CN114997195B - A component inventory positioning method based on inspection robot - Google Patents
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Abstract
Description
技术领域Technical Field
本发明涉及建筑工业领域，具体涉及一种基于巡检机器人的构件盘点定位方法。The invention relates to the field of construction industry, and in particular to a component inventory positioning method based on an inspection robot.
背景技术Background Art
为了响应国家绿色建筑的号召，装配式建筑产业化应运而生。装配式建筑主要包括预制装配式混凝土结构、钢结构、现代木结构建筑等，因为采用标准化设计、工厂化生产、装配化施工、信息化管理、智能化应用，是现代工业化生产方式的代表。In response to the national green building call, the industrialization of prefabricated buildings came into being. Prefabricated buildings mainly include prefabricated concrete structures, steel structures, modern wooden structures, etc., which are representatives of modern industrialized production methods because they adopt standardized design, factory production, assembly construction, information management, and intelligent applications.
在整个装配式建筑工厂中，堆场作为生产线与装配现场中间的缓冲池，其地位异常重要。而在巨大的堆场中，如何确定构件的具体位置，以及如何进行全体构件的盘点确认，这无疑是目前整个装配式建筑行业痛点。In the entire prefabricated building factory, the yard is an extremely important buffer pool between the production line and the assembly site. In the huge yard, how to determine the specific location of the components and how to check and confirm all the components are undoubtedly the pain points of the entire prefabricated building industry.
现阶段国内装配式建筑处于刚起步，堆场缺乏高效的管理方式，目前仍停留在纯人工管理层面，无法实现预制构件数据获取的自动化，构件数据孤岛问题严重，堆场管理效率低下。大量企业仍然在采用人工管理的方式盘点、定位堆场中的构件；堆场构件人工盘点与定位效率低、难度大。At present, the prefabricated construction in China is just starting, and the yard lacks efficient management methods. It is still at the level of pure manual management, unable to realize the automation of prefabricated component data acquisition, and the problem of component data islands is serious, and the yard management efficiency is low. A large number of companies still use manual management to count and locate components in the yard; manual inventory and positioning of yard components is inefficient and difficult.
现有基于RFID射频技术应用与预制构件堆场盘点的研究，单纯使用RFID进行构件盘点可行，但由于RFID精度较差，易受干扰，所以不适用于堆场环境中构件的精确定位。而计算机视觉技术也有在堆场中应用，但由于同一批次构件外观相同，计算机视觉或深度学习技术无法辨别相同类型构件，故也不适用于堆场中构件的定位。Existing research based on the application of RFID radio frequency technology and inventory of prefabricated component yards shows that it is feasible to use RFID alone for component inventory, but due to its poor accuracy and susceptibility to interference, it is not suitable for accurate positioning of components in the yard environment. Computer vision technology is also used in the yard, but since the components of the same batch have the same appearance, computer vision or deep learning technology cannot distinguish the same type of components, so it is not suitable for positioning components in the yard.
综上，对于预制构件堆场中构件的盘点与定位问题，还需要开发新的检测方法，不仅要实现构件的无感化自动化盘点，还要实现构件的精确定位，以打通整个堆场数据孤岛，降低堆场管理成本，提高盘点效率。In summary, for the inventory and positioning of components in the prefabricated component yard, new detection methods need to be developed, not only to achieve non-sensitive and automated inventory of components, but also to achieve precise positioning of components, so as to break through the data island of the entire yard, reduce yard management costs, and improve inventory efficiency.
发明内容Summary of the invention
基于此，有必要针对现有技术中预制构件堆场中构件盘点与定位问题，提供一种基于巡检机器人的装配式预制构件堆场中构件盘点定位方法。Based on this, it is necessary to provide a component inventory and positioning method in an assembled prefabricated component yard based on an inspection robot to address the problem of component inventory and positioning in a prefabricated component yard in the prior art.
