在无人机辅助无线网络中使用主动推理进行目标定向的轨迹规划
Ali Krayani1,2, Khalid Khan1, Lucio Marcenaro1,2
1Department of Electrical, Electronic, Telecommunications Engineering and Naval Architecture, University of Genoa, 16145 Genoa, Italy.
Sensors (Basel, Switzerland)
|August 12, 2023
概括
无人驾驶飞行器 (UAV) 通过主动推断路径规划来增强无线连接. 这种方法优化了无人机路线的可靠通信,超过了传统的Q学习.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 无线通信无线通信
- 人工智能的人工智能
背景情况:
- 无人驾驶飞行器 (UAV) 提供灵活的空中基站部署,以增强地面网络.
- 有效的飞行轨迹规划对于最大限度地利用无人机辅助无线通信的好处至关重要.
- 目前的方法可能缺乏动态无人机通信场景所需的适应性和效率.
研究的目的:
- 引入一种新的,以目标为导向的无人机轨迹规划方法,使用主动推理.
- 加强无人机与地面用户之间的无线连接.
- 为无人机基站开发一个强大而高效的路径规划解决方案.
主要方法:
- 一个代表世界模式的全球字典是使用旅行销售员带利问题 (TSPWP) 的实例创建的.
- 世界模型使用"字母" (热点),"令牌" (局部路径) 和"单词" (轨迹) 来编码决策语法.
- 积极推断使无人机能够解释世界模型,并根据信念状态推断出最佳路线.
主要成果:
- 与传统的Q学习相比,拟议的积极推断方法显示出更高的性能.
- 该方法为无人机轨迹规划提供了快速,稳定和可靠的解决方案.
- 该方法在各种场景中表现出良好的概括能力.
结论:
- 积极推断为无人机辅助无线网络的智能轨迹规划提供了一个强大的框架.
- 发达世界模型为无人机提供了有效的路线优化和决策.
- 这种方法显著改善了无线连接和网络增强能力.
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