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混合式巡线机器人环境感知方法研究
常文凯1,2
学位类型工学博士
导师梁自泽
2018-05-24
学位授予单位中国科学院研究生院
学位授予地点北京
关键词巡线机器人 多任务学习 绝缘子分割 融合感知 联合标定
摘要目前,输电线路的定期巡检依然以人工巡检为主,其劳动强度高、巡检效率低、安全风险大。而有人直升机可在一定程度上提高巡检质量,但运营费用昂贵。已有的悬挂式巡线机器人机构复杂、越障能力有限、上下线非常困难。虽然小型多旋翼无人机具有一定的灵活性,但续航时间短、辅助作用有限。
本文提出了一种飞行与滚动相结合的混合式巡线机器人,它可以方便地飞行上下线,在避雷线(架空线地线)上滚轮移动,遇到障碍物飞行跨越,具备灵活的越障能力和可观续航潜力,为环境恶劣地区的输电线路巡检提供了一种新的途径。论文针对混合式巡线机器人全线路、自动化、远距离的巡检需求,围绕其环境感知方法展开研究,主要内容如下:
第一,研究了混合式巡线机器人机构设计及其传感器联合标定的方法。针对混合式巡线机器人飞行越障和线上移动的需求,设计了相应的滚轮结构以及二维激光雷达与摄像头相结合的环境感知系统,使其具备在导线上降落和越障的功能。建立了雷达与摆动舵机之间的数据迟延模型、摆动雷达的机械参数模型和雷达与摄像头的相对位姿模型,并针对相互耦合的机械参数和迟延参数提出了一种基于单个或多个平面的联合标定方法。通过模拟和实验验证了所提方法的准确性以及稳定性。
第二,研究了基于神经网络识别线状物体位姿的方法。针对物体检测中常规的方框标注不能有效表示出线状物体方向这一问题,构建了一个多目标约束的端到端神经网络,并基于条件生成式对抗框架设计了相应的损失函数。网络包含的两个分支中,位置分支输出导线的位置和方向,显著图分支输出导线在图像中的显著区域。在此基础上,提出了一种基于Census变换的导线动态分割方法,使得导线区域的分割更加精细。解决了混合式巡线机器人趋近并降落在导线上的过程中,导线图像从近似直线过度为螺旋纹理区域的识别问题。
第三,研究了图像分割样本合成方法及其训练策略。针对架空线路中目标样本稀少的问题,提出了一种利用图像片段合成全部分割样本的方法。合成过程分别考虑了目标和背景的多样性,利用绝缘子或导线在图像中纹理的重复性,把不同样本片段拼接和增强后与背景图像按比例融合为训练样本,并提出了利用目标物体的透明度调控学习难度和分割效果的训练策略。解决了有监督图像分割训练样本稀少、合成样本资源消耗大的问题。该合成方法和训练策略在多种分割网络中均达到了良好训练效果,并在实验中得到验证。
第四,研究了基于感知信息融合的混合式巡线机器人线上降落和越障的方法。针对不同距离下实时检测导线位姿的需求,提出了一种融合深度和图像纹理信息的神经网络降落点检测方法,并在子图像分割过程中结合了深度信息,解决了杆状物体对导线检测的干扰问题并缩短了整体降落时间。针对线上障碍物和关键目标(绝缘子)的识别,构建了一种紧凑的三维卷积神经网络,并提出了合成三维点云样本和抖动模糊图像样本的方法,解决了识别形态多样的线上障碍物和视频中关键目标的问题,在降落和越障实验中获得了良好效果。同时,实验也分析了机器人不同状态下的功耗,验证了其长续航的潜力。
最后,对本文的工作进行了总结,并指出了需要进一步开展的工作。
其他摘要At present, regular inspections of power transmission lines are still based on manual inspections, which have high labor intensity, low inspection efficiency, and high safety risks. Some helicopters can improve the inspection quality, but the operating costs are expensive. Existing hang-up inspection robots have complex mechanisms, limited obstacle avoidance capabilities, and difficulty in getting on and off the line. Although the small multi-rotor UAV is flexible, it has a short flying duration and limited assistance.
This paper proposes a hybrid inspections robot with a combination of flying and rolling. It can fly on and off the wire easily, moves on the lightning protection wire (overhead ground wire) and fly across the obstacles with potential long duration, which provides a new way for the inspection of transmission lines in harsh areas. In this paper, the full line, automation, and long-distance for hybrid inspections robot are studied around their environmental perception methods. The main contents are as follows:
Firstly, the design of the hybrid inspections robot and the joint calibration method of the sensors are studied. For the obstacle avoidance and online movement requirements of the hybrid inspections robots, a corresponding roller structure and a two-dimensional environment perception system combining the laser ranger finder (LRF) and the camera are designed to enable them to land and cross obstacles on the wire. The data delay model between LRF and swing servo motor, the mechanical parameter model of the swing LRF, and the relative pose model between the LRF and the camera are established. The coupled mechanical parameters and delay parameters are calibrated by the joint calibration method with a single or multiple plane. The simulation and experiment verify the accuracy and stability of the proposed method.
Secondly, the method of identifying the pose of linear objects based on neural network is studied. Aiming at the problem that the conventional box annotation in object detection cannot effectively represent the direction of the linear object, a multi-objective end-to-end neural network is constructed, and a corresponding loss function is designed based on the conditional generative adversarial nets (cGAN). In the two branches contained in the network, the location branch outputs the position and direction of the wire, and the saliency map branch outputs the saliency area of the wire in the image. Based on this, a dynamic segmentation method for conductors based on Census transformation is proposed, which makes the segmentation of conductor area more precise. It solves the problem of identifying the wire that from approximate straight line to spiral texture region in the process of the hybrid inspections robot approaching and landing on the wire.
Thirdly, the synthesis method of image segmentation samples and its training strategy are studied. Aiming at the scarce training samples in overhead lines, a method of synthesizing segmented samples using image segments is proposed. The synthesis process takes into account the diversity of targets and backgrounds respectively. Using the repeatability of insulators or wires in the image, the different sample segments are spliced and enhanced and then blended with the background image in proportion to the training sample, and the training strategies to control learning difficulty and segmentation effects using the transparency of the target object is proposed. It solves the problem of the scarcity of supervised image segmentation training samples and reduces the consumption of synthetic sample. The synthesis method and training strategy have achieved good training effect in various segmentation networks and have been verified in experiments.
Fourthly, the landing and obstacle crossing of hybrid inspections robot based on perceptual information fusion is studied. Aiming at the requirement of real-time detection of the wire pose at different distances, a neural network landing point detection method is proposed, which combines depth and image texture information. The depth information is also combined in the process of sub-image segmentation. The fusion method reduces the interference of rod-shaped objects and shorten the overall landing time. A compact three-dimensional convolutional neural network is constructed for the identification of online obstacles and key targets (insulators), and the methods for synthesizing three-dimensional point cloud samples and jitter blurred image samples are proposed to recognize various of online objects and the key targets in videos, which has yielded good results in landing and crossing obstacle experiments. At the same time, the experiment also analyzed the power consumption of the robot under different conditions, and verified its long-term potential.
Finally, it summarizes the work of this paper and points out the work that needs further development.
文献类型学位论文
条目标识符http://ir.ia.ac.cn/handle/173211/20972
专题毕业生_博士学位论文
作者单位1.中国科学院自动化研究所
2.中国科学院大学
推荐引用方式
GB/T 7714
常文凯. 混合式巡线机器人环境感知方法研究[D]. 北京. 中国科学院研究生院,2018.
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