为解决上述技术问题，本发明采用如下技术方案：In order to solve the above technical problems, the present invention adopts the following technical solutions:
一种基于巡检机器人的构件盘点定位方法，用于对预制构件堆场中各货架上的构件进行盘点和定位，构件具有RFID标签，巡检机器人具有相机、LF频率触发激励器、RFID阅读器；构件盘点定位方法包括以下步骤：A component inventory positioning method based on an inspection robot is used to inventory and position components on each shelf in a prefabricated component yard. The components have RFID tags, and the inspection robot has a camera, an LF frequency trigger exciter, and an RFID reader. The component inventory positioning method includes the following steps:
步骤一：巡检机器人使用预先标定的相机，通过货架上的ARTAG标识码获取巡检机器人在ARTAG标识码坐标系下的定位坐标数据；定位坐标数据包括平移向量和旋转矩阵 Step 1: The inspection robot uses a pre-calibrated camera to obtain the positioning coordinate data of the inspection robot in the ARTAG identification code coordinate system through the ARTAG identification code on the shelf; the positioning coordinate data includes the translation vector and the rotation matrix
步骤二：根据ARTAG标识码预先标定的位置以及定位坐标数据，计算得到巡检机器人的位置；Step 2: Calculate the position of the inspection robot based on the pre-calibrated position of the ARTAG identification code and the positioning coordinate data;
步骤三：巡检机器人靠近货架时，货架上构件的RFID标签被巡检机器人的LF频率触发激励器唤醒，构件向巡检机器人的RFID阅读器发送自身ID和LF频率触发激励器的ID，得到构件盘点数据；Step 3: When the inspection robot approaches the shelf, the RFID tag of the component on the shelf is awakened by the LF frequency trigger exciter of the inspection robot, and the component sends its own ID and the ID of the LF frequency trigger exciter to the RFID reader of the inspection robot to obtain the component inventory data;
步骤四：根据步骤二中巡检机器人的位置反推货架的位置，将货架位置与构件盘点数据结合，得到构件定位数据。Step 4: Infer the position of the shelf based on the position of the inspection robot in step 2, and combine the shelf position with the component inventory data to obtain the component positioning data.
具体地，将构件盘点数据、构件定位数据与云端数据库对应的数据合并，并将最新数据与云端数据库中原有数据比对，如不一致自动上报堆场管理员，进行进一步核查；若云端数据库中不存在此构件的数据，则进行更新录入；若云端数据库中已有此构件的数据，且与最新数据一致，则不做修改。Specifically, the component inventory data, component positioning data and corresponding data in the cloud database are merged, and the latest data is compared with the original data in the cloud database. If there is any inconsistency, it is automatically reported to the yard manager for further verification; if the data of this component does not exist in the cloud database, it is updated and entered; if the data of this component already exists in the cloud database and is consistent with the latest data, no modification is made.
具体地，步骤一中，使用预先标定的相机并通过货架上的ARTAG标识码获取巡检机器人在ARTAG标识码坐标系下的定位坐标数据时，首先获取包含ARTAG标识码的视频图像，将图片缩小至500像素x500像素，然后将每一帧视频图像进行对比度和亮度调整、滤波降噪、反二值化，其中对比度因子设为1.8和亮度因子设为-30，再通过腐蚀膨胀处理视频图像上的污点，对ARTAG标识码进行边缘检测，找出需要的四边形图案并进行筛选，最后进行编码、匹配、解码、检查，从而得到巡检机器人在ARTAG标识码坐标系下的定位坐标数据。Specifically, in step one, when using a pre-calibrated camera and obtaining the positioning coordinate data of the inspection robot in the ARTAG identification code coordinate system through the ARTAG identification code on the shelf, first obtain a video image containing the ARTAG identification code, reduce the image to 500 pixels x 500 pixels, and then adjust the contrast and brightness of each frame of the video image, filter and reduce noise, and debinarize, where the contrast factor is set to 1.8 and the brightness factor is set to -30, and then process the stains on the video image through corrosion and expansion, perform edge detection on the ARTAG identification code, find the required quadrilateral pattern and screen it, and finally encode, match, decode, and check to obtain the positioning coordinate data of the inspection robot in the ARTAG identification code coordinate system.
具体地，巡检机器人的相机为单目广角摄像头，相机的安装位置位于巡检机器人的顶端侧向。Specifically, the camera of the inspection robot is a monocular wide-angle camera, and the camera is installed on the side of the top of the inspection robot.
具体地，LF频率触发激励器的频率为125KHz，LF频率触发激励器安装在巡检机器人靠近货架的一侧。Specifically, the frequency of the LF frequency trigger exciter is 125KHz, and the LF frequency trigger exciter is installed on a side of the inspection robot close to the shelf.
