CN114995414A - Wall surface quality detection robot and method, electronic device and storage medium - Google Patents
Wall surface quality detection robot and method, electronic device and storage medium Download PDFInfo
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Abstract
The application provides a wall surface quality detection robot and method, electronic equipment and a storage medium, and belongs to the technical field of robots. The robot comprises a mobile platform, wherein a path planning module, a motion control module and a data acquisition module are arranged on the mobile platform, and the robot comprises: the path planning module is used for acquiring environmental information of a target area through a laser radar to establish an environmental map corresponding to the target area, so that a path planning movement strategy comprising a plurality of target points is established; the motion control module is used for controlling the mobile platform to move to each target point; the data acquisition module is used for monitoring the stability of the visual sensor through the stability detection unit, and when the data monitored by the stability meet the stability condition, the data acquisition module acquires image data of each target point through the visual sensor. This application carries out stability monitoring to visual sensor through stabilizing the detecting element, has improved data acquisition's quality and efficiency, can accomplish the detection task to the wall quality better.
Description
Technical Field
The application relates to the technical field of robots, in particular to a wall surface quality detection robot and method, electronic equipment and a storage medium.
Background
At present, in the actual operation process, when the wall surface quality detection robot performs data acquisition operation, when the machine body moves from one sampling point to the next sampling point to stop sampling, the vision collector usually placed on the moving platform can slightly shake due to factors such as insufficient rigidity of the machine body and unevenness of the partial ground in the moving process under the inertia generated by movement. However, when some high-definition cameras and scanners are used to collect data of a certain area, the high-definition cameras and scanners need to be kept completely still for a period of working time, that is, a small shake has a great influence on the quality of data collected by a vision collector, and has a great probability of causing data distortion and being unusable. The existing design scheme of the quality inspection robot has high requirements on a mobile platform and hardware, and has low data acquisition efficiency, so that the quality inspection robot cannot meet the requirements of the current building industry.
Disclosure of Invention
The main purpose of the embodiment of the application is to provide a wall surface quality detection robot and method, an electronic device, and a storage medium, which can perform stability monitoring and judgment on a visual sensor through a stability detection unit so as to improve the quality and efficiency of data acquisition and better complete the detection task of the wall surface quality.
In order to achieve the above object, a first aspect of the embodiments of the present application provides a wall quality detection robot, the robot includes a mobile platform, a path planning module, a motion control module, and a data acquisition module are provided on the mobile platform, wherein:
the path planning module is used for acquiring environmental information of a 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 comprising a plurality of target points according to the environmental map;
the motion control module is used for controlling the mobile platform to move to each target point according to the path planning movement strategy;
the data acquisition module is used for monitoring the stability of the visual sensor through the stability detection unit, and when the data obtained after the stability monitoring is carried out by the stability detection unit meets a preset stability condition, image data acquisition is carried out on the area corresponding to each target point through the visual sensor.
In some embodiments, the mobile platform further comprises:
and the data processing module is used for analyzing and processing the sampling data acquired by the image data to obtain the wall surface quality information of the area corresponding to each target point.
In some embodiments, the sampled data after the image data is collected includes RGB data and point cloud data, and the data processing module further includes:
the color difference data processing submodule is used for converting the RGB data into HSV data and performing grouping identification distance calculation on the HSV data to obtain a distance calculation result; performing wall surface quality judgment on the target area according to a preset first distance threshold and the distance calculation result;
the point cloud data processing sub-module is used for carrying out plane fitting on the plurality of point cloud data to obtain a plurality of target planes corresponding to the point cloud data; and calculating the distance between each target point and the target plane to obtain a plurality of target points exceeding a preset second distance threshold, and judging the wall surface quality of the target area according to the plurality of target points exceeding the preset second distance threshold.
In some embodiments, the vision sensor is disposed on the mobile platform; the path planning module specifically includes:
the environment information acquisition sub-module is used for acquiring environment information of a target area through a laser radar and establishing an environment map corresponding to the target area;
the sampling interval submodule is used for determining the sampling interval distance on the environment map according to the sampling range of the visual sensor;
and the path planning submodule is used for determining a path planning movement strategy which comprises a plurality of target points and corresponds to the target area according to the environment map and the sampling interval distance.
In some embodiments, the motion control module specifically includes:
the initial position obtaining submodule is used for obtaining the initial position of the mobile platform in the target area;
the distance calculation submodule is used for calculating the distance between the mobile platform and the position of each target point and determining the initial target point of the mobile platform according to the obtained distance lengths;
and the motion control sub-module is used for 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.
