A multi-joint industrial robot 3D printing path optimization method

Abstract

The application discloses a kind of multi-joint industrial robot 3D printing path optimization method, it is related to 3D printing path optimization field.The method includes: according to the joint torque of mechanical arm generation normalized torque load index, and the cutting layer thickness sequence is generated in combination with model surface curvature;Through joint angular acceleration, construct vibration risk index, dynamically adjust pose interpolation weight, generate pose interpolation trajectory;According to the distribution characteristics of temperature field around path point, calculate thermal coupling factor and generate thermal avoidance path offset, correct path point;Collision risk score is calculated based on the minimum Euclidean distance of path point and obstacle model;Fusion cutting layer thickness, vibration risk, thermal coupling factor and collision risk score, construct global path score function.By comprehensively considering the load of mechanical arm, vibration risk, thermal accumulation and collision risk, effectively improve printing precision and quality, optimize path smoothness, reduce vibration and thermal influence, avoid material deformation, ensure printing process stability.

Description

Technical Field

[0001] This invention relates to the field of 3D printing path optimization, specifically a method for optimizing the 3D printing path of a multi-joint industrial robot. Background Technology

[0002] With the widespread application of industrial robots in high-precision manufacturing, 3D printing technology is finding increasingly wider applications in various industries, such as aerospace, automotive manufacturing, and medical devices. However, the application of existing path planning technologies in 3D printing still faces many challenges, especially in terms of accuracy, vibration control, heat accumulation, and collision avoidance.

[0003] Existing technical problems: Current path planning methods mainly focus on geometric optimization or shortest path, ignoring dynamic factors that may arise during the printing process, resulting in difficulties in meeting high requirements for printing accuracy and quality. Key issues include: Insufficient vibration control: During robot movement, especially when the path makes sharp turns or accelerates, vibrations are frequently generated, affecting printing accuracy; Heat accumulation: During 3D printing, nozzle heating and material melting cause localized temperature increases, leading to heat accumulation and printing defects; Poor collision and obstacle avoidance capabilities: Existing technologies mainly rely on offline planning, ignoring changes in obstacles in the dynamic environment and failing to achieve real-time obstacle avoidance.

[0004] Limitations of existing technologies: Most existing path planning methods only consider a single objective, such as finding the shortest path or optimizing path geometry, neglecting factors such as vibration, thermal effects, and collisions. Therefore, they cannot meet the requirements for high precision and high quality in complex 3D printing tasks.

[0005] Technical challenges: Path smoothness: Traditional methods fail to effectively handle path vibration and heat accumulation, leading to decreased printing accuracy; Multi-objective optimization: Existing technologies typically only consider single factors such as path length and speed, failing to comprehensively consider vibration, thermal control, and collision risks; Dynamic adaptability: Existing methods cannot adjust the path in real time when facing dynamically changing environmental conditions.

[0006] The limitations of existing technologies mean that 3D printing path optimization still faces many challenges, especially in terms of precision control, vibration suppression, thermal effects, and collision avoidance. More efficient and accurate path optimization methods are urgently needed. Summary of the Invention

[0007] Based on the shortcomings of the prior art described above, the purpose of this invention is to provide a method for optimizing the 3D printing path of a multi-joint industrial robot to solve the above-mentioned technical problems.

[0008] To achieve the above objectives, the present invention provides the following technical solution: a method for optimizing the 3D printing path of a multi-joint industrial robot, comprising:

[0009] S1. Based on the joint torque of the robotic arm, a normalized torque load index is generated and combined with the surface curvature of the model. The thickness values ​​of each layer are generated through inverse proportional mapping to construct a layer thickness sequence.

[0010] S2. Construct a vibration risk index by combining the joint angular acceleration during the movement of the robotic arm, and use the vibration risk index to dynamically adjust the weight allocation during attitude interpolation to generate an attitude interpolation trajectory sequence that minimizes vibration risk;

[0011] S3. Based on the distribution characteristics of the temperature field around the path point, calculate the thermal coupling factor and generate the thermal avoidance path offset, and correct the path point based on the thermal avoidance path offset.

