Environment model construction method applied to intelligent inspection robot

Abstract

The invention discloses an environment model construction method applied to an intelligent inspection robot, and relates to the technical field of robots, inspection key points are set in a target inspection area according to requirements, and edge terminals are deployed at the inspection key points; obtaining infrared data at the inspection key point through the edge terminal, and constructing a local environment model corresponding to the edge terminal based on the obtained infrared data; setting an inspection initial position of the inspection robot, and acquiring infrared data and image data of the inspection initial position through the inspection robot; constructing a reference environment model taking the inspection robot as the center according to the infrared data and the image data; according to the inspection progress of the inspection robot, fitting the local environment model corresponding to each edge terminal with the reference environment model to obtain an inspection environment model; therefore, the routing inspection environment model construction process of the routing inspection robot is optimized, the routing inspection environment model generation process is smoother, and the routing inspection environment model construction efficiency is improved.

Description

Technical Field

[0001] This invention relates to the field of robotics, specifically to a method for constructing an environment model for an intelligent inspection robot. Background Technology

[0002] Building accurate environmental models is crucial for intelligent inspection robots. Early methods relied on simple two-dimensional map construction, which provided basic environmental layout information but lacked three-dimensional information such as height and object shape, failing to meet the needs of complex scenarios. With technological advancements, methods like 3D laser scanning emerged, acquiring 3D point cloud data of the environment; however, the processing is complex, the data volume is large, and real-time performance is poor. How to reduce the burden of building 3D environment models on inspection robots and thus improve the smoothness of environment model building is a problem we need to solve. To this end, we now provide a method for building environment models for intelligent inspection robots. Summary of the Invention

[0003] The purpose of this invention is to provide a method for constructing an environmental model for intelligent inspection robots.

[0004] The objective of this invention can be achieved through the following technical solution: a method for constructing an environment model for an intelligent inspection robot, comprising: Set up key inspection points within the target inspection area according to requirements, and deploy edge terminals at the key inspection points; Infrared data at key inspection points is acquired through edge terminals, and a local environment model corresponding to the edge terminals is constructed based on the acquired infrared data. Set the initial inspection position of the inspection robot, and obtain infrared data and image data of the initial inspection position through the inspection robot; A baseline environment model centered on the inspection robot is constructed based on infrared and image data. Based on the inspection progress of the inspection robot, the local environment model corresponding to each edge terminal is fitted with the benchmark environment model to obtain the inspection environment model.

[0005] Furthermore, the process of setting key inspection points within the target inspection area according to requirements and deploying edge terminals at these key inspection points includes: The target inspection area is equipped with robot inspection routes for inspection robots to perform inspections. Based on the actual situation, set key inspection points on the robot's inspection route; Edge terminals are set up at key inspection points, and each edge terminal is configured with communication coverage and data detection range.

[0006] Furthermore, the process of acquiring infrared data at key inspection points through edge terminals and constructing a local environment model corresponding to the edge terminals based on the acquired infrared data includes: Infrared data at each key inspection point is periodically acquired by edge terminals set up at each key inspection point. Obtain the data detection range of the edge terminal, and mark the overlapping part of the data detection range and the robot inspection route as the target area corresponding to the edge terminal; The location of the edge terminal is recorded as the reference point, and a three-dimensional spatial coordinate system with the reference point as the origin is constructed. Based on the obtained infrared data, obstacle information within the target area is detected, and the coordinate positions of the detected obstacles are marked in a three-dimensional spatial coordinate system. The coordinates of each detected obstacle are compared with those of the obstacles obtained in the previous cycle. If there are obstacles with the same coordinates, the corresponding obstacle is marked as a fixed obstacle; otherwise, it is marked as a non-fixed obstacle. Based on the obstacle information of the fixed obstacles, a corresponding obstacle model is generated at the corresponding position in the three-dimensional spatial coordinate system, thereby obtaining a local environment model corresponding to the target area.

[0007] Furthermore, the process of setting the initial inspection position of the inspection robot and acquiring infrared and image data of the initial inspection position through the inspection robot includes: Set the initial inspection position for the inspection robot to perform inspections; After the inspection robot is placed at the initial inspection position, it acquires infrared data and image data in the direction corresponding to the initial inspection position according to the robot's inspection route.

[0008] Furthermore, the process of constructing a baseline environment model centered on the inspection robot based on infrared and image data includes: Construct a three-dimensional spatial coordinate system centered on the inspection robot; The image data obtained by the inspection robot is converted into image frames, the image frames are sorted according to time, and the image frames are rasterized. The rasterized image data is then converted into grayscale images. Feature extraction is performed on the obtained grayscale image to identify obstacles and obstacle types contained in the image data, including non-fixed obstacles and fixed obstacles; Based on the identified contours of fixed and non-fixed obstacles, corresponding obstacle models are generated, and the obstacle models are mapped into a three-dimensional spatial coordinate system. Then, based on the infrared data, the robot detects obstacle information within its infrared scanning range and marks the coordinates of the detected obstacles in a three-dimensional spatial coordinate system. The coordinates of obstacles detected by infrared data are fitted with the locations of obstacles identified by image data to obtain a corresponding baseline environment model.

