Building robot construction evaluation and control method and device and electronic equipment

Abstract

The invention provides a building robot construction evaluation and control method and device and electronic equipment. According to the implementation scheme, the method comprises the steps that based on spreading viscosity distribution and spreading speed distribution of a first spreading area in a first wall surface, spreading fluidity distribution of the first spreading area is determined, so that a local fluidity abnormal area is determined; on the basis of the local liquidity abnormal area, the movement track of the plastering mechanical arm is compensated, and a target movement track of the plastering mechanical arm is obtained; and when the plastering mechanical arm conducts plastering based on the target movement track and plastering of the first plaster spreading area is completed, the plaster layer flatness of the first plaster spreading area is determined based on plaster layer thickness distribution of the first plaster spreading area, and if the plastering flatness meets the preset condition, it is determined that plaster spreading of the first plaster spreading area in the first wall is completed, and then the first wall in the second wall is plastered. And the plastering mechanical arm is triggered to plaster the second plaster spreading area in the first wall surface. By adopting the robot, the plastering efficiency of the robot and the flatness of a plastered layer of a wall surface can be improved.

Description

Technical Field

[0001] The present invention relates to the field of electromechanical control technology, and in particular to a construction evaluation and control method, device and electronic equipment for a construction robot. Background Art

[0002] With the development of artificial intelligence technology, the application of robots in the construction field can improve construction quality and reduce labor costs.

[0003] Among them, when plastering the walls of buildings, plastering robots are used for plastering. During the plastering process, how to control the robot to achieve uniform laying of the plaster layer and control the surface flatness is a technical problem that those skilled in the art need to solve. Summary of the Invention

[0004] The present invention provides a construction evaluation and control method, device and electronic equipment for a construction robot, which can solve at least one of the above technical problems.

[0005] According to one aspect of the present invention, a construction robot construction evaluation and control method is provided, comprising: When a plastering robot arm of a wall plastering robot is plastering a first wall, determining a plastering fluidity distribution in a first plastering area on the first wall based on a plastering viscosity distribution and a plastering speed distribution in the first plastering area; and determining a local fluidity abnormality area based on the plastering fluidity distribution in the first plastering area; Based on the position information of the local fluidity abnormal area, the motion trajectory of the plastering robot arm is compensated to obtain the target motion trajectory of the plastering robot arm; When the plastering robot arm is plastering based on the target motion trajectory and the first plastering area has been completed, determining the flatness of the plaster layer in the first plastering area based on the thickness distribution of the plaster layer in the first plastering area; When the plastering flatness of the first plastering area meets the preset conditions, it is determined that the first plastering area in the first wall has been completed, and the plastering robot arm is triggered to plaster the second plastering area in the first wall.

[0006] According to another aspect of the present invention, there is provided a construction robot construction evaluation and control device, comprising: a fluidity distribution determining module, configured to determine, when a plastering robot arm of a wall plastering robot is plastering a first wall, a fluidity distribution of a first plastering area on the first wall based on a plastering viscosity distribution and a plastering speed distribution of the first plastering area; An abnormal region determining module, configured to determine a local fluidity abnormal region based on the ash spreading fluidity distribution of the first ash spreading region; a trajectory compensation module, configured to compensate the motion trajectory of the plastering robot arm based on the position information of the local fluidity abnormality area to obtain a target motion trajectory of the plastering robot arm; a flatness determination module, configured to determine the flatness of the plaster layer in the first plastering area based on a thickness distribution of the plaster layer in the first plastering area, when the plastering robot arm is plastering based on the target motion trajectory and the plastering of the first plastering area has been completed; The plastering control module is used to determine that the plastering of the first plastering area in the first wall has been completed when the plastering flatness of the first plastering area meets the preset conditions, and trigger the plastering robot arm to plaster the second plastering area in the first wall.

[0007] According to another aspect of the present invention, a construction robot construction assessment and control system is provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the construction robot construction assessment and control method described in any one of the embodiments of the present invention.

[0008] According to another aspect of the present invention, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to enable a computer to execute the construction evaluation and control method of a construction robot described in any one of the embodiments of the present invention.

