技术领域technical field
本发明属于电力巡检技术领域，尤其涉及一种巡检飞行机器人与架空电力线路距离预测和保持方法。The invention belongs to the technical field of electric power inspection, and in particular relates to a method for predicting and maintaining the distance between an inspection flying robot and an overhead power line.
背景技术Background technique
飞行机器人在对偏远地区进行电力线路巡检时，会面临巡检效率与飞行机器人自身安全两大问题。巡检飞行机器人与架空电力线路距离过远会导致巡检消耗时间过长、精度偏低等问题，过近则会对飞行机器人自身安全构成严重威胁。When flying robots inspect power lines in remote areas, they will face two major problems: inspection efficiency and the safety of flying robots. If the distance between the inspection flying robot and the overhead power line is too far, it will lead to problems such as long inspection time and low accuracy, and if it is too close, it will pose a serious threat to the safety of the flying robot itself.
目前已有的距离预测与保持技术大致分为以下三个方面，均存在严重不足At present, the existing distance prediction and maintenance technology can be roughly divided into the following three aspects, all of which have serious deficiencies
1.人工预测与控制的方法。该方法的效果取决于驾驶员的技术和经验，存在很大风险。1. The method of manual prediction and control. The effect of this method depends on the skill and experience of the driver, and there is a great risk.
2.提前规划路径的方法。该方法必须预先制定，无法针对具体的实时环境做出灵活调整。2. The method of planning the route in advance. This method must be formulated in advance and cannot be flexibly adjusted for specific real-time environments.
3.基于机器视觉和全球定位系统GPS的定位方法。该方法无法高效精准地获取和利用当前电力线路状况与空间位置等信息，也没有充分考虑飞行机器人安全问题。3. Positioning method based on machine vision and global positioning system GPS. This method cannot efficiently and accurately acquire and utilize information such as the current power line status and spatial location, and does not fully consider the safety of flying robots.
发明内容Contents of the invention
针对上述现有技术存在的问题，本发明提出一种巡检飞行机器人与架空电力线路距离预测和保持方法，其特征在于，该方法包括距离预测方法和距离保持方法，所述距离预测方法为：In view of the problems existing in the above-mentioned prior art, the present invention proposes a distance prediction and maintenance method between the inspection flying robot and the overhead power line, which is characterized in that the method includes a distance prediction method and a distance maintenance method, and the distance prediction method is:
11)确定当前飞行机器人的地理位置坐标，将其映射到已有的三维走廊模型中，获取当前巡检范围内架空电力线路走势，计算三维走廊模型中的飞行机器人与架空电力线路的最小距离，并将其乘以比例因子转化为实际距离；11) Determine the geographic location coordinates of the current flying robot, map it to the existing three-dimensional corridor model, obtain the trend of the overhead power line within the current inspection range, and calculate the minimum distance between the flying robot and the overhead power line in the three-dimensional corridor model, And multiply it by the scale factor to convert it into the actual distance;
所述距离保持方法为：The distance keeping method is:
21)针对相关架空电力线路巡检标准，利用机载高清摄像头采集巡检目标图像，计算机视觉和模式识别方法和已有的常见故障图像进行离线训练，获得架空电力线路的故障诊断模型；21) Aiming at relevant overhead power line inspection standards, use the airborne high-definition camera to collect inspection target images, computer vision and pattern recognition methods and existing common fault images for offline training, and obtain a fault diagnosis model for overhead power lines;
22)将飞行机器人机身电位水平、电磁场场强、风速、湿度和步骤11)中获取的实际距离，作为安全评估模型的输入，分析当前飞行机器人的安全状况；22) Use the actual distance obtained in the flying robot fuselage potential level, electromagnetic field strength, wind speed, humidity and step 11) as the input of the safety assessment model to analyze the safety status of the current flying robot;
23)进行安全自适应动态路径规划。23) Perform security adaptive dynamic path planning.