[0009] Furthermore, fitting the coordinates of the obstacle detected by infrared data with the coordinates of the obstacle identified by image data means that when there is a difference between the coordinates of the two identified obstacles, the midpoint between the two obstacle coordinates is taken as the new obstacle coordinates.

[0010] Furthermore, the process of identifying obstacles and their types contained in image data includes: Extract features from each image frame and mark the outlines of obstacles in each image frame based on the extracted features; The contours of each obstacle extracted in adjacent image frames are matched, and the positions of obstacles with the same contour are compared. If the position of an obstacle with the same outline changes, it indicates that the corresponding obstacle is a non-fixed obstacle; if the position of an obstacle with the same outline does not change, it indicates that the corresponding obstacle is a fixed obstacle.

[0011] Furthermore, based on the inspection progress of the inspection robot, the process of fitting the local environment model corresponding to each edge terminal with the baseline environment model to obtain the inspection environment model includes: When the inspection robot enters the communication coverage area of ​​the edge terminal, it invokes the local environment model corresponding to the edge terminal. The coordinate deviation is obtained based on the current location of the inspection robot and the location of the edge terminal; The coordinates of the obstacle models within the local environment model are updated based on the coordinate deviation. Then, based on the updated coordinates, the obstacle models are mapped to the corresponding coordinates within the reference environment model, thereby completing the fitting between the local environment model and the reference environment model and obtaining the corresponding inspection environment model.

[0012] Compared with the prior art, the beneficial effects of the present invention are: By setting up edge terminals along the robot's inspection route, the edge terminals periodically acquire infrared data within their detection range to identify the distribution of fixed obstacles within that range. When the inspection robot enters the communication coverage area of ​​the edge terminals, the pre-built model of the fixed obstacles within the edge terminals can be directly sent to the inspection robot's model. This optimizes the inspection environment model building process, making the generation of the inspection environment model smoother and improving its efficiency. Attached Figure Description

[0013] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0014] Figure 1 This is a flowchart of the present invention. Detailed Implementation

[0015] like Figure 1 As shown, the method for constructing an environmental model for intelligent inspection robots includes: Set up key inspection points within the target inspection area according to requirements, and deploy edge terminals at the key inspection points; Infrared data at key inspection points is acquired through edge terminals, and a local environment model corresponding to the edge terminals is constructed based on the acquired infrared data. Set the initial inspection position of the inspection robot, and obtain infrared data and image data of the initial inspection position through the inspection robot; A baseline environment model centered on the inspection robot is constructed based on infrared and image data. Based on the inspection progress of the inspection robot, the local environment model corresponding to each edge terminal is fitted with the benchmark environment model to obtain the inspection environment model.

[0016] It should be further explained that, in the specific implementation process, the process of setting up key inspection points within the target inspection area according to needs and deploying edge terminals at these key inspection points includes: The target inspection area is equipped with robot inspection routes for inspection robots to perform inspections. Based on the actual situation, key inspection points are set on the robot's inspection route; it should be noted that the key inspection points are equipped with ports for the inspection robot to acquire inspection data. Edge terminals are set up at key inspection points. These edge terminals can communicate with the inspection robot to complete data interaction between the edge terminals and the inspection robot. Each edge terminal is equipped with a communication coverage range and a data detection range.

[0017] It should be further explained that, in the specific implementation process, the process of acquiring infrared data at key inspection points through edge terminals and constructing a local environment model corresponding to the edge terminals based on the acquired infrared data includes: By setting up edge terminals at various key inspection points, infrared data at these key inspection points is acquired periodically. It should be noted that acquiring infrared data periodically means acquiring a set of infrared data at regular intervals. Based on the order of the edge terminals on the robot's inspection route, each edge terminal is labeled and denoted as i, where i = 1, 2, ..., n, and n is an integer greater than 0; Obtain the data detection range of the edge terminal labeled i, and mark the overlapping part of the data detection range and the robot inspection route as the target area corresponding to the edge terminal; The location of the edge terminal is recorded as the reference point, and a three-dimensional spatial coordinate system with the reference point as the origin is constructed. Based on the obtained infrared data, obstacle information within the target area is detected, and the coordinate positions of the detected obstacles are marked in a three-dimensional spatial coordinate system. The coordinates of each detected obstacle are compared with those of the obstacles obtained in the previous cycle. If there are obstacles with the same coordinates, the corresponding obstacle is marked as a fixed obstacle; otherwise, it is marked as a non-fixed obstacle. Based on the obstacle information of the fixed obstacles, a corresponding obstacle model is generated at the corresponding position in the three-dimensional spatial coordinate system, thereby obtaining a local environment model corresponding to the target area.

