四旋翼无人机矿浆管道辅助巡线系统的研究
本文选题:四旋翼无人机 切入点:管道巡线系统 出处:《昆明理工大学》2017年硕士论文 论文类型:学位论文
【摘要】:长距离浆体管道输送技术,已经成功解决偏远山区矿产资源输送问题。浆体管道大部分铺设在山地、丘陵地带,极端天气一旦发生极易对管道造成危害,产生巨大的经济损失。管道巡线人员沿.山路对管线进行巡视,无法及时发现并确定事故管线地点,导致故障无法及时得到处理。因此,采取一种既能快速查找定位矿浆管道事故地点,又能节省人力物力的方式具有重要的意义。针对管道所处山地位置环境复杂,巡线人员不易到达现场,造成管道巡线维护成本较大问题,本文设计了一种四旋翼无人机的辅助巡线系统,用于矿浆管道运行状态的监测。巡线人员可以通过辅助巡线机器人,在无法到达管道位置的情况下,通过无人机上所携带的图像处理设备和图传设备自动地对管道进行跟踪巡视,观察矿浆管道运行状态。本文的主要工作内容和研究成果如下:(1)建立基于深度卷积神经网络(CNN)的管道分类模型。采集了实验室条件下的模拟管道数据作为数据集,对神经网络管道分类模型进行训练。通过在测试集上对模型进行验证,该管道识别分类模型可以识别复杂条件下的管道,结果显示其分类正确率基本满足要求。(2)设计了基于STM32F405单片机的飞行控制系统。首先对四旋翼飞行器进行数学建模,分析其模型特性,确定飞行器的控制策略。其次,对各个传感器,包括加速度计、陀螺仪和磁力计等进行拟合校正和数据滤波,计算得到较为准确的姿态信息。最后实现了基于串级PID算法的飞行控制器。(3)实现了管道分类模型软件、飞控系统软件和飞控系统硬件。使用Python脚本语言和TensorFlow深度学习框架实现了模型的软件部分。使用C语言完成了飞行控制系统的软件部分、传感器数据的处理与校准程序、姿态控制程序和位置控制程序,并最终在STM32单片机上运行。(4)将机载电脑和飞控系统搭载到四旋翼飞行器上,在实验室模拟管道下进行模拟矿浆管道巡线飞行,飞行器可按模拟管道路线自主飞行,达到了矿浆管道巡线要求,实现了对矿浆管道运行状态的监测。
[Abstract]:Long distance slurry pipeline transportation technology has successfully solved the problem of mineral resources transportation in remote mountainous areas. Most of the slurry pipelines are laid in mountainous and hilly areas. If extreme weather occurs, it is easy to cause harm to pipelines. Great economic losses are caused. Pipeline patrol personnel patrol the pipeline along the. Mountain road, unable to find and determine the location of the accident pipeline in time, resulting in failure can not be dealt with in time. Therefore, It is of great significance to adopt a way that can locate the accident site of the slurry pipeline quickly and save manpower and material resources. In view of the complex environment of the mountain location of the pipeline, it is difficult for the line patrol personnel to get to the site. In this paper, a kind of auxiliary line patrol system for four-rotor UAV is designed to monitor the running state of slurry pipeline. When the pipeline position cannot be reached, the pipeline is automatically tracked and patrolled through the image processing and transmission equipment carried on the UAV. The main contents and research results of this paper are as follows: 1) the classification model of pipelines based on deep convolution neural network (CNN) is established. The simulated pipeline data under laboratory conditions are collected as data sets. The neural network pipeline classification model is trained. By verifying the model on the test set, the pipeline identification and classification model can identify the pipeline under complex conditions. The results show that the classification accuracy basically meets the requirements. (2) the flight control system based on STM32F405 microcontroller is designed. Firstly, the mathematical model of the four-rotor aircraft is established, and its model characteristics are analyzed, and the control strategy of the aircraft is determined. Fitting and correcting various sensors, including accelerometers, gyroscopes and magnetometers, and data filtering, Finally, a flight controller based on cascade PID algorithm. The software of the flight control system and the hardware of the flight control system are realized by using Python script language and TensorFlow depth learning framework. The software part of the flight control system and the processing and calibration program of the sensor data are completed by using C language. Attitude control program and position control program, and finally run on the STM32 microcontroller. 4) the airborne computer and flight control system are mounted on the four-rotor aircraft, and simulated slurry pipeline inspection flight is carried out under the laboratory simulated pipeline. The aircraft can fly independently according to the simulated pipeline route, which can meet the requirements of slurry pipeline inspection, and realize the monitoring of slurry pipeline running state.
【学位授予单位】:昆明理工大学
【学位级别】:硕士
【学位授予年份】:2017
【分类号】:TD561
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