所述安全自适应动态路径规划包括：The safety adaptive dynamic path planning includes:
(1)以三维走廊模型，飞行机器人在三维走廊模型中的位置坐标和预设的安全距离为基础，自动规划计算全局安全巡检飞行路径；(1) Based on the three-dimensional corridor model, the position coordinates of the flying robot in the three-dimensional corridor model and the preset safety distance, automatically plan and calculate the global safety inspection flight path;
(2)若安全评估模型报告不安全状态而飞行机器人仍处于全局安全巡检飞行路径时，加大安全距离的阈值，根据(1)中的方法重新规划全局安全巡检飞行路径；(2) If the safety assessment model reports an unsafe state and the flying robot is still in the global safety inspection flight path, increase the threshold of the safety distance, and re-plan the global safety inspection flight path according to the method in (1);
(3)若安全评估模型报告不安全状态，并且飞行机器人在全局安全巡检飞行路径以外，对飞行机器人进行局部安全巡检飞行路径规划，及时飞离危险区域，实现安全距离的保持。(3) If the safety assessment model reports an unsafe state, and the flying robot is outside the global safety inspection flight path, plan the local safety inspection flight path for the flying robot, and fly away from the dangerous area in time to maintain a safe distance.
发明的有益效果：该方法结合传感器数据和架空电力线路的三维走廊模型，精确计算飞行机器人相对于架空电力线路的距离，并在考虑各类安全因素基础上实时评估飞行机器人的安全性。与此同时，全面针对架空电力线路巡检标准，实时规划飞行巡检路径，实现飞行机器人对架空电力线路的距离保持。Beneficial effects of the invention: the method combines the sensor data and the three-dimensional corridor model of the overhead power line to accurately calculate the distance of the flying robot relative to the overhead power line, and evaluates the safety of the flying robot in real time on the basis of considering various safety factors. At the same time, according to the inspection standards of overhead power lines, the flight inspection path is planned in real time, and the distance between the flying robot and the overhead power lines is maintained.
附图说明Description of drawings
图1为巡检飞行机器人与架空电力线路距离预测和保持方法的流程图；Fig. 1 is the flowchart of the distance prediction and maintenance method between the inspection flying robot and the overhead power line;
图2为架空电力线路的三维走廊模型图；Fig. 2 is a three-dimensional corridor model diagram of an overhead power line;
图3为架空电力线路和飞行机器人的坐标投影图；Fig. 3 is the coordinate projection diagram of overhead power line and flying robot;
图4为全局安全巡检飞行路径规划图；Fig. 4 is the global security inspection flight path planning diagram;
图5为局部安全巡检飞行路径规划图；Fig. 5 is the flight path planning diagram of local security inspection;
图6为本发明方法具体实施所依赖的飞行机器人平台上的系统架构图。Fig. 6 is a system architecture diagram on the flying robot platform on which the method of the present invention is implemented.
具体实施方式Detailed ways
下面结合附图对本发明方法作进一步的说明。The method of the present invention will be further described below in conjunction with the accompanying drawings.
图1为本发明方法的流程图，该方法包括距离预测方法和距离保持方法，距离预测方法为：Fig. 1 is the flowchart of the method of the present invention, and this method comprises distance prediction method and distance keeping method, and distance prediction method is:
首先，确定当前飞行机器人的地理位置坐标：地理位置坐标通过机载全球定位系统(Global Positioning System，GPS)天线和地面GPS基站所提供的飞行机器人的地理空间坐标数据获取。First, determine the geographic location coordinates of the current flying robot: the geographic location coordinates are obtained from the geospatial coordinate data of the flying robot provided by the airborne Global Positioning System (GPS) antenna and the ground GPS base station.
然后，设定三维走廊模型的三维坐标系XYZ，将当前飞行机器人的地理位置坐标映射到已有的三维走廊模型中，获取飞行机器人在三维走廊模型中的坐标PF(x0，y0，z0)，三维走廊模型示意图如图2所示。Then, set the three-dimensional coordinate system XYZ of the three-dimensional corridor model, map the current geographic location coordinates of the flying robot to the existing three-dimensional corridor model, and obtain the coordinates PF(x0 , y0 , z0 ), the schematic diagram of the 3D corridor model is shown in Fig. 2.