[0012] S4. Based on the minimum three-dimensional Euclidean distance between the path point and the obstacle model, calculate the collision risk score using an exponential decay function;

[0013] S5. Construct a heterogeneous weighted global path scoring function by integrating slice thickness, vibration risk index, thermal coupling factor, and collision risk score.

[0014] The present invention is further configured such that S1 includes:

[0015] Paths are generated using path planning algorithms;

[0016] The torque value of each joint is obtained by inverse kinematics using a robotic arm, and then normalized with the maximum allowable torque of each joint to eliminate dimensional differences and obtain the normalized torque load index.

[0017] The present invention is further configured to calculate the principal curvature by performing local quadratic surface fitting on discrete points on the surface of the three-dimensional model and combining it with Gaussian filtering to obtain the curvature, and then fuse the curvature with the normalized torque load index through a nonlinear function to obtain the layer thickness.

[0018] The layer thickness is calculated point by point by traversing all discrete points on the surface of the 3D model to form a layer thickness sequence that matches the geometric features and the load of the robotic arm.

[0019] The present invention is further configured such that S2 includes:

[0020] The vibration risk index is obtained by quantifying the vibration intensity by calculating the sum of the squares of the angular accelerations of each joint of the robotic arm.

[0021] The interpolation weight is obtained by dynamically adjusting the attitude interpolation ratio based on the vibration risk index and time parameters at the endpoint.

[0022] The present invention is further configured to generate a smooth attitude interpolation trajectory through quaternion weighted interpolation;

[0023] The attitude interpolation trajectory sequence is obtained by connecting the generated attitude interpolation trajectories in chronological order.

[0024] The present invention is further configured such that S3 includes:

[0025] The thermal coupling factor is obtained by quantifying the cumulative temperature in the local area around the path point.

[0026] The present invention is further configured to estimate the spatial thermal gradient vector by the ratio of the temperature difference to the distance between adjacent path points, and to obtain the thermal avoidance path offset by combining the offset sensitivity coefficient:

[0027] The original path point is superimposed with the thermal avoidance path offset to obtain a new corrected path point.

[0028] The present invention is further configured such that the collision risk score generation included in S4 includes:

[0029] The planned path generated by the path planning algorithm is discretized to obtain the set of discrete points of the robotic arm end effector on the path segment;

[0030] Using LiDAR and RGB-D cameras, the position information of the printed parts, the robotic arm, and environmental obstacles is collected in real time to construct an obstacle model;

[0031] The minimum three-dimensional Euclidean distance between the discrete point set of the path and the obstacle model is extracted. The cube value of the minimum three-dimensional Euclidean distance is nonlinearly mapped by an exponential decay function to generate a normalized collision risk score sequence.

[0032] The present invention is further configured such that the global path score included in S5 is obtained by weighted and fused the reciprocal of the slice thickness, vibration risk index, thermal coupling factor and collision risk score of each path point to obtain the path score of the path point of the individual path point, and then the path scores of all path points are summed to obtain the global path score.

[0033] The present invention is further configured such that the method also includes a path determination module:

[0034] The generated path is scored globally. If the global path score is greater than the threshold, it is determined to be an invalid path and the system is fed back to regenerate the path. If the score is less than the threshold, all parameters contained in the path are fed back to each execution module based on the current path to complete the 3D model printing.

[0035] This invention provides a 3D printing path optimization method for multi-joint industrial robots. The method comprises: S1. Generating a normalized torque load index based on the joint torque of the robotic arm and combining it with the surface curvature of the model to generate layer thickness values ​​through inverse proportional mapping, thus constructing a layer thickness sequence; S2. Constructing a vibration risk index based on the joint angular acceleration during the robotic arm's movement, and dynamically adjusting the weight allocation during attitude interpolation using the vibration risk index to generate an attitude interpolation trajectory sequence that minimizes vibration risk; S3. Calculating a thermal coupling factor and generating a thermal avoidance path offset based on the temperature field distribution characteristics around the path points, and correcting the path points based on the thermal avoidance path offset; S4. Calculating a collision risk score based on the minimum three-dimensional Euclidean distance between the path points and the obstacle model using an exponential decay function; S5. Constructing a non-homogeneously weighted global path scoring function by fusing layer thickness, vibration risk index, thermal coupling factor, and collision risk score. The beneficial effects include:

