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In recent years, unmanned aerial vehicles (UAVs) have become a crucial component in the development of wireless sensor networks (WSNs) due to their significant advantages, such as high flexibility, low cost, and broad applicability. In certain scenarios, the target area can be extremely large and often divided into multiple sub-regions due to geographic features and other factors. In large-scale target areas divided into multiple sub-regions, existing multi-UAV data collection methods suffer from three core limitations: 1. high path redundancy due to suboptimal inter-UAV coordination; 2. excessive energy consumption from inefficient trajectory planning; 3. unstable connectivity caused by insufficient hierarchical collaboration mechanisms. To address this challenge, this paper proposes a trajectory path plan algorithm for collaborative UAV swarm data collection across multiple sub-regions in a large-scale target area. Specifically, we introduce the enhanced dueling double DQN - genetic particle swarm optimization (ED3QN-GPSO) algorithm, which is based on the hierarchical leader-follower formation strategy. This algorithm stratifies the UAV swarms into upper and lower layers, assigning distinct tasks to each layer for efficient deployment. Simulation results demonstrate that the proposed algorithm effectively reduces both overall path redundancy and energy consumption of the UAV swarm compared to other benchmark algorithms, while simultaneously maintaining high connectivity and coverage rate.

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UAVs’ Cooperative Trajectory Plan for Large-Scale Cluster Zone Data Collection

  • 会议论文

  • 发表日期：

    2026年

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  • Xueying Wang,
  • Furong Yang,
  • Lijuan Zhang,
  • Xiaoqin Song

摘要

In recent years, unmanned aerial vehicles (UAVs) have become a crucial component in the development of wireless sensor networks (WSNs) due to their significant advantages, such as high flexibility, low cost, and broad applicability. In certain scenarios, the target area can be extremely large and often divided into multiple sub-regions due to geographic features and other factors. In large-scale target areas divided into multiple sub-regions, existing multi-UAV data collection methods suffer from three core limitations: 1. high path redundancy due to suboptimal inter-UAV coordination; 2. excessive energy consumption from inefficient trajectory planning; 3. unstable connectivity caused by insufficient hierarchical collaboration mechanisms. To address this challenge, this paper proposes a trajectory path plan algorithm for collaborative UAV swarm data collection across multiple sub-regions in a large-scale target area. Specifically, we introduce the enhanced dueling double DQN - genetic particle swarm optimization (ED3QN-GPSO) algorithm, which is based on the hierarchical leader-follower formation strategy. This algorithm stratifies the UAV swarms into upper and lower layers, assigning distinct tasks to each layer for efficient deployment. Simulation results demonstrate that the proposed algorithm effectively reduces both overall path redundancy and energy consumption of the UAV swarm compared to other benchmark algorithms, while simultaneously maintaining high connectivity and coverage rate.