最后，在三维走廊模型中，获取飞行机器人当前巡检范围内架空电力线路走势，将架空电力线路数据和飞行机器人的坐标PF(x0，y0，z0)垂直投影到水平地面XY上，如图3所示，记飞行机器人的二维投影坐标为PF'(x0，y0)，以PF'(x0，y0)为中心，构建一个矩形A＝{(x，y)|a＜x＜b，m＜y＜n}，其中，(x，y)为当前巡检范围内架空电力线路上的一点坐标；a，b，m，n都为常量值，根据巡检时架空电力线路所在的具体地理环境由人工设置；该矩形满足一个原则：必须与架空电力线路有交集，一般情况下|b-a|和|m-n|所代表的真实距离均大于10米。计算此范围内所有架空电力线路上的点到PF'(x0，y0)的距离值，求得最小距离值d，则飞行机器人距离架空电力线路的实际距离D为最小距离值d乘以比例因子X。Finally, in the three-dimensional corridor model, the trend of the overhead power lines within the current inspection range of the flying robot is obtained, and the data of the overhead power lines and the coordinates PF (x0 , y0 , z0 ) of the flying robot are vertically projected onto the horizontal ground XY, As shown in Figure 3, remember that the two-dimensional projected coordinates of the flying robot are PF'(x0 , y0 ), with PF'(x0 , y0 ) as the center, construct a rectangle A={(x,y)| a<x<b, m<y<n}, where (x, y) is a point coordinate on the overhead power line within the current inspection range; a, b, m, n are all constant values, according to the inspection The specific geographical environment where the power line is located is set manually; the rectangle meets a principle: it must intersect with the overhead power line. In general, the real distances represented by |ba| and |mn| are both greater than 10 meters. Calculate the distance value from all points on the overhead power line to PF'(x0 , y0 ) within this range, and obtain the minimum distance value d, then the actual distance D between the flying robot and the overhead power line is the minimum distance value d multiplied by the ratio Factor X.
所述距离保持方法为：The distance keeping method is:
21)针对相关架空电力线路巡检标准(杆塔安全性评估原则和标准、架线强度安全性评估标准和疲劳判别标准)，利用机载高清摄像头采集巡检目标图像，计算机视觉和模式识别方法和已有常见故障图像进行离线训练，获得架空电力线路的故障诊断模型。将巡检目标图像作为故障诊断模型的输入，对架空电力线路进行实时故障检测，智能确定巡检飞行距离保持方式；若架空电力线路出现故障迹象，则飞行机器人以悬停的检测方式保持距离；若无故障迹象，则飞行机器人继续向前飞行。21) Aiming at relevant overhead power line inspection standards (principles and standards for pole safety evaluation, safety evaluation standards for stringing strength, and fatigue discrimination standards), use airborne high-definition cameras to collect inspection target images, computer vision and pattern recognition methods and Common fault images have been used for offline training to obtain a fault diagnosis model for overhead power lines. Using the image of the inspection target as the input of the fault diagnosis model, real-time fault detection is performed on the overhead power line, and the method of maintaining the inspection flight distance is intelligently determined; if the overhead power line shows signs of failure, the flying robot maintains the distance by means of hovering detection; If there is no indication of failure, the flying robot continues to fly forward.
22)将飞行机器人机身电位水平E、电磁场场强M、风速W、湿度H和实际距离D，组成向量(E，M，W，H，D)作为安全评估模型的输入，由其分析当前飞行机器人的安全状况，输出结果为1(安全)与0(不安全)。22) Take the potential level E of the fuselage of the flying robot, the strength of the electromagnetic field M, the wind speed W, the humidity H, and the actual distance D, and form the vector (E, M, W, H, D) as the input of the safety assessment model, which analyzes the current The safety status of the flying robot, the output results are 1 (safe) and 0 (unsafe).