In some embodiments, the controlling the mobile platform to move according to the path planning movement policy includes:
determining a next target point behind the initial target point according to the path planning movement strategy;
and controlling the mobile platform to move to the next target point for image data acquisition.
In some embodiments, the mobile platform is further provided with a rudder unit, and the data acquisition module specifically includes:
the attitude adjusting submodule is used for adjusting the attitude of the visual sensor through the steering gear set to obtain the stable information of the visual sensor;
the stability monitoring submodule is used for monitoring the stability of the visual sensor through the stability detection unit to obtain a stability monitoring result;
and the stability judgment sub-module is used for judging whether the stability monitoring result meets a preset stability condition or not, and when the stability monitoring result meets the preset stability condition, image data acquisition is carried out on the area corresponding to each target point through the visual sensor and the steering engine group.
In order to achieve the above object, a second aspect of the embodiments of the present application provides a wall surface quality detection method applied to the wall surface quality detection robot of the first aspect, where the method includes:
acquiring environmental information of a 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 comprising a plurality of target points according to the environmental map;
controlling the mobile platform to move to each target point according to the path planning movement strategy;
and carrying out stability monitoring on the visual sensor through a stability detection unit, and when the data obtained after the stability monitoring is carried out by the stability detection unit meets a preset stability condition, carrying out image data acquisition on the area corresponding to each target point through the visual sensor.
In order to achieve the above object, a third aspect of the embodiments of the present application provides an electronic device, which includes a memory, a processor, a program stored on the memory and executable on the processor, and a data bus for implementing connection communication between the processor and the memory, wherein the program, when executed by the processor, implements the method of the second aspect.
To achieve the above object, a fourth aspect of the embodiments of the present application proposes a storage medium, which is a computer-readable storage medium for computer-readable storage, and stores one or more programs, which are executable by one or more processors to implement the method of the second aspect.
According to the wall surface quality detection robot and the method, the electronic device and the storage medium, the environment information of the target area is obtained through the laser radar, the environment map corresponding to the target area is established according to the environment information, and the path planning movement strategy comprising a plurality of target points is established according to the environment map, so that the path planning of the wall surface quality detection robot is accurately achieved. And in order to completely 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. And carrying out stability monitoring on the visual sensor through the stability detection unit, wherein the data obtained after the stability monitoring is carried out on the stability detection unit meets the preset stability condition, and carrying out image data acquisition on the area corresponding to each target point through the visual sensor. The embodiment of the application can monitor and judge the stability of the visual sensor through the stability detection unit so as to improve the quality and efficiency of data acquisition and better complete the detection task of the wall surface quality.
Drawings
FIG. 1 is a block diagram of a wall quality inspection robot provided in an embodiment of the present application;
FIG. 2 is a flow chart illustrating the steps corresponding to the motion control sub-module provided in the embodiments of the present application;
FIG. 3 is a first schematic view of a wall quality inspection robot provided by an embodiment of the present application;
FIG. 4 is a second schematic view of a wall quality inspection robot provided by an embodiment of the present application;
fig. 5 is a flowchart of a wall surface quality detection method according to an embodiment of the present application;
fig. 6 is a flowchart of step S510 in fig. 5;
fig. 7 is a flowchart of step S520 in fig. 5;
fig. 8 is a flowchart of step S530 in fig. 5;
fig. 9 is a schematic hardware structure diagram of an electronic device according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more clearly understood, the present application is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
It should be noted that although functional blocks are partitioned in a schematic diagram of an apparatus and a logical order is shown in a flowchart, in some cases, the steps shown or described may be performed in a different order than the partitioning of blocks in the apparatus or the order in the flowchart. The terms first, second and the like in the description and in the claims, and the drawings described above, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of the present application only and is not intended to be limiting of the application.
First, several terms referred to in the present application are resolved:
inertial Measurement Unit (IMU): is a device for measuring the three-axis attitude angle (or angular velocity) and acceleration of an object. Gyroscopes and accelerometers are the main components of the IMU, the accuracy of which directly affects the accuracy of the inertial system. Generally, an IMU includes three single-axis accelerometers and three single-axis gyroscopes, the accelerometers detecting acceleration signals of the object in three independent axes of the carrier coordinate system, and the gyroscopes detecting angular velocity signals of the carrier relative to the navigation coordinate system, measuring the angular velocity and acceleration of the object in three-dimensional space, and calculating the attitude of the object based thereon.
