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<p>Search-and-Rescue (SaR) missions often expose first responders to extreme risks, particularly when operating in hazardous or unstable environments. Recent advances have made it possible to deploy swarms of Unmanned Aerial Vehicles (UAVs) to assist in these operations, offering enhanced situational awareness, faster coverage, and reduced human exposure. Yet, the dynamic and unpredictable nature of SaR environments poses persistent challenges in coordinating drone teams, ensuring communication, and maintaining adaptability in the face of unexpected changes. Command &amp; Control (C2) is a long-established military concept concerned with how entities and resources are organised, coordinated, and directed to achieve strategic objectives. In recent years, C2 has found renewed relevance in highly distributed and information-rich domains, such as disaster relief, humanitarian response, and large-scale coordination systems. Its core principles, namely structured communication, adaptive organization, and mission-driven alignment, make it an appealing framework for managing drones fleets in SaR missions. In this paper, we extend our previous work by providing a refined and formalised representation of C2 for drone-based SaR operations. First, we revisit and clarify NATO’s concepts of C2 Approaches and C2 Agility, grounding them in the operational realities of autonomous drone coordination. Second, we introduce a formal specification of C2 Systems, expressed through both a Domain-Specific Modeling Language (DSML) for system design, and a Structural Operational Semantics (SOS) that formally defines their dynamic behaviour. This dual representation enables systematic reasoning about the correctness and adaptability of C2-driven drone coordination. Third, we report on a simulation environment that operationalises our metamodel, allowing the design, execution, and observation of C2 System behaviours under evolving mission conditions. Altough preliminary, our paper aims at pushing forward formal foundations of C2 for (semi-)autonomous drone systems, and provide a way toward designing fleets capable of adaptive, reliable, and verifiable behaviour.</p>

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Using command & control for coordinating UAV fleets for missions with changing contexts

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月24日

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  • Moussa Amrani,
  • Abdelkader Ouared

摘要

Search-and-Rescue (SaR) missions often expose first responders to extreme risks, particularly when operating in hazardous or unstable environments. Recent advances have made it possible to deploy swarms of Unmanned Aerial Vehicles (UAVs) to assist in these operations, offering enhanced situational awareness, faster coverage, and reduced human exposure. Yet, the dynamic and unpredictable nature of SaR environments poses persistent challenges in coordinating drone teams, ensuring communication, and maintaining adaptability in the face of unexpected changes. Command & Control (C2) is a long-established military concept concerned with how entities and resources are organised, coordinated, and directed to achieve strategic objectives. In recent years, C2 has found renewed relevance in highly distributed and information-rich domains, such as disaster relief, humanitarian response, and large-scale coordination systems. Its core principles, namely structured communication, adaptive organization, and mission-driven alignment, make it an appealing framework for managing drones fleets in SaR missions. In this paper, we extend our previous work by providing a refined and formalised representation of C2 for drone-based SaR operations. First, we revisit and clarify NATO’s concepts of C2 Approaches and C2 Agility, grounding them in the operational realities of autonomous drone coordination. Second, we introduce a formal specification of C2 Systems, expressed through both a Domain-Specific Modeling Language (DSML) for system design, and a Structural Operational Semantics (SOS) that formally defines their dynamic behaviour. This dual representation enables systematic reasoning about the correctness and adaptability of C2-driven drone coordination. Third, we report on a simulation environment that operationalises our metamodel, allowing the design, execution, and observation of C2 System behaviours under evolving mission conditions. Altough preliminary, our paper aims at pushing forward formal foundations of C2 for (semi-)autonomous drone systems, and provide a way toward designing fleets capable of adaptive, reliable, and verifiable behaviour.