[0036] Improving printing accuracy and quality: This invention comprehensively considers multiple factors such as the load on the robotic arm joints, vibration risk, heat accumulation and collision risk, and performs multi-objective optimization to ensure that each path meets the printing accuracy requirements while avoiding printing defects caused by vibration, heat effects or collisions, thereby significantly improving the overall accuracy and quality of 3D printing.

[0037] Optimized path smoothness and vibration control: By dynamically adjusting the weight distribution of attitude interpolation and combining it with a vibration risk index to minimize vibration, this invention effectively reduces micro-vibrations or errors caused by abrupt attitude changes in the path, ensuring smooth nozzle movement during printing, thereby improving printing accuracy, especially in applications requiring high precision.

[0038] Reduced thermal impact and material deformation: This invention effectively avoids material warping, deformation, or poor interlayer adhesion caused by excessive heat accumulation during printing by calculating the thermal coupling factor and generating a thermal avoidance path offset. Intelligent path adjustment avoids thermal impact in high-temperature areas, improving the stability and quality of printed parts.

[0039] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0040] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments 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. In the drawings:

[0041] Figure 1 The flowchart illustrates a method for optimizing the 3D printing path of a multi-joint industrial robot, as shown in an exemplary embodiment of the present invention. Detailed Implementation

[0042] The embodiments of the present invention will be described below with reference to the accompanying drawings and preferred embodiments. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be understood that the preferred embodiments are only for illustrating the present invention and not for limiting the scope of protection of the present invention.

[0043] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Therefore, the drawings only show the components related to the present invention and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0044] In the following description, numerous details are explored to provide a more thorough explanation of embodiments of the invention. However, it will be apparent to those skilled in the art that embodiments of the invention may be practiced without these specific details. In other embodiments, well-known structures and devices are shown in block diagram form rather than in detail to avoid obscuring embodiments of the invention.

[0045] Example 1

[0046] A method for optimizing the 3D printing path of a multi-joint industrial robot, such as Figure 1 As shown, it includes:

[0047] S1. Based on the joint torque of the robotic arm, a normalized torque load index is generated and combined with the surface curvature of the model. The thickness values ​​of each layer are generated through inverse proportional mapping to construct a layer thickness sequence.

[0048] S2. Construct a vibration risk index by combining the joint angular acceleration during the movement of the robotic arm, and use the vibration risk index to dynamically adjust the weight allocation during attitude interpolation to generate an attitude interpolation trajectory sequence that minimizes vibration risk;

[0049] S3. Based on the distribution characteristics of the temperature field around the path point, calculate the thermal coupling factor and generate the thermal avoidance path offset, and correct the path point based on the thermal avoidance path offset.

[0050] S4. Based on the minimum three-dimensional Euclidean distance between the path point and the obstacle model, calculate the collision risk score using an exponential decay function;

[0051] S5. Construct a heterogeneous weighted global path scoring function by integrating slice thickness, vibration risk index, thermal coupling factor, and collision risk score.

[0052] The present invention is further configured such that S1 includes:

[0053] Paths are generated using path planning algorithms;

[0054] The torque value of each joint is obtained through inverse kinematics using a robotic arm, and then normalized to the maximum allowable torque of each joint to eliminate dimensional differences and obtain a normalized torque load index. Specifically, an initial path is generated using a fast exploration random tree algorithm in path planning, output as a continuous curve using a B-spline algorithm, and then smoothed using a trajectory optimization algorithm to obtain the final path. The path contains waypoints This method is existing technology and will not be elaborated upon here. The normalized torque load index is a standardized parameter that comprehensively reflects the load state of each joint of the robotic arm. The specific calculation logic is as follows: ,in, path point The index indicates which path point; For each joint of the robotic arm at the path point Normalized torque load index at the location; This is a joint index, indicating which joint is in a multi-joint robotic arm; For the first Each joint at the path point The real-time torque at the location is collected in real time by sensors; The maximum permissible torque for the i-th joint is provided by the robotic arm manufacturer. The total number of joints is used; the real-time torque of each joint is compared with its maximum allowable value, and the overall load status of the robotic arm is comprehensively evaluated through the sum of squares.