Red-Green-Blue color pattern (Red-Green-Blue, RGB): is a color standard, and various colors are obtained by changing three color channels of red (R), green (G) and blue (B) and superposing the three color channels with each other. The RGB color mode can be used to represent any color that can be seen by the naked eye in nature, i.e., the three colors can be mixed and superimposed, and is also called additive color mode.
chroma-Saturation-luminance pattern (Hue-Saturation-Brightness, HSB): a color pattern representing a chromaticity (H), a saturation (S), and a brightness (B) describes three basic characteristics of a color based on human perception of the color. The HSB color mode is represented by three attributes of color, i.e., the three attributes of color are quantized, the saturation S and the brightness B are represented by percentage values, and the chroma is represented by angles. The saturation S represents the purity of the color, and the saturation is zero, i.e., gray. White, black and other gray-scale colors have no saturation, and the colors are purer the greater the saturation. The brightness B is the brightness of the color, which is black when the brightness is zero, and the brightness is the most vivid state of the color at the maximum.
At present, in the actual operation process, when the wall surface quality detection robot carries out data acquisition operation, when the machine body moves from one sampling point to the next sampling point to stop sampling, the visual acquisition device placed on the moving platform can slightly shake due to factors such as insufficient rigidity of the machine body and unevenness of the partial ground in the moving process under the inertia generated by movement. However, some high-definition cameras and scanners need to be kept completely still for a certain working time when acquiring data of a certain area, so that such a slight shake has a great influence on the quality of data acquired by a vision acquirer, and has a great possibility of causing data distortion and being unusable. The existing design scheme of the quality inspection robot has high requirements on a mobile platform and hardware, and has low data acquisition efficiency, so that the quality inspection robot cannot meet the requirements of the current building industry.
Based on this, the embodiment of the application provides a wall surface quality detection robot and method, an electronic device and a storage medium, and aims to improve the quality and efficiency of data acquisition so as to complete a task of detecting the wall surface quality.
The wall surface quality detection robot, the wall surface quality detection method, the electronic device, and the storage medium provided in the embodiments of the present application are specifically described with reference to the following embodiments, in which first a wall surface quality detection robot is described in the embodiments of the present application.
Referring to fig. 1, fig. 1 is a schematic block diagram of a wall quality inspection robot according to an embodiment of the present disclosure, where 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 disposed on the mobile platform.
The path planning module 110 is configured to 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 a plurality of target points according to the environmental map;
a motion control module 120, configured to control the mobile platform to move to each target point according to a path planning movement policy;
and the data acquisition module 130 is configured to perform stability monitoring on the visual sensor through the stability detection unit, and perform image data acquisition on an area corresponding to each target point through the visual sensor when data obtained after the stability monitoring is performed by the stability detection unit meets a preset stability condition.
In the path planning module 110 of some embodiments, in order to accurately implement path planning for the wall quality detection robot, the present application obtains environment information of a target area through a laser radar, establishes an environment map corresponding to the target area according to the environment information, and establishes a path planning movement strategy including a plurality of target points according to the environment map. Specifically, when the wall surface quality detection robot enters a target area, a task of detecting the wall surface quality is executed. The robot surrounds the target area along the wall under the guidance of the laser radar to acquire the environmental information of the target area, so that an environmental map corresponding to the target area is established according to the environmental information. After the map is built, the background module corresponding to the robot carries out path planning and target point selection on the target area according to the environment information and the built environment map so as to build a path planning movement strategy comprising a plurality of target points.
It should be noted that, when the robot performs wall-surrounding on the target area under the guidance of the laser radar, the number of the target points corresponding to the path planning movement strategy may be determined according to the sampling range of the vision sensor, for example, the number of the target points corresponding to the path planning movement strategy may be determined according to the sampling range of the binocular three-dimensional scanner, or the number of the target points to be sampled may be preset according to a requirement, which is not specifically limited herein.
It should be noted that the mobile platform may adopt a background command automatic setting mode, which is not limited herein.
It should be noted that the visual sensor may be a scanner, camera, or other device that involves point cloud data and image recognition and needs to be stabilized to collect valid data.
In the motion control module 120 of some embodiments, in order to completely implement the task of detecting the wall 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 according to the path execution command corresponding to the path planning movement strategy through the moving platform, that is, the operation that the control points need to be arranged in advance and cleaned after the work is completed can be avoided, and the quality and the efficiency of data acquisition are improved.
In the data collecting module 130 of some embodiments, in order to improve the quality and efficiency of data collection, stability monitoring is performed on the visual sensor through the stability detecting unit, and when the data obtained after the stability monitoring is performed by the stability detecting unit meets a preset stability condition, image data collection is performed on the area corresponding to each target point through the visual sensor. The embodiment of the application can monitor and judge the stability of the visual sensor through the stability detection unit so as to improve the quality and efficiency of data acquisition and better complete the detection task of the wall surface quality.