[0009] According to the technical solution of the present invention, when a plastering robot arm is plastering a first wall, the plastering fluidity distribution of the first plastering area is determined based on the plastering viscosity distribution and plastering speed distribution of the first plastering area. Based on the plastering fluidity distribution of the first plastering area, a localized abnormal fluidity region is determined. Based on the location information of the localized abnormal fluidity region, the motion trajectory of the plastering robot arm is compensated to obtain a target motion trajectory of the plastering robot arm. In this way, during the plastering process on the first plastering area, the motion trajectory of the plastering robot arm can be dynamically adjusted to avoid the occurrence of abnormal plastering regions, thereby protecting the smoothness of the plaster layer during the plastering process. Furthermore, when the plastering robot arm is plastering based on the target motion trajectory and the first plastering area has been completed, the smoothness of the plaster layer in the first plastering area is determined based on the plastering layer thickness distribution in the first plastering area. If the plastering smoothness of the first plastering area meets a preset condition, the first plastering area on the first wall is determined to be completed, and the plastering robot arm is triggered to plaster the second plastering area on the first wall. In this way, after the first plastering area is finished, the flatness of that area is evaluated, and only if it meets the requirements will the next plastering area be plastered. This further ensures the flatness of the wall plaster layer. Therefore, the technical solution of the present invention can improve the flatness of the wall plaster layer.

[0010] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present invention, nor is it intended to limit the scope of the present invention. Other features of the present invention will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] The accompanying drawings are provided for a better understanding of the present invention and do not constitute a limitation of the present invention. Figure 1 is a flow chart of a construction robot construction evaluation and control method according to an embodiment of the present invention; Figure 2 This is a schematic structural diagram of a wall plastering robot according to an embodiment of the present invention; Figure 3 Schematic diagram of wall plastering according to an embodiment of the present invention.

[0012] Figure 4 This is a structural block diagram of a construction robot construction evaluation and control device according to an embodiment of the present invention; Figure 5 is a block diagram of an electronic device for implementing the method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0013] The following description of exemplary embodiments of the present invention is made in conjunction with the accompanying drawings, and various details of the embodiments of the present invention are included to facilitate understanding. These details should be considered as merely exemplary. Therefore, it should be appreciated by those skilled in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope of the present invention. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0014] Figure 1 This is a flow chart of a construction robot construction evaluation and control method according to an embodiment of the present invention.

[0015] like Figure 1 As shown, the construction robot construction evaluation and control method may include: S110, when a plastering robot arm of a wall plastering robot is plastering a first wall, determining a plastering fluidity distribution in a first plastering area on the first wall based on a plastering viscosity distribution and a plastering speed distribution in the first plastering area; S120, determining a local fluidity abnormality region based on the ash spreading fluidity distribution in the first ash spreading region; S130, compensating the motion trajectory of the plastering robot arm based on the position information of the local fluidity abnormality area to obtain a target motion trajectory of the plastering robot arm; S140, when the plastering robot arm is plastering based on the target motion trajectory and the plastering of the first plastering area has been completed, determining the flatness of the plaster layer in the first plastering area based on the thickness distribution of the plaster layer in the first plastering area; S150, when the plastering flatness of the first plastering area meets the preset conditions, determine that the first plastering area in the first wall surface has been completed, and trigger the plastering robot arm to plaster the second plastering area in the first wall surface.

[0016] For example, the plastering area can be the area where the plastering robot arm spreads the slurry sprayed on the wall. The first plastering area can include an unplastered area and a slurry area. The second plastering area refers to the next plastering area. The first plastering area is connected to the second plastering area.

[0017] For example, the ash spreading viscosity distribution may include the viscosity of each ash spreading position at the current moment. The ash spreading speed distribution may include the speed of each ash spreading position at the current moment. Viscosity and speed information can be collected using sensors or image information.

[0018] For example, the ash spreading fluidity distribution may include a fluidity index of each ash spreading position at the current moment. The fluidity index may be a numerical value.

[0019] For example, the local fluidity abnormal region refers to a gray spreading region where the gray spreading fluidity does not meet preset requirements, and is a partial region within the first gray spreading region.