安全评估模型主要由深度学习构建出的深度神经网络组成，记为SDNN。首先，在实验室环境下，人工模拟自然环境下飞行机器人机身电位水平E、电磁场场强M、风速W、湿度H等数据。在区间[5m，15m]中每0.5米选择一个距离值组成距离样本SD。依据巡检技术规范和飞行机器人相关部件表现性能，评估在条件(E，M，W，H，SD)下飞行机器人的安全性，输出结果S，即“安全”(S＝1)和“不安全”(S＝0)。其次，将(E，M，W，H，SD，S)作为深度神经网络的训练数据，自底向上学习特征，并在网络顶层设置一个分类器，自顶向下调整参数，计算获取完整的安全评估模型SDNN。对于训练数据(E，M，W，H，SD，S)而言，该模型满足SDNN(E，M，W，H，SD)的输出结果与实际S之间的错误率最小。The security assessment model is mainly composed of a deep neural network constructed by deep learning, denoted as SDNN. First, in the laboratory environment, artificially simulate the data of the potential level E of the flying robot fuselage, the field strength M of the electromagnetic field, the wind speed W, and the humidity H in the natural environment. In the interval [5m, 15m], select a distance value every 0.5m to form the distance sample SD. According to the inspection technical specifications and the performance performance of the relevant parts of the flying robot, evaluate the safety of the flying robot under the conditions (E, M, W, H, SD), and output the result S, namely "safe" (S=1) and "no Security" (S=0). Secondly, use (E, M, W, H, SD, S) as the training data of the deep neural network, learn the features from the bottom up, and set a classifier on the top layer of the network, adjust the parameters from the top down, and calculate the complete Security assessment model SDNN. For the training data (E, M, W, H, SD, S), the model satisfies the minimum error rate between the output of SDNN (E, M, W, H, SD) and the actual S.
23)安全自适应动态路径规划，包括：23) Safety adaptive dynamic path planning, including:
(1)以三维走廊模型，飞行机器人在三维走廊模型中的位置坐标和预设的安全距离Pd为基础，自动规划全局安全巡检飞行路径，具体方法为：(1) Based on the three-dimensional corridor model, the position coordinates of the flying robot in the three-dimensional corridor model and the preset safety distance Pd, the global safety inspection flight path is automatically planned, and the specific method is as follows:
首先，在三维走廊模型中，对架空电力线路按一定比率采样，得到采样点PL(i)，将其投影到水平地面XY上，设为PL_XY(i)，计算每个采样点PL_XY(i)的切线斜率K(i)，以预设安全距离Pd为半径作圆，使得该圆与该斜率代表的直线相切，得到该二维圆的圆心坐标CC(i)，如图4所示。First, in the three-dimensional corridor model, the overhead power line is sampled at a certain rate to obtain the sampling point PL(i), which is projected onto the horizontal ground XY, set as PL_XY(i), and each sampling point PL_XY(i) is calculated The slope K(i) of the tangent line is drawn with the preset safety distance Pd as the radius, so that the circle is tangent to the straight line represented by the slope, and the center coordinate CC(i) of the two-dimensional circle is obtained, as shown in Figure 4.
其次，在三维走廊模型中，获得采样点PL(i)对应的高度坐标值PL_Z(i)，计算得到一个三维点V(i)＝(CC(i)，PL_Z(i)+L)，其中，L为常量，表示飞行机器人与架空电力线路的恒定高度差；Secondly, in the three-dimensional corridor model, the height coordinate value PL_Z(i) corresponding to the sampling point PL(i) is obtained, and a three-dimensional point V(i)=(CC(i), PL_Z(i)+L) is calculated, where , L is a constant, representing the constant height difference between the flying robot and the overhead power line;
最后，将所有的点V(i)通过插值运算连接起来，得到一条完整的曲线，将该曲线映射到真实地理坐标，并设置航点，从而完成全局路径的规划。Finally, connect all points V(i) through interpolation to obtain a complete curve, map the curve to real geographic coordinates, and set waypoints to complete the planning of the global path.
(2)若安全评估模型报告不安全状态而飞行机器人仍处于全局安全巡检飞行路径时，加大安全距离的阈值，根据(1)中的方法重新规划全局安全巡检飞行路径；(2) If the safety assessment model reports an unsafe state and the flying robot is still in the global safety inspection flight path, increase the threshold of the safety distance, and re-plan the global safety inspection flight path according to the method in (1);
(3)若安全评估模型报告不安全状态，并且飞行机器人在全局安全巡检飞行路径以外，将对飞行机器人进行局部安全巡检飞行路径规划，帮助飞行机器人及时飞离危险区域，实现安全距离的保持。(3) If the safety assessment model reports an unsafe state, and the flying robot is outside the global safety inspection flight path, it will plan the local safety inspection flight path for the flying robot to help the flying robot fly away from the dangerous area in time and achieve a safe distance. Keep.