It should be noted that the preset stable condition refers to that the stability detection unit performs stability monitoring on the visual sensor, specifically, the acceleration of the visual sensor in each direction is monitored to be close to zero, or whether the shake of the visual sensor is stable in a negligible range is monitored.
It should be noted that the stability detection unit may be an inertial measurement unit IMU, that is, the IMU is used to perform stability monitoring on the visual sensor, monitor the stability information of the visual sensor, and when the data obtained after the IMU performs stability monitoring meets the preset stability condition, the visual sensor is used to perform image data acquisition on the area corresponding to each target point.
In some embodiments, the mobile platform of the wall quality detection robot further includes a data processing module, and the data processing module is configured to analyze and process the sampled data after the image data is collected, so as to obtain wall quality information of an area corresponding to each target point.
Specifically, in order to better complete the task of detecting the wall surface quality and judge the problem of the problem area in the wall surface, the sampled data after the image data is collected is analyzed and processed to obtain the wall surface quality information of the area corresponding to each target point.
In some embodiments, the sampling data after the image data is collected includes RGB data and point cloud data, and the data processing module on the wall quality inspection robot further includes:
the color difference data processing submodule is used for converting the RGB data into HSV data and performing grouping identification distance calculation on the HSV data to obtain a distance calculation result; judging the wall surface quality of the target area according to a preset first distance threshold and a distance calculation result;
the point cloud data processing submodule is used for carrying out plane fitting on the plurality of point cloud data to obtain target planes corresponding to the plurality of point cloud data; and calculating the distance between each target point and the target plane to obtain a plurality of target points exceeding a preset second distance threshold, and judging the wall surface quality of the target area according to the plurality of target points exceeding the preset second distance threshold.
Specifically, the sampling data after image data acquisition comprises RGB data and point cloud data, when the sampling data is RGB data, the RGB data is firstly converted into HSV data, and clustering identification distance calculation is carried out on the HSV data to obtain a distance calculation result. Specifically, the distance is identified in groups by using a K-Means algorithm, and a distance calculation result is obtained. And judging the wall surface quality of the target area according to a preset first distance threshold and a distance calculation result so as to judge whether the wall surface of the area corresponding to the current target point in the target area has cracks or fissures. And when the sampling data is point cloud data, performing plane fitting on the plurality of point cloud data to obtain a target plane corresponding to the plurality of point cloud data, namely the target plane is an ideal plane fitted by screening the plurality of point cloud data. And calculating the distance between each target point and the target plane to obtain a plurality of target points exceeding a preset second distance threshold, namely, gathering the plurality of target points exceeding the preset second distance threshold. And judging the wall surface quality of the target area according to the plurality of target points, specifically judging whether the current area is a crack or not according to the collected data, and calculating the length of the crack.
In order to facilitate the repair process of the crack or crack area, the position of the crack or crack in the target area is marked and retained in the established environment map.
It should be noted that, when the sampling data is RGB data, that is, the first distance threshold is a threshold processed according to color, for example, when the collected color of the wall surface is light gray, the color at the cracked place may be dark black, and by identifying the corresponding HSV data, a point on the color where a sudden change occurs may be found, that is, a problem wall surface area in the target area is determined.
In some embodiments, the vision sensor is disposed on a mobile platform of the wall quality inspection robot, and the path planning module 110 specifically includes:
the environment information acquisition submodule is used for acquiring environment information of the target area through the laser radar and establishing an environment map corresponding to the target area;
the sampling interval submodule is used for determining the sampling interval distance on the environment map according to the sampling range of the visual sensor;
and the path planning submodule is used for determining a path planning movement strategy which comprises a plurality of target points and corresponds to the target area according to the environment map and the sampling interval distance.
Specifically, in order to accurately implement path planning for the wall quality detection robot, environment information is acquired for a target area through a laser radar, and an environment map corresponding to the target area is established. And determining a sampling interval distance on the environment map according to the sampling range of the visual sensor, namely determining the sampling interval distance for detecting the wall surface quality of the robot on the path corresponding to the current target area. And determining a path planning movement strategy comprising a plurality of target points corresponding to the target area according to the environment map and the sampling interval distance. The embodiment of the application can monitor and judge the stability of the visual sensor through the stability detection unit so as to improve the quality and efficiency of data acquisition and better complete the detection task of the wall surface quality.
In some embodiments, the motion control module 120 on the mobile platform specifically includes:
the initial position acquisition submodule is used for acquiring the initial position of the mobile platform in a target area;
the distance calculation submodule is used for calculating the distance between the mobile platform and the position of each target point and determining the initial target point of the mobile platform according to the obtained distance lengths;
and the motion control sub-module is used for 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.