具体地，步骤三中，构件的RFID标签巡检机器人的LF频率触发激励器唤醒，并发送自身ID和LF频率触发激励器的ID的具体方法如下：Specifically, in step 3, the LF frequency trigger exciter of the component RFID tag inspection robot wakes up and sends its own ID and the ID of the LF frequency trigger exciter. The specific method is as follows:
巡检机器人上的LF(125KHz)激励触发器信号覆盖范围为2米；The LF (125KHz) excitation trigger signal on the inspection robot has a coverage range of 2 meters;
任意一个构件在被LF(125KHz)激励触发器信号覆盖之前，RFID信号只有自身ID，当一个构件进入到LF(125KHz)激励触发器信号覆盖范围时，构件中的RFID芯片会被LF(125KHz)激励触发器激活，并向外广播自身ID和LF(125KHz)激励触发器ID；Before any component is covered by the LF (125KHz) excitation trigger signal, the RFID signal only has its own ID. When a component enters the coverage range of the LF (125KHz) excitation trigger signal, the RFID chip in the component will be activated by the LF (125KHz) excitation trigger and broadcast its own ID and LF (125KHz) excitation trigger ID.
巡检机器人上的RFID阅读器接收到RFID自身ID和LF(125KHz)激励触发器ID组成的包，进行解码后即可判断该构件在巡检机器人附近。The RFID reader on the inspection robot receives a packet consisting of the RFID's own ID and the LF (125KHz) excitation trigger ID, and after decoding, it can be determined that the component is near the inspection robot.
与现有技术相比，本发明的有益技术效果是：Compared with the prior art, the beneficial technical effects of the present invention are:
1.本发明中在预制构件适当位置，安装了RFID芯片，在巡检机器人适当位置安装视觉设备与RFID设备，将射频技术与机器视觉相结合，进而可以及时准确的获取各个预制构件的数据与准确位置，并能有针对性地获取各个预制构件的类型信息；这极大提高堆场中构件盘点与定位的效率和准确率。由于本发明提供的构件盘点定位方法对图像处理及射频信号处理的硬件的性能要求较低，系统的实时性好，因而能够实现在边缘端对预制构件进行盘点与定位，能够实现自动化盘点与定位。1. In the present invention, RFID chips are installed at appropriate positions of prefabricated components, and visual equipment and RFID equipment are installed at appropriate positions of inspection robots, combining radio frequency technology with machine vision, thereby being able to timely and accurately obtain the data and accurate position of each prefabricated component, and being able to obtain the type information of each prefabricated component in a targeted manner; this greatly improves the efficiency and accuracy of component inventory and positioning in the yard. Since the component inventory and positioning method provided by the present invention has low performance requirements for image processing and radio frequency signal processing hardware, and the system has good real-time performance, it is possible to realize inventory and positioning of prefabricated components at the edge, and can realize automated inventory and positioning.
2.本发明还可以在检测构件位置信息错误时，及时向管理员发出警报，待管理员核查后更新构件正确位置信息，从而避免构件发货或转运时找不到构件的问题。2. The present invention can also promptly send an alarm to the administrator when detecting component position information errors, and the administrator will update the correct component position information after verification, thereby avoiding the problem of not being able to find the component when the component is shipped or transported.
附图说明BRIEF DESCRIPTION OF THE DRAWINGS
图1为本发明构件盘点定位方法的流程图。FIG. 1 is a flow chart of a component inventory positioning method according to the present invention.
具体实施方式DETAILED DESCRIPTION
下面将结合本发明实施例中的附图，对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本发明一部分实施例，而不是全部的实施例。基于本发明中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都属于本发明保护的范围。The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.
除非另有定义，本文所使用的所有的技术和科学术语与属于本发明的技术领域的技术人员通常理解的含义相同。在本发明的说明书中所使用的术语只是为了描述具体的实施例，不是旨在限制本发明。Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in the specification of the present invention are only for describing specific embodiments and are not intended to limit the present invention.
实施例1Example 1
本实施例的操作环境为搭载WINDOWS10系统的PC与搭载Ubuntu系统的树莓派，使用PC处理RFID数据，用树莓派识别ARTAG标识码获取定位坐标数据。搭建RFID数据处理的程序设计语言为C#，在树莓派中识别ARTAG标识码的程序设计语言为python。The operating environment of this embodiment is a PC equipped with WINDOWS10 system and a Raspberry Pi equipped with Ubuntu system. The PC is used to process RFID data, and the Raspberry Pi is used to identify the ARTAG identification code to obtain the positioning coordinate data. The programming language for building RFID data processing is C#, and the programming language for identifying the ARTAG identification code in the Raspberry Pi is Python.
