An off-line trajectory planning method and system applied to a masonry plastering robot

Abstract

The application provides an off-line trajectory planning method and system applied to a masonry mud daubing robot, relates to the field of mud daubing in the masonry industry, and comprises the following steps: S1, laser scanning is performed on a target object, and a three-dimensional model of the target object is constructed according to the laser scanning; S2, a brick to be daubed with mud is identified, and brick parameters and a surface to be daubed are obtained; S3, a trajectory planning system is established according to working parameters of the masonry mud daubing robot, mud parameters, the three-dimensional model and the brick parameters, and a planned trajectory is obtained; and S4, the masonry mud daubing robot moves according to the planned trajectory and daubs mud on the target object. According to the position, joint speed, angular velocity, angular acceleration and joint torque of the masonry mud daubing robot, the trajectory planning system is optimized, the masonry mud daubing robot moves according to the planned trajectory, mud is daubed on the target object, and accurate control of the movement trajectory of the masonry mud daubing robot is realized.

Description

Technical Field

[0001] This invention relates to the field of slurry application technology in the masonry industry, and in particular to an offline trajectory planning method and system for masonry slurry application robots. Background Technology

[0002] Currently, the main technical methods used in the masonry industry include traditional brick and stone masonry, standardized concrete construction, and new masonry materials and technologies such as high-strength blocks and reinforced concrete. Traditional brick and stone masonry buildings exhibit excellent structural stability, but have a long construction cycle and require highly skilled workers. On the other hand, standardized concrete construction uses prefabricated components, allowing for rapid assembly and shortening the construction cycle, but it is somewhat limited in terms of design freedom and personalization. The development of new masonry materials and technologies, such as high-strength blocks and reinforced concrete, has improved the seismic resistance and durability of buildings, but the characteristics of the materials and construction techniques must be considered during construction.

[0003] For robot trajectory planning, existing industrial production methods mainly include manual teaching and offline programming. Manual teaching suffers from drawbacks such as low programming efficiency, poor trajectory accuracy, and low paint utilization, thus it is only suitable for some simple painting situations. While existing offline programming software offers significant improvements in efficiency, painting trajectory accuracy, and adaptability compared to traditional manual teaching methods, it cannot precisely control various robot parameters, which in turn affects the robot's efficiency in mud-spreading.

[0004] Therefore, finding an offline trajectory method that can both accurately control robot motion estimation and achieve efficient bricklaying is a technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0005] In view of this, the present invention proposes an offline trajectory planning method and system for a masonry and slurry robot. It can optimize the trajectory planning system based on the position, joint velocity, angular velocity, angular acceleration and joint torque of the masonry and slurry robot, so that the masonry and slurry robot moves according to the planned trajectory and applies actual slurry to the target object. This achieves precise control of the movement trajectory of the masonry and slurry robot and improves the accuracy of masonry and slurry application.

[0006] The technical solution of this invention is implemented as follows:

[0007] In a first aspect, the present invention provides an offline trajectory planning method for a masonry and plastering robot, comprising the following steps:

[0008] S1. Perform laser scanning on the target object and construct a three-dimensional model of the target object based on the laser scan.

[0009] S2. Identify the bricks to be plastered and obtain the brick parameters and the surface to be plastered.

[0010] S3. Establish a trajectory planning system based on the working parameters, slurry parameters, three-dimensional model and brick parameters of the masonry and plastering robot to obtain the planned trajectory;

[0011] S4. The masonry and slurry robot moves according to the planned trajectory and applies actual slurry to the target object.

[0012] Based on the above technical solutions, preferably, step S3 specifically includes:

[0013] S31. Determine the variables of mud slurry application based on the working parameters and mud parameters of the masonry slurry application robot, and design the target response function of mud slurry during the application process based on the variables of mud slurry application.

[0014] S32. Establish constraints on the variables and objective response function of mud application, and use the constraints to optimize and obtain the objective response value;

[0015] S33. Input the mud parameters and target response values ​​into the radial basis function neural network to establish a mud slurry model;

[0016] S34. Determine the variable parameters of the mud slurry model based on the mud slurry model, and establish a trajectory planning system based on the three-dimensional model and brick parameters;

[0017] S35. Optimize the trajectory planning system based on the position, joint velocity, angular velocity, angular acceleration, and joint torque of the masonry and plastering robot to obtain the planned trajectory; where angular velocity is the speed at which the joints of the masonry and plastering robot rotate around the origin, and angular acceleration is the rate of change of angular velocity.

