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This paper presents a taxonomy for classifying heterogeneity in uncrewed vehicle swarms based on agent nature, hardware structure, and operational space. It argues that strategically incorporating heterogeneity improves swarm resilience by enabling diverse capabilities, adaptive role allocation, and multi-sensor data integration. The authors identify key challenges for implementing heterogeneous swarms, including sim-to-real transfer, standardized evaluation metrics, and control architecture integration, while also highlighting advancements in learning-based coordination and GPS-denied SLAM.
Heterogeneous uncrewed vehicle swarms aren't just a collection of different robots; they're a fundamentally more resilient architecture, provided you navigate the complexities of sim-to-real transfer and standardized evaluation.
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.
