基于时间序列的管道地面透雷达校准角度预测算法研究
Maoxuan Xu1, Feng Yang1, Yuanjin Fang1
1School of Mechanical Electronic and Information Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China.
Sensors (Basel, Switzerland)
|January 23, 2024
概括
使用长短期记忆 (LSTM) 的深度学习模型有效地预测了管道机器人的引导轮角度和扭矩. 这提高了探测地下管道缺陷的地面透雷达准确性.
科学领域:
- 地质物理学 地质物理学
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
背景情况:
- 地下基础设施的安全依赖于管道内的缺陷的准确检测.
- 导航地下排水系统的管道机器人由于各种因素而经历姿势偏差.
- 精确的地面透雷达 (GPR) 天线的空间定位对于缺陷检测至关重要.
研究的目的:
- 开发一个智能控制系统来纠正管道机器人的姿势.
- 提出基于时间序列的算法,用于预测导轮校正角度和扭矩.
- 利用深度学习来提高GPR缺陷定位的准确性.
主要方法:
- 配备有指导轮的轮式管道机器人被用于姿势纠正.
- 使用长短期记忆 (LSTM) 深度学习模型来预测校正角度和扭矩.
- 将LSTM模型的性能与自回归集成移动平均线 (ARIMA) 模型进行了比较.
主要成果:
- 在LSTM模型中,对角度的平均绝对误差 (MAE) 降低了4.11°,对扭矩降低了8.25N·m.
- 预测角度和扭矩的平均平方误差 (MSE) 分别下降了10.66%和7.27%.
- 实验结果显示,平均校正速度为5秒,角误差为±1°.
结论:
- 基于LSTM的预测模型有效地以高准确度实时纠正管道机器人的姿势.
- 这种智能态度校正显著提高了GPR天线在定位管道缺陷方面的精度.
- 拟议的方法提供了一个强大的解决方案,通过先进的机器人GPR系统来改善地下空间安全.
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