Cooperative Search Method of Multi-UAVs for Mountain Search and Rescue Missions

Mountain search-and-rescue missions require multiple unmanned aerial vehicles (UAVs) to cooperate efficiently under limited communication and constrained operational resources. Existing multi-UAV search methods often rely on centralized task allocation or explicit coordination mechanisms, while exhaustive coverage strategies may allocate excessive search effort to low-value regions, reducing search efficiency in large-scale environments. To address these challenges, this paper proposes a probability-guided and pheromone-assisted distributed cooperative search framework that enables autonomous UAV decision-making without centralized control. Each UAV independently selects its motion according to local observations, prior target probability, and digital pheromone information, while intermittent communication and occupancy-map fusion enable implicit coordination through local interactions. The proposed framework balances target-oriented exploitation and spatial exploration by combining probability attraction with pheromone repulsion, thereby suppressing redundant revisits while maintaining effective distributed cooperation. It is evaluated through representative two-dimensional mountain search simulations under distance-limited communication constraints. In the baseline (2km×2km) scenario, the proposed framework achieves search success rates comparable to conventional coverage-based methods while reducing the average search cost from 1074.2 and 1005.0 steps to 581.9 steps. As the search environment expands, its performance advantage becomes increasingly pronounced, demonstrating higher search efficiency and improved mission reliability under limited search resources. Parameter sensitivity and scalability analyses further show that the proposed framework maintains favorable robustness and cooperative efficiency across different parameter settings and search scales. These results demonstrate that the proposed distributed search framework provides an effective and scalable solution for multi-UAV mountain search-and-rescue missions under limited communication and operational resources.

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Publication Details

Journal
Drones
Published
2026-06-28
DOI
https://doi.org/10.3390/drones10070493
Primary Topic
UAV Applications and Optimization
Type
article
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article

Cooperative Search Method of Multi-UAVs for Mountain Search and Rescue Missions

Zheng Zi, Zhizhou Zhang, Junxi Zhu, Xiaokun Jiang et al.
Drones
UAV Applications and Optimization
article

Cooperative Search Method of Multi-UAVs for Mountain Search and Rescue Missions

Zheng Zi, Zhizhou Zhang, Junxi Zhu, Xiaokun Jiang, Xitong Zhou, Yuan Xie
article en

Abstract

Mountain search-and-rescue missions require multiple unmanned aerial vehicles (UAVs) to cooperate efficiently under limited communication and constrained operational resources. Existing multi-UAV search methods often rely on centralized task allocation or explicit coordination mechanisms, while exhaustive coverage strategies may allocate excessive search effort to low-value regions, reducing search efficiency in large-scale environments. To address these challenges, this paper proposes a probability-guided and pheromone-assisted distributed cooperative search framework that enables autonomous UAV decision-making without centralized control. Each UAV independently selects its motion according to local observations, prior target probability, and digital pheromone information, while intermittent communication and occupancy-map fusion enable implicit coordination through local interactions. The proposed framework balances target-oriented exploitation and spatial exploration by combining probability attraction with pheromone repulsion, thereby suppressing redundant revisits while maintaining effective distributed cooperation. It is evaluated through representative two-dimensional mountain search simulations under distance-limited communication constraints. In the baseline (2km×2km) scenario, the proposed framework achieves search success rates comparable to conventional coverage-based methods while reducing the average search cost from 1074.2 and 1005.0 steps to 581.9 steps. As the search environment expands, its performance advantage becomes increasingly pronounced, demonstrating higher search efficiency and improved mission reliability under limited search resources. Parameter sensitivity and scalability analyses further show that the proposed framework maintains favorable robustness and cooperative efficiency across different parameter settings and search scales. These results demonstrate that the proposed distributed search framework provides an effective and scalable solution for multi-UAV mountain search-and-rescue missions under limited communication and operational resources.

DronesVol. 10(7)
Sun Yat-sen University (CN)
Peace, Justice and strong institutions
Openalex Percentile: Top 3%
UAV Applications and Optimization
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