[0018] Based on the above technical solution, the preferred constraint in step S32 is:

[0019]

[0020] Where f1 represents the mud application time, f2 represents the mud saturation, f3 represents the mud thickness error, and V B V represents the speed at which the paint is applied at the starting position B. N The value represents the mud output speed of the mud-plastering robot's spray gun, h represents the height between the spray gun and the brick surface, S represents the mud coating thickness, d represents the overlap width between the two trajectories, and subjectto represents the constraint conditions.

[0021] Based on the above technical solutions, the preferred optimization of the constraints specifically includes:

[0022] Divide the variable into n sample intervals with equal probability. in, This represents the upper limit value of the i-th variable. The lower limit value, This represents the i-th variable in the n-th interval;

[0023] For n sample intervals, extract m random samples to form m sample points;

[0024] Randomly and without repetition, pair m random samples from each sample with m random samples from another sample until all sample points of all variables have been combined.

[0025] The distance between combinations of sample points is calculated, and the constraints are optimized. The formula for calculating the distance between combinations of sample points is as follows:

[0026]

[0027] in, y i ,y j Representing two distinct sample intervals, y i ε Represents the sample interval y i The ε-th sample, y j ε Represents the sample interval y j The εth sample.

[0028] Based on the above technical solutions, the preferred optimized constraint condition in step S35 is:

[0029]

[0030] Where, x i (t) represents the position of the i-th segment of the trajectory along the x-axis at time t, z i (t) represents the position of the i-th segment of the trajectory along the z-axis at time t, x min Let x represent the minimum value of the i-th segment of the trajectory along the x-axis. max Let z represent the maximum value of the i-th segment of the trajectory in the x-axis direction. min This represents the minimum position of the i-th segment of the trajectory along the z-axis. max δ represents the maximum value of the i-th segment of the trajectory in the z-axis direction. max This represents the maximum straightness error of the trajectory segment AB. τ represents the upper and lower limits of the angular velocity and angular acceleration of joint m, respectively; UB Indicates the upper limit of the output torque; T represents the time it takes for the masonry and plastering robot to move from the initial pose to the final pose along a certain trajectory; J represents the upper limit of the output torque. MAXThis represents the maximum acceleration of the masonry and plastering robot in the x-direction; This represents the angular velocity of joint m at time t; This represents the angular acceleration of joint m at time t; τ represents the jerk of joint m at time t, where jerk is the rate of change of joint velocity with time; τ represents the joint torque of the plastering robot; x straight It represents the position of the straight line in the x-direction at time t.

[0031] More preferably, the planned trajectory includes a first planned trajectory segment and a second planned trajectory segment. Step S4 specifically includes:

[0032] S41, S41, the masonry and plastering robot moves according to the first planned trajectory, so that the spray gun moves to the starting position of the first planned trajectory;

[0033] S42. The spray gun of the masonry and plastering robot moves according to the second planned trajectory to spray slurry.

[0034] Based on the above technical solutions, preferably, step S41 specifically includes:

[0035] The three-dimensional model is sliced ​​to obtain surface trajectory points;

[0036] The surface estimated points are interpolated seven times using the B-spline interpolation algorithm to obtain the interpolated trajectory points.

[0037] Offset the interpolated trajectory points and constrain the spray gun's speed and attitude;

[0038] The constrained spray gun is moved according to the second planned trajectory and mud is sprayed.

[0039] Secondly, the present invention provides an offline trajectory planning system for a masonry and plastering robot, employing the offline trajectory planning method as described in any of the above claims, including:

[0040] The 3D reconstruction scanning module is used to perform laser scanning on the target object and construct a 3D model of the target object based on the laser scan.

[0041] The brick recognition module is used to identify the bricks to be laid and plastered, and to obtain the brick parameters and the surface to be plastered.

[0042] The trajectory planning module is used to establish a trajectory planning system based on the working parameters, mud parameters, three-dimensional model and brick parameters of the masonry and plastering robot, and obtain the planned trajectory.