[0055] The present invention is further configured to calculate the principal curvature by performing local quadratic surface fitting on discrete points on the surface of the three-dimensional model and combining it with Gaussian filtering to obtain the curvature, and then fuse the curvature with the normalized torque load index through a nonlinear function to obtain the layer thickness.

[0056] The process iterates through all discrete points on the surface of the 3D model, calculating the layer thickness point by point to form a sequence of layer thicknesses that matches the geometric features and the load of the robotic arm. Specifically, directly reading the vertex coordinates of the 3D digital model from mesh models such as STL as discrete points and calculating curvature based on these discrete points is an existing technique, which will not be elaborated upon here. The layer thickness is used to control printing precision based on the curvature of the model surface. The greater the local curvature, the smaller the layer thickness, resulting in finer printing; conversely, the smaller the curvature in smoother areas, the larger the layer thickness is used to speed up printing. The layer thickness calculation logic is as follows: ,in, The thickness of the cut layer is represented by the thickness at the first cut layer. Layer thickness at each path point The maximum allowable layer thickness is... For curvature, specifically represented as the first... The curvature of the model surface at each path point is calculated using existing technology, which will not be elaborated upon here. This is a normalized torque load index; , The exponential factor for adjusting curvature and normalized torque load index can be set between 1.5 and 2.5, and can be adjusted according to different usage scenarios. This is a numerical stability term used to prevent errors caused by a denominator of zero; it is usually set to... The slice thickness set is a set that includes the slice thickness of each path point calculated, ultimately resulting in a slice thickness set containing the slice thickness of all path points.

[0057] The present invention is further configured such that S2 includes:

[0058] The vibration risk index is obtained by quantifying the vibration intensity by calculating the sum of the squares of the angular accelerations of each joint of the robotic arm.

[0059] The interpolation weight is obtained by dynamically adjusting the attitude interpolation ratio based on the vibration risk index of the endpoint and time parameters. Specifically, to prevent the robotic arm from moving from the current path point to the next path point, if there are sharp turns in the trajectory segment and the print head flips, it is necessary to calculate the vibration risk index of the trajectory segment and use the vibration risk index to calculate the weight. If the vibration risk index is high, the attitude change rate is slowed down by reducing the weight, the transition time is extended, and a smooth attitude sequence is generated to reduce the impact on the robotic arm joints. The vibration risk index reflects the potential threat of mechanical vibration to accuracy and stability during movement. The greater the angular acceleration of the path segment with rapid acceleration, sudden stop, or high curvature, and the greater the joint angular acceleration, the higher the vibration risk index. The vibration risk index calculation logic is as follows: ,in, path point The index indicates which path point is being accessed. This is the vibration risk index. For the first The joint angles of each joint. For the first The angular acceleration of each joint is obtained by performing a cubic spline fit on the joint angles in the path and then taking the second derivative. This represents the total number of joints. The interpolation weight calculation logic is as follows: ,in, For interpolation weights; The interpolation time variable is defined in The interval between these intervals represents the interpolation progress from the initial attitude to the final attitude. As the endpoint of the difference, Indicates the starting point, Indicates the endpoint; This refers to the vibration risk index of the starting and ending points of the current path points, i.e., the vibration risk index of the current path point and the next path point. This is a vibration risk growth control factor used to control the rate of weight decrease in high-risk segments. Its value ranges from 1.0 to 2.0, with a default value of 1.5. Indicates the interpolation time variable Applying a nonlinear transformation makes the interpolation weights more sensitive to progress. It is the vibration risk index scheduling term, representing the interpolation endpoint. Corresponding vibration risk index For the current interpolation time variable The intensity of the impact.

