通过PPO-LSTM增强地磁导航:利用观测到的地磁场数据进行强大的导航.
Xiaohui Zhang1, Wenqi Bai1, Jun Liu1
1School of Automation and Information Engineering, Xi'an University of Technology, Xi'an 710048, China.
Sensors (Basel, Switzerland)
|June 27, 2025
概括
自动驾驶汽车可以使用地球的地磁场来导航GPS被拒绝的区域. 深度强化学习方法,近距离策略优化与长期短期记忆 (PPO-LSTM),提高了导航准确性和轨迹流性.
科学领域:
- 机器人技术和自主系统
- 地质物理学 地质物理学
- 人工智能的人工智能
背景情况:
- 无GPS环境中的地理空间导航对自动驾驶汽车来说是一个挑战.
- 地球的地磁场提供了一个潜在的替代导航资源.
- 不完整的传感器数据需要先进的导航策略.
研究的目的:
- 为无GPS环境中的自动驾驶汽车开发强大的导航系统.
- 为了利用地球的地磁场来实现可靠的地理空间导航.
- 解决地磁导航中部分可观测性和不确定性的挑战.
主要方法:
- 制定导航问题作为一个部分观察的马尔科夫决策过程 (POMDP).
- 采用近距离政策优化与长期短期记忆 (PPO-LSTM),一个深度强化学习框架.
- 在噪音条件下使用国际地磁参考场 (IGRF) 模型中的真实地磁数据验证方法.
主要成果:
- 与基线算法相比,PPO-LSTM表现出优越的性能.
- 拟议的方法实现了更顺的车辆轨迹.
- 观察到更高的航向准确度,表明导航精度有所提高.
结论:
- PPO-LSTM框架有效地处理地磁导航中的不确定性和部分可观测性.
- 这种方法使得强有力的政策能够适应复杂的地磁梯度.
- 它为在充满挑战的环境中进行地理空间导航提供了可行的和强大的解决方案.
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