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一个新的增强现实和强化学习授权的通信框架,用于水下无人驾驶自动驾驶汽车
Abdullah Lakhan1,2, Mazin Abed Mohammed3,4,5,6, Mohd Khanapi Abd Ghani7
1Department of Computer System Engineering, Dawood University of Engineering and Technology, Karachi, Pakistan.
Scientific reports
|January 26, 2026
概括
本研究介绍了无人水下车辆 (UUV) 的自适应式增强现实和强化学习调度策略 (AARLSS). AARLSS增强了通信,物体检测和安全性,显著提高了UUV性能.
科学领域:
- 机器人技术和自主系统
- 海洋技术 海洋技术
- 人工智能的人工智能
背景情况:
- 无人驾驶水下车辆 (UUV) 对于海洋勘探,监视和监控至关重要.
- 在UUV操作中的挑战包括通信延迟,有限的带宽,安全漏洞和对象识别方面的困难.
- 现有的UUV框架在关键的水下环境中难以实现动态通信.
研究的目的:
- 为UUVs提供一个新的沟通框架,利用增强现实和强化学习.
- 提高通信质量,对象检测准确性,并识别UUV应用程序中的系统漏洞.
- 优化UUV性能,最大限度地减少能源消耗和延迟,减少安全风险.
主要方法:
- 拟议的自适应增强现实和强化学习调度策略 (AARLSS) 以优化通信和对象识别.
- 利用深度Q学习 (DQN) 进行工作负载培训,并使用自适应入侵检测系统 (IDS) 进行安全验证.
- 开发了一个增强现实和虚拟现实测试台,用于实验评估.
主要成果:
- 通过AARLSS,UUV能耗降低了20-21%,通信延迟降低了18-20%.
- 该框架在实验设置中提高了对象检测准确度,达到97-98%.
- 在UUV运营效率和安全方面取得了显著的改进.
结论:
- 拟议的AARLSS框架有效地解决了UUV通信和运行的关键挑战.
- 增强现实和强化学习的整合为改善UUV能力提供了强大的解决方案.
- 该框架显示出在探索和监测方面推进自主海洋系统的前景.
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