[0018] It should be further explained that, in the specific implementation process, the process of setting the initial inspection position of the inspection robot and acquiring infrared data and image data of the initial inspection position through the inspection robot includes: Set the initial inspection position for the inspection robot; it should be noted that the initial inspection position is located on the robot's inspection route; After the inspection robot is placed at the initial inspection position, it acquires infrared data and image data in the direction corresponding to the initial inspection position according to the robot's inspection route.

[0019] It should be further explained that, in the specific implementation process, the process of constructing a baseline environment model centered on the inspection robot based on infrared data and image data includes: Construct a three-dimensional spatial coordinate system centered on the inspection robot; The image data obtained by the inspection robot is converted into image frames, the image frames are sorted according to time, the image frames are rasterized, and the rasterized image data is converted into grayscale images. Feature extraction is performed on the obtained grayscale image to identify obstacles and their types in the image data. The obstacle types include non-fixed obstacles and fixed obstacles. It should be noted that the obstacle identification in the image data in this invention uses an edge detection algorithm and the Sobel operator. The process of using the edge detection algorithm to identify obstacles in the image data is a common technique used by those skilled in the art and will not be described in detail here. Based on the identified contours of fixed and non-fixed obstacles, corresponding obstacle models are generated, and the obstacle models are mapped into a three-dimensional spatial coordinate system. Then, based on the infrared data, the robot detects obstacle information within its infrared scanning range and marks the coordinates of the detected obstacles in a three-dimensional spatial coordinate system. The coordinates of obstacles detected by infrared data are fitted with the coordinates of obstacles identified by image data to obtain a corresponding baseline environment model. Fitting the coordinates of obstacles detected by infrared data with the coordinates of obstacles identified by image data means that when there is a difference between the coordinates of the obstacles identified by the two, the midpoint between the two obstacle coordinates is taken as the new obstacle coordinates. It should be noted that the specific process of identifying obstacles and their types contained in image data includes: Based on the order of the image frames, the image frames are sequentially labeled and denoted as j, where j = 1, 2, ..., m, and m is an integer greater than 0; Extract features from each image frame and mark the outlines of obstacles in each image frame based on the extracted features; Match the contours of each obstacle extracted from the image frame labeled j=1 and the image frame labeled j=2, and compare the positions of obstacles with the same contour. If the position of an obstacle with the same outline changes, it indicates that the corresponding obstacle is a non-fixed obstacle; if the position of an obstacle with the same outline does not change, it indicates that the corresponding obstacle is a fixed obstacle. Next, the contours of each obstacle extracted from the image frames labeled j=3 and j=2 are matched, and the positions of obstacles with the same contour are compared, and so on.

[0020] It should be further explained that, in the specific implementation process, the process of fitting the local environment model corresponding to each edge terminal with the baseline environment model according to the inspection progress of the inspection robot to obtain the inspection environment model includes: When the inspection robot enters the communication coverage area of ​​the edge terminal, it invokes the local environment model corresponding to the edge terminal. The coordinate deviation is obtained based on the current location of the inspection robot and the location of the edge terminal; The coordinates of the obstacle models within the local environment model are updated based on the coordinate deviation. Then, based on the updated coordinates, the obstacle models are mapped to the corresponding coordinates within the reference environment model, thereby completing the fitting between the local environment model and the reference environment model and obtaining the corresponding inspection environment model.

[0021] It should be noted that when the inspection robot enters the communication coverage area of ​​the edge terminal, it will no longer generate corresponding obstacle models for the fixed obstacles it has identified. Instead, it will only generate obstacle models for the non-fixed obstacles. For fixed obstacles, it will directly call the obstacle model in the local environment model corresponding to the edge terminal. This reduces the model generation process after the inspection robot enters the communication coverage area of ​​the edge terminal, thereby optimizing the nodes in the inspection environment model construction process and making the process of generating the inspection environment model smoother. It should be noted that in actual situations, there may be communication blind spots along the robot's inspection route, meaning that the corresponding area is not within the communication coverage of any edge terminal. When the inspection robot is in a communication blind spot, the generation of the obstacle model of the fixed obstacle is restored, referring to the obstacle model generation process at the initial inspection position, which will not be elaborated here.

[0022] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any modifications or equivalent substitutions made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A method for constructing an environmental model for intelligent inspection robots, characterized in that, include: Set up key inspection points within the target inspection area according to requirements, and deploy edge terminals at the key inspection points; Infrared data at key inspection points is acquired through edge terminals, and a local environment model corresponding to the edge terminals is constructed based on the acquired infrared data. Set the initial inspection position of the inspection robot, and obtain infrared data and image data of the initial inspection position through the inspection robot; A baseline environment model centered on the inspection robot is constructed based on infrared and image data. Based on the inspection progress of the inspection robot, the local environment model corresponding to each edge terminal is fitted with the benchmark environment model to obtain the inspection environment model.

