道事故的端到端应急响应协议增加了强化学习
Hafiz Muhammad Raza Ur Rehman1, M Junaid Gul1, Rabbiya Younas1
1Department of Information and Communication Engineering, Yeungnam University, Gyeongsan, 38541, Republic of Korea.
Scientific reports
|January 26, 2026
概括
自主无人机 (UAV) 提高了道事故应对能力. 多代理增强学习与灰狼优化能够在复杂的环境中实现更快,更有效的紧急救援.
科学领域:
- 机器人和人工智能 机器人和人工智能
- 灾害管理和应急响应
- 自主导航系统 自主导航系统
背景情况:
- 自主无人飞行器 (UAV) 为危险环境提供了具有成本效益的解决方案.
- 无人机在动态道基础设施中的实时路径规划是具有挑战性的,因为可见度和导航信号有限.
- 现有的导航方法在道内的不可预测事故场景中经常失败.
研究的目的:
- 为道事故提出一个端到端的应急响应框架,使用多剂强化学习 (MARL).
- 增强无人机在封闭,动态道环境中的自主导航和决策.
- 提高道基础设施紧急响应行动的速度和效率.
主要方法:
- 实施一个端到端的应急响应框架,利用多代理强化学习 (MARL).
- 每个无人机都作为一个独立的代理运作,使用独立的Q学习来实时决策.
- 集成灰狼优化 (GWO) 以减轻勘探中的过早收和局部优化.
- 开发一个定制的奖励功能,优先考虑受害者发现,并惩罚不安全/冗余的行为.
主要成果:
- 拟议的MARL框架显示,与基于GWO的探索和随机搜索相比,目标的发现速度更快.
- 在模拟道环境中实现了更好的地图覆盖范围和减少了整体救援时间.
- 验证了基于轻量级MARL的自动无人机在紧急响应中的协调效果.
结论:
- 开发的MARL框架为道事故中无人机辅助的自主应急响应提供了有效的解决方案.
- 集成GWO和定制奖励功能可以增强探索和决策能力.
- 这种方法显示出在关键道救援场景中提高局势意识和运营效率的巨大潜力.
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