[0043] The execution module is used by the masonry and slurry robot to move according to the planned trajectory and apply actual slurry to the target object.

[0044] Thirdly, the present invention provides a computer-readable storage medium storing computer instructions that cause the computer to implement the offline trajectory planning method as described in any of the preceding claims.

[0045] Fourthly, the present invention provides an electronic device, comprising: at least one processor, at least one memory, a communication interface, and a bus; wherein,

[0046] The processor, memory, and communication interface communicate with each other through the bus;

[0047] The memory stores program instructions that can be executed by the processor, which calls the program instructions to implement the offline trajectory planning method as described above.

[0048] The offline trajectory planning method of the present invention has the following advantages over the prior art:

[0049] (1) By setting up a trajectory planning system for the masonry and smearing robot, the trajectory planning system is optimized according to the position, joint speed, angular velocity, angular acceleration and joint torque of the masonry and smearing robot to obtain the planned trajectory, so that the masonry and smearing robot moves according to the planned trajectory and actually applies mud to the target object, thereby achieving precise control of the movement trajectory of the masonry and smearing robot and improving the accuracy of masonry and smearing.

[0050] (2) Set variables for masonry plastering and target response function of slurry during the plastering process. By establishing constraints on slurry parameters and target response function, and using the constraints for optimization, the target response value is obtained and a slurry plastering model is established. This enables the optimal planning trajectory to be obtained based on the variable parameters of the slurry plastering model during the plastering process, thereby improving the effect and quality of masonry plastering. Attached Figure Description

[0051] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0052] Figure 1 This is a flowchart of the offline trajectory planning method of the present invention;

[0053] Figure 2 This is a flowchart of the offline trajectory planning method of the present invention.

[0054] Figure 3 This is a topology diagram of the radial basis function neural network for the offline trajectory planning method of the present invention;

[0055] Figure 4 This is a mud smear model diagram of the offline trajectory planning method of the present invention;

[0056] Figure 5 This is a comparison diagram of the interpolation smoothing effect of the second segment of the planned trajectory in the offline trajectory planning method of the present invention;

[0057] Figure 6 This is the end-point attitude constraint diagram of the second segment of the planned trajectory in the offline trajectory planning method of the present invention;

[0058] Figure 7 This is a block diagram of the offline trajectory planning system of the present invention.

[0059] Key markers:

[0060] P, cutting plane; Q, mud coating surface. Detailed Implementation

[0061] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0062] like Figure 1 and Figure 2 As shown, this invention provides an offline trajectory planning method for a masonry and plastering robot, comprising the following steps:

[0063] S1. Perform laser scanning on the target object and construct a three-dimensional model of the target object based on the laser scan.

[0064] In this embodiment of the application, a large amount of point cloud data of the surface of the target object is obtained by laser scanning. Then, through data processing and modeling, a real three-dimensional model of the target object is constructed. The three-dimensional model provides high-density and high-precision point cloud data to ensure the accuracy and comprehensiveness of the three-dimensional model.

[0065] Specifically, a laser scanner is used to scan the target object. The scanner emits a laser beam and records the data reflected back from the laser beam. This data can include information such as distance, angle, and intensity.

[0066] The point cloud data acquired by the laser scanner is processed, including noise reduction, registration, and filtering, to obtain high-quality point cloud data.

[0067] Various algorithms can be used for 3D reconstruction using point cloud data, such as mesh-based reconstruction and voxel-based reconstruction, to convert point cloud data into realistic 3D models.

[0068] Post-processing is performed on the constructed 3D model, including model smoothing, detail enhancement, and texture mapping, to obtain a more realistic and lifelike model.

[0069] S2. Identify the bricks to be plastered and obtain the brick parameters and the surface to be plastered. The brick parameters include the brick model, brick size and brick thickness.

[0070] In this embodiment of the application, by identifying the bricks to be plastered and obtaining the brick parameters and the surface to be plastered, the accuracy and quality of the plastering are ensured, avoiding plastering problems caused by mismatches in parameters such as brick size and shape, improving the efficiency of plastering, reducing labor costs, and lowering the risks of plastering.

[0071] Furthermore, in one embodiment of this application, the camera of the masonry and plastering robot or other image acquisition device is used to photograph the bricks to be masonred and the surface to be plastered to obtain image data.

