一种用于林业环境的无人机辅助轨迹定位的新方法
1Department of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.
Sensors (Basel, Switzerland)
|June 19, 2024
概括
这项研究引入了一种新的无人机辅助森林定位方法,克服了GPS的局限性. 该系统使用多代理深度增强学习优化无人机定位,以在具有挑战性的环境中准确追踪目标.
科学领域:
- 机器人技术和自主系统
- 地理空间智能是什么?
- 无线通信无线通信
背景情况:
- 全球定位系统 (GPS) 在密集的森林中不可靠.
- 现有的本地化方法需要固定基础设施,不适合动态林业.
- 环境不确定性会降低信号质量,降低定位准确度.
研究的目的:
- 为林业环境开发一种创新的轨迹定位方法.
- 解决复杂地形中传统定位系统的局限性.
- 使用无人机 (UAV) 提高定位准确性和可靠性.
主要方法:
- 使用多代理深度强化学习 (DRL) 来实时优化无人机拓.
- 使用从无人机到目标的接收信号强度 (RSS) 测量.
- 实施最小平方算法,以实现灵活可靠的位置估计.
- 整合共享重播内存以提高DRL系统的性能和效率.
主要成果:
- 拟议的无人机辅助方法实现了灵活和高精度的轨迹定位.
- 与现有系统相比,在高维异质数据上表现出卓越的稳定性.
- 通过模拟验证适用于具有挑战性的林业环境的适用性.
结论:
- 新型的多代理DRL方法为森林中的定位提供了强大而准确的解决方案.
- 无人机在战略部署时,在GPS被拒绝的地区显著提高了定位能力.
- 这种方法为需要精确空间意识的林业应用提供了可靠的替代方案.
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