基于深度强化学习的联合轨迹设计,用于使用移动地面设备的无人机辅助网络
Yunfei Gao1,2, Mingliu Liu1,3, Xiaopeng Yuan1,2
1School of Electronic Information, Wuhan University, Wuhan, 430072, China.
Scientific reports
|September 30, 2024
概括
这项研究优化了使用多个无人机 (UAV) 的移动地面设备的数据收集. 一种新的多代理联合增强学习方法动态地管理无人机轨迹和设备调度,以实现高效运行.
科学领域:
- 无线通信无线通信
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
背景情况:
- 无人驾驶飞行器 (UAV) 提供了高效的无线数据收集,但面临着诸如轨迹规划,隐私和使用移动地面设备 (GD) 的复杂环境等挑战.
- 现有的方法与动态环境和来自禁飞区和避免碰撞的非凸式优化问题作斗争.
研究的目的:
- 在多个无人机辅助数据收集系统中,尽量减少整体操作时间成本.
- 共同优化无人机的3D轨迹和GD的通信安排.
- 为了应对禁飞区,避免碰撞和GDs的动态移动所带来的挑战.
主要方法:
- 这个问题被转化为马尔科夫决策过程.
- 为动态优化提出了一种多代理联合强化学习 (MAFRL) 方法.
- 使用多步传播技术和决斗网络架构来增强代理训练和稳定性.
主要成果:
- 基于MAFRL的方法有效地优化无人机轨迹和GD通信调度.
- 拟议的方法在动态情景中显示出更好的趋同率和整体稳定性.
- 实验结果验证了该方法在实际数据收集场景中的有效性.
结论:
- MAFRL方法为动态无人机辅助数据收集提供了有效的解决方案.
- 对无人机轨迹和GD调度的联合优化对于高效运行至关重要.
- 该研究强调了复杂的无线系统中先进的强化学习技术的潜力.
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