[0020] For example, the motion trajectory of the plastering robot arm can include the robot arm's plastering position, plastering force, and plastering direction at each point in time. Therefore, using the position information of the local fluidity abnormality area and the fluidity index corresponding to each position, at least one of the robot arm's plastering position, plastering force, and plastering direction at each point in time can be adjusted to obtain the target motion trajectory of the plastering robot arm, and targeted plastering can be strengthened in the local fluidity abnormality area to ensure the smoothness of the local fluidity abnormality area after plastering. For example, for locations with poor fluidity indicators, the plastering force can be increased at that location in the motion trajectory.

[0021] Exemplarily, a statistical analysis is performed on the thickness distribution of the plaster layer in the first plastering area to obtain parameters such as the average value and the range, and the parameters obtained by the statistical analysis are used to determine the flatness of the plaster layer in the first plastering area.

[0022] Figure 2 Schematic diagram of the structure of a wall plastering robot according to an embodiment of the present invention. The robot comprises a plastering robot arm 2, a plastering device 1 mounted on the plastering robot arm 2, a slurry preparation device 3 and a robot support plate 4. The method according to the embodiment of the present invention can be applied to Figure 2 The robot in the.

[0023] According to the above embodiment, when a plastering robot arm is plastering a first wall, the plastering fluidity distribution of the first plastering region is determined based on the plastering viscosity distribution and plastering speed distribution of the first plastering region. Based on the plastering fluidity distribution of the first plastering region, a localized fluidity anomaly region is determined. Based on the location information of the localized fluidity anomaly region, the motion trajectory of the plastering robot arm is compensated to obtain a target motion trajectory of the plastering robot arm. In this way, during the plastering process on the first plastering region, the motion trajectory of the plastering robot arm can be dynamically adjusted to avoid the occurrence of the plastering anomaly region, thereby protecting the smoothness of the plaster layer during the plastering process. Furthermore, when the plastering robot arm is plastering based on the target motion trajectory and the first plastering region has been completed, the smoothness of the plaster layer in the first plastering region is determined based on the plastering layer thickness distribution in the first plastering region. If the plastering smoothness of the first plastering region meets a preset condition, the first plastering region of the first wall is determined to be completed, and the plastering robot arm is triggered to plaster the second plastering region of the first wall. In this way, after the first plastering area is finished, the flatness of that area is evaluated, and only if it meets the requirements will the next plastering area be plastered. This further ensures the flatness of the wall plaster layer. Therefore, the technical solution of the present invention can improve the flatness of the wall plaster layer.

[0024] In one embodiment, based on the plastering viscosity distribution and the plastering speed distribution of the first plastering area in the first wall surface, the plastering fluidity distribution of the first plastering area is determined, including: determining the slurry thickness variation curve of the plastering line of the first plastering area at the current moment based on the thickness distribution of the slurry area in the first plastering area, wherein the plastering line is the boundary line between the slurry area and the unplastered area in the first plastering area; predicting the slurry thickness variation curve of the plastering line of the first plastering area at the next moment based on the slurry thickness variation curve of the plastering line of the first plastering area at the current moment, as well as the slurry viscosity of each plastering position in the plastering viscosity distribution at the current moment and the slurry viscosity of each plastering position in the plastering speed distribution at the current moment; determining the plastering fluidity distribution of the first plastering area based on the plastering thickness variation curve of the plastering line of the first plastering area at the current moment and the slurry line position change curve and thickness change of the plastering line between the plaster thickness variation curve of the plastering line of the first plastering area at the next moment, wherein the plastering fluidity distribution is used to describe the slurry fluidity of each plastering position.

[0025] For example, Figure 3 As shown, Figure 3 The dotted line in the figure is the spread line. Under the action of gravity, the slurry will flow downward along the wall, the spread line will continue to move downward, and the slurry thickness on the spread line will be different at each spread line position on the spread line.

[0026] For example, a pre-trained neural network model can be used to predict the next gray line of the first gray area and the position-dependent curve of the slurry thickness on the gray line. Alternatively, a cloud-based simulation model can be used to perform online simulation to obtain the next gray line of the first gray area and the position-dependent curve of the slurry thickness on the gray line.