2. The environmental model construction method for intelligent inspection robots according to claim 1, characterized in that, The process of setting key inspection points within the target inspection area according to requirements and deploying edge terminals at these key inspection points includes: The target inspection area is equipped with robot inspection routes for inspection robots to perform inspections. Based on the actual situation, set key inspection points on the robot's inspection route; Edge terminals are set up at key inspection points, and each edge terminal is configured with communication coverage and data detection range.

3. The environmental model construction method for intelligent inspection robots according to claim 2, characterized in that, The process of acquiring infrared data at key inspection points through edge terminals and constructing a local environment model corresponding to the edge terminals based on the acquired infrared data includes: Infrared data at each key inspection point is periodically acquired by edge terminals set up at each key inspection point. Obtain the data detection range of the edge terminal, and mark the overlapping part of the data detection range and the robot inspection route as the target area corresponding to the edge terminal; The location of the edge terminal is recorded as the reference point, and a three-dimensional spatial coordinate system with the reference point as the origin is constructed. Based on the obtained infrared data, obstacle information within the target area is detected, and the coordinate positions of the detected obstacles are marked in a three-dimensional spatial coordinate system. The coordinates of each detected obstacle are compared with those of the obstacles obtained in the previous cycle. If there are obstacles with the same coordinates, the corresponding obstacle is marked as a fixed obstacle; otherwise, it is marked as a non-fixed obstacle. Based on the obstacle information of the fixed obstacles, a corresponding obstacle model is generated at the corresponding position in the three-dimensional spatial coordinate system, thereby obtaining a local environment model corresponding to the target area.

4. The environmental model construction method for intelligent inspection robots according to claim 3, characterized in that, The process of setting the initial inspection position of the inspection robot and acquiring infrared and image data of the initial inspection position through the inspection robot includes: Set the initial inspection position for the inspection robot to perform inspections; After the inspection robot is placed at the initial inspection position, it acquires infrared data and image data in the direction corresponding to the initial inspection position according to the robot's inspection route.

5. The environmental model construction method for intelligent inspection robots according to claim 4, characterized in that, The process of constructing a baseline environment model centered on the inspection robot based on infrared and image data includes: Construct a three-dimensional spatial coordinate system centered on the inspection robot; The image data obtained by the inspection robot is converted into image frames, the image frames are sorted according to time, and the image frames are rasterized. The rasterized image data is then converted into grayscale images. Feature extraction is performed on the obtained grayscale image to identify obstacles and obstacle types contained in the image data, including non-fixed obstacles and fixed obstacles; Based on the identified contours of fixed and non-fixed obstacles, corresponding obstacle models are generated, and the obstacle models are mapped into a three-dimensional spatial coordinate system. Then, based on the infrared data, the robot detects obstacle information within its infrared scanning range and marks the coordinates of the detected obstacles in a three-dimensional spatial coordinate system. The coordinates of obstacles detected by infrared data are fitted with the locations of obstacles identified by image data to obtain a corresponding baseline environment model.

6. The environmental model construction method for intelligent inspection robots according to claim 5, characterized in that, Fitting the coordinates of obstacles detected by infrared data with those identified by image data means that when there is a difference between the two identified obstacle coordinates, the midpoint between the two obstacle coordinates is taken as the new obstacle coordinates.

7. The environmental model construction method for intelligent inspection robots according to claim 5, characterized in that, The process of identifying obstacles and their types contained in image data includes: Extract features from each image frame and mark the outlines of obstacles in each image frame based on the extracted features; The contours of each obstacle extracted in adjacent image frames are matched, and the positions of obstacles with the same contour are compared. If the position of an obstacle with the same outline changes, it indicates that the corresponding obstacle is a non-fixed obstacle; if the position of an obstacle with the same outline does not change, it indicates that the corresponding obstacle is a fixed obstacle.

8. The environmental model construction method for intelligent inspection robots according to claim 7, characterized in that, Based on the inspection progress of the inspection robot, the process of fitting the local environment model corresponding to each edge terminal with the baseline environment model to obtain the inspection environment model includes: When the inspection robot enters the communication coverage area of ​​the edge terminal, it invokes the local environment model corresponding to the edge terminal. The coordinate deviation is obtained based on the current location of the inspection robot and the location of the edge terminal; The coordinates of the obstacle models within the local environment model are updated based on the coordinate deviation. Then, based on the updated coordinates, the obstacle models are mapped to the corresponding coordinates within the reference environment model, thereby completing the fitting between the local environment model and the reference environment model and obtaining the corresponding inspection environment model.

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