A wall quality detection robot and method, electronic equipment, and storage medium

Abstract

The present application provides a wall quality inspection robot and method, electronic equipment, and storage medium, and belongs to the field of robot technology. The robot includes a mobile platform, and a path planning module, a motion control module, and a data acquisition module are arranged on the mobile platform, wherein: the path planning module is used to obtain environmental information of the target area through a laser radar to establish an environmental map corresponding to the target area, thereby establishing a path planning movement strategy including multiple target points; the motion control module is used to control the mobile platform to move to each target point; the data acquisition module is used to monitor the stability of the visual sensor through a stability detection unit, and when the data obtained by stability monitoring meets the stability condition, the image data of each target point is collected through the visual sensor. The present application monitors the stability of the visual sensor through a stability detection unit, improves the quality and efficiency of data acquisition, and can better complete the task of wall quality inspection.

Description

Technical Field

[0001] The present application relates to the field of robotics technology, and in particular to a wall quality inspection robot and method, electronic equipment, and storage medium. Background Art

[0002] At present, in the actual operation process, when the wall quality inspection robot performs data collection operations, every time the body moves from one sampling point to the next sampling point and stops sampling, the visual collector usually placed on the mobile platform will shake slightly due to the inertia generated by the movement due to factors such as insufficient body rigidity and uneven ground during movement. However, when using some high-definition cameras and scanners to collect data from a certain area, the high-definition cameras and scanners need to remain completely still for a period of working time, that is, slight shaking has a great impact on the quality of the data collected by the visual collector, and there is a high probability that the data will be distorted and unusable. The existing design schemes of quality inspection robots have high requirements for mobile platforms and hardware, and have low data collection efficiency, which cannot meet the requirements of the current construction industry. Summary of the invention

[0003] The main purpose of the embodiments of the present application is to propose a wall quality inspection robot and method, electronic equipment, and storage medium, which can monitor and judge the stability of the visual sensor through a stability detection unit to improve the quality and efficiency of data collection and better complete the wall quality inspection task.

[0004] To achieve the above-mentioned purpose, a first aspect of an embodiment of the present application proposes a wall quality inspection robot, the robot comprising a mobile platform, the mobile platform being provided with a path planning module, a motion control module, and a data acquisition module, wherein:

[0005] The path planning module is used to obtain environmental information of the target area through a laser radar, establish an environmental map corresponding to the target area according to the environmental information, and establish a path planning movement strategy including multiple target points according to the environmental map;

[0006] The motion control module is used to control the mobile platform to move to each target point according to the path planning movement strategy;

[0007] The data acquisition module is used to monitor the stability of the visual sensor through a stability detection unit. When the data obtained after the stability detection unit performs the stability monitoring meets the preset stability condition, the visual sensor collects image data for the area corresponding to each target point.

[0008] In some embodiments, the mobile platform further includes:

[0009] The data processing module is used to obtain the wall quality information of the area corresponding to each target point by analyzing and processing the sampling data after the image data is collected.

[0010] In some embodiments, the sampled data after the image data acquisition includes RGB data and point cloud data, and the data processing module further includes:

[0011] A color difference data processing submodule is used to convert the RGB data into HSV data, perform group identification distance calculation on the HSV data, and obtain a distance calculation result; and judge the wall quality of the target area according to a preset first distance threshold and the distance calculation result;

[0012] The point cloud data processing submodule is used to perform plane fitting on the multiple point cloud data to obtain target planes corresponding to the multiple point cloud data; by calculating the distance between each target point and the target plane, a plurality of target points exceeding a preset second distance threshold are obtained, and the wall quality of the target area is judged based on the plurality of target points exceeding the preset second distance threshold.

[0013] In some embodiments, the visual sensor is disposed on the mobile platform; the path planning module specifically includes:

[0014] The environmental information acquisition submodule is used to acquire environmental information of the target area through a laser radar and establish an environmental map corresponding to the target area;

[0015] A sampling interval submodule, used to determine the sampling interval distance on the environment map according to the sampling range of the visual sensor;

[0016] The path planning submodule is used to determine a path planning movement strategy including multiple target points corresponding to the target area according to the environmental map and the sampling interval distance.

[0017] In some embodiments, the motion control module specifically includes:

[0018] An initial position acquisition submodule, used to acquire the initial position of the mobile platform in the target area;

[0019] A distance calculation submodule, used for calculating the distance between the mobile platform and the location of each target point, and determining the initial target point for the mobile platform to move according to the obtained multiple distance lengths;

[0020] The motion control submodule is used to control the mobile platform to move to the initial target point and control the mobile platform to move according to the path planning movement strategy.

[0021] In some embodiments, controlling the mobile platform to move according to the path planning movement strategy includes:

[0022] Determining the next target point after the initial target point according to the path planning movement strategy;

[0023] The mobile platform is controlled to move to the next target point to collect image data.

[0024] In some embodiments, the mobile platform is further provided with a steering gear unit, and the data acquisition module specifically includes:

[0025] The attitude adjustment submodule is used to adjust the attitude of the visual sensor through the steering gear group to obtain stable information of the visual sensor;

[0026] The stability monitoring submodule is used to monitor the stability of the visual sensor through a stability detection unit to obtain a stability monitoring result;

[0027] The stability judgment submodule is used to judge whether the stability monitoring result meets the preset stability condition. When the stability monitoring result meets the preset stability condition, image data of the area corresponding to each target point is collected through the visual sensor and the steering gear group.

[0028] To achieve the above object, a second aspect of an embodiment of the present application proposes a wall quality detection method, which is applied to the wall quality detection robot described in the first aspect. The method includes:

[0029] Acquire environmental information of a target area through a laser radar, establish an environmental map corresponding to the target area according to the environmental information, and establish a path planning movement strategy including multiple target points according to the environmental map;

[0030] Control the mobile platform to move to each target point according to the path planning movement strategy;

[0031] The stability of the visual sensor is monitored by a stability detection unit. When the data obtained after the stability detection unit performs the stability monitoring meets a preset stability condition, image data is collected for the area corresponding to each target point by the visual sensor.

[0032] To achieve the above-mentioned purpose, the third aspect of an embodiment of the present application proposes an electronic device, which includes a memory, a processor, a program stored in the memory and executable on the processor, and a data bus for realizing connection and communication between the processor and the memory, and the program, when executed by the processor, implements the method described in the second aspect above.

[0033] To achieve the above-mentioned purpose, the fourth aspect of an embodiment of the present application proposes a storage medium, which is a computer-readable storage medium used for computer-readable storage, and the storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the method described in the second aspect above.

[0034] The embodiments of the present application propose a wall quality inspection robot and method, electronic device, and storage medium. The robot acquires environmental information of a target area through a laser radar, establishes an environmental map corresponding to the target area based on the environmental information, and establishes a path planning mobile strategy including multiple target points based on the environmental map, thereby accurately realizing the path planning of the wall quality inspection robot. In order to fully realize the wall quality inspection task, the mobile platform is controlled to move to each target point according to the obtained path planning mobile strategy. The stability of the visual sensor is monitored by a stability detection unit. When the data obtained after the stability detection unit performs stability monitoring meets the preset stability conditions, the image data of the area corresponding to each target point is collected by the visual sensor. The embodiments of the present application can monitor and judge the stability of the visual sensor through the stability detection unit to improve the quality and efficiency of data collection, thereby better completing the wall quality inspection task. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is a module schematic diagram of a wall quality inspection robot provided in an embodiment of the present application;

[0036] Figure 2 is a flowchart of steps corresponding to the motion control submodule provided in an embodiment of the present application;

[0037] Figure 3 is a first schematic diagram of a wall quality inspection robot provided in an embodiment of the present application;

[0038] Figure 4 is a second schematic diagram of the wall quality inspection robot provided in an embodiment of the present application;

[0039] Figure 5 is a flow chart of a wall quality detection method provided in an embodiment of the present application;

[0040] Figure 6 yes Figure 5 Flow chart of step S510 in FIG.

[0041] Figure 7 yes Figure 5 Flow chart of step S520 in FIG.

[0042] Figure 8 yes Figure 5 Flow chart of step S530 in FIG.

[0043] Fig. 9 It is a schematic diagram of the hardware structure of the electronic device provided in the embodiment of the present application. DETAILED DESCRIPTION

[0044] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0045] It should be noted that, although the functional modules are divided in the device schematic diagram and the logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first", "second", etc. in the specification, claims and the above drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0046] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0047] First, some nouns involved in this application are analyzed:

[0048] Inertial Measurement Unit (IMU): is a device that measures the three-axis attitude angle (or angular rate) and acceleration of an object. Gyroscopes and accelerometers are the main components of IMU, and their accuracy directly affects the accuracy of the inertial system. Usually, an IMU contains three single-axis accelerometers and three single-axis gyroscopes. The accelerometer detects the acceleration signal of the object in the independent three axes of the carrier coordinate system, while the gyroscope detects the angular velocity signal of the carrier relative to the navigation coordinate system, measures the angular velocity and acceleration of the object in three-dimensional space, and uses this to calculate the object's attitude.

[0049] Red-Green-Blue (RGB): is a color standard that obtains a variety of colors by changing the three color channels of red (R), green (G), and blue (B), and superimposing them on each other. The RGB color model can be used to represent any color that can be seen by the naked eye in nature, that is, it can be formed by mixing and superimposing these three colors, so it is also called the additive color model.

[0050] Hue-Saturation-Brightness (HSB) mode: represents a color mode of hue (H), saturation (S), and brightness (B). It is based on human perception of color and describes the three basic characteristics of color. The HSB color mode uses the three attributes of color to represent, that is, the three attributes of color are quantified, saturation S and brightness B are expressed as percentage values, and hue is expressed as an angle. Saturation S represents the purity of the color. When the saturation is zero, it is gray. White, black, and other grayscale colors have no saturation. The greater the saturation, the purer the color. Brightness B refers to the brightness of the color. When the brightness is zero, it is black. The maximum brightness is the most vivid state of the color.

[0051] At present, in the actual operation process, when the wall quality inspection robot performs data collection operations, every time the body moves from one sampling point to the next sampling point and stops sampling, the visual collector placed on the mobile platform will shake slightly due to the inertia generated by the movement due to factors such as insufficient body rigidity and uneven ground during movement. However, some high-definition cameras and scanners need to remain completely still for a period of working time when collecting data from a certain area. Therefore, such slight shaking has a great impact on the quality of the data collected by the visual collector, and there is a high probability that the data will be distorted and unusable. The existing design schemes of quality inspection robots have high requirements for mobile platforms and hardware, and have low data collection efficiency, which cannot meet the requirements of the current construction industry.

[0052] Based on this, the embodiments of the present application provide a wall quality inspection robot and method, electronic equipment, and storage medium, which aim to improve the quality and efficiency of data collection to complete the task of inspecting wall quality.

[0053] A wall quality inspection robot and method, electronic device, and storage medium provided in the embodiments of the present application are specifically illustrated through the following embodiments. First, a wall quality inspection robot in the embodiments of the present application is described.

[0054] See also Figure 1 , Figure 1 It is a module schematic diagram of a wall quality inspection robot provided in an embodiment of the present application. The wall quality inspection robot includes a mobile platform, and a path planning module 110, a motion control module 120, and a data acquisition module 130 are arranged on the mobile platform.

[0055] The path planning module 110 is used to obtain environmental information of the target area through a laser radar, establish an environmental map corresponding to the target area according to the environmental information, and establish a path planning movement strategy including multiple target points according to the environmental map;

[0056] The motion control module 120 is used to control the mobile platform to move to each target point according to the path planning movement strategy;

[0057] The data acquisition module 130 is used to monitor the stability of the visual sensor through a stability detection unit. When the data obtained after the stability detection unit performs stability monitoring meets the preset stability condition, the visual sensor collects image data for the area corresponding to each target point.

[0058] In the path planning module 110 of some embodiments, in order to accurately implement the path planning of the wall quality inspection robot, the present application obtains environmental information of the target area through a laser radar, establishes an environmental map corresponding to the target area based on the environmental information, and establishes a path planning movement strategy including multiple target points based on the environmental map. Specifically, when the wall quality inspection robot enters the target area to perform a wall quality inspection task. Under the guidance of the laser radar, the robot circles the target area along the wall to obtain the environmental information of the target area, and then establishes an environmental map corresponding to the target area based on the environmental information. After the map is built, the background module corresponding to the robot performs path planning and target point selection for the target area based on the environmental information and the established environmental map to establish a path planning movement strategy including multiple target points.

[0059] It should be noted that when the robot circles the target area along the wall under the guidance of the lidar, the number of target points corresponding to the path planning movement strategy can be determined according to the sampling range of the visual sensor. For example, the number of target points corresponding to the path planning movement strategy can be determined according to the sampling range of the binocular three-dimensional scanner, and the number of target points to be sampled can also be pre-set according to needs, and no specific limitation is made here.

[0060] It should be noted that the mobile platform can adopt the method of automatic setting by background commands, which is not limited here.

[0061] It should be noted that the visual sensor can be a scanner, a camera, or other devices that involve point cloud data and image recognition and need to be stable to collect valid data.

[0062] In the motion control module 120 of some embodiments, in order to fully realize the task of detecting the wall surface quality, the mobile platform is controlled to move to each target point according to the obtained path planning movement strategy. Specifically, the robot moves to each selected target point through the mobile platform according to the path execution command corresponding to the path planning movement strategy, that is, the embodiment of the present application can avoid the need to arrange the control points in advance and clean up the control points after the work is completed, thereby improving the quality and efficiency of data collection.

[0063] In the data acquisition module 130 of some embodiments, in order to improve the quality and efficiency of data acquisition, the stability of the visual sensor is monitored by the stability detection unit. When the data obtained after the stability detection unit performs stability monitoring meets the preset stability condition, the image data of the area corresponding to each target point is collected by the visual sensor. The embodiment of the present application can monitor and judge the stability of the visual sensor through the stability detection unit to improve the quality and efficiency of data acquisition, thereby better completing the task of detecting the quality of the wall.

[0064] It should be noted that the preset stability condition refers to the stability detection unit monitoring the stability of the visual sensor, specifically monitoring whether the acceleration of the visual sensor in all directions is close to zero, or monitoring whether the shaking of the visual sensor is stable within a negligible range.

[0065] It should be noted that the stability detection unit can be an inertial measurement unit IMU, that is, the stability of the visual sensor is monitored by the IMU to monitor the stability information of the visual sensor. When the data obtained after the IMU performs stability monitoring meets the preset stability conditions, the image data of the area corresponding to each target point is collected by the visual sensor.

[0066] In some embodiments, the mobile platform of the wall quality inspection robot also includes a data processing module, which is used to obtain wall quality information of the area corresponding to each target point by analyzing and processing the sampling data after image data acquisition.

[0067] Specifically, in order to better complete the task of detecting wall quality and realize problem judgment on problem areas in the wall, the sampling data after image data acquisition is analyzed and processed to obtain the wall quality information of the area corresponding to each target point.

[0068] In some embodiments, the sampled data after image data acquisition includes RGB data and point cloud data, and the data processing module on the wall quality inspection robot further includes:

[0069] The color difference data processing submodule is used to convert the RGB data into HSV data, perform group identification distance calculation on the HSV data, and obtain the distance calculation result; and judge the wall quality of the target area according to the preset first distance threshold and the distance calculation result;

[0070] The point cloud data processing submodule is used to perform plane fitting on multiple point cloud data to obtain target planes corresponding to the multiple point cloud data; by calculating the distance between each target point and the target plane, multiple target points exceeding a preset second distance threshold are obtained, and the wall quality of the target area is judged based on the multiple target points exceeding the preset second distance threshold.

[0071] Specifically, the sampled data after the image data is collected includes RGB data and point cloud data. When the sampled data is RGB data, the RGB data is first converted into HSV data, and the HSV data is grouped and identified by distance calculation to obtain the distance calculation result. Specifically, the K-Means algorithm is used to group and identify the distance to obtain the distance calculation result. The wall quality of the target area is judged according to the preset first distance threshold and the distance calculation result to judge whether the wall of the area corresponding to the current target point in the target area has cracks or cracks. When the sampled data is point cloud data, a plane fitting is performed on multiple point cloud data to obtain a target plane corresponding to multiple point cloud data, that is, the target plane is an ideal plane fitted by screening multiple point cloud data. By calculating the distance between each target point and the target plane, multiple target points exceeding the preset second distance threshold are obtained, that is, multiple target points exceeding the preset second distance threshold are collected. The wall quality of the target area is judged according to the multiple target points, specifically judging whether the current area is a crack according to the collected data, and calculating the length of the crack.

[0072] It should be noted that in order to facilitate the subsequent repair of the area with cracks or fissures, the locations of the cracks or fissures in the target area are marked and archived in the established environmental map.

[0073] It should be noted that when the sampling data is RGB data, that is, the first distance threshold is a threshold based on color processing. For example, when the collected wall color is light gray, the color at the cracked area may be dark black. By identifying the corresponding HSV data, the point where the color mutation occurs can be found, that is, the problem wall area in the target area can be determined.

[0074] In some embodiments, the visual sensor is disposed on the mobile platform of the wall quality inspection robot, and the path planning module 110 specifically includes:

[0075] The environmental information acquisition submodule is used to acquire environmental information of the target area through a laser radar and establish an environmental map corresponding to the target area;

[0076] A sampling interval submodule is used to determine the sampling interval distance on the environment map according to the sampling range of the visual sensor;

[0077] The path planning submodule is used to determine the path planning movement strategy including multiple target points corresponding to the target area according to the environment map and the sampling interval distance.

[0078] Specifically, in order to accurately implement the path planning of the wall quality inspection robot, the environmental information of the target area is acquired through the laser radar, and the environmental map corresponding to the target area is established. According to the sampling range of the visual sensor, the sampling interval distance on the environmental map is determined, that is, the sampling interval distance for determining the wall quality inspection of the robot on the path corresponding to the current target area. According to the environmental map and the sampling interval distance, the path planning movement strategy including multiple target points corresponding to the target area is determined. The embodiment of the present application can monitor and judge the stability of the visual sensor through the stability detection unit to improve the quality and efficiency of data acquisition, thereby better completing the wall quality inspection task.

[0079] In some embodiments, the motion control module 120 on the mobile platform specifically includes:

[0080] The initial position acquisition submodule is used to obtain the initial position of the mobile platform in the target area;

[0081] The distance calculation submodule is used to calculate the distance between the mobile platform and the location of each target point, and determine the initial target point for the mobile platform to move according to the obtained multiple distance lengths;

[0082] The motion control submodule is used to control the mobile platform to move to the initial target point and control the mobile platform to move according to the path planning movement strategy.

[0083] Specifically, when the mobile platform is controlled to move to each target point according to the path planning mobile strategy, the initial position of the mobile platform in the target area is first obtained, the distance between the mobile platform and the position of each target point is calculated, and the initial target point for the mobile platform to move is determined according to the obtained multiple distance lengths. For example, the robot can be selected to start from the wall closest to the entrance in the target area. The mobile platform is controlled to move to the initial target point and is controlled to move according to the path planning mobile strategy.

[0084] See also Figure 2 In some embodiments, the steps in the motion control submodule for controlling the movement of the mobile platform according to the path planning movement strategy specifically include but are not limited to step S210 and step S220.

[0085] Step S210, determining the next target point after the initial target point according to the path planning movement strategy;

[0086] Step S220, controlling the mobile platform to move to the next target point to collect image data.

[0087] Specifically, in order to better complete the task of detecting wall quality, after completing data collection for the area corresponding to the initial target point, the next target point after the initial target point is determined according to the path planning movement strategy, and the mobile platform is controlled to move to the next target point for image data collection.

[0088] It should be noted that after data collection for all target points selected in the path planning mobile strategy is completed, the mobile platform in the wall quality inspection robot drives out of the current target area and enters the next target area to perform the wall quality inspection task.

[0089] In some embodiments, a steering gear group is also provided on the mobile platform, and the data acquisition module 130 specifically includes:

[0090] The attitude adjustment submodule is used to adjust the attitude of the visual sensor through the steering gear group to obtain stable information of the visual sensor;

[0091] The stability monitoring submodule is used to monitor the stability of the visual sensor through a stability detection unit to obtain a stability monitoring result;

[0092] The stability judgment submodule is used to judge whether the stability monitoring result meets the preset stability condition. When the stability monitoring result meets the preset stability condition, image data of the area corresponding to each target point is collected through the visual sensor and the steering gear group.

[0093] Specifically, in order to maintain the stability of the visual sensor and improve the quality and efficiency of data collection, a steering gear group is also provided on the mobile platform, and the steering gear group includes three steering gears. The embodiment of the present application sets up the visual sensor on the mobile platform through the steering gear group, and adjusts the posture of the visual sensor through the steering gear group to obtain the stability information of the visual sensor. The stability of the visual sensor is monitored by the stability detection unit. When the data obtained after the stability detection unit performs stability monitoring meets the preset stability conditions, the image data of the area corresponding to each target point is collected by the visual sensor. The embodiment of the present application can avoid the need to arrange the control points in advance and clean up the control points after the work is completed. The stability of the visual sensor is monitored and judged by the stability detection unit, thereby improving the quality and efficiency of data collection.

[0094] It should be noted that when the wall quality inspection robot enters the target area through a narrow door, the visual sensor can change the original horizontal visual sensor to a vertical posture through the servo unit to achieve smooth passage.

[0095] In a specific embodiment, see Figure 3A wall quality inspection robot includes a mobile platform 310, on which a laser radar 320, a steering gear group 330, and a visual sensor 340 are arranged. The steering gear group consists of three steering gears. In order to maintain the stability of the visual sensor and improve the quality and efficiency of data collection, please refer to Figure 4 As shown, a stability detection unit 350, such as an IMU, is provided near the visual sensor 340. Environmental information of the target area is acquired through the laser radar 320, and an environmental map corresponding to the target area is established. According to the sampling range of the visual sensor 340, the sampling interval distance on the environmental map is determined, so as to determine the path planning movement strategy including multiple target points corresponding to the target area. The initial position of the mobile platform 310 in the target area is acquired, the distance between the mobile platform 310 and the position of each target point is calculated, and the initial target point for the mobile platform 310 to move is determined according to the obtained multiple distance lengths, and the mobile platform 310 is controlled to move to the initial target point, so as to control the mobile platform to move according to the path planning movement strategy. When data collection starts for the area corresponding to the initial target point, the visual sensor 340 is adjusted in posture through the steering gear group 330 to obtain the stability information of the visual sensor 340. The stability of the visual sensor 340 is monitored by the stability detection unit 350. When the result of the stability monitoring meets the preset stability condition, the image data of the area corresponding to each target point is collected by the visual sensor 340 and the steering gear group 330. The embodiments of the present application can avoid the need to arrange control sites in advance and clean up the control sites after the work is completed. The stability of the visual sensor is monitored and judged by the stability detection unit, thereby improving the quality and efficiency of data collection.

[0096] See also Figure 5 , Figure 5 It is a method flow chart of a wall quality detection method provided in an embodiment of the present application. The method can be applied to a wall quality detection robot provided in the above embodiment. The method includes but is not limited to steps S510 to S530.

[0097] Step S510, obtaining environmental information of the target area through a laser radar, establishing an environmental map corresponding to the target area according to the environmental information, and establishing a path planning movement strategy including multiple target points according to the environmental map;

[0098] Step S520, controlling the mobile platform to move to each target point according to the path planning movement strategy;

[0099] Step S530, monitoring the stability of the visual sensor through the stability detection unit. When the data obtained after the stability detection unit performs stability monitoring meets the preset stability condition, the visual sensor collects image data for the area corresponding to each target point.

[0100] The specific implementation of the wall quality inspection method is basically the same as the specific implementation of the above-mentioned wall quality inspection robot, and will not be repeated here.

[0101] In some embodiments, the wall quality detection method provided in the embodiments of the present application further includes: obtaining wall quality information of the area corresponding to each target point by analyzing and processing the sampling data after image data acquisition.

[0102] In some embodiments, the sampled data after image data acquisition includes RGB data and point cloud data. Step 530 in the wall quality detection method provided in the embodiment of the present application also includes: converting the RGB data into HSV data, performing group identification distance calculation on the HSV data, and obtaining a distance calculation result; and judging the wall quality of the target area according to a preset first distance threshold and the distance calculation result; performing plane fitting on multiple point cloud data to obtain a target plane corresponding to the multiple point cloud data; obtaining multiple target points exceeding a preset second distance threshold by calculating the distance between each target point and the target plane, and judging the wall quality of the target area according to the multiple target points exceeding the preset second distance threshold.

[0103] See also Figure 6 In some embodiments, the visual sensor is arranged on the mobile platform, and step S510 of the wall quality detection method provided in the embodiment of the present application specifically includes but is not limited to steps S610 to S630.

[0104] Step S610, acquiring environmental information of the target area through a laser radar, and establishing an environmental map corresponding to the target area;

[0105] Step S620, determining the sampling interval distance on the environment map according to the sampling range of the visual sensor;

[0106] Step S630: Determine a path planning movement strategy including multiple target points corresponding to the target area according to the environment map and the sampling interval distance.

[0107] See also Figure 7 In some embodiments, step S520 of the wall quality detection method provided in the embodiment of the present application specifically includes but is not limited to steps S710 to S730.

[0108] Step S710, obtaining the initial position of the mobile platform in the target area;

[0109] Step S720, calculating the distance between the mobile platform and each target point, and determining the initial target point for the mobile platform to move according to the obtained multiple distance lengths;

[0110] Step S730, controlling the mobile platform to move to the initial target point, and controlling the mobile platform to move according to the path planning movement strategy.

[0111] See also Figure 8 In some embodiments, a steering gear group is also provided on the mobile platform. Step S530 of the wall quality detection method provided in the embodiment of the present application specifically includes but is not limited to steps S810 to S830.

[0112] Step S810, adjusting the attitude of the visual sensor by the steering gear group to obtain stable information of the visual sensor;

[0113] Step S820, performing stability monitoring on the visual sensor through a stability detection unit to obtain a stability monitoring result;

[0114] Step S830, determining whether the stability monitoring result meets the preset stability condition. When the stability monitoring result meets the preset stability condition, image data is collected for the area corresponding to each target point through the visual sensor and the steering gear group.

[0115] The wall quality detection method provided in the embodiment of the present application can be applied to the terminal, can also be applied to the server side, and can also be software running in the terminal or the server side. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, etc.; the server side can be configured as an independent physical server, or a server cluster or distributed system composed of multiple physical servers, and can also be configured as a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application that implements the wall quality detection method, etc., but is not limited to the above forms.

[0116] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0117] The embodiment of the present application also provides an electronic device, the electronic device comprising: a memory, a processor, a program stored in the memory and executable on the processor, and a data bus for realizing connection and communication between the processor and the memory, and the program is executed by the processor to realize the above-mentioned wall quality detection method. The electronic device can be any intelligent terminal including a tablet computer, a car computer, etc.

[0118] See also Fig. 9 , Fig. 9 The hardware structure of an electronic device of another embodiment is illustrated, and the electronic device includes:

[0119] The processor 910 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application;

[0120] The memory 920 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 920 can store an operating system and other application programs. When the technical solution provided in the embodiment of this specification is implemented by software or firmware, the relevant program code is stored in the memory 920, and the processor 910 calls and executes the wall quality detection method of the embodiment of this application;

[0121] Input / output interface 930, used to implement information input and output;

[0122] Communication interface 940, used to realize communication interaction between the device and other devices, which can be realized through wired mode (such as USB, network cable, etc.) or wireless mode (such as mobile network, WIFI, Bluetooth, etc.);

[0123] bus 950 , which transmits information between the various components of the device (e.g., processor 910 , memory 920 , input / output interface 930 , and communication interface 940 );

[0124] The processor 910 , the memory 920 , the input / output interface 930 , and the communication interface 940 are connected to each other in communication within the device via a bus 950 .

[0125] An embodiment of the present application also provides a storage medium, which is a computer-readable storage medium used for computer-readable storage. The storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the above-mentioned wall quality detection method.

[0126] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0127] The embodiments of the present application provide a wall quality inspection robot and method, an electronic device, and a storage medium, which acquire environmental information of a target area through a laser radar, establish an environmental map corresponding to the target area based on the environmental information, and establish a path planning movement strategy including multiple target points based on the environmental map, thereby accurately realizing the path planning of the wall quality inspection robot. In order to fully realize the wall quality inspection task, the mobile platform is controlled to move to each target point according to the obtained path planning movement strategy. The stability of the visual sensor is monitored by a stability detection unit. When the data obtained after the stability detection unit performs stability monitoring meets the preset stability conditions, the image data of the area corresponding to each target point is collected by the visual sensor. The embodiments of the present application can monitor and judge the stability of the visual sensor through the stability detection unit to improve the quality and efficiency of data collection, thereby better completing the wall quality inspection task.

[0128] The embodiments described in the embodiments of the present application are intended to more clearly illustrate the technical solutions of the embodiments of the present application and do not constitute a limitation on the technical solutions provided in the embodiments of the present application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0129] It can be understood by those skilled in the art that Figures 1 to 8 The technical solutions shown in the figure do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than those shown in the figure, or a combination of certain steps, or different steps.

[0130] The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0131] Those skilled in the art will appreciate that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices may be implemented as software, firmware, hardware, or a suitable combination thereof.

[0132] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0133] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0134] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the above units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0135] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0136] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0137] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including multiple instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read Only Memory, referred to as ROM), random access memory (Random Access Memory, referred to as RAM), disk or optical disk and other media that can store programs.

[0138] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but the scope of the rights of the present invention is not limited thereto. Any modification, equivalent substitution and improvement made by a person skilled in the art without departing from the scope and essence of the present invention should be within the scope of the rights of the present invention.

