威胁感知的能耗高效动态无人机网络部署：一种多智能体强化学习方法
Threat-Aware Energy-Efficient Deployment for Dynamic UAV Networks: A Multi-Agent RL Approach
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中文总结 AI 辅助
针对威胁环境下多无人机空中基站部署，提出基于威胁感知聚类与多智能体MATD3的框架，实现零安全违规、高能量效率及低计算复杂度。
中文摘要 AI 辅助
在威胁密集环境中确保运行安全仍然是作为空中基站的多无人机网络面临的关键挑战。本文提出了一种高效框架，通过威胁感知聚类和基于奖励的安全强制机制，在促进安全运行的同时最大化全局能量效率（EE）。所提出的框架分三步执行。首先，威胁感知K均值（TAKM）算法确定所需的最少无人机数量并计算安全初始位置。其次，最优匹配阶段将物理无人机分配到这些质心以最小化能量消耗。第三，威胁感知多智能体双延迟深度确定性策略梯度（MATD3）算法动态优化轨迹、功率和用户关联。仿真结果表明，在考虑的场景中，所提出的框架实现了零观察到的安全违规，同时实现了优于其他学习方法和非聚类基线的能量效率和更快的收敛速度。与启发式优化相比，所提出的框架优于贪婪粒子群优化（GPSO），并达到与优化粒子群优化（OPSO）相当的性能，同时在线部署计算复杂度显著降低。此外，所提出的框架对未见过的用户分布、大型无人机编队和不同威胁几何形状展现出有效的泛化能力，同时保持零安全违规。
英文摘要
Ensuring operational safety in threat-prone environments remains a critical challenge for multi-UAV networks serving as aerial base stations. This paper proposes an efficient framework to maximize global energy efficiency (EE) while promoting safe operation through threat-aware clustering and reward-based safety enforcement. The proposed framework is executed in three steps. First, a threat-aware K-means (TAKM) algorithm determines the minimum required UAVs and computes safe initial placements. Second, an optimal matching stage assigns physical UAVs to these centroids to minimize energy expenditure. Third, a threat-aware multi-agent twin delayed deep deterministic policy gradient (MATD3) algorithm dynamically optimizes trajectories, power, and user associations. Simulation results show that the proposed framework achieves zero observed safety violations in the considered scenarios while achieving superior EE and faster convergence than other learning methods and non-clustering baselines. Compared to heuristic optimization, the proposed framework outperforms the greedy particle swarm optimization (GPSO) and achieves performance comparable to that of the optimized PSO (OPSO), while incurring significantly lower online deployment computational complexity. Furthermore, the proposed framework demonstrates effective generalization to unseen user distributions, large UAV fleets, and different threat geometries, while maintaining zero safety violations.
发表机构
- University of Ottawa（渥太华大学）
- Ibb University（伊卜大学）
- Royal Military College（皇家军事学院）
- The American University in Cairo（开罗美国大学）
机构由 AI 辅助整理，请以论文原文为准。