Specifically, when the mobile platform is controlled to move to each target point according to a path planning movement strategy, the initial position of the mobile platform in a target area is firstly obtained, the distance between the mobile platform and the position of each target point is calculated, and the initial target point of the mobile platform for moving is determined according to the obtained distance lengths. For example, the robot may specifically be selected to start entering the wall closest to the entrance in the target area. The mobile platform is controlled to move to an initial target point and is controlled to move according to a path planning movement strategy.
Referring to fig. 2, in some embodiments, the steps of the motion control sub-module for controlling the mobile platform to move according to the path planning movement strategy include, but are not limited to, step S210 and step S220.
Step S210, determining a next target point after the initial target point according to the path planning movement strategy;
and step S220, controlling the mobile platform to move to the next target point for image data acquisition.
Specifically, in order to better complete the task of detecting the wall quality, after data acquisition is completed for the area corresponding to the initial target point, a next target point behind 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 acquisition.
It should be noted that, after data collection is completed for all target points selected in the path planning movement strategy, the mobile platform in the wall surface quality detection robot exits the current target area and enters the next target area to perform a task of detecting the wall surface quality.
In some embodiments, the mobile platform is further provided with a rudder unit, and the data acquisition module 130 specifically includes:
the attitude adjustment sub-module is used for adjusting the attitude of the visual sensor through the steering engine group to obtain the stable information of the visual sensor;
the stability monitoring submodule is used for monitoring the stability of the visual sensor through the stability detection unit to obtain a stability monitoring result;
and the stability judgment submodule is used for judging whether the stability monitoring result meets the preset stability condition or not, and when the stability monitoring result meets the preset stability condition, image data acquisition is carried out on the area corresponding to each target point through the visual sensor and the steering engine group.
Specifically, in order to keep the stability of vision sensor to improve data acquisition's quality and efficiency, still be provided with the rudder unit on mobile platform, this rudder unit includes that three steering wheel is constituteed, this application embodiment establishes vision sensor rack on mobile platform through the rudder unit, carries out gesture adjustment through steering unit group to vision sensor, obtains vision sensor's stable information. And carrying out stability monitoring on the visual sensor through the stability detection unit, and carrying out image data acquisition on an area corresponding to each target point through the visual sensor when data obtained after the stability monitoring is carried out by the stability detection unit meets a preset stability condition. The embodiment of the application can avoid the operations of arranging the control sites in advance and cleaning the control sites after the work is finished, and the stability monitoring and judgment are carried out on the visual sensor through the stable detection unit, so that the quality and the efficiency of data acquisition are improved.
It should be noted that, when the wall surface quality detection robot enters a target area through a narrow door, the vision sensor can change the original transverse vision sensor into a vertical posture through the steering unit, so as to realize smooth passing.
In a specific embodiment, referring to fig. 3, a wall surface quality detection robot includes a moving platform 310, and a laser radar 320, a steering engine set 330, and a visual sensor 340 are further disposed on the moving platform, where the steering engine set is composed of three steering engines. In order to maintain the stability of the vision sensor and improve the quality and efficiency of data acquisition, referring to fig. 4, a stability detection unit 350, such as an IMU, is disposed near the vision sensor 340. The environment information of the target area is acquired through the laser radar 320, an environment map corresponding to the target area is established, and the sampling interval distance on the environment map is determined according to the sampling range of the visual sensor 340, so that the path planning movement strategy comprising a plurality of target points corresponding to the target area is determined. The initial position of the mobile platform 310 in the target area is obtained, the distance between the mobile platform 310 and the position of each target point is calculated, the initial target point of the mobile platform 310 for moving is determined according to the obtained distance lengths, the mobile platform 310 is controlled to move to the initial target point, and therefore the mobile platform is controlled to move according to the path planning movement strategy. When data acquisition is started for the area corresponding to the initial target point, the attitude of the vision sensor 340 is adjusted by the rudder unit 330, so as to obtain the stable information of the vision sensor 340. And the stability detection unit 350 monitors the stability of the vision sensor 340, and when the stability monitoring result meets a preset stability condition, the vision sensor 340 and the steering unit 330 acquire image data of an area corresponding to each target point. The embodiment of the application can avoid the operations of arranging the control sites in advance and cleaning the control sites after the work is finished, and the stability monitoring and judgment are carried out on the visual sensor through the stable detection unit, so that the quality and the efficiency of data acquisition are improved.
Referring to fig. 5, fig. 5 is a flowchart of a method of detecting wall quality according to an embodiment of the present application, where the method is applicable to a wall quality detection robot according to the above embodiment, and the method includes, but is not limited to, steps S510 to S530.
