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Optimization and Tactical Deconfliction for Drone Search-and-Rescue in Low-Altitude Airspace: A Systematic Literature Review
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DOI：10.3390/drones10080596.png)
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Abstract
En 中文
Unmanned Aerial Vehicle (UAV) swarms are increasingly deployed in search and rescue (SAR) missions to rapidly locate survivors. However, deploying autonomous swarms in low-altitude airspace shared with crewed rescue aircraft poses significant algorithmic and safety challenges. Following PRISMA 2020 guidelines, this systematic review synthesizes 44 peer-reviewed studies (2022–2026) to evaluate the literature across algorithmic optimization, reality-gap limitations, tactical deconfliction, and validation maturity. The synthesis reveals a consistent trend toward decentralized swarms, driven by Deep Reinforcement Learning in dynamic environments and by bio-inspired metaheuristics for static coverage. Despite these algorithmic advancements, the literature exhibits a severe reality gap: approximately 86% of evaluated models rely exclusively on idealized software simulations, abstracting away critical constraints like communication denial and sensor noise. Furthermore, most models assume uncontested airspace and lack the Manned–Unmanned Teaming (MUM-T) and tactical deconfliction protocols necessary for safe coexistence with rescue helicopters. To achieve true operational readiness within the critical “Golden 72 Hours” of disaster response, the discipline must transition toward hardware-in-the-loop and physical field trials, natively integrating airspace deconfliction into core swarm optimization loops.
Keywords:
unmanned aerial vehicles (UAVs)
drone swarms
search and rescue (SAR)
deep reinforcement learning
manned–unmanned teaming (MUM-T)
coverage path planning (CPP)
tactical deconfliction
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