如图1所示，本发明公开的一种基于巡检机器人的构件盘点定位方法，包括以下步骤：As shown in FIG1 , the present invention discloses a component inventory positioning method based on an inspection robot, comprising the following steps:
S1：巡检机器人使用单目摄像头获取货架上的ARTAG标识码，得到巡检机器人和相机在ARTAG标识码坐标系下的定位坐标数据；巡检机器人为堆场巡检配套硬件设备，其单目摄像头已经提前标定，具备距离与角度的计算条件。单目摄像头扫描货架上的ARTAG标识码(即AprilTag码)并得到定位坐标数据的步骤为：首先获取视频图像，然后将每一帧视频图像进行对比度和亮度调整、滤波降噪，通过反二值化来强化边界、增强分离度，再进行腐蚀膨胀，处理图像上的小污点，再进行AprilTag码边缘检测，然后找出需要的四边形图案并进行筛选，最后进行编码、匹配、解码、检查，从而获取到巡检机器人和相机在ARTAG标识码坐标系下的定位坐标数据。S1: The inspection robot uses a monocular camera to obtain the ARTAG identification code on the shelf and obtains the positioning coordinate data of the inspection robot and the camera in the ARTAG identification code coordinate system; the inspection robot is a hardware device for yard inspection, and its monocular camera has been calibrated in advance and has the conditions for calculating distance and angle. The steps for the monocular camera to scan the ARTAG identification code (i.e., AprilTag code) on the shelf and obtain the positioning coordinate data are as follows: first obtain the video image, then adjust the contrast and brightness of each frame of the video image, filter and reduce noise, strengthen the boundary and enhance the separation through debinarization, then perform corrosion and expansion, process small stains on the image, and then perform AprilTag code edge detection, then find the required quadrilateral pattern and screen it, and finally perform encoding, matching, decoding, and inspection to obtain the positioning coordinate data of the inspection robot and the camera in the ARTAG identification code coordinate system.
将获取的定位坐标数据输入到云数据库中，等待下一步坐标系转换与分析。The acquired positioning coordinate data is input into the cloud database, waiting for the next step of coordinate system conversion and analysis.
巡检机器人上的单目摄像头为广角摄像头，安装位置为巡检机器人顶端侧向。The monocular camera on the inspection robot is a wide-angle camera, which is installed on the side of the top of the inspection robot.
S2：根据ARTAG标识码预先标定的位置以及定位坐标数据，计算得到巡检机器人的位置；定位坐标数据，包含平移向量(也是位置坐标)与旋转矩阵，将货架坐标系与世界坐标系合并，通过平移矢量与旋转矩阵可以计算出巡检机器人目前位置。S2: Calculate the position of the inspection robot based on the pre-calibrated position of the ARTAG identification code and the positioning coordinate data; the positioning coordinate data includes the translation vector (also the position coordinate) and the rotation matrix, which merges the shelf coordinate system with the world coordinate system. The current position of the inspection robot can be calculated through the translation vector and the rotation matrix.
由于货架上的ARTAG标识码已经事先已经标定位置，故当获取到巡检机器人在ARTAG标识码坐标系下的定位坐标数据，即可反推出巡检机器人当前所处位置，再将巡检机器人位置存入云数据库，等待下一步构件位置的确定。Since the ARTAG identification code on the shelf has been calibrated in advance, when the positioning coordinate data of the inspection robot in the ARTAG identification code coordinate system is obtained, the current position of the inspection robot can be inferred, and then the position of the inspection robot can be stored in the cloud database, waiting for the next step of determining the component position.
