强化学习用于端到端的无人机悬挂载荷导航和避难障碍的学习.
Mohammed Basheer Mohiuddin1, Igor Boiko2,3, Vu Phi Tran4
1Interdisciplinary Research Centre for Aviation and Space Exploration (IRC-ASE), King Fahd University of Petroleum and Minerals (KFUPM), 31261, Dhahran, Kingdom of Saudi Arabia. mohammed.mohiuddin@kfupm.edu.sa.
Scientific reports
|October 3, 2025
概括
本研究提出了一种统一的强化学习 (RL) 方法,用于控制带有悬挂载荷的无人机 (UAV),改善导航和避开障碍物. 新的 CompactRL-8 型号可以提高速度和安全性,而不需要预先训练.
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:
- 使用悬挂载荷的无人机 (UAV) 的传统控制涉及复杂的,独立的导航,路径规划和避难障碍系统.
- 现有的强化学习 (RL) 方法通常需要广泛的预训练和全状态观察,包括潜在的噪音负载摆动率.
研究的目的:
- 开发一个集成的,端到端的RL方法,用于无人机悬挂载荷控制,简化设计和计算.
- 研究一个缩小的观测空间RL模型 (CompactRL-8) 以提高效率和性能.
- 为了证明拟议的RL方法的实际适用性和Sim2Real传输能力.
主要方法:
- 开发了一个端到端的RL框架,以统一导航,路径规划和避难障碍的无人机与悬挂载荷.
- 设计了一个新的 CompactRL-8 模型,仅使用八个观测结果,不包括负载摆动率测量.
- 通过使用详细的系统模型验证了RL方法,实现了成功的Sim2Real传输,而无需重新调整.
主要成果:
- 与完全观察模型相比,CompactRL-8模型显示速度增加了58.79%,障碍物清除率提高了10倍.
- 拟议的RL方法优于最先进的自适应控制技术,显示路径效率提高了8%,负载摆动稳定性增加了四倍.
- 成功的Sim2Real转移证实了基于RL的控制策略的稳定性和实际适用性.
结论:
- 统一的RL方法提供了一个计算高效和有效的解决方案,用于控制带有斜载荷的无人机.
- 紧型RL-8型号提供了卓越的性能和稳定性,突出了减少观测空间的好处.
- 这项研究为更可靠,更高效的自主空中系统铺平了道路,用于城市货物运输等应用.
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