无人系统群体异质性分类与韧性影响
提出基于代理性质、硬件结构与作业空间的异质性分类框架，综述表明战略性异质化可提升群体性能与韧性；讨论通信架构、能耗感知协调、控制集成、sim-to-real迁移和统一评估等实现挑战。结合学习型协调、无GPS多机器人SLAM与行业部署，认为异质化群体正趋近实用化，并提供统一分类与证据支持。
原文内容
arXiv:2603.28831v1 Announce Type: new
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 im
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 im