[0060] The present invention is further configured to generate a smooth attitude interpolation trajectory through quaternion weighted interpolation;

[0061] The attitude interpolation trajectory sequence is obtained by connecting the generated attitude interpolation trajectories in chronological order. Specifically, compared with traditional linear or SLERP interpolation, a nonlinear weight combination is directly applied to the start and end attitudes, and the vibration risk index of the path endpoints is introduced to modulate the weights, making the attitude transition more inclined to low vibration risk points. The attitude interpolation calculation logic is as follows: Here, i represents a path segment, i.e., a path point. To the waypoint The path range For the path segment in time The interpolated quaternion at that point represents the spatial orientation of the printhead. The initial orientation indicates the print head's position at the path point. The posture at the place, The termination attitude indicates that the print head is at the next path point. The initial and final poses are used to define the initial and target orientations of the robotic arm's end effector. , The time weighting factor is related to vibration risk and is used to satisfy the heterogeneous scheduling strategy. This is used to normalize the denominator, ensuring that the output attitude quaternion remains valid on the unit sphere.

[0062] The present invention is further configured such that S3 includes:

[0063] The thermal coupling factor is obtained by quantifying the degree of temperature accumulation in the local area surrounding the waypoint. Specifically, the thermal coupling factor is used to quantify the degree of temperature accumulation in the local area surrounding the waypoint, reflecting the risk of material melting and collapse due to heat. The specific calculation formula is as follows: ,in, It is the thermal coupling factor. The radius of thermal influence indicates that the calculation needs to consider the m neighboring path points before and after the point of influence. The estimated temperature for nearby path points is obtained through actual measurements using infrared sensors. For path points, For nearby path points, The Euclidean distance between a path point and its neighboring path points is used to weight the temperature contribution; the closer the distance, the more significant the temperature impact. In high-temperature dense areas, when the temperature is high and the distance is large, the thermal coupling factor increases significantly, indicating a high risk of melt collapse. In low-temperature sparse areas, points with low temperature or large distance contribute less to the thermal coupling factor, indicating that the risk is controllable.

[0064] The present invention is further configured to estimate the spatial thermal gradient vector by the ratio of the temperature difference to the distance between adjacent path points, and to obtain the thermal avoidance path offset by combining the offset sensitivity coefficient:

[0065] The original path point is superimposed with the thermal avoidance path offset to obtain a new corrected path point. Specifically, the thermal avoidance offset represents the displacement vector by which the path point needs to move towards the low-temperature region, adjusting the path point to avoid local overheating; the specific calculation logic is as follows: ,in, This is the thermal avoidance path offset, used to represent the distance that needs to be displaced. This is the offset sensitivity coefficient, used to control the amplification of the offset by the thermal gradient. Its value ranges from 0.1 to 0.5, with a default value of 0.3. It can be modified according to the actual scenario. The spatial thermal gradient vector is estimated by the ratio of the temperature difference to the distance between adjacent path points. It represents the rate of change of temperature distribution around a path point. The spatial thermal gradient vector reflects the spatial variation of temperature in a local area and affects path adjustment. The specific calculation formula is as follows: ,in, The spatial thermal gradient vector, This represents the distance between the current path point and the two path points before and after it. This represents the thermal coupling factor between the current path point and the two path points before and after it; the corrected path point represents the path point adjusted based on the thermal coupling factor, obtained by adding the thermal avoidance path offset to the path point. The specific calculation formula is as follows: ,in, To correct the path points.