[0027] For example, by comparing the gray line position information in the slurry thickness variation with position curve at moment A with the gray line position information in the slurry thickness variation with position curve at moment B, the gray line position change can be determined; by comparing the thickness at each gray line position in the slurry thickness variation with position curve at moment A with the thickness at the corresponding gray line position in the slurry thickness variation with position curve at moment B, the thickness change at each corresponding gray line position can be determined.

[0028] For example, based on the gray line position change and the thickness change at each corresponding gray line position, the position change speed and thickness change speed at each corresponding gray line position are determined. Based on the position change speed and thickness change speed at each corresponding gray line position, the gray spread fluidity at each corresponding gray line position is determined. For example, a complex number obtained by taking the position change speed as the real part and the thickness change speed as the imaginary part is used as the gray spread fluidity.

[0029] For example, the ash spreading fluidity distribution in the first ash spreading area can be predicted by using the slurry fluidity at the ash spreading line position.

[0030] According to the above embodiment, the change in the gray line position between two previous and subsequent moments and the change in the slurry thickness at each corresponding gray line position can be used to determine the slurry fluidity at the gray line position, thereby accurately predicting the gray fluidity distribution in the first gray area.

[0031] In one embodiment, based on the ash spreading fluidity distribution in the first ash spreading area, a local fluidity abnormality area is determined, including: determining the slurry fluidity change between two adjacent ash spreading positions based on the slurry fluidity of each ash spreading position in the ash spreading fluidity distribution; determining a continuous ash spreading position change interval in which the slurry fluidity change is greater than a preset threshold based on the slurry fluidity change between each two adjacent ash spreading positions; and determining the local fluidity abnormality area based on the position of the continuous ash spreading position change interval in the first ash spreading area.

[0032] For example, the fluidity distribution along the gray line is determined from the gray line fluidity distribution. Based on the slurry fluidity at each gray line location, the slurry fluidity change between two adjacent gray line locations is determined. It is understood that there are multiple gray line locations along the gray line, and each gray line location can be represented by a two-dimensional or three-dimensional coordinate.

[0033] Exemplarily, based on the slurry fluidity changes at two adjacent gray spreading line positions, a continuous gray spreading line position change interval in which the slurry fluidity change is greater than a preset threshold is determined; Exemplarily, with a preset neighborhood radius, a neighborhood range including a position change interval of continuous gray lines is determined in the first gray area, and the neighborhood range is used as a local fluidity abnormality area.

[0034] According to the above embodiment, the mobility distribution on the gray line is used to determine the abnormal continuous gray line position change interval, and thus, the abnormal continuous gray line position change interval is used to determine the corresponding mobility abnormal area in the first gray area. In this way, the mobility abnormal area can be quickly and accurately determined.

[0035] In one embodiment, based on the position information of the local fluidity abnormality area, the motion trajectory of the plastering robot arm is compensated to obtain the target motion trajectory of the plastering robot arm, including: determining the initial motion trajectory of the plastering robot arm based on the slurry thickness variation curve of the plastering line of the first plastering area at the next moment, wherein the initial motion trajectory includes the plastering position, plastering force and plastering direction corresponding to each time point; determining the slurry fluidity of each plastering position in the local fluidity abnormality area based on the slurry fluidity of each plastering position in the plastering fluidity distribution; adjusting the plastering force and plastering direction at the corresponding plastering position in the initial motion trajectory based on the slurry fluidity of each plastering position in the local fluidity abnormality area to obtain the target motion trajectory of the plastering robot arm.

[0036] For example, the slurry thickness at the plastering line in the slurry thickness versus position curve of the plastering line is used to determine the plastering force applied by the plastering robot arm at that plastering line and at other locations along a direction perpendicular to the plastering line and passing through the plastering line. For example, the greater the slurry thickness, the greater the plastering force at the corresponding location, and the relationship between the two can be linearly proportional. The plastering direction at the plastering line is the direction perpendicular to the plastering line and passing through the plastering line.

[0037] For example, if the slurry fluidity at the ash spreading position in the local fluidity abnormality area is large, the ash spreading force at the corresponding ash spreading position in the initial motion trajectory is reduced; if the slurry fluidity at the ash spreading position in the local fluidity abnormality area is small, the ash spreading force at the corresponding ash spreading position in the initial motion trajectory is increased.