[0072] The acquired image data is processed, including image enhancement, edge detection, and feature extraction, in order to identify bricks and surfaces;

[0073] Image processing technology can be used to identify parameters such as the position, size, and shape of bricks. Computer vision technology and deep learning algorithms can be used to automatically identify bricks and extract parameters such as the position, shape, and size of the surface to be coated, so as to carry out subsequent construction planning and control.

[0074] Integrate brick parameters and surface data to be coated into a single system for construction planning and control.

[0075] S3. Establish a trajectory planning system based on the working parameters, slurry parameters, three-dimensional model and brick parameters of the masonry and plastering robot to obtain the planned trajectory.

[0076] In this embodiment, the masonry and plastering process is automated by planning the motion trajectory of the masonry and plastering robot, reducing manual intervention and improving construction efficiency. The trajectory of the masonry and plastering robot is precisely planned based on a three-dimensional model and brick parameters to ensure uniform application of mortar and accurate laying of bricks, thereby improving the quality of masonry and plastering. Through the three-dimensional model, the movement trajectory of the masonry and plastering robot can be intelligently planned according to the shape and size of the surface to be plastered, so that the three-dimensional model can adapt to different shapes of surfaces to be plastered, improving the flexibility and applicability of masonry and plastering.

[0077] Furthermore, step S3 specifically includes:

[0078] S31. Determine the variables of slurry application based on the working parameters and slurry parameters of the masonry slurry robot, and design the target response function of slurry during the application process based on the variables of slurry application; wherein the target response function includes slurry application time, slurry fullness, and slurry thickness error after brick placement.

[0079] S32. Establish constraints on the variables and objective response function of mud application, and use the constraints to optimize and obtain the objective response value;

[0080] S33. Input the mud parameters and target response values ​​into the radial basis function neural network to establish a mud slurry model;

[0081] S34. Determine the variable parameters of the mud slurry model based on the mud slurry model, and establish a trajectory planning system based on the three-dimensional model and brick parameters;

[0082] S35. Optimize the trajectory planning system based on the position, joint velocity, angular velocity, angular acceleration, and joint torque of the masonry and plastering robot to obtain the planned trajectory; where angular velocity is the speed at which the joints of the masonry and plastering robot rotate around the origin, and angular acceleration is the rate of change of angular velocity.

[0083] like Figure 3 and Figure 4 As shown, based on the working parameters and slurry parameters of the masonry and plastering robot, a target response function for the slurry during the plastering process is designed to ensure the uniformity and adhesion of the slurry. Constraints are established on the slurry parameters and the target response function, and the optimal slurry parameters are obtained through optimization to ensure that the optimal target response value is achieved during the masonry and plastering process. A slurry plastering model is established using methods such as radial basis function neural networks, where point A is the starting position of the masonry and plastering robot's movement, point B is the starting position of the plastering robot's application, and point C is the midpoint position of the masonry and plastering robot's movement. The slurry parameters and target response value are modeled and predicted, and the optimal slurry parameters are determined. Then, these parameters and target response values ​​are input into the trajectory planning system to establish a trajectory planning system for specific construction tasks. The trajectory planning system is optimized based on the position, joint velocity, angular velocity, angular acceleration, and joint torque of the masonry and plastering robot to obtain the final planned trajectory, thereby achieving precise motion control of the masonry and plastering robot during the masonry and plastering process.

[0084] In one embodiment of this application, the variables for masonry plastering include the speed V of the masonry plastering robot at the starting position (point B). B Mud ejection speed V from the spray gun NThe parameters are: height h between the spray gun and the brick surface, slurry thickness S, and overlap width d between the two trajectories. The target response function is:

[0085]

[0086] Where Plumpness represents the fullness of the slurry, n is the number of measurements taken on the surface of each brick, and x i These are the values ​​measured on the surface of each brick. This is the average of all measured data.

[0087]

[0088] Where Error represents the error in mortar thickness after brick placement, Δ x The ideal thickness of the mortar on the brick surface.

[0089] The constraints in step S32 are as follows:

[0090]

[0091] Where f1 represents the mud application time, f2 represents the mud saturation, f3 represents the mud thickness error, and V B V represents the speed at which the paint is applied at the starting position B. N The value represents the mud output speed of the mud-plastering robot's spray gun, h represents the height between the spray gun and the brick surface, S represents the mud coating thickness, d represents the overlap width between the two trajectories, and subjectto represents the constraint conditions.