[0066] The present invention is further configured such that the collision risk score generation included in S4 includes:

[0067] The planned path generated by the path planning algorithm is discretized to obtain the set of discrete points of the robotic arm end effector on the path segment;

[0068] Using LiDAR and RGB-D cameras, the position information of the printed parts, the robotic arm, and environmental obstacles is collected in real time to construct an obstacle model;

[0069] The minimum 3D Euclidean distance between the discrete point set of the path and the obstacle model is extracted. A nonlinear mapping of the cubic value of this minimum 3D Euclidean distance is then performed using an exponential decay function to generate a normalized collision risk score sequence. Specifically, the collision risk score Ri quantifies the minimum distance between the path segment and the obstacle. Combining the exponential function and cubic operations, it maps the safe distance in physical space to a risk value in the [0,1] interval, providing a calculable safety indicator for path optimization. Collision risk score calculation logic: ,in, Assess collision risk. The path is a discrete set of points, representing the points on the path of the robotic arm's end effector. The set of trajectory points on the path is a discrete set of points generated by sampling along the path at fixed or adaptive step sizes. This method is existing technology and will not be elaborated on here. The obstacle set is constructed by collecting point cloud data of the printed parts, the robotic arm itself, and environmental obstacles in real time using LiDAR and RGB-D cameras, then combining the kinematic model of the robotic arm to calculate the point cloud on its own surface, and predicting the future position of dynamic obstacles using Kalman filtering or particle filtering. This method is existing technology and will not be elaborated on here. The path points are the discrete point set of the path. The obstacle point is a point within the obstacle set. This is the risk sensitivity coefficient, used to control the steepness of risk variation with distance. Its value needs to be adjusted based on the robotic arm's movement speed and the dynamics of the obstacle; in high-dynamic scenarios, it needs to be increased. To enhance security sensitivity, typically in Between, risk sensitivity coefficient The larger the value, the higher the collision risk score at the same distance. The closer it is to 1; It is an exponential function used to compress the calculation results into an exponential space. between; To calculate the minimum safe distance, it iterates through all points in the path segment, calculates the Euclidean distance to all points in the obstacle set, and finds the minimum distance of the path segment. This represents the cube operation on the minimum distance, used to represent a nonlinear risk mapping, with the aim of amplifying the collision risk in the distance region; finally, an exponential function is used to compress the risk value, compressing the output result to a smaller value. Between, when the risk of collision is extremely high When the risk of collision is extremely low and it is completely safe .

[0070] The present invention is further configured such that the global path score included in S5 is obtained by weighted and fused the inverse of the slice thickness, vibration risk index, thermal coupling factor, and collision risk score of each path point to obtain the path score of each path point, and then the path scores of all path points are summed to obtain the global path score. Specifically, the global path score scores multiple performance indicators of each path point, and constructs a global path cost function through weighted combination, which is used to drive subsequent path selection or optimizer to find the optimal printing path. The specific calculation formula is as follows: ,in, Score the global path; This is the exponential scaling factor, used to control the compression speed of the mapping. A larger value results in greater sensitivity to path scoring. The value range is... Between, the default is ; This represents the total number of path points. It is the reciprocal of the thickness of the slice where the path point is located. The thinner the slice, the higher the printing accuracy, but it also means higher time cost. By taking the reciprocal, the thinner slice corresponds to a greater cost, and the thicker slice has a smaller cost. Assess collision risk. It is the thermal coupling factor; As a vibration risk indicator; , , , These are weighting parameters used to control the relative influence of each scoring item in the global path scoring function. Used to control the impact of layer thickness on printing efficiency and detail, with a value range between 0.1 and 0.4. Used to control obstacle avoidance risk, with a value ranging from 0.2 to 0.4. Used to control thermal deformation, with a value ranging from 0.1 to 0.3. Used to control vibration stability, the value ranges from 0.1 to 0.3, and needs to meet the following requirements. .

[0071] The present invention is further configured such that the method also includes a path determination module:

[0072] The generated paths are scored globally. If the global path score is greater than a threshold, the path is deemed invalid, and the feedback system regenerates the path. If the score is less than the threshold, all parameters contained in the path are fed back to the respective execution modules to complete the 3D model printing. Specifically, the path judgment module, through multi-objective quantization scoring, threshold judgment, parameter mapping, and closed-loop feedback, realizes path decision-making and execution control in complex scenarios. It is the decision-making unit of the 3D printing path optimization system, used to transform discrete path planning results into an executable, verifiable, and adjustable industrial control process. The lower the global path score, the lower the total cost of the path. A threshold is set, with a default value of 0.45. A higher threshold requires higher accuracy in path planning. Paths with scores less than the threshold are designated as optimal paths. The printing point coordinates and layer thickness data corresponding to the optimal path are converted into control commands that the execution modules can recognize. Finally, the control commands are sent to the execution terminal through a real-time communication protocol.