如图5所示为局部安全巡检飞行路径规划图，具体为：飞行机器人先保持悬停状态，在三维走廊模型中，将全局安全巡检飞行路径数据和飞行机器人坐标数据垂直投影到水平地面XY上，记飞行机器人二维投影坐标为PF″，以PF″为中心，构建一个矩形B＝{(x′，y′)|a＜x′＜b，m＜y′＜n}，计算在此范围内的全局安全路径上的点到PF″的距离值，取距离值最小的那个点P。增设对应于点P坐标的新航点，自动导航飞行机器人回到全局安全路径上来。Figure 5 shows the planning diagram of the local safety inspection flight path, specifically: the flying robot remains in the hovering state first, and in the 3D corridor model, the global safety inspection flight path data and the coordinate data of the flying robot are vertically projected onto the horizontal ground On XY, record the two-dimensional projected coordinates of the flying robot as PF″, with PF″ as the center, construct a rectangle B={(x′, y′)|a<x′<b, m<y′<n}, calculate The distance value from the point on the global safe path within this range to PF " is the point P with the smallest distance value. Add a new waypoint corresponding to the coordinates of point P, and the automatic navigation flying robot gets back to the global safe path.
该方法的实施需要依赖如图6所示的飞行机器人平台上的系统，首先是数据采集分析平台1，其能够汇集机载传感器7(GPS导航仪、机载高清摄像头、电磁场检测器等)所采集的数据，并对其进行初步分类加工，为机载高性能计算平台2提供可靠数据来源。同时，它还存储了架空输电线路的三维走廊模型。The implementation of this method needs to rely on the system on the flying robot platform as shown in Figure 6, first of all, the data acquisition and analysis platform 1, which can gather all the data collected by the airborne sensors 7 (GPS navigator, airborne high-definition camera, electromagnetic field detector, etc.) The collected data will be preliminarily classified and processed to provide a reliable data source for the airborne high-performance computing platform 2. At the same time, it also stores the 3D corridor model of the overhead transmission line.
其次是机载高性能计算平台2，其配备了以下模块：This is followed by the Airborne High Performance Computing Platform 2, which is equipped with the following modules:
(1)故障检测模块3，其内置已训练的基于图像的故障诊断模型，故障检测模块3利用机载高清相机采集的检测目标的图像，实时判定架空电力线路是否存在故障，从而决定飞行机器人的距离保持方式。(1) Fault detection module 3, which has a built-in fault diagnosis model based on images that has been trained. Fault detection module 3 uses the image of the detection target collected by the airborne high-definition camera to determine in real time whether there is a fault in the overhead power line, thereby determining the fault of the flying robot. Keeping distance.
(2)实时距离预测模块4，其能结合数据采集分析平台1中的实时传感数据和三维走廊模型数据，计算出飞行机器人与架空电力线路之间的实际距离。(2) Real-time distance prediction module 4, which can combine the real-time sensing data and three-dimensional corridor model data in the data collection and analysis platform 1 to calculate the actual distance between the flying robot and the overhead power line.
(3)安全评估模块5，其内置安全评估模型，结合数据采集分析平台1中的实时传感数据，对当前飞行机器人所处安全状况进行评估，评估的结果供给路径规划模块7，用以实时调整路径规划结果。(3) Safety evaluation module 5, which has a built-in safety evaluation model, combines the real-time sensing data in the data acquisition and analysis platform 1, evaluates the safety status of the current flying robot, and the evaluation result is supplied to the path planning module 7 for real-time Adjust path planning results.