[0038] Exemplarily, for the abnormal area, the gray spreading force and gray spreading direction at the corresponding gray spreading position in the initial motion trajectory are adjusted.

[0039] According to the above embodiment, the initial motion trajectory of the plastering robot is determined based on the slurry thickness versus position curve of the plastering line in the first plastering area at the next moment. Then, using the slurry fluidity at each plastering location within the localized fluidity anomaly region, the plastering force and direction at the corresponding plastering location in the initial motion trajectory are adjusted to obtain the target motion trajectory of the plastering robot. This allows for targeted plastering with increased or decreased force in areas with fluidity anomalies, improving the plastering smoothness of the entire area along the plastering line.

[0040] In one embodiment, it also includes: determining the degree of slurry deterioration based on the slurry fluidity at each plastering position in the local fluidity abnormality area; when the degree of slurry deterioration is greater than a preset degradation degree threshold, adjusting the stirring parameters of the slurry stirring robotic arm of the wall plastering robot based on the slurry deterioration degree, wherein the slurry stirring robotic arm is used to stir and modulate the plastering slurry.

[0041] For example, if the slurry fluidity at multiple locations within the local fluidity abnormality region, among the ash spreading locations, is less than a preset threshold, the slurry degradation degree is determined by taking the average of the slurry fluidities at these multiple locations. For example, the smaller the average of the slurry fluidity, the greater the degree of slurry degradation.

[0042] For example, the stirring parameters may include stirring frequency and stirring duration. For example, the greater the degree of slurry degradation, the greater the stirring frequency and the longer the stirring duration.

[0043] According to the above embodiment, the degree of slurry deterioration is determined based on the slurry fluidity in the area of ​​local fluidity abnormality. If the slurry fluidity is too small, the greater the degree of slurry deterioration. When it is greater than a preset threshold, the stirring parameters of the slurry stirring robot arm of the wall plastering robot can be adjusted based on the degree of slurry deterioration to increase the slurry fluidity of subsequent plastering and avoid abnormalities during plastering.

[0044] In one embodiment, based on the plaster layer thickness distribution in the first plastering area, the flatness of the plaster layer in the first plastering area is determined, including: performing gradient calculation on the plaster layer thickness at each position in the plaster layer thickness distribution to obtain a gradient variation curve with position; based on the gradient variation curve with position, determining the flatness of the plaster layer in the first plastering area.

[0045] For example, based on the gradient-position variation curve, a gradient standard deviation or variance is determined, and the flatness of the plaster layer in the first plastering area is evaluated using the gradient standard deviation or variance.

[0046] According to the above embodiment, the gradient of the plaster layer thickness at each position in the plaster layer thickness distribution is calculated to obtain a gradient change curve with position. Based on the gradient change curve with position, the flatness of the plaster layer in the first plastering area is determined, so that the flatness of the plaster layer in the first plastering area can be accurately obtained.

[0047] Figure 4 It is a structural block diagram of a construction robot construction evaluation and control device according to an embodiment of the present invention.

[0048] like Figure 4 As shown, the construction robot construction evaluation and control device includes: a fluidity distribution determining module 410 for determining, when a plastering robot arm is plastering a first wall, a fluidity distribution of a first plastering region on the first wall based on a plastering viscosity distribution and a plastering velocity distribution of the first plastering region; An abnormal region determining module 420 is configured to determine a local abnormal fluidity region based on the ash spreading fluidity distribution of the first ash spreading region; A trajectory compensation module 430 is configured to compensate the motion trajectory of the plastering robot arm based on the position information of the local fluidity abnormality area to obtain a target motion trajectory of the plastering robot arm; a flatness determination module 440 for determining the flatness of the plaster layer in the first plastering area based on a thickness distribution of the plaster layer in the first plastering area when the plastering robot arm is plastering based on the target motion trajectory and plastering of the first plastering area has been completed; The plastering control module 450 is used to determine that the first plastering area in the first wall has been completed when the plastering flatness of the first plastering area meets the preset conditions, and trigger the plastering robot arm to plaster the second plastering area in the first wall.

