使用MADRL对双电池FANET系统进行建模和模拟,以优化能源使用
A Sophia Mary1, T S Pradeep Kumar2
1School of Electronics Engineering, Vellore Institute of Technology, Chennai, 600127, India.
Scientific reports
|December 19, 2025
概括
这项研究优化了无人机 (UAV) 在飞行特设网络 (FANET) 中的电池寿命,使用多代理深度增强学习. 这种新的方法节约了对延长搜救任务的能量.
科学领域:
- 机器人技术和自主系统
- 网络工程 网络工程
- 人工智能的人工智能
背景情况:
- 无人机对于搜索和救援 (SAR) 等关键任务至关重要,但有限的电池寿命阻碍了运营寿命.
- 飞行特设网络 (FANET) 能够实现动态无人机通信,但能源消耗仍然是一个重大挑战.
- 优化能源效率和通信稳定性对于FANET中持续的无人机运行至关重要.
研究的目的:
- 在基于FANET的无人机操作中解决能源消耗和通信链路稳定性挑战.
- 为节能无人机行为建模提出一种新的战略.
- 在关键任务场景中延长无人机的作战时间.
主要方法:
- 基于飞行动态,有效载荷运行和无线通信 (80-85%的使用量) 的模拟无人机能耗.
- 实施了使用近接政策优化 (PPO) 代理的多代理深度强化学习 (MADRL) 框架.
- 利用由MADRL代理人管理的双电池系统 (B1用于飞行/有效载荷,B2用于通信/处理).
主要成果:
- 动态管理的MADRL代理电池B2用于UAV之间关于能源和环境条件的通信.
- 显著减少能源浪费和增强网络连接.
- 在模拟的SAR场景中,使无人机在较长时间内持续运行.
结论:
- 拟议的MADRL战略有效地优化了FANET中的能源消耗.
- 这种方法提高了无人机网络连接,并延长了运营寿命.
- 这项研究为大规模的自主部署中节能无人机系统提供了基础.
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