[0092] It is understandable that the constraints in the conditions can be specifically limited by parameters according to the actual situation, and this application does not make specific limitations in this regard.

[0093] In this embodiment of the application, by constraining and optimizing the mud parameters and the target response function, the slurry application parameters are precisely controlled, making the use of mud in the masonry slurry process more reasonable, reducing resource waste, and making the masonry slurry application more accurate and efficient.

[0094] In one embodiment of this application, optimizing the constraints specifically includes:

[0095] Divide the variable into n sample intervals with equal probability. in, This represents the upper limit value of the i-th variable. The lower limit value, This represents the i-th variable in the n-th interval;

[0096] For n sample intervals, extract m random samples to form m sample points;

[0097] Randomly and without repetition, pair m random samples from each sample with m random samples from another sample until all sample points of all variables have been combined.

[0098] The distance between combinations of sample points is calculated, and the constraints are optimized. The formula for calculating the distance between combinations of sample points is as follows:

[0099]

[0100] in, y i ,y j Representing two distinct sample intervals, y i ε Represents the sample interval y i The ε-th sample, y j ε Represents the sample interval y j The ε-th sample, where n, u, m and ε are all non-zero natural numbers.

[0101] By optimizing the constraints to obtain the optimal target response value, the masonry and plastering process becomes more stable, reducing unnecessary adjustments and repeated application by the masonry and plastering robot, thereby improving the efficiency of masonry and plastering.

[0102] S4. The masonry and slurry robot moves according to the planned trajectory and applies actual slurry to the target object.

[0103] By planning its trajectory, the masonry and plastering robot can precisely control the thickness and uniformity of the plaster application, ensuring the quality and consistency of the plastering and improving the overall aesthetics and quality of the target object's surface. The robot moves according to the planned trajectory, precisely controlling the amount of plaster used, avoiding waste and over-application. While achieving automated plastering, it greatly improves construction speed and efficiency, avoids some safety hazards in manual operations, such as the risks of working at heights and plaster splashing, and ensures the safety of personnel.

[0104] In one embodiment of this application, the optimized constraint in step S35 is:

[0105]

[0106] Where, x i (t) represents the position of the i-th segment of the trajectory along the x-axis at time t, z i (t) represents the position of the i-th segment of the trajectory along the z-axis at time t, x min Let x represent the minimum value of the i-th segment of the trajectory along the x-axis. max Let z represent the maximum value of the i-th segment of the trajectory in the x-axis direction. minThis represents the minimum position of the i-th segment of the trajectory along the z-axis. max δ represents the maximum value of the i-th segment of the trajectory in the z-axis direction. max This represents the maximum straightness error of the trajectory segment AB. τ represents the upper and lower limits of the angular velocity and angular acceleration of joint m, respectively; UB Indicates the upper limit of the output torque; T represents the time it takes for the masonry and plastering robot to move from the initial pose to the final pose along a certain trajectory; J represents the upper limit of the output torque. MAX This represents the maximum acceleration of the masonry and plastering robot in the x-direction; This represents the angular velocity of joint m at time t; This represents the angular acceleration of joint m at time t; τ represents the jerk of joint m at time t, where jerk is the rate of change of joint velocity with time; τ represents the joint torque of the plastering robot; x straight It represents the position of the straight line in the x-direction at time t.

[0107] In this embodiment of the application, the upper limit of the output torque is defined as 80% of the maximum torque of the joint motor.

[0108] Understandably, when performing masonry and plastering tasks, the masonry and plastering robot needs to move precisely along a predetermined trajectory, while also meeting the requirements for the continuity and smoothness of the joint angle curves, and not exceeding the range of motion and torque that the joint can withstand during the movement.

[0109] The optimization model is:

[0110]

[0111] Among them, S1—motion time, measures the motion efficiency of the masonry and plastering robot; S2—average angular velocity of the joint, is an indicator of the energy consumed by the joints of the masonry and plastering robot; S3—average joint pulsation, measures the smoothness of the trajectory, v i ,a i ,j i These represent joint velocity, angular velocity, and jerk, respectively; J MAX This represents the maximum acceleration of the masonry and plastering robot in the x-direction.