[0049] In one embodiment, the liquidity distribution determination module 410 includes: a first curve determining unit, configured to determine a slurry thickness variation curve of a plastering line of the first plastering area at a current moment based on a thickness distribution of the slurry area in the first plastering area, wherein the plastering line is a boundary between the slurry area and an unplastered area in the first plastering area; The second curve determination unit is used to predict the slurry thickness change curve of the ash spreading line in the first ash spreading area at the next moment based on the slurry thickness change curve of the ash spreading line, the slurry viscosity of each ash spreading position in the ash spreading viscosity distribution at the current moment, and the slurry viscosity of each ash spreading position in the ash spreading velocity distribution at the current moment; the fluidity distribution determination unit is used to determine the ash spreading fluidity distribution of the first ash spreading area based on the slurry thickness change curve of the ash spreading line in the first ash spreading area at the current moment and the slurry thickness change curve of the ash spreading line in the first ash spreading area at the next moment, wherein the ash spreading fluidity distribution is used to describe the slurry fluidity at each ash spreading position.

[0050] In one embodiment, the abnormal region determination module 420 includes: a fluidity change determining unit, configured to determine a fluidity change of slurry between two adjacent ash spreading positions based on the slurry fluidity at each ash spreading position in the ash spreading fluidity distribution; an interval determining unit, configured to determine, based on the slurry fluidity changes at each of the two adjacent ash spreading positions, a continuous ash spreading position change interval in which the slurry fluidity change is greater than a preset threshold; The region determining unit is configured to determine the local fluidity abnormal region based on the position of the continuous gray spreading position change interval in the first gray spreading region.

[0051] In one embodiment, the trajectory compensation module 430 includes: an initial trajectory determining unit, configured to determine an initial motion trajectory of the plastering robot arm based on a curve of a slurry thickness versus position change of a plastering line of the first plastering area at a next moment, wherein the initial motion trajectory includes a plastering position, a plastering force, and a plastering direction corresponding to each time point; a fluidity determination unit, configured to determine the slurry fluidity at each ash spreading position in the local fluidity abnormality region based on the slurry fluidity at each ash spreading position in the ash spreading fluidity distribution; The target trajectory determination unit is used to adjust the spreading force and spreading direction at the corresponding spreading positions in the initial motion trajectory based on the slurry fluidity at each spreading position in the local fluidity abnormality area, so as to obtain the target motion trajectory of the plastering robot arm.

[0052] In one embodiment, the above device further comprises: a slurry degradation degree determination module, configured to determine the slurry degradation degree based on the slurry fluidity at each ash spreading position in the local fluidity abnormality area; A stirring parameter adjustment module is used to adjust the stirring parameters of the slurry stirring robotic arm of the wall plastering robot based on the degree of slurry degradation when the degree of slurry degradation is greater than a preset degradation degree threshold, wherein the slurry stirring robotic arm is used to stir and modulate the plastering slurry.

[0053] In one embodiment, the flatness determination module 340 includes: a gradient calculation unit, configured to perform gradient calculation on the thickness of the plaster layer at each position in the plaster layer thickness distribution to obtain a gradient variation curve with respect to position; The flatness calculation unit is used to determine the flatness of the plaster layer in the first plastering area based on the gradient-position change curve.

[0054] For the description of specific functions and examples of each module and submodule of the system in the embodiment of the present invention, please refer to the relevant description of the corresponding steps in the above method embodiment, which will not be repeated here.

[0055] In the technical solution of the present invention, the acquisition, storage and application of user personal information involved are in compliance with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0056] According to an embodiment of the present invention, the present invention further provides a system and a readable storage medium.

[0057] Figure 5 A schematic block diagram of an example electronic device 800 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0058] like Figure 5 As shown, electronic device 800 includes a computing unit 801, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 802 or a computer program loaded from a storage unit 808 into a random access memory (RAM) 803. Various programs and data required for the operation of device 800 can also be stored in RAM 803. Computing unit 801, ROM 802, and RAM 803 are connected to each other via a bus 804. An input / output (I / O) interface 805 is also connected to bus 804.

