复杂的动态环境中的多无人机的自动避免碰撞:由事件触发的PPO方法与LSTM注意力集成
Chengqing Liang1, Lei Liu2, Jinde Cao3
1College of Artificial Intelligence and Automation, Hohai University, Changzhou,213200, China.
概括
本研究介绍了一种事件触发的近接政策优化 (ETPPO) 策略,用于避免碰撞,平衡性能和通信资源. 这种新的方法通过复合奖励机制和LSTM-Attention融合,提高了动态环境中的决策能力.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
- 控制系统 控制系统
背景情况:
- 避免集群碰撞需要在动态环境中平衡智能决策与资源效率.
- 深度强化学习 (DRL) 奖励功能对于避免碰撞往往缺乏定量基础.
研究的目的:
- 提出一个事件触发的近距离政策优化 (ETPPO) 策略,以避免集群碰撞.
- 改进在复杂的动态环境中避免碰撞的奖励功能的设计.
- 通过LSTM-Attention融合模块来提高算法稳定性和训练效率.
主要方法:
- 使用事件触发机制 (ETM) 进行资源性能平衡的内置间歇通信成本.
- 设计了一个复合的避免奖励机制,将动态模型成本和DRL奖励结合起来.
- 引入了一个LSTM-Attention (LA) 融合模块来处理历史和关键状态信息,创建了ETPPO-LA算法.
主要成果:
- ETPPO-LA算法证明了网络稳定性和培训效率的提高.
- 在Ros-Stage模拟平台上的验证显示,累积奖励的显著优势.
- 与现有方法相比,拟议的策略实现了较高的避免成功率.
结论:
- 开发的ETPPO-LA战略有效地解决了涌动碰撞避免的挑战.
- 复合奖励机制和LA融合模块增强了决策和资源管理.
- 该方法为复杂环境中的自主系统提供了强大而高效的解决方案.
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