[0112] Furthermore, the planned trajectory includes a first planned trajectory segment and a second planned trajectory segment; step S4 specifically includes:

[0113] S41. The masonry and plastering robot moves according to the first planned trajectory, so that the spray gun moves to the starting position of the first planned trajectory.

[0114] S42. The spray gun of the masonry and plastering robot moves according to the second planned trajectory to spray slurry.

[0115] In this embodiment, the masonry slurry robot moves to the target position according to the first planned trajectory, and the spray gun of the masonry slurry robot sprays mud according to the second planned trajectory, ensuring that the masonry slurry robot maintains a certain speed and pressure during the slurrying process, thereby improving the uniformity and consistency of the slurrying, and improving the accuracy and stability during the movement and spraying process.

[0116] In one embodiment of this application, point B is the starting position of the smearing robot, segment AB is the first planned trajectory, that is, the AB segment trajectory planning is the joint space trajectory of the smearing robot when performing a specific task, and segment BC is the second planned trajectory, that is, the BC segment trajectory planning is the movement trajectory of the spray gun of the smearing robot when performing the spraying operation.

[0117] Step S41 specifically includes:

[0118] The three-dimensional model is sliced ​​to obtain surface trajectory points;

[0119] The surface estimated points are interpolated seven times using the B-spline interpolation algorithm to obtain the interpolated trajectory points.

[0120] Offset the interpolated trajectory points and constrain the spray gun's speed and attitude;

[0121] The constrained spray gun is moved according to the second planned trajectory and mud is sprayed.

[0122] like Figure 5 and Figure 6 As shown, it is understandable that the triangular facet structure of the 3D model is first extracted, and then sliced, i.e., L = Dd, where L represents the slicing distance, D represents the width of the spray gun of the masonry and plastering robot, and d represents the overlap width between the two trajectories, as shown. Figure 5 As shown in (a); after slicing, the surface trajectory points of the three-dimensional model will be obtained, and the trajectory points will be interpolated by seven B-splines to ensure that the masonry and plastering robot can smoothly pass through the trajectory turning points, such as Figure 5 As shown in (b) and (c), where (c) is a magnified view of (b); the processed trajectory points are offset, where the offset is equal to the spraying height h, and the spray gun's movement speed and attitude are constrained (i.e., the spray gun's movement speed and V...). B Equal, the X-axis direction of the spray gun is consistent with the direction of movement, and the Z-axis direction of the spray gun is perpendicular to the tangent direction of the coating surface, such as... Figure 6As shown in (a) and (b), where (b) is a partial enlarged view of (a), the x-direction is the movement direction of the spray gun, and the z-direction is the orientation direction of the spray gun. The constrained spray gun is moved according to the second planned trajectory and mud spraying is performed to ensure that the spray gun maintains a suitable posture during the movement to ensure the quality of the spraying effect.

[0123] In this embodiment, by slicing the three-dimensional model to obtain surface trajectory points, and then using the B-spline interpolation algorithm for seven interpolations, more accurate smearing trajectory points are obtained, improving the accuracy of smearing. By offsetting the interpolated trajectory points and constraining the speed and posture of the spray gun, the masonry smearing robot can better adapt to target objects of different shapes and curved surfaces, optimizing the smearing effect. Through seven-fold B-spline interpolation and trajectory point offset, the masonry smearing robot can complete the task more efficiently during the smearing process, saving time and costs and improving construction efficiency. By moving the constrained spray gun according to the second planned trajectory for smearing, automated smearing can be achieved, avoiding some safety hazards in manual smearing and improving construction safety.

[0124] In one embodiment of this application, the first planned trajectory is also subjected to seven B-spline trajectory planning. To ensure that the joint angle curve is continuous and smooth at the path points, it is necessary to ensure that the angle, angular velocity, and angular acceleration of any two adjacent curve segments are equal at the path points, that is:

[0125]

[0126] Where, θ mi (t i+1 ) represents the angle of the i-th joint curve of joint m at time ti+1; Let represent the angular velocity of the i-th joint curve of joint m at time ti+1; θ represents the angular acceleration of the i-th joint curve of joint m at time ti+1; m,i+1 (t i+1 ) represents the angle of the (i+1)th joint curve of joint m at time ti+1; This represents the angular velocity of the (i+1)th joint curve of joint m at time ti+1; θ represents the angular acceleration of the (i+1)th joint curve segment of joint m at time ti+1; mi Indicates the angle of the m joint. This represents the angular velocity of joint m. This represents the angular acceleration of joint m.

