节能多代理深度强化学习算法用于无人机路由问题
Xiulan Shu1, Anping Lin2, Xupeng Wen3
1School of Intelligent Manufacturing Engineering, Zhanjiang University of Science and Technology, Zhanjiang 524000, China.
Sensors (Basel, Switzerland)
|October 26, 2024
概括
一个新的多代理深度强化学习算法,EMADRL,优化无人机路线的能源效率. 这种方法在最后一英里无人机配送中显著降低了能源消耗,优于传统方法.
科学领域:
- 机器人和自动化 机器人和自动化
- 运营研究 运营研究
- 人工智能的人工智能
背景情况:
- 无人机技术正在快速发展,增加了对高效分发策略的需求.
- 能源消耗是评估无人机配送效率的关键因素.
- 传统的路由算法在找到最佳,节能无人机路线方面存在局限性.
研究的目的:
- 解决无人机分发中传统路由算法的局限性.
- 提出一种新的算法,以优化无人机路由中的能源消耗.
- 为了提高最后一英里无人机配送的能源效率.
主要方法:
- 在多代理强化学习框架内制定了无人机路由问题.
- 开发了能源意识的多代理深度强化学习 (EMADRL) 算法.
- 集成了一个无人机能耗模型,并使用了战略梯度算法与注意力机制.
主要成果:
- 埃马德尔一直致力于快速实现高质量的解决方案.
- 与当代算法相比,证明了更高的能源效率.
- 实现了平均节能5.96%和最大节能12.45%.
结论:
- 埃马德尔为优化无人机配送的能源消耗提供了一个有前途的解决方案.
- 该算法有效地解决了多仓库车辆路由问题的复杂性.
- 这种方法推进了对最后一英里物流的节能战略.
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