Step S510, acquiring environment information of a target area through a laser radar, establishing an environment map corresponding to the target area according to the environment information, and establishing a path planning movement strategy comprising a plurality of target points according to the environment map;
step S520, controlling the mobile platform to move to each target point according to the path planning movement strategy;
step S530, stability monitoring is carried out on the visual sensor through the stability detection unit, when data obtained after the stability monitoring is carried out on the stability detection unit meets preset stability conditions, image data collection is carried out on the area corresponding to each target point through the visual sensor.
The specific implementation of the wall quality detection method is basically the same as that of the wall quality detection robot, and is not described herein again.
In some embodiments, the wall surface quality detection method provided in the embodiment of the present application further includes: and analyzing and processing the sampled data after the image data is collected to obtain the wall surface quality information of the area corresponding to each target point.
In some embodiments, the sampling data after the image data is acquired includes RGB data and point cloud data, and step 530 in the wall surface quality detection method provided in this embodiment further includes: converting the RGB data into HSV data, and performing clustering identification distance calculation on the HSV data to obtain a distance calculation result; judging the wall surface quality of the target area according to a preset first distance threshold and a distance calculation result; performing plane fitting on the plurality of point cloud data to obtain target planes corresponding to the plurality of point cloud data; and calculating the distance between each target point and the target plane to obtain a plurality of target points exceeding a preset second distance threshold, and judging the wall surface quality of the target area according to the plurality of target points exceeding the preset second distance threshold.
Referring to fig. 6, in some embodiments, the visual sensor is disposed on the mobile platform, and step S510 of the wall quality detection method provided in the embodiments of the present application specifically includes, but is not limited to, step S610 to step S630.
Step S610, acquiring environment information of a target area through a laser radar, and establishing an environment map corresponding to the target area;
step S620, determining a sampling interval distance on the environment map according to the sampling range of the visual sensor;
step S630, according to the environment map and the sampling interval distance, determining a path planning movement strategy which comprises a plurality of target points and corresponds to the target area.
Referring to fig. 7, in some embodiments, the step S520 of the wall surface quality detection method provided in the embodiments of the present application specifically includes, but is not limited to, the steps S710 to S730.
Step S710, acquiring the initial position of the mobile platform in the target area;
step S720, calculating the distance between the mobile platform and the position of each target point, and determining the initial target point of the mobile platform according to the obtained distance lengths;
and step S730, controlling the mobile platform to move to an initial target point, and controlling the mobile platform to move according to the path planning movement strategy.
Referring to fig. 8, in some embodiments, a rudder unit is further disposed on the mobile platform, and step S530 of the wall surface quality detection method provided in the embodiment of the present application specifically includes, but is not limited to, step S810 to step S830.
Step S810, carrying out attitude adjustment on the visual sensor through a steering engine group to obtain stable information of the visual sensor;
step S820, carrying out stability monitoring on the visual sensor through a stability detection unit to obtain a stability monitoring result;
and S830, judging whether the stability monitoring result meets a preset stability condition, and when the stability monitoring result meets the preset stability condition, acquiring image data of an area corresponding to each target point through the visual sensor and the steering gear set.
The wall surface quality detection method provided by the embodiment of the application can be applied to a terminal, a server side and software running in the terminal or the server side. In some embodiments, the terminal may be a smartphone, tablet, laptop, desktop computer, or the like; the server side can be configured into an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, and cloud servers for providing basic cloud computing services such as cloud service, a cloud database, cloud computing, cloud functions, cloud storage, network service, cloud communication, middleware service, domain name service, security service, CDN (content delivery network) and big data and artificial intelligence platforms; the software may be an application for implementing a wall surface quality detection method, and the like, but is not limited to the above form.
The application is operational with numerous general purpose or special purpose computing system environments or configurations. For example: personal computers, server computers, hand-held or portable devices, tablet-type devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like. The application may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The application may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
An embodiment of the present application further provides an electronic device, where the electronic device includes: the wall surface quality detection method comprises a memory, a processor, a program stored on the memory and capable of running on the processor, and a data bus for realizing connection communication between the processor and the memory, wherein the program realizes the wall surface quality detection method when being executed by the processor. The electronic equipment can be any intelligent terminal including a tablet computer, a vehicle-mounted computer and the like.