[0127] Understandably, by generating smooth joint angle curves through B-spline trajectory planning, the masonry and plastering robot can move smoothly along the trajectory when performing masonry and plastering tasks. The movement trajectory of the masonry and plastering robot can be adjusted by using constraints such as position, velocity, angular velocity, angular acceleration, and torque.

[0128] This application acquires a 3D model of the target object and identifies bricks through laser scanning. It then establishes a trajectory planning system using the 3D model and brick parameters to precisely control the movement trajectory and smearing posture of the masonry and plastering robot, achieving accurate smearing of the target object. Through laser scanning and recognition technology, the masonry and plastering robot can adapt to target objects and bricks of different shapes and sizes, realizing automated masonry and plastering for different objects. The masonry and plastering robot moves according to the planned trajectory to achieve automated construction and precise smearing.

[0129] like Figure 7 As shown, the present invention also provides an offline trajectory planning system for a masonry and plastering robot, employing the offline trajectory planning method described above, including:

[0130] The 3D reconstruction scanning module is used to perform laser scanning on the target object and construct a 3D model of the target object based on the laser scan.

[0131] The brick recognition module is used to identify the bricks to be laid and plastered, and to obtain the brick parameters and the surface to be plastered.

[0132] The trajectory planning module is used to establish a trajectory planning system based on the working parameters, mud parameters, three-dimensional model and brick parameters of the masonry and plastering robot, and obtain the planned trajectory.

[0133] The execution module is used by the masonry and slurry robot to move according to the planned trajectory and apply actual slurry to the target object.

[0134] This application uses a 3D reconstruction scanning module to quickly and accurately obtain a 3D model of the target object, a brick recognition module to quickly and accurately obtain brick parameters and the surface to be coated, and a trajectory planning module to quickly and accurately establish a trajectory planning system based on actual conditions and needs. The execution module is used for the masonry and plastering robot to move according to the planned trajectory, thereby improving construction efficiency and safety, while also ensuring the accuracy and consistency of the coating.

[0135] The present invention also provides a computer-readable storage medium storing computer instructions that cause the computer to implement the offline trajectory planning method as described in any of the preceding claims.

[0136] The present invention also provides an electronic device, comprising: at least one processor, at least one memory, a communication interface, and a bus; wherein the processor, memory, and communication interface communicate with each other through the bus; the memory stores program instructions executable by the processor, and the processor calls the program instructions to implement the offline trajectory planning method as described in any of the preceding claims.

[0137] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An offline trajectory planning method applied to a masonry and plastering robot, characterized in that, Includes the following steps: S1. Perform laser scanning on the target object and construct a three-dimensional model of the target object based on the laser scan. S2. Identify the bricks to be plastered and obtain the brick parameters and the surface to be plastered. S3. Establish a trajectory planning system based on the working parameters, slurry parameters, three-dimensional model and brick parameters of the masonry and plastering robot to obtain the planned trajectory; S4. The masonry and slurry robot moves according to the planned trajectory and applies actual slurry to the target object. Step S3 specifically includes: S31. Determine the variables for mud application based on the working parameters and mud parameters of the masonry mud application robot, and design the target response function of mud application during the mud application process based on the variables of mud application. S32. Establish constraints on the variables and objective response function of mud application, and use the constraints to optimize and obtain the objective response value; S33. Input the mud parameters and target response values ​​into the radial basis function neural network to establish a mud slurry model; S34. Determine the variable parameters of the mud slurry model based on the mud slurry model, and establish a trajectory planning system based on the three-dimensional model and brick parameters; S35. Optimize the trajectory planning system based on the position, joint velocity, angular velocity, angular acceleration, and joint torque of the masonry and plastering robot to obtain the planned trajectory; where angular velocity is the speed at which the joints of the masonry and plastering robot rotate around the origin, and angular acceleration is the rate of change of angular velocity; The constraints in step S32 are: ,subject to ; Where f1 represents the mud application time, f2 represents the mud saturation, f3 represents the mud thickness error, and V B V represents the speed at which the paint is applied at the starting position B. N The value represents the mud output speed of the mud-plastering robot's spray gun, h represents the height between the spray gun and the brick surface, S represents the mud coating thickness, d represents the overlap width between the two trajectories, and subject to represents the constraint conditions.