[0073] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0074] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. A and B can be singular or plural. Additionally, the character " / " in this article generally indicates an "or" relationship between the preceding and following related objects, but it can also represent an "and / or" relationship. Please refer to the context for a more accurate understanding.

[0075] In this application, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or multiple items. For example, at least one of a, b, or c can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0076] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0077] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0078] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0079] In the several embodiments provided in this application, it should be understood that the disclosed system can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For instance, the division of units 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 devices or units may be electrical, mechanical, or other forms.

[0080] The units described 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.

[0081] In addition, 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.

[0082] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they 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 a portion 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 several 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 described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0083] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for optimizing the 3D printing path of a multi-joint industrial robot, characterized in that, include: S1. Based on the joint torque of the robotic arm, a normalized torque load index is generated and combined with the surface curvature of the model. The thickness values ​​of each layer are generated through inverse proportional mapping to construct a layer thickness sequence. S2. Construct a vibration risk index by combining the joint angular acceleration during the movement of the robotic arm, and dynamically adjust the weight allocation during attitude interpolation using the vibration risk index to generate an attitude interpolation trajectory sequence that minimizes vibration risk. S2 includes: quantifying the vibration intensity by calculating the sum of the squares of the angular accelerations of each joint of the robotic arm to obtain a vibration risk index, and dynamically adjusting the attitude interpolation ratio based on the vibration risk index of the endpoints and time parameters to obtain weights. S3. Based on the distribution characteristics of the temperature field around the path point, calculate the thermal coupling factor and generate the thermal avoidance path offset, and correct the path point based on the thermal avoidance path offset. S4. Based on the minimum three-dimensional Euclidean distance between the path points and the obstacle model, a collision risk score is calculated using an exponential decay function. The collision risk score generation includes: discretizing the planned path generated by the path planning algorithm, obtaining the path discrete point set of the robotic arm end effector on the path segment, using LiDAR and RGB-D camera to collect the position information of the printed part, the robotic arm body, and environmental obstacles in real time, constructing an obstacle model, extracting the minimum three-dimensional Euclidean distance between the path discrete point set and the obstacle model, and performing nonlinear mapping on the cube value of the minimum three-dimensional Euclidean distance using an exponential decay function to generate a normalized collision risk score sequence. S5. Construct a heterogeneous weighted global path score function by fusing the slice thickness, vibration risk index, thermal coupling factor and collision risk score of each path point. The global path score is obtained by weighting and fusing the inverse of the slice thickness, vibration risk index, thermal coupling factor and collision risk score of each path point to obtain the path score of a single path point. Then, the path scores of all path points are summed to obtain the global path score.

2. The method for optimizing the 3D printing path of a multi-joint industrial robot according to claim 1, characterized in that, S1 includes: Paths are generated using path planning algorithms; The torque value of each joint is obtained by inverse kinematics using a robotic arm, and then normalized with the maximum allowable torque of each joint to eliminate dimensional differences and obtain the normalized torque load index.

3. The method for optimizing the 3D printing path of a multi-joint industrial robot according to claim 2, characterized in that, By fitting local quadratic surfaces to discrete points on the surface of a 3D model, the principal curvature is calculated and combined with Gaussian filtering to obtain the curvature. The curvature and normalized torque load index are then fused with a nonlinear function to obtain the layer thickness. The layer thickness is calculated point by point by traversing all discrete points on the surface of the 3D model to form a layer thickness sequence that matches the geometric features and the load of the robotic arm.

4. The method for optimizing the 3D printing path of a multi-joint industrial robot according to claim 1, characterized in that, A smooth attitude interpolation trajectory is generated by quaternion weighted interpolation; The attitude interpolation trajectory sequence is obtained by connecting the generated attitude interpolation trