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<p>It is essential to deploy efficient unmanned aerial vehicle (UAV) swarms for time-sensitive search and rescue (SAR) missions, as they require quick reactions, energy efficiency, and complete spatial coverage. Existing approaches often rely on complex optimization methods or assume homogeneous UAV swarms, which limit their real-world applicability. Therefore, this work introduces a lightweight framework for deploying heterogeneity-aware UAV swarms that incorporate diversity in detection range, communication range, maximum flight speed, and payload using a geometric optimization model. The proposed method consists of an analytical model for optimal inter-UAV distances, a triangular subswarm structure, and a hierarchical orbit-based deployment algorithm to ensure gapless coverage with minimal energy use. Our simulation results show near-complete area coverage in simulation with movement cost reduction and deployment time compared to a standard virtual force algorithm. The proposed work provides a computationally efficient and scalable framework for heterogeneous UAV swarms in simulation-based SAR environments, with discussion of practical deployment considerations.</p>

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A Heterogeneity-Aware Geometric Framework for UAV Swarm Deployment in Time-Critical Search and Rescue Missions

  • 期刊论文

  • 发表日期：

    2026年5月20日

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  • Moteb Alghamdi,
  • Salah Abdelmageid

摘要

It is essential to deploy efficient unmanned aerial vehicle (UAV) swarms for time-sensitive search and rescue (SAR) missions, as they require quick reactions, energy efficiency, and complete spatial coverage. Existing approaches often rely on complex optimization methods or assume homogeneous UAV swarms, which limit their real-world applicability. Therefore, this work introduces a lightweight framework for deploying heterogeneity-aware UAV swarms that incorporate diversity in detection range, communication range, maximum flight speed, and payload using a geometric optimization model. The proposed method consists of an analytical model for optimal inter-UAV distances, a triangular subswarm structure, and a hierarchical orbit-based deployment algorithm to ensure gapless coverage with minimal energy use. Our simulation results show near-complete area coverage in simulation with movement cost reduction and deployment time compared to a standard virtual force algorithm. The proposed work provides a computationally efficient and scalable framework for heterogeneous UAV swarms in simulation-based SAR environments, with discussion of practical deployment considerations.