Referring to fig. 9, fig. 9 illustrates a hardware structure of an electronic device according to another embodiment, where the electronic device includes:
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 configured to execute a relevant program to implement the technical solution provided in the embodiment of the present Application;
the Memory 920 may 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 may store an operating system and other application programs, and when the technical solution provided by the embodiments of the present disclosure is implemented by software or firmware, the relevant program codes are stored in the memory 920 and called by the processor 910 to execute the wall surface quality detection method according to the embodiments of the present disclosure;
an input/output interface 930 for implementing information input and output;
the communication interface 940 is configured to implement communication interaction between the device and other devices, and may implement communication in a wired manner (e.g., USB, network cable, etc.) or in a wireless manner (e.g., mobile network, WIFI, bluetooth, etc.);
a bus 950 that transfers information between various components of the device (e.g., the processor 910, the memory 920, the input/output interface 930, and the communication interface 940);
wherein the processor 910, the memory 920, the input/output interface 930, and the communication interface 940 are communicatively coupled to each other within the device via a bus 950.
The embodiment of the application also provides a storage medium, which is a computer-readable storage medium and is used for computer-readable storage, where 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 wall surface quality detection method.
The memory, which is a non-transitory computer readable storage medium, may be used to store non-transitory software programs as well as non-transitory computer executable programs. Further, the memory may include high speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory optionally includes memory located remotely from the processor, and these remote memories may be connected to the processor through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
According to the wall surface quality detection robot and the method, the electronic device and the storage medium, the laser radar is used for acquiring the environment information of the target area, the environment map corresponding to the target area is established according to the environment information, and the path planning movement strategy comprising a plurality of target points is established according to the environment map, so that the path planning of the wall surface quality detection robot is accurately realized. And in order to completely 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. And carrying out stability monitoring on the visual sensor through the stability detection unit, wherein the data obtained after the stability monitoring is carried out on the stability detection unit meets the preset stability condition, and carrying out image data acquisition on the area corresponding to each target point through the visual sensor. The embodiment of the application can monitor and judge the stability of the visual sensor through the stability detection unit so as to improve the quality and efficiency of data acquisition and better complete the detection task of the wall surface quality.
The embodiments described in the embodiments of the present application are for more clearly illustrating the technical solutions of the embodiments of the present application, and do not constitute a limitation to the technical solutions provided in the embodiments of the present application, and it is obvious to those skilled in the art that the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems with the evolution of technology and the emergence of new application scenarios.
Those skilled in the art will appreciate that the solutions shown in fig. 1 to 8 do not constitute a limitation on the embodiments of the present application, and may include more or less steps than those shown, or combine some steps, or different steps.
The above described embodiments of the apparatus are merely illustrative, wherein the units illustrated as separate components may or may not be physically separate, may be located in one place, or may be distributed over a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
One of ordinary skill in the art will appreciate that all or some of the steps of the methods, systems, functional modules/units in the devices disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof.
The terms "first," "second," "third," "fourth," and the like in the description of the application and the above-described figures, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
It should be understood that in the present application, "at least one" means one or more, "a plurality" means two or more. "and/or" for describing an association relationship of associated objects, indicating that there may be three relationships, e.g., "a and/or B" may indicate: only A, only B and both A and B are present, wherein A and B may be singular or plural. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. "at least one of the following" or similar expressions refer to any combination of these items, including any combination of single item(s) or plural items. For example, at least one (one) of a, b, or c, may represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", wherein a, b, c may be single or plural.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the above-described units is only one type of logical functional division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit may be implemented in the form of hardware, or may also be implemented in the form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a separate product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be substantially implemented or contributed to by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes multiple instructions for causing a computer device (which may be a personal computer, a server, or a network device) to perform all or part of the steps of the method of the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing programs, such as a usb disk, a removable hard disk, a Read Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
The preferred embodiments of the present application have been described above with reference to the accompanying drawings, and the scope of the claims of the embodiments of the present application is not limited thereto. Any modifications, equivalents and improvements that may occur to those skilled in the art without departing from the scope and spirit of the embodiments of the present application are intended to be within the scope of the claims of the embodiments of the present application.
Claims (10)
1. The utility model provides a wall quality testing robot, its characterized in that, the robot includes moving platform, last path planning module, the motion control module, the data acquisition module of being provided with of moving platform, wherein:
the path planning module is used for acquiring environmental information of a 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 comprising a plurality of target points according to the environmental map;
the motion control module is used for controlling the mobile platform to move to each target point according to the path planning movement strategy;
the data acquisition module is used for monitoring the stability of the visual sensor through the stability detection unit, and when the data obtained after the stability monitoring is carried out by the stability detection unit meets a preset stability condition, image data acquisition is carried out on the area corresponding to each target point through the visual sensor.
2. The wall surface quality detection robot of claim 1, wherein the mobile platform further comprises:
and the data processing module is used for analyzing and processing the sampled data after the image data is collected to obtain the wall surface quality information of the area corresponding to each target point.