(4)路径规划模块7，其内置安全自适应动态路径规划方法，能利用数据采集分析平台1中的三维走廊模型数据，以及当前飞行机器人所处的位置安全状况，自适应动态规划飞行机器人的飞行路径。(4) path planning module 7, its built-in safety self-adaptive dynamic path planning method, can utilize the three-dimensional corridor model data in the data collection and analysis platform 1, and the position security situation that current flying robot is in, self-adaptive dynamic planning flying robot flight path.
以上所述，仅为本发明较佳的具体实施方式，但本发明的保护范围并不局限于此，任何熟悉本技术领域的技术人员在本发明揭露的技术范围内，可轻易想到的变化或替换，都应涵盖在本发明的保护范围之内。因此，本发明的保护范围应该以权利要求的保护范围为准。The above is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed in the present invention can easily think of changes or Replacement should be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention should be determined by the protection scope of the claims.
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410211164.9ACN103984355B (en) | 2014-05-19 | 2014-05-19 | Routing inspection flying robot and overhead power line distance prediction and maintaining method |
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410211164.9ACN103984355B (en) | 2014-05-19 | 2014-05-19 | Routing inspection flying robot and overhead power line distance prediction and maintaining method |
| Publication Number | Publication Date |
|---|---|
| CN103984355Atrue CN103984355A (en) | 2014-08-13 |
| CN103984355B CN103984355B (en) | 2017-01-11 |
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201410211164.9AExpired - Fee RelatedCN103984355B (en) | 2014-05-19 | 2014-05-19 | Routing inspection flying robot and overhead power line distance prediction and maintaining method |
| Country | Link |
|---|---|
| CN (1) | CN103984355B (en) |
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104865971A (en)* | 2015-05-26 | 2015-08-26 | 广西大学 | Power transmission line inspection unmanned plane control method and unmanned plane |
| CN106156850A (en)* | 2015-04-24 | 2016-11-23 | 江苏卓顿信息科技有限公司 | A kind of psychological consultant's robot system based on cloud computing |
| CN106441286A (en)* | 2016-06-27 | 2017-02-22 | 上海大学 | Unmanned aerial vehicle tunnel inspection system based on BIM technology |
| CN108073180A (en)* | 2016-11-08 | 2018-05-25 | 北京金风科创风电设备有限公司 | Control method, the device and system of unmanned plane |
| CN108415453A (en)* | 2018-01-24 | 2018-08-17 | 上海大学 | UAV tunnel inspection method based on BIM technology |
| CN108830965A (en)* | 2018-05-31 | 2018-11-16 | 中国舰船研究设计中心 | A kind of novel cabin inspection monitoring system |
| CN110362098A (en)* | 2018-03-26 | 2019-10-22 | 北京京东尚科信息技术有限公司 | Unmanned plane vision method of servo-controlling, device and unmanned plane |
| CN111044044A (en)* | 2019-12-06 | 2020-04-21 | 国网安徽省电力有限公司淮南供电公司 | A method and device for planning an inspection route of an electric unmanned aerial vehicle |
| CN111506116A (en)* | 2020-05-29 | 2020-08-07 | 中国南方电网有限责任公司超高压输电公司昆明局 | Unmanned aerial vehicle safety control method based on strong magnetic field interference of converter station |
| CN112230680A (en)* | 2020-10-29 | 2021-01-15 | 国网河南省电力公司济源供电公司 | A UAV power line inspection control method |
| CN115202390A (en)* | 2022-06-29 | 2022-10-18 | 国家电网有限公司 | A method and system for analyzing safety distance of unmanned aerial vehicle inspection |
| CN115511112A (en)* | 2022-08-23 | 2022-12-23 | 中国华能集团清洁能源技术研究院有限公司 | Operation and maintenance method and system for offshore wind farm equipment |
| CN117435998A (en)* | 2023-12-21 | 2024-01-23 | 天津博优智能科技有限公司 | Fault detection method of intelligent robot |
| CN119322941A (en)* | 2024-12-18 | 2025-01-17 | 国网江西省电力有限公司南昌供电分公司 | Small unmanned aerial vehicle inspection data management method |
