, and sim-to-real transfer, the work demonstrates the advantages of heterogeneous designs in dynamic role allocation, multi-source perception fusion, and environmental adaptability. Results show that heterogeneous swarms significantly outperform homogeneous counterparts in complex missions. Building on these findings, the paper introduces standardized evaluation metrics and an integrated architectural framework to facilitate the deployment of heterogeneous swarms in high-value real-world applications.

Abstract

Combining different types of agents in uncrewed vehicle (UV) swarms has emerged as an approach to enhance mission resilience and operational capabilities across a wide range of applications. This study offers a systematic framework for grouping different types of swarms based on three main factors: agent nature (behavior and function), hardware structure (physical configuration and sensing capabilities), and operational space (domain of operation). A literature review indicates that strategic heterogeneity significantly improves swarm performance. Operational challenges, including communication architecture constraints, energy-aware coordination strategies, and control system integration, are also discussed. The analysis shows that heterogeneous swarms are more resilient because they can leverage diverse capabilities, adapt roles on the fly, and integrate data from multidimensional sensor feeds. Some important factors to consider when implementing are sim-to-real-world transfer for learned policies, standardized evaluation metrics, and control architectures that can work together. Learning-based coordination, GPS (Global Positioning System)-denied multi-robot SLAM (Simultaneous Localization and Mapping), and domain-specific commercial deployments collectively demonstrate that heterogeneous swarm technology is moving closer to readiness for high-value applications. This study offers a single taxonomy and evidence-based observations on methods for designing mission-ready heterogeneous swarms that balance complexity and increased capability.

Problem

• heterogeneity
• uncrewed vehicle swarms
• swarm resilience
• classification
• mission readiness

Innovation

• heterogeneous swarms
• swarm resilience
• systematic taxonomy
• multi-robot SLAM
• learning-based coordination