3. The wall surface quality detection robot as claimed in claim 2, wherein the sampled data after the image data is collected includes RGB data and point cloud data, and the data processing module further includes:
the color difference data processing submodule is used for converting the RGB data into HSV data and performing grouping identification distance calculation on the HSV data to obtain a distance calculation result; performing wall surface quality judgment on the target area according to a preset first distance threshold and the distance calculation result;
the point cloud data processing submodule is used for carrying out plane fitting on the plurality of point cloud data to obtain a plurality of target planes corresponding to the point cloud data; and calculating the distance between each target point and the target plane to obtain a plurality of target points exceeding a preset second distance threshold, and judging the wall surface quality of the target area according to the plurality of target points exceeding the preset second distance threshold.
4. A wall surface quality inspection robot as claimed in any one of claims 1 to 3, wherein the vision sensor is provided on the mobile platform; the path planning module specifically includes:
the environment information acquisition sub-module is used for acquiring environment information of a target area through a laser radar and establishing an environment map corresponding to the target area;
the sampling interval submodule is used for determining a sampling interval distance on the environment map according to the sampling range of the visual sensor;
and the path planning submodule is used for determining a path planning movement strategy which comprises a plurality of target points and corresponds to the target area according to the environment map and the sampling interval distance.
5. The wall surface quality detection robot according to any one of claims 1 to 3, wherein the motion control module specifically comprises:
the initial position obtaining submodule is used for obtaining the initial position of the mobile platform in the target area;
the distance calculation sub-module is used for calculating the distance between the mobile platform and the position of each target point and determining the initial target point of the mobile platform according to the obtained distance lengths;
and the motion control sub-module is used for 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.
6. The wall surface quality detection robot of claim 5, wherein the controlling the mobile platform to move according to the path planning movement strategy comprises:
determining a next target point after the initial target point according to the path planning movement strategy;
and controlling the mobile platform to move to the next target point for image data acquisition.
7. The wall surface quality detection robot according to any one of claims 1 to 3, wherein a rudder unit is further provided on the mobile platform, and the data acquisition module specifically includes:
the attitude adjusting submodule is used for adjusting the attitude of the visual sensor through the steering gear set to obtain the stable information of the visual sensor;
the stability monitoring submodule is used for monitoring the stability of the visual sensor through the stability detection unit to obtain a stability monitoring result;
and the stability judgment submodule is used for judging whether the stability monitoring result meets a preset stability condition or not, and when the stability monitoring result meets the preset stability condition, image data acquisition is carried out on the area corresponding to each target point through the visual sensor and the steering gear group.
8. A wall surface quality detection method applied to the wall surface quality detection robot of any one of claims 1 to 7, the method comprising:
acquiring environmental information of a 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 comprising a plurality of target points according to the environmental map;
controlling the mobile platform to move to each target point according to the path planning movement strategy;
and carrying out stability monitoring on the visual sensor through a stability detection unit, and when the data obtained after the stability monitoring is carried out by the stability detection unit meets a preset stability condition, carrying out image data acquisition on the area corresponding to each target point through the visual sensor.
9. An electronic device, characterized in that the electronic device comprises a memory, a processor, a program stored on the memory and executable on the processor, and a data bus for enabling a connection communication between the processor and the memory, which program, when executed by the processor, realizes the steps of the method as claimed in claim 8.
10. A storage medium, the storage medium being a computer-readable storage medium for computer-readable storage, wherein the storage medium stores one or more programs executable by one or more processors to implement the steps of the method of claim 8.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117563843A (en) * | 2023-11-14 | 2024-02-20 | 广州赛特智能科技有限公司 | Automatic spraying method, device, intelligent mobile robot and storage medium |
| CN117742338A (en) * | 2023-12-26 | 2024-03-22 | 天津河工大先进装备研究院有限公司 | Curtain wall repair robot scheduling system based on images |
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| CN118379640A (en) * | 2024-06-26 | 2024-07-23 | 山东光安智能科技有限公司 | Intelligent inspection method, system, equipment and medium for granary |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117563843A (en) * | 2023-11-14 | 2024-02-20 | 广州赛特智能科技有限公司 | Automatic spraying method, device, intelligent mobile robot and storage medium |
| CN117742338A (en) * | 2023-12-26 | 2024-03-22 | 天津河工大先进装备研究院有限公司 | Curtain wall repair robot scheduling system based on images |
| CN117852567A (en) * | 2024-03-08 | 2024-04-09 | 广东电网有限责任公司梅州供电局 | RFID tag-based power appliance data processing and analyzing method and system |
| CN117852567B (en) * | 2024-03-08 | 2024-06-11 | 广东电网有限责任公司梅州供电局 | RFID tag-based power appliance data processing and analyzing method and system |
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