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH0724751A (en)* | 1989-02-13 | 1995-01-27 | Toshiba Corp | Inspection work robot |
| CN1645284A (en)* | 2004-12-17 | 2005-07-27 | 华北电力大学（北京） | Power circuit scanning test robot airplane and controlling system |
| CN101477169A (en)* | 2009-01-16 | 2009-07-08 | 华北电力大学 | Electric power circuit detection method by polling flying robot |
| CN101807080A (en)* | 2010-03-16 | 2010-08-18 | 浙江大学 | Robot airship control system for overhead line inspection and control method thereof |
| CN102591355A (en)* | 2012-02-24 | 2012-07-18 | 山东电力研究院 | Method for detecting safe power-line-cruising distance of UAV (unmanned aerial vehicle) |
| US20120250010A1 (en)* | 2011-03-31 | 2012-10-04 | Richard Charles Hannay | Aerial Inspection System(s) and Method(s) |
| CN102780177A (en)* | 2012-07-19 | 2012-11-14 | 华北电力大学 | Overhead power line inspection data collection method based on flying robot |
| CN102880186A (en)* | 2012-08-03 | 2013-01-16 | 北京理工大学 | Flight path planning method based on sparse A* algorithm and genetic algorithm |
| CN102941920A (en)* | 2012-12-05 | 2013-02-27 | 南京理工大学 | High-tension transmission line inspection robot based on multi-rotor aircraft and method using robot |
| CN103135550A (en)* | 2013-01-31 | 2013-06-05 | 南京航空航天大学 | Multiple obstacle-avoidance control method of unmanned plane used for electric wire inspection |
| CN103235830A (en)* | 2013-05-13 | 2013-08-07 | 北京臻迪科技有限公司 | Unmanned aerial vehicle (UAV)-based electric power line patrol method and device and UAV |
| CN103472847A (en)* | 2013-08-30 | 2013-12-25 | 广东电网公司电力科学研究院 | Method and system for monitoring track of unmanned aerial vehicle power line inspection |
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH0724751A (en)* | 1989-02-13 | 1995-01-27 | Toshiba Corp | Inspection work robot |
| CN1645284A (en)* | 2004-12-17 | 2005-07-27 | 华北电力大学（北京） | Power circuit scanning test robot airplane and controlling system |
| CN101477169A (en)* | 2009-01-16 | 2009-07-08 | 华北电力大学 | Electric power circuit detection method by polling flying robot |
| CN101807080A (en)* | 2010-03-16 | 2010-08-18 | 浙江大学 | Robot airship control system for overhead line inspection and control method thereof |
| US20120250010A1 (en)* | 2011-03-31 | 2012-10-04 | Richard Charles Hannay | Aerial Inspection System(s) and Method(s) |
| CN102591355A (en)* | 2012-02-24 | 2012-07-18 | 山东电力研究院 | Method for detecting safe power-line-cruising distance of UAV (unmanned aerial vehicle) |
| CN102780177A (en)* | 2012-07-19 | 2012-11-14 | 华北电力大学 | Overhead power line inspection data collection method based on flying robot |
| CN102880186A (en)* | 2012-08-03 | 2013-01-16 | 北京理工大学 | Flight path planning method based on sparse A* algorithm and genetic algorithm |
| CN102941920A (en)* | 2012-12-05 | 2013-02-27 | 南京理工大学 | High-tension transmission line inspection robot based on multi-rotor aircraft and method using robot |
| CN103135550A (en)* | 2013-01-31 | 2013-06-05 | 南京航空航天大学 | Multiple obstacle-avoidance control method of unmanned plane used for electric wire inspection |
| CN103235830A (en)* | 2013-05-13 | 2013-08-07 | 北京臻迪科技有限公司 | Unmanned aerial vehicle (UAV)-based electric power line patrol method and device and UAV |
| CN103472847A (en)* | 2013-08-30 | 2013-12-25 | 广东电网公司电力科学研究院 | Method and system for monitoring track of unmanned aerial vehicle power line inspection |
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106156850A (en)* | 2015-04-24 | 2016-11-23 | 江苏卓顿信息科技有限公司 | A kind of psychological consultant's robot system based on cloud computing |
| CN104865971A (en)* | 2015-05-26 | 2015-08-26 | 广西大学 | Power transmission line inspection unmanned plane control method and unmanned plane |
| CN106441286A (en)* | 2016-06-27 | 2017-02-22 | 上海大学 | Unmanned aerial vehicle tunnel inspection system based on BIM technology |
| CN106441286B (en)* | 2016-06-27 | 2019-11-19 | 上海大学 | UAV tunnel inspection system based on BIM technology |
| CN108073180A (en)* | 2016-11-08 | 2018-05-25 | 北京金风科创风电设备有限公司 | Control method, the device and system of unmanned plane |
| CN108073180B (en)* | 2016-11-08 | 2020-07-28 | 北京金风科创风电设备有限公司 | Control method, device and system of unmanned aerial vehicle |
| CN108415453A (en)* | 2018-01-24 | 2018-08-17 | 上海大学 | UAV tunnel inspection method based on BIM technology |
| CN110362098B (en)* | 2018-03-26 | 2022-07-05 | 北京京东尚科信息技术有限公司 | Unmanned aerial vehicle visual servo control method and device and unmanned aerial vehicle |
| CN110362098A (en)* | 2018-03-26 | 2019-10-22 | 北京京东尚科信息技术有限公司 | Unmanned plane vision method of servo-controlling, device and unmanned plane |
| CN108830965A (en)* | 2018-05-31 | 2018-11-16 | 中国舰船研究设计中心 | A kind of novel cabin inspection monitoring system |
| CN111044044A (en)* | 2019-12-06 | 2020-04-21 | 国网安徽省电力有限公司淮南供电公司 | A method and device for planning an inspection route of an electric unmanned aerial vehicle |
| CN111044044B (en)* | 2019-12-06 | 2023-04-07 | 国网安徽省电力有限公司淮南供电公司 | Electric unmanned aerial vehicle routing inspection route planning method and device |
| CN111506116A (en)* | 2020-05-29 | 2020-08-07 | 中国南方电网有限责任公司超高压输电公司昆明局 | Unmanned aerial vehicle safety control method based on strong magnetic field interference of converter station |
| CN111506116B (en)* | 2020-05-29 | 2021-08-17 | 中国南方电网有限责任公司超高压输电公司昆明局 | Unmanned aerial vehicle safety control method based on strong magnetic field interference of converter station |
| CN112230680A (en)* | 2020-10-29 | 2021-01-15 | 国网河南省电力公司济源供电公司 | A UAV power line inspection control method |
| CN115202390A (en)* | 2022-06-29 | 2022-10-18 | 国家电网有限公司 | A method and system for analyzing safety distance of unmanned aerial vehicle inspection |
| CN115511112A (en)* | 2022-08-23 | 2022-12-23 | 中国华能集团清洁能源技术研究院有限公司 | Operation and maintenance method and system for offshore wind farm equipment |
| CN115511112B (en)* | 2022-08-23 | 2025-08-12 | 中国华能集团清洁能源技术研究院有限公司 | Operation and maintenance method and system for offshore wind farm equipment |
| CN117435998A (en)* | 2023-12-21 | 2024-01-23 | 天津博优智能科技有限公司 | Fault detection method of intelligent robot |
| CN117435998B (en)* | 2023-12-21 | 2024-03-08 | 天津博优智能科技有限公司 | A fault detection method for intelligent robots |
| CN119322941A (en)* | 2024-12-18 | 2025-01-17 | 国网江西省电力有限公司南昌供电分公司 | Small unmanned aerial vehicle inspection data management method |
| CN119322941B (en)* | 2024-12-18 | 2025-05-02 | 国网江西省电力有限公司南昌供电分公司 | Small unmanned aerial vehicle inspection data management method |
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| CN103984355B (en) | 2017-01-11 |
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| CN103984355B (en) | Routing inspection flying robot and overhead power line distance prediction and maintaining method | |
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| Park et al. | Automatic inspection drone with deep learning-based auto-tracking camera gimbal to detect defects in power lines | |
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| Jiang et al. | Robust visual positioning of the uav for the regional operation |
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| CF01 | Termination of patent right due to non-payment of annual fee | Granted publication date:20170111 Termination date:20170519 | |
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