2. The offline trajectory planning method for a masonry and plastering robot as described in claim 1, characterized in that, Optimizing constraints specifically includes: Divide the variable into n sample intervals with equal probability. ;in, This represents the upper limit value of the i-th variable. The lower limit value, This represents the i-th variable in the n-th interval; For n sample intervals, extract m random samples to form m sample points; Randomly and without repetition, pair m random samples from each sample with m random samples from another sample until all sample points of all variables have been combined. The distance between combinations of sample points is calculated, and the constraints are optimized. The formula for calculating the distance between combinations of sample points is as follows: ； in, Indicates two different sample intervals. Indicates the sample interval The One sample, Indicates the sample interval The One sample.

3. The offline trajectory planning method for a masonry and plastering robot as described in claim 1, characterized in that, The constraints for optimization in step S35 are: ； Where, x i (t) represents the position of the i-th segment of the trajectory along the x-axis at time t, z i (t) represents the position of the i-th segment of the trajectory along the z-axis at time t, x min Let x represent the minimum value of the i-th segment of the trajectory along the x-axis. max Let z represent the maximum value of the i-th segment of the trajectory in the x-axis direction. min This represents the minimum position of the i-th segment of the trajectory along the z-axis. max This represents the maximum value of the i-th segment of the trajectory in the z-axis direction. This represents the maximum straightness error of the trajectory segment AB. , , , These represent the upper and lower limits of the angular velocity and angular acceleration of joint m, respectively. This indicates the upper limit of the output torque; T represents the time it takes for the masonry robot to move from its initial pose to its final pose along a certain trajectory. This represents the maximum acceleration of the masonry and plastering robot in the x-direction; This represents the angular velocity of joint m at time t; This represents the angular acceleration of joint m at time t; Let represent the jerk of joint m at time t, where jerk is the rate of change of joint velocity with time; This indicates the joint torque of the plastering and masonry robot; It represents the position of the straight line in the x-direction at time t.

4. The offline trajectory planning method for a masonry and plastering robot as described in claim 1, characterized in that, The planned trajectory includes a first planned trajectory segment and a second planned trajectory segment. Step S4 specifically includes: S41. The masonry and plastering robot moves according to the first planned trajectory, so that the spray gun moves to the starting position of the first planned trajectory. S42. The spray gun of the masonry and plastering robot moves according to the second planned trajectory to spray slurry.

5. The offline trajectory planning method for a masonry and plastering robot as described in claim 4, characterized in that, Step S41 specifically includes: The three-dimensional model is sliced ​​to obtain surface trajectory points; The surface estimated points are interpolated seven times using the B-spline interpolation algorithm to obtain the interpolated trajectory points. Offset the interpolated trajectory points and constrain the spray gun's speed and attitude; The constrained spray gun is moved according to the second planned trajectory and mud is sprayed.

6. An offline trajectory planning system for a masonry and plastering robot, characterized in that, The offline trajectory planning method as described in any one of claims 1-5 includes: The 3D reconstruction scanning module is used to perform laser scanning on the target object and construct a 3D model of the target object based on the laser scan. The brick recognition module is used to identify the bricks to be laid and plastered, and to obtain the brick parameters and the surface to be plastered. The trajectory planning module is used to establish a trajectory planning system based on the working parameters, mud parameters, three-dimensional model and brick parameters of the masonry and plastering robot, and obtain the planned trajectory. The execution module is used by the masonry and slurry robot to move according to the planned trajectory and apply actual slurry to the target object.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause the computer to implement the offline trajectory planning method as described in any one of claims 1-5.

8. An electronic device, characterized in that, include: At least one processor, at least one memory, a communication interface, and a bus; wherein, The processor, memory, and communication interface communicate with each other through the bus; The memory stores program instructions that can be executed by the processor, which calls the program instructions to implement the offline trajectory planning method as described in any one of claims 1-5.

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