Welding track generation method, welding system and storage medium

Abstract

The embodiment of the invention provides a welding track generation method, a welding system and a storage medium, and belongs to the technical field of industrial control. The method comprises the steps that welding gun posture data corresponding to welding gun discrete control points in a one-to-one mode and workpiece posture data corresponding to workpiece discrete control points are combined in a one-to-one correspondence mode, and target posture data of a plurality of reference control points are obtained; taking every two adjacent reference control points in the welding direction as a trajectory segment starting point and a trajectory segment ending point of the same trajectory segment, and calling a Bezier curve model to solve the trajectory segment to obtain target attitude data of a plurality of target control points of the same trajectory segment; the speeds of the trajectory segment starting point, the trajectory segment middle point and the trajectory segment end point of the trajectory segment represented by the Bezier curve model are the same; and a B spline curve is constructed according to the target posture data of the multiple target control points of the same track section, and a welding gun welding track and a workpiece moving track are determined, so that the multiple robots can synchronously and smoothly move during collaborative welding while the welding efficiency is considered.

Description

Technical Field

[0001] This application relates to the field of industrial control technology, and in particular to a method for generating welding trajectories, a welding system, and a storage medium. Background Technology

[0002] Industrial automation typically enables the automatic production of products using machinery without direct human intervention. For example, welding can be automated by welding robots moving along predetermined trajectories. However, with increasingly stringent product quality requirements, new demands are being placed on welding processes, especially when welding complex workpieces. This often requires synchronized changes in the workpiece's pose, necessitating the collaboration of multiple robots. However, current technologies often rely on independent trajectory planning and control for each robot, making it difficult to balance efficiency with smooth, synchronized movement to meet welding requirements. Therefore, a trajectory planning method is urgently needed that can balance welding efficiency with smooth, synchronized movement of multiple robots during collaborative welding. Summary of the Invention

[0003] The main objective of this application is to propose a method for generating welding trajectories, a welding system, and a storage medium that can balance welding efficiency while enabling multiple robots to move synchronously and smoothly during collaborative welding.

[0004] To achieve the above objectives, a first aspect of this application proposes a method for generating a welding trajectory, comprising: acquiring welding torch posture data corresponding to discrete control points of a welding torch and workpiece posture data of discrete control points of a workpiece along a preset welding direction; setting a one-to-one correspondence between the discrete control points of the welding torch and the discrete control points of the workpiece; combining the welding torch posture data and the corresponding workpiece posture data to obtain target posture data of reference control points corresponding to the discrete control points of the welding torch; using each pair of adjacent reference control points along the welding direction as the starting point and ending point of the same trajectory segment, respectively, and calling a preset Bézier curve model to solve for the control points of the starting point and ending point of the same trajectory segment to obtain target posture data of multiple target control points of the same trajectory segment; wherein, the Bézier curve model represents that the velocities of the starting point, intermediate point, and ending point of the same trajectory segment are all the same; constructing a B-spline curve based on the target posture data of the multiple target control points of the same trajectory segment to generate a target trajectory segment; and determining the welding torch welding trajectory and the workpiece movement trajectory based on the target trajectory segment.

[0005] To achieve the above objectives, a second aspect of this application provides a welding system including a welding robot, a positioning robot, and a controller. The controller executes a welding trajectory generation method as described in any of the first aspects to obtain a welding torch welding trajectory and a workpiece movement trajectory. The welding robot moves according to the welding torch welding trajectory, and the positioning robot moves according to the workpiece movement trajectory.

[0006] To achieve the above objectives, a third aspect of the present application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the welding trajectory generation method described in any of the first aspects.