[0059] Multiple components in electronic device 800 are connected to I / O interface 805, including: an input unit 806, such as a keyboard, mouse, etc.; an output unit 807, such as various types of displays, speakers, etc.; a storage unit 808, such as a magnetic disk, optical disk, etc.; and a communication unit 809, such as a network card, modem, wireless communication transceiver, etc. The communication unit 809 allows device 800 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0060] The computing unit 801 can be various general-purpose and / or specialized processing components with processing and computing capabilities. Some examples of the computing unit 801 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units that run machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The computing unit 801 performs the various methods and processes described above, such as the construction robot construction evaluation and control method. For example, in some embodiments, the construction robot construction evaluation and control method can be implemented as a computer software program that is tangibly contained in a machine-readable medium, such as a storage unit 808. In some embodiments, part or all of the computer program can be loaded and / or installed on the device 800 via the ROM 802 and / or the communication unit 809. When the computer program is loaded into the RAM 803 and executed by the computing unit 801, one or more steps of the construction robot construction evaluation and control method described above can be performed. Alternatively, in other embodiments, the computing unit 801 may be configured to execute the construction robot construction evaluation and control method in any other appropriate manner (for example, by means of firmware).

[0061] Various embodiments of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system comprising at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0062] The program code for implementing the method of the present invention can be written in any combination of one or more programming languages. Such program code can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device so that when the program code is executed by the processor or controller, the functions / operations specified in the flow chart and / or block diagram are implemented. The program code can be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0063] In the context of the present invention, machine-readable medium can be a tangible medium that can contain or store a program for use with an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. Machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. More specific examples of machine-readable storage media can include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0064] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0065] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), and the Internet.

[0066] A computer system may include a client and a server. The client and server are generally remote from each other and typically interact through a communication network. The client-server relationship arises through computer programs running on the respective computers and having a client-server relationship with each other. The server may be a cloud server, a server in a distributed system, or a server integrated with a blockchain.

[0067] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in the present invention can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions disclosed in the present invention can be achieved. This is not limited herein.

[0068] The above specific embodiments do not limit the scope of protection of the present invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the principles of the present invention are intended to be included within the scope of protection of the present invention.

Claims

1. A construction robot construction evaluation and control method, characterized in that: include: When a plastering robot arm of a wall plastering robot is plastering a first wall, determining a plastering fluidity distribution in a first plastering area on the first wall based on a plastering viscosity distribution and a plastering speed distribution in the first plastering area; determining a local fluidity abnormality region based on the ash spreading fluidity distribution in the first ash spreading region; Based on the position information of the local fluidity abnormal area, the motion trajectory of the plastering robot arm is compensated to obtain the target motion trajectory of the plastering robot arm; When the plastering robot arm is plastering based on the target motion trajectory and the first plastering area has been completed, determining the flatness of the plaster layer in the first plastering area based on the thickness distribution of the plaster layer in the first plastering area; When the plastering flatness of the first plastering area meets the preset conditions, it is determined that the first plastering area in the first wall has been completed, and the plastering robot arm is triggered to plaster the second plastering area in the first wall.

2. The method according to claim 1, characterized in that The determining of the ash spreading fluidity distribution of the first ash spreading area based on the ash spreading viscosity distribution and the ash spreading speed distribution of the first ash spreading area on the first wall surface includes: Based on the thickness distribution of the slurry area in the first plastering area, determining a slurry thickness variation curve of a plastering line in the first plastering area at a current moment, wherein the plastering line is a boundary between the slurry area and the unplastered area in the first plastering area; Based on the slurry thickness variation curve of the lime spreading line as a function of position, the slurry viscosity at each lime spreading position in the lime spreading viscosity distribution at the current moment, and the slurry viscosity at each lime spreading position in the lime spreading velocity distribution at the current moment, predict the slurry thickness variation curve of the lime spreading line in the first lime spreading area at the next moment; Based on the slurry thickness change curve of the ash spreading line in the first ash spreading area at the current moment and the slurry thickness change curve of the ash spreading line in the first ash spreading area at the next moment, the ash spreading fluidity distribution of the first ash spreading area is determined, wherein the ash spreading fluidity distribution is used to describe the slurry fluidity at each ash spreading position.