S3：货架上构件的RFID标签被巡检机器人的LF频率触发激励器唤醒，向巡检机器人的RFID阅读器发送自身ID和LF频率触发激励器的ID，得到构件盘点数据；当巡检机器人行驶靠近待巡检货架时，构件上的RFID标签接近巡检机器人上的LF(125KHz)激励触发器，被LF(125KHz)信号唤醒，并接收LF频率(125KHz)触发激励器的ID数据，然后启动UHF频率(2.4GHz)部件发送自身ID和LF频率(125KHz)触发激励器的ID给巡检机器人上的RFID阅读器。S3: The RFID tag of the component on the shelf is awakened by the LF frequency trigger exciter of the inspection robot, and sends its own ID and the ID of the LF frequency trigger exciter to the RFID reader of the inspection robot to obtain the component inventory data; when the inspection robot drives close to the shelf to be inspected, the RFID tag on the component approaches the LF (125KHz) excitation trigger on the inspection robot, is awakened by the LF (125KHz) signal, and receives the ID data of the LF frequency (125KHz) trigger exciter, and then starts the UHF frequency (2.4GHz) component to send its own ID and the ID of the LF frequency (125KHz) trigger exciter to the RFID reader on the inspection robot.
货架上的任意一个构件在被LF(125KHz)激励触发器信号覆盖之前，RFID信号只有自身ID。当其中一个预制进入到LF(125KHz)激励触发器信号覆盖范围时，预制构件中的RFID标签会被LF(125KHz)激励触发器激活，并向外广播自身ID和LF(125KHz)激励触发器ID；此时就可以根据这两个ID来确定构件与巡检机器人之间的关系，如果两个ID都在，则确定构件就在巡检机器人附近。Before any component on the shelf is covered by the LF (125KHz) excitation trigger signal, the RFID signal only has its own ID. When one of the prefabricated components enters the coverage range of the LF (125KHz) excitation trigger signal, the RFID tag in the prefabricated component will be activated by the LF (125KHz) excitation trigger and broadcast its own ID and the LF (125KHz) excitation trigger ID. At this time, the relationship between the component and the inspection robot can be determined based on these two IDs. If both IDs are present, it is determined that the component is near the inspection robot.
LF频率(125KHz)触发激励器应安装在巡检机器人右侧，巡检机器人正向行驶时LF频率(125KHz)触发激励器应尽量靠近货架。The LF frequency (125KHz) trigger exciter should be installed on the right side of the inspection robot. When the inspection robot is moving forward, the LF frequency (125KHz) trigger exciter should be as close to the shelf as possible.
S4：根据S2中巡检机器人的位置反推货架的位置，将货架位置与构件盘点数据结合，得到构件定位数据。S4: The position of the shelf is inferred based on the position of the inspection robot in S2, and the shelf position is combined with the component inventory data to obtain the component positioning data.
S5：将构件盘点数据、构件定位数据与云端数据库对应的数据合并，并将最新数据与云端数据库中原有数据比对，如不一致自动上报堆场管理员，进行进一步核查；若云端数据库中不存在此构件的数据，则进行更新录入；若云端数据库中已有此构件的数据，且与最新数据一致，则不做修改。S5: The component inventory data, component location data and corresponding data in the cloud database are merged, and the latest data is compared with the original data in the cloud database. If there is any inconsistency, it is automatically reported to the yard manager for further verification; if the data of this component does not exist in the cloud database, it is updated and entered; if the data of this component already exists in the cloud database and is consistent with the latest data, no modification is made.
经实验表明，本发明中的构件盘点定位方法在预制构件堆场中可行，且盘点准确率为100％，定位准确率可达到99％以上，可有效提高堆场中构件盘点与定位的效率和准确率。Experiments have shown that the component inventory and positioning method of the present invention is feasible in prefabricated component yards, and the inventory accuracy is 100%, and the positioning accuracy can reach more than 99%, which can effectively improve the efficiency and accuracy of component inventory and positioning in the yard.
以上所述实施例的各技术特征可以进行任意的组合，为使描述简洁，未对上述实施例中的各个技术特征所有可能的组合都进行描述，然而，只要这些技术特征的组合不存在矛盾，都应当认为是本说明书记载的范围。The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.
以上所述实施例仅表达了本发明的一种实施方式，其描述较为具体和详细，但并不能因此而理解为对发明范围的限制。应当指出的是，对于本领域的普通技术人员来说，在不脱离本发明构思的前提下，还可以做出若干变形和改进，这些都属于本发明的保护范围。因此，本发明的保护范围应以所附权利要求为准。The above-described embodiment only expresses one implementation mode of the present invention, and its description is relatively specific and detailed, but it cannot be understood as limiting the scope of the invention. It should be pointed out that for ordinary technicians in this field, several modifications and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the attached claims.
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