Claims

1. A wall quality inspection robot, characterized in that: The robot comprises a mobile platform, on which a path planning module, a motion control module and a data acquisition module are arranged, wherein: The path planning module is used to obtain environmental information of the target area through a laser radar, establish an environmental map corresponding to the target area according to the environmental information, and establish a path planning movement strategy including multiple target points according to the environmental map; The motion control module is used to control the mobile platform to move to each target point according to the path planning movement strategy; The data acquisition module is used to monitor the stability of the visual sensor through a stability detection unit. When the data obtained after the stability detection unit performs the stability monitoring meets the preset stability conditions, the visual sensor is used to collect image data for the area corresponding to each target point; wherein the preset stability conditions refer to monitoring the acceleration of the visual sensor in all directions to be close to zero, or monitoring whether the shaking of the visual sensor is stable within a negligible range.

2. A wall quality inspection robot according to claim 1, characterized in that: The mobile platform also includes: The data processing module is used to obtain the wall quality information of the area corresponding to each target point by analyzing and processing the sampling data after the image data is collected.

3. A wall quality inspection robot according to claim 2, characterized in that: The sampled data after the image data acquisition includes RGB data and point cloud data, and the data processing module also includes: A color difference data processing submodule is used to convert the RGB data into HSV data, perform group identification distance calculation on the HSV data, and obtain a distance calculation result; and judge the wall quality of the target area according to a preset first distance threshold and the distance calculation result; The point cloud data processing submodule is used to perform plane fitting on the multiple point cloud data to obtain target planes corresponding to the multiple point cloud data; by calculating the distance between each target point and the target plane, a plurality of target points exceeding a preset second distance threshold are obtained, and the wall quality of the target area is judged based on the plurality of target points exceeding the preset second distance threshold.

4. A wall quality inspection robot according to any one of claims 1 to 3, characterized in that: The visual sensor is arranged on the mobile platform; the path planning module specifically comprises: The environmental information acquisition submodule is used to acquire environmental information of the target area through a laser radar and establish an environmental map corresponding to the target area; A sampling interval submodule, used to determine the sampling interval distance on the environment map according to the sampling range of the visual sensor; The path planning submodule is used to determine a path planning movement strategy including multiple target points corresponding to the target area according to the environmental map and the sampling interval distance.

5. A wall quality inspection robot according to any one of claims 1 to 3, characterized in that: The motion control module specifically includes: An initial position acquisition submodule, used to acquire the initial position of the mobile platform in the target area; A distance calculation submodule, used for calculating the distance between the mobile platform and the location of each target point, and determining the initial target point for the mobile platform to move according to the obtained multiple distance lengths; The motion control submodule is used to control the mobile platform to move to the initial target point and control the mobile platform to move according to the path planning movement strategy.

6. A wall quality inspection robot according to claim 5, characterized in that: The controlling the mobile platform to move according to the path planning movement strategy includes: Determining the next target point after the initial target point according to the path planning movement strategy; The mobile platform is controlled to move to the next target point to collect image data.

7. A wall quality inspection robot according to any one of claims 1 to 3, characterized in that: The mobile platform is also provided with a steering gear unit, and the data acquisition module specifically includes: The attitude adjustment submodule is used to adjust the attitude of the visual sensor through the steering gear group to obtain stable information of the visual sensor; The stability monitoring submodule is used to monitor the stability of the visual sensor through a stability detection unit to obtain a stability monitoring result; The stability judgment submodule is used to judge whether the stability monitoring result meets the preset stability condition. When the stability monitoring result meets the preset stability condition, image data of the area corresponding to each target point is collected through the visual sensor and the steering gear group.

8. A wall quality inspection method, applied to the wall quality inspection robot according to any one of claims 1 to 7, characterized in that: The method comprises: Acquire environmental information of a target area through a laser radar, establish an environmental map corresponding to the target area according to the environmental information, and establish a path planning movement strategy including multiple target points according to the environmental map; Control the mobile platform to move to each target point according to the path planning movement strategy; The stability of the visual sensor is monitored by a stability detection unit. When the data obtained after the stability detection unit performs the stability monitoring meets the preset stability conditions, the image data of the area corresponding to each target point is collected by the visual sensor; wherein the preset stability conditions refer to monitoring the acceleration of the visual sensor in all directions to be close to zero, or monitoring whether the shaking of the visual sensor is stable within a negligible range.

9. An electronic device, characterized in that: The electronic device includes a memory, a processor, a program stored in the memory and executable on the processor, and a data bus for realizing connection and communication between the processor and the memory. When the program is executed by the processor, the steps of the method according to claim 8 are realized.

10. A storage medium, the storage medium being a computer-readable storage medium, used for computer-readable storage, characterized in that: The storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps of the method according to claim 8.

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