3. The method according to claim 2, characterized in that The determining of a local fluidity abnormality region based on the ash spreading fluidity distribution in the first ash spreading region includes: Determining a change in slurry fluidity between two adjacent ash spreading positions based on the slurry fluidity at each ash spreading position in the ash spreading fluidity distribution; Based on the slurry fluidity changes at each of the two adjacent ash spreading positions, determining a continuous ash spreading position change interval in which the slurry fluidity change is greater than a preset threshold; The local fluidity abnormality region is determined based on the position of the continuous gray spreading position change interval in the first gray spreading area.

4. The method according to claim 3, characterized in that The method of compensating the motion trajectory of the plastering robot arm based on the position information of the local fluidity abnormal area to obtain the target motion trajectory of the plastering robot arm includes: determining the initial motion trajectory of the plastering robot arm based on a slurry thickness versus position curve of the plastering line of the first plastering area at the next moment, wherein the initial motion trajectory includes the plastering position, plastering force, and plastering direction corresponding to each time point; Determining the slurry fluidity at each ash spreading position in the local fluidity abnormality region based on the slurry fluidity at each ash spreading position in the ash spreading fluidity distribution; Based on the slurry fluidity at each plastering position in the local fluidity abnormality area, the plastering force and direction at the corresponding plastering position in the initial motion trajectory are adjusted to obtain the target motion trajectory of the plastering robot arm.

5. The method according to claim 4, characterized in that Also includes: determining the degree of slurry degradation based on the slurry fluidity at each ash spreading position in the local fluidity abnormality area; When the slurry degradation degree is greater than a preset degradation degree threshold, the stirring parameters of the slurry stirring robotic arm of the wall plastering robot are adjusted based on the slurry degradation degree, wherein the slurry stirring robotic arm is used to stir and modulate the plastering slurry.

6. The method according to claim 1, characterized in that The determining the flatness of the plaster layer in the first plastering area based on the thickness distribution of the plaster layer in the first plastering area includes: The thickness of the plaster layer at each position in the plaster layer thickness distribution is gradient calculated to obtain a gradient-position variation curve; and the flatness of the plaster layer in the first plastering area is determined based on the gradient-position variation curve.

7. A construction robot construction evaluation and control device, characterized in that: include: a fluidity distribution determining module, configured to determine, when a plastering robot arm of a wall plastering robot is plastering a first wall, a fluidity distribution of a first plastering area on the first wall based on a plastering viscosity distribution and a plastering speed distribution of the first plastering area; An abnormal region determining module, configured to determine a local fluidity abnormal region based on the ash spreading fluidity distribution of the first ash spreading region; a trajectory compensation module, configured to compensate the motion trajectory of the plastering robot arm based on the position information of the local fluidity abnormality area to obtain a target motion trajectory of the plastering robot arm; a flatness determination module, configured to determine the flatness of the plaster layer in the first plastering area based on a thickness distribution of the plaster layer in the first plastering area, when the plastering robot arm is plastering based on the target motion trajectory and the plastering of the first plastering area has been completed; The plastering control module is used to determine that the plastering of the first plastering area in the first wall has been completed when the plastering flatness of the first plastering area meets the preset conditions, and trigger the plastering robot arm to plaster the second plastering area in the first wall.

8. The device according to claim 7, characterized in that The liquidity distribution determination module includes: a first curve determining unit, configured to determine a slurry thickness variation curve of a plastering line of the first plastering area at a current moment based on a thickness distribution of the slurry area in the first plastering area, wherein the plastering line is a boundary between the slurry area and an unplastered area in the first plastering area; a second curve determining unit, configured to predict a slurry thickness variation curve of the ash spreading line in the first ash spreading area at a next moment based on the slurry thickness variation curve of the ash spreading line, the slurry viscosity at each ash spreading position in the ash spreading viscosity distribution at a current moment, and the slurry viscosity at each ash spreading position in the ash spreading velocity distribution at a current moment; A fluidity distribution determination unit is used to determine the ash spreading fluidity distribution of the first ash spreading area based on the slurry thickness change curve of the ash spreading line of the first ash spreading area at the current moment and the slurry thickness change curve of the ash spreading line of the first ash spreading area at the next moment, wherein the ash spreading fluidity distribution is used to describe the slurry fluidity at each ash spreading position.

9. A construction robot construction evaluation and control system, comprising: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 6.

10. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to enable a computer to execute the method according to any one of claims 1 to 6.

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