[0007] The welding trajectory generation method, welding system, and storage medium proposed in this application combine welding torch posture data and corresponding workpiece posture data in a one-to-one correspondence to obtain target posture data for new reference control points. Based on the target posture data of the reference control points and a Bézier curve model, target control points are planned. This allows the control points of the welding robot (moving based on the discrete control points of the welding torch) and the positioning robot (moving based on the discrete control points of the workpiece) to jointly plan their movement trajectories, ensuring synchronization between the welding robot and the positioning robot. Furthermore, the welding torch welding trajectory and workpiece movement trajectory are obtained by planning B-spline curves based on the target control points, resulting in higher movement control efficiency for both the welding robot and the positioning robot, thereby improving welding efficiency. Therefore, the welding trajectory generation method based on the embodiments of this application can balance welding efficiency while enabling multiple robots to move synchronously and smoothly during collaborative welding. Attached Figure Description

[0008] Figure 1 This is a schematic flowchart of the welding trajectory generation method provided in the embodiments of this application; Figure 2 This is a flowchart illustrating one embodiment of the welding trajectory method provided in this application. Figure 3 This is a schematic diagram of the welding system provided in the embodiments of this application; Figure 4 This is a schematic diagram of the hardware structure corresponding to the control method provided in the embodiments of this application. Detailed Implementation

[0009] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0010] It should be noted that although functional modules are divided in the device schematic diagram and a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first," "second," etc., in the specification, claims, and the aforementioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0011] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0012] First, let's analyze some of the terms used in this application: Yaw: Rotation about a vertical axis (such as the Z-axis), corresponding to left or right rotation in the horizontal direction (similar to a change in heading).

[0013] Pitch: Rotation about a horizontal axis (such as the Y-axis), corresponding to rotation in the up and down direction (similar to looking up or down).

[0014] Roll: Rotation about a longitudinal axis (such as the X-axis), corresponding to rotation about its own direction of travel (similar to tilting).

[0015] Industrial automation refers to the collective term for the measurement, manipulation, and process control of machinery and equipment or production processes according to expected goals without direct human intervention. With the increasing maturity of industrial automation, more and more industries are adopting it for product processing. For example, in welding, welding automation is achieved through planning welding trajectories. However, as the quality requirements for welded products become increasingly stringent, new demands are being placed on welding processes, especially in the welding of complex workpieces. Often, the workpiece's position needs to be changed during welding, and the weld seam must be continuous and uniform (i.e., the movement of the welding torch needs to be smooth). Therefore, for complex welded workpieces, at least two types of robots, such as welding robots and position-shifting robots, are often required for collaborative control. However, in existing technologies, multiple robot collaboration scenarios often involve each robot independently planning and controlling its trajectory, making it difficult to ensure that multiple robots can move synchronously and smoothly while maintaining efficiency, thus failing to meet welding process requirements. Based on this, this application provides a method for generating welding trajectories, a welding system, and a storage medium that can balance welding efficiency while enabling multiple robots to move synchronously and smoothly during collaborative welding.

[0016] Understandably, referring to Figure 1As shown, the welding trajectory generation method provided in the embodiments of this application includes: Step S100: Obtain the welding torch posture data corresponding to the discrete control points of the welding torch in the preset welding direction and the workpiece posture data of the discrete control points of the workpiece; set the discrete control points of the welding torch and the discrete control points of the workpiece to correspond one-to-one. Step S200: Combine the welding torch posture data and the corresponding workpiece posture data one by one to obtain the target posture data of the reference control point that corresponds one-to-one with the discrete control point of the welding torch. Step S300: Take every two adjacent reference control points in the welding direction as the starting point and ending point of the same trajectory segment, respectively. Call the preset Bézier curve model to solve the control points of the starting point and ending point of the same trajectory segment to obtain the target attitude data of multiple target control points of the same trajectory segment. Among them, the Bézier curve model represents that the velocities of the starting point, the middle point and the ending point of the same trajectory segment are the same. Step S400: Construct B-spline curves based on the target attitude data of multiple target control points in the same trajectory segment to generate the target trajectory segment; Step S500: Determine the welding torch trajectory and workpiece movement trajectory based on the target trajectory segment.

[0017] Therefore, by combining the welding torch posture data and the corresponding workpiece posture data in a one-to-one correspondence, new target posture data for the reference control points are obtained. Based on this target posture data and a Bézier curve model, the target control points are planned, enabling the control points of the welding robot (moving based on the discrete control points of the welding torch) and the positioning robot (moving based on the discrete control points of the workpiece) to jointly plan their movement trajectories. This ensures synchronization between the welding robot and the positioning robot. Furthermore, the welding torch trajectory and workpiece movement trajectory are obtained by planning B-spline curves based on the target control points, resulting in higher movement control efficiency for both the welding robot and the positioning robot, thereby improving welding efficiency. Therefore, the welding trajectory generation method based on this embodiment can balance welding efficiency while enabling multiple robots to move synchronously and smoothly during collaborative welding.

[0018] The discrete control points of the welding torch are the critical path points obtained by discretizing the teaching path of the welding robot. The discrete control points of the workpiece are the critical path points obtained by discretizing the teaching path of the positioning robot.

[0019] The welding torch attitude data represents the spatial position information of the corresponding discrete control point of the welding torch, including at least one of position coordinates and Euler angles. The position coordinates include coordinates in at least two coordinate axes, and the Euler angles correspond one-to-one with the coordinate axes corresponding to the position coordinates. For example, if the position coordinates include x-axis coordinates and y-axis coordinates, then the Euler angles include Euler angles about the x-axis and Euler angles about the y-axis.

[0020] Workpiece posture data characterizes the joint parameters of the displacement robot at the location of the corresponding discrete control point of the workpiece. In some embodiments, the displacement robot includes two-axis joints, then the workpiece posture data includes one-axis joint angles and two-axis joint angles. The welding torch posture data and the workpiece posture data are data in the same coordinate system.

[0021] For example, taking a welding robot as a six-axis robot and a positioner robot as a two-link robot, the posture data of the six-axis robot is converted into data based on the coordinate system of the positioner robot's end effector, resulting in welding torch posture data [x,y,z,u,v,w]; the workpiece posture data of the positioner robot is [j1,j2]. These are then combined to obtain the target posture data [x,y,z,j1,j2,u,v,w] of the reference control points. Path planning is then performed based on the target posture data [x,y,z,j1,j2,u,v,w] of the reference control points.

[0022] There are at least four target control points for the same trajectory segment. Among them, the multiple target control points for the same trajectory segment include the reference control points located at the endpoints (i.e., the start point and end point of the trajectory segment).

[0023] This application does not limit the order of the Bézier curve model; those skilled in the art can selectively set it according to actual needs.

[0024] The target trajectory segment is a B-spline curve planned based on two adjacent reference control points and the target control point located between the reference control points. The number of target trajectory segments is the number of reference control points minus 1.

[0025] The welding direction indicates the direction of movement of the welding robot, which determines the order in which the welding robot passes through various reference control points.

[0026] The Bézier curve model represents a Bézier curve segment planned based on the starting point and ending point of the trajectory segment. The midpoint of the trajectory segment is the point at the middle position on the Bézier curve. In this case, the independent variable of the Bézier curve model takes the value of 0.5.

[0027] For example, taking the welding trajectory as and , ,in, This represents the first welding point on the expected welding trajectory of the welding robot. One key point, The first part represents the expected welding trajectory of the displacement robot. Key points. These key points are the minimum number of control points required to satisfy the expected welding trajectory control. In some embodiments, the two welding trajectories can be discretized separately; and in some embodiments, the point density is increased during discretization, thus obtaining... Workpiece attitude data and discrete control points of each workpiece , ,according to The transformation relationship from the end effector of the displacement robot to the base coordinate system of the six-axis robot can be determined. ,according to and ,Will Transform to the base coordinate system to obtain the welding torch attitude data of each discrete control point of the welding torch. ,Will One-to-one correspondence Combine, obtain One benchmark control point , and then Each pair of adjacent reference control points The trajectory segment control points are solved by using a set of reference control points as a group, resulting in each set of reference control points. The corresponding multiple target control points, with the first group of reference control points, that is... For example, multiple target control points of the first set of reference control points can be obtained. ,in, The starting points of the trajectory segments corresponding to the first group of reference control points are respectively and the end point of the trajectory segment , These are the target control points obtained based on the Bézier curve model. At this point, based on... Solving for the B-spline curve yields the target trajectory segment corresponding to the first set of reference control points. Similarly, for the second set of reference control points, i.e. The reference control point group can be planned using the method described above. This allows for the creation of a welding trajectory composed of multiple independently controlled target trajectory segments by planning each reference control point in pairs. In some embodiments, when the number of key points on the welding trajectory meets the requirements, it is not necessary to increase the point density. Those skilled in the art can selectively set this according to actual needs.

[0028] Understandably, the Bézier curve model is a third-order Bézier curve model. By calling a preset Bézier curve model to solve for the control points at the start and end points of the same trajectory segment, target attitude data for multiple target control points of the same trajectory segment is obtained, including: Based on the first derivative model of the third-order Bézier curve model, the first expression relationship between the first intermediate control point and the starting point of the trajectory segment and the second expression relationship between the second intermediate control point and the ending point of the trajectory segment are determined respectively. Based on the first derivative model, a third expression relationship is determined between the midpoint of the trajectory segment and the starting point, ending point, first intermediate control point, and second intermediate control point of the trajectory segment. Determine the speed based on the first, second, and third expression relationships; Based on the target attitude data of the first expression relationship, velocity, trajectory segment starting point, and control point tangent vector, the target attitude data of the first intermediate control point is obtained; Based on the target attitude data of the second intermediate control point, the second expression relationship, velocity, target attitude data of the trajectory segment endpoint, and control point tangent vector, the target attitude data of the second intermediate control point is obtained. Among them, the first intermediate control point, the second intermediate control point, the starting point of the trajectory segment, and the ending point of the trajectory segment are all target control points.

[0029] By setting the Bézier curve model to a third-order Bézier curve model, both the trajectory planning requirements and the trajectory planning efficiency can be met.

[0030] The control point tangent vector can be obtained by solving a preset tangent vector constraint model. In some embodiments, the tangent vector constraint model includes a starting point tangent vector calculation model, an ending point tangent vector calculation model, and an intermediate node tangent vector calculation model, which are used to define the relationship between the control point tangent vector of the first reference control point in the welding direction and the control point tangent vectors of adjacent reference control points, the relationship between the control point tangent vector of the last reference control point in the welding direction and the control point tangent vectors of adjacent reference control points, and the tangent vector relationship between two adjacent reference control points other than the first and last reference control points. In other embodiments, it can also be configured directly. This application does not limit this, and those skilled in the art can selectively set it according to the actual situation.

[0031] The first derivative model is obtained by taking the first derivative of the curve corresponding to the Bézier curve model.

[0032] The first relational expression characterizes the coordinate vector mapping relationship between the first intermediate control point and the starting point of the trajectory segment. The second relational expression characterizes the coordinate vector mapping relationship between the second intermediate control point and the ending point of the trajectory segment.

[0033] In some embodiments, the third-order Bézier curve model is shown below: Formula 1-1 in, These are the parameters of the third-order Bézier curve model. The target attitude data representing the starting point of the trajectory segment to which it belongs. This represents the target attitude data at the endpoint of the trajectory segment to which it belongs. This represents the target attitude data of the first intermediate control point of the trajectory segment. This represents the target attitude data of the second intermediate control point of the trajectory segment. For example, the target attitude data of two adjacent reference control points are respectively... time For example, The target attitude data, which serves as the starting point of the trajectory segment, is denoted as... ,Will The target attitude data at the end of the trajectory segment is denoted as... For example, the target attitude data of the first intermediate control point is denoted as: The target attitude data of the second intermediate control point is denoted as .

[0034] At this point, based on Equation 1-1, the first derivative model is as follows: Formula 1-2 Since the velocities at the starting point, midpoint, and ending point of each trajectory segment are the same, the constraint conditions can be obtained based on Formula 1-2 as shown in the following formula: Formula 1-3 in, Let be the velocity, and be the value to be solved. The velocity corresponding to the starting point of the trajectory segment, The velocity corresponding to the end of the trajectory segment, The velocity at the midpoint of the corresponding trajectory segment; At this time, when Based on formulas 1-3 and 1-2, we can obtain Thus, we can obtain Among them, due to For the The unit vector of direction, therefore, Represents a vector At this point, the first expression relation can be obtained according to the vector calculation method. Similarly, when The second expression relation can be obtained. ;in, for tangent vector of the control point, for The control point tangent vector.

[0035] At this point, for the midpoint of the trajectory segment, we can... And from formulas 1-2, we obtain the third expression. Based on the first, second, and third expressions, we obtain the following formula: .

[0036] At this time, The result of squaring is: Formula 1-4 in, , All are known quantities. and It can be derived from a preset tangent vector constraint model or pre-configured known quantities. At this point, , , and Substituting into formula 1-4 and solving, we can obtain two roots, one positive and one negative. We take the positive root as the result. The value of .

[0037] At this point, based on the solution obtained... The target attitude data of the first intermediate control point can be obtained by relating the first expression relationship to the first expression relationship, based on the solved data. The target attitude data of the second intermediate control point can be obtained by combining the second expression.

[0038] Understandably, when the starting point of the trajectory segment is the first reference control point in the welding direction, the tangent vector of the control point at the starting point of the trajectory segment is obtained by performing the following steps through the starting point tangent vector calculation model: The control point tangent vector of the next reference control point of the trajectory segment starting point is obtained through the starting point tangent vector calculation model and used as the first control point tangent vector, and the first curve smoothing index of the next reference control point of the trajectory segment starting point is obtained. The tangent vector of the first control point is obtained by performing a weighted difference calculation on the tangent vector of the first control point and the first curve smoothing index through the starting point tangent vector calculation model; the curve smoothing index represents the curvature change relationship between the reference control point and the previous reference control point.

[0039] In some embodiments, the starting point tangent vector calculation model is as follows: Formula 1-5 in, The first curve smoothing index, express time , ; N represents the number of reference control points. This represents the (k+1)th reference control point. This represents the k-th reference control point. Indicates the first The node vector scalar of a trajectory segment starting from a reference control point. This represents the control point tangent vector of the next reference control point after the first reference control point, which is also the control point tangent vector of the second reference control point.

[0040] Understandably, when the endpoint of the trajectory segment is the last reference control point in the welding direction, the control point tangent vector at the endpoint of the trajectory segment is determined by performing the following steps through the endpoint tangent vector calculation model: The control point tangent vector of the previous reference control point at the end of the trajectory segment is obtained by the end-point tangent vector calculation model and used as the second control point tangent vector. The control point tangent vector is obtained by performing a weighted difference calculation on the tangent vector of the second control point and the curve smoothing index of the trajectory segment endpoint using the endpoint tangent vector calculation model.

[0041] The weighted difference operation means subtracting the second control point tangent vector and the curve smoothing index after weighting them respectively.

[0042] For example, the endpoint tangent vector calculation model is as follows: Formula 1-6 in, This represents the tangent vector of the second control point, which is the tangent vector of the control point preceding the last reference control point. The curve smoothing index for the last baseline control point. .in, The weighting coefficient (i.e., 2 in Formula 1-6) can be selectively adjusted according to the actual situation.

[0043] Understandably, the tangent vectors of the control points at the start and end points of the trajectory segment are determined by performing the following steps through the intermediate node tangent vector calculation model: The curve smoothing index of the current reference control point and the curve smoothing index of the next reference control point are obtained through the intermediate node tangent vector calculation model and used as the third curve smoothing index and the fourth curve smoothing index, respectively. The node scalar variation coefficient of the current reference control point is obtained by calculating the intermediate node tangent vector. The node scalar variation coefficient represents the relationship between the node scalar difference of the current reference control point and the node scalar difference of the next reference control point. The control point tangent vector of the current benchmark control point is determined by weighted summation of the node scalar change coefficient, the third curve smoothing index, and the fourth curve smoothing index through the intermediate node tangent vector calculation model. The starting point of a trajectory segment is any reference control point other than the first reference control point, and the ending point of a trajectory segment is any reference control point other than the last reference control point.

[0044] In some embodiments, the intermediate node tangent vector calculation model is as follows: Formula 1-7 in, denoted as the scalar variation coefficient of the node. This is the third curve smoothing index (i.e., the curve smoothing index of the current baseline control point). This is the fourth curve smoothing index (i.e., the curve smoothing index of the next reference control point after the current reference control point). .

[0045] In some embodiments, the node scalar change coefficient satisfies the following formula: Formula 1-8 For example, assuming the current reference control point is the second reference control point, then Assuming the current reference control point is the third reference control point, then .

[0046] Since the target attitude data of each reference control point is known, and the node vector scalar corresponding to each reference control point is determined, the control point tangent vectors of the starting point and ending point of each trajectory segment can be calculated based on formulas 1-5 to 1-8. Thus, the first intermediate control point and the second intermediate control point can be obtained by solving the first, second, and third expressions obtained from the third-order Bessel model.

[0047] Understandably, the curve smoothing index is the ratio of the coordinate vector difference between two adjacent reference control points to the node scalar difference; the node scalar change coefficient is the ratio of the node scalar difference of the current reference control point to the total node vector difference, and the ratio of the total node vector difference is the sum of the node scalar difference of the current reference control point and the node scalar difference of the next reference control point.

[0048] Understandably, B-spline curves are constructed based on the target attitude data of multiple target control points on the same trajectory segment to generate the target trajectory segment, including: Input the target attitude data of the reference control points of different trajectory segments into the preset node scalar constraint model to obtain the node vector corresponding to the trajectory segment where the starting point of the trajectory segment is located. B-spline curves are constructed based on the node vectors and the target attitude data of the corresponding multiple target control points.

[0049] The nodal scalar constraint model characterizes the relationship between the nodal vectors of two B-spline curves with coincident endpoints at a preset order.

[0050] In some embodiments, the node scalar constraint model is as follows: Formula 1-9 in, This represents the node vector of the (K+2)th target trajectory segment, also known as the node vector scalar. and This represents the target attitude data of the starting point and ending point of the K+1th target trajectory segment.

[0051] For example, the two sets of target control points obtained based on the third-order Bézier curve model are respectively , For example, among which, For continuous reference control points, when k=1, for , for , that is For example, when k=2, for , for , that is .

[0052] Therefore, by constructing a node scalar constraint model between two adjacent target trajectory segments, it is possible to further ensure that the planned welding trajectory path is smoother.

[0053] This application does not elaborate on how to construct B-spline curves. Given the target attitude data of the control points and the corresponding tangent vectors, those skilled in the art can refer to existing B-spline curve construction methods to construct them.

[0054] For example, refer to Figure 2 As shown, the specific steps of the welding trajectory generation process in this application embodiment are as follows: S1. Obtain welding torch attitude data for discrete control points on the expected welding trajectory. and workpiece attitude data of workpiece discrete control points ; ; ;

[0055] S2. Combine the welding torch posture data and the corresponding workpiece posture data one by one to obtain the target posture data of the reference control point. ; S3. Combine every two adjacent reference control points along the preset welding direction into pairs, using them as the start and end points of the same trajectory segment, respectively, to obtain... A group of reference control points, for example, the first group of reference control points. For example, that is ,but The target attitude data is recorded as the starting point of the trajectory segment corresponding to the first set of reference control points. The target attitude data is recorded as the endpoint of the trajectory segment corresponding to the first set of reference control points. and The target attitude data of the first intermediate control point and the second intermediate control point of the trajectory segment corresponding to the first group of reference control points are denoted as the first intermediate control point and the second intermediate control point. S4. Use the third-order Bézier curve model to determine the speed control point relationship model, that is, obtain the speed control point relationship model (i.e., Formula 1-4) based on Formula 1-2 and Formula 1-3. S5. Determine the control point tangent vectors of the trajectory segment start point and trajectory segment end point based on the start point tangent vector calculation model, the end point tangent vector calculation model and the intermediate node tangent vector calculation model; S6. Target attitude data at the starting point of the trajectory segment, based on the velocity control point relationship model (i.e., formulas 1-4). Target attitude data at the end of the trajectory segment The target attitude data of the first intermediate control point is obtained by taking the tangent vectors of the control points at the start and end points of the trajectory segment. Target attitude data of the second intermediate control point ; S7. Call the node scalar constraint model to determine the node vectors of each target trajectory segment to be calculated; and obtain the target trajectory segment based on the node vectors of the same trajectory segment, the target attitude data of the trajectory segment start point and trajectory segment end point.

[0056] Understandably, referring to Figure 3 As shown, the welding system provided according to an embodiment of this application includes: Welding robots; Displacement robot, The controller performs the following steps: Acquire the welding torch posture data corresponding to the discrete control points of the welding torch in the preset welding direction and the workpiece posture data of the discrete control points of the workpiece; set the discrete control points of the welding torch and the discrete control points of the workpiece to correspond one-to-one. By combining the welding torch posture data and the corresponding workpiece posture data one by one, the target posture data of the reference control point corresponding one-to-one with the discrete control point of the welding torch is obtained. Each pair of adjacent reference control points in the welding direction is taken as the start and end points of the same trajectory segment. The preset Bézier curve model is called to solve for the control points of the start and end points of the same trajectory segment, so as to obtain the target attitude data of multiple target control points of the same trajectory segment. The velocities of the start, middle and end points of each trajectory segment are the same. B-spline curves are constructed based on the target attitude data of multiple target control points on the same trajectory segment to generate the target trajectory segment; Based on the target trajectory segment, determine the welding torch trajectory and the workpiece movement trajectory.

[0057] Among them, the welding robot moves according to the welding torch trajectory; the positioning robot moves according to the workpiece movement trajectory.

[0058] The controller can be integrated into the welding robot or stand independently of the welding robot or the positioning robot. This application does not limit this, and those skilled in the art can selectively configure it according to the actual situation.

[0059] Understandably, the welding robot is a six-axis robot, and the welding torch posture data includes coordinates of multiple axes and Euler angles; the workpiece posture data includes one-axis joint angles and two-axis joint angles.

[0060] Euler angles include at least one of yaw, pitch, and roll. Multiple coordinate axes include at least one of the X-axis, Y-axis, and Z-axis.

[0061] This application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described method for generating welding trajectories. This welding system can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0062] Please see Figure 4 , Figure 4 The hardware structure of an electronic device according to another embodiment is illustrated. The electronic device includes: The processor 401 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application. The memory 402 can be a NAND flash, and the relevant program code is stored in the memory 402 and called by the processor 401 to execute the welding trajectory generation method of the embodiment of this application. Input / output interface 403 is used to implement information input and output; The communication interface 404 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.). Bus 405 transmits information between various components of the device (e.g., processor 401, memory 402, input / output interface 403, and communication interface 404); The processor 401, memory 402, input / output interface 403 and communication interface 404 are connected to each other within the device via bus 405.

[0063] This application also provides a computer-readable storage medium that stores a computer program that, when executed by a processor, implements the above-described method for generating welding trajectories.

[0064] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0065] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0066] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0067] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0068] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0069] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0070] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0071] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0072] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0073] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0074] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0075] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A method for generating a welding trajectory, characterized in that, The method includes: Acquire the welding torch posture data corresponding to the discrete control points of the welding torch in the preset welding direction and the workpiece posture data of the discrete control points of the workpiece; the discrete control points of the welding torch and the discrete control points of the workpiece are set to correspond one-to-one. The welding torch posture data and the corresponding workpiece posture data are combined one by one to obtain the target posture data of the reference control points that correspond one-to-one with the discrete control points of the welding torch. Each pair of adjacent reference control points in the welding direction is taken as the starting point and ending point of the same trajectory segment. A preset Bézier curve model is called to solve for the control points of the starting point and ending point of the same trajectory segment, thereby obtaining the target attitude data of multiple target control points of the same trajectory segment. The Bézier curve model represents that the velocities of the starting point, the middle point, and the ending point of the same trajectory segment are all the same. B-spline curves are constructed based on the target attitude data of multiple target control points in the same trajectory segment to generate the target trajectory segment; Based on the target trajectory segment, determine the welding torch trajectory and the workpiece movement trajectory.

2. The method for generating welding trajectories according to claim 1, characterized in that, The Bézier curve model is a third-order Bézier curve model. The process of calling a preset Bézier curve model to solve for control points at the start and end points of the same trajectory segment yields target attitude data for multiple target control points of the same trajectory segment, including: Based on the first derivative model of the third-order Bézier curve model, the first expression relationship between the first intermediate control point and the starting point of the trajectory segment and the second expression relationship between the second intermediate control point and the ending point of the trajectory segment are determined respectively. Based on the first derivative model, a third expression relationship is determined between the midpoint of the trajectory segment and the starting point, the ending point, the first intermediate control point, and the second intermediate control point of the trajectory segment. The speed is determined based on the first expression relationship, the second expression relationship, and the third expression relationship; Based on the first expression relationship, the velocity, the target attitude data of the trajectory segment starting point, and the control point tangent vector, the target attitude data of the first intermediate control point is obtained; Based on the second expression relationship, the velocity, the target attitude data of the trajectory segment endpoint, and the control point tangent vector, the target attitude data of the second intermediate control point is obtained; Among them, the first intermediate control point, the second intermediate control point, the starting point of the trajectory segment, and the ending point of the trajectory segment are all target control points.

3. The method for generating welding trajectories according to claim 2, characterized in that, When the starting point of the trajectory segment is the first reference control point in the welding direction, the tangent vector of the control point at the starting point of the trajectory segment is obtained by performing the following steps through the starting point tangent vector calculation model: The control point tangent vector of the next reference control point of the trajectory segment starting point is obtained through the starting point tangent vector calculation model and used as the first control point tangent vector, and the first curve smoothing index of the next reference control point of the trajectory segment starting point is obtained. The control point tangent vector is obtained by performing a weighted difference operation on the first control point tangent vector and the first curve smoothing index using the starting point tangent vector calculation model; the curve smoothing index characterizes the curvature change relationship between the reference control point and the previous reference control point.

4. The method for generating welding trajectories according to claim 3, characterized in that, When the endpoint of the trajectory segment is the last reference control point in the welding direction, the tangent vector of the control point at the endpoint of the trajectory segment is determined by performing the following steps through the endpoint tangent vector calculation model: The control point tangent vector of the previous reference control point of the trajectory segment endpoint is obtained through the endpoint tangent vector calculation model and used as the second control point tangent vector. The control point tangent vector is obtained by performing a weighted difference calculation on the curve smoothing index of the second control point tangent vector and the endpoint of the trajectory segment using the endpoint tangent vector calculation model.

5. The method for generating welding trajectories according to claim 3 or 4, characterized in that, The control point tangent vectors at the starting point and the ending point of the trajectory segment are both determined by performing the following steps through the intermediate node tangent vector calculation model: The curve smoothing index of the current reference control point and the curve smoothing index of the next reference control point are obtained through the intermediate node tangent vector calculation model and used as the third curve smoothing index and the fourth curve smoothing index, respectively. The node scalar change coefficient of the current reference control point is obtained through the intermediate node tangent vector calculation model. The node scalar change coefficient represents the relationship between the node scalar difference of the current reference control point and the node scalar difference of the next reference control point. The control point tangent vector of the current reference control point is determined by weighted summation of the node scalar change coefficient, the third curve smoothing index, and the fourth curve smoothing index using the intermediate node tangent vector calculation model. The starting point of the trajectory segment is any reference control point other than the first reference control point, and the ending point of the trajectory segment is any reference control point other than the last reference control point.

6. The method for generating welding trajectories according to claim 5, characterized in that, The curve smoothing index is the ratio of the coordinate vector difference between two adjacent reference control points to the node scalar difference; the node scalar change coefficient is the ratio of the node scalar difference of the current reference control point to the total node vector difference, and the ratio of the total node vector difference is the sum of the node scalar difference of the current reference control point and the node scalar difference of the next reference control point.

7. The method for generating welding trajectories according to claim 1, characterized in that, The step of constructing a B-spline curve based on the target attitude data of multiple target control points in the same trajectory segment to generate the target trajectory segment includes: The target attitude data of the reference control points of different trajectory segments are respectively input into the preset node scalar constraint model to obtain the node vector corresponding to the trajectory segment where the starting point of the trajectory segment is located. Based on the node vectors and the target attitude data of the corresponding multiple target control points, a B-spline curve is constructed.

8. A welding system, characterized in that, include: Welding robots; Displacement robot, A controller that executes the welding trajectory generation method as described in any one of claims 1 to 7 to obtain a welding torch welding trajectory and a workpiece movement trajectory; The welding robot moves according to the welding torch trajectory; the positioning robot moves according to the workpiece movement trajectory.

9. The welding system according to claim 8, characterized in that, The welding robot is a six-axis robot, and the welding torch posture data includes coordinates of multiple axes and Euler angles; the workpiece posture data includes one-axis joint angles and two-axis joint angles.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the method for generating the welding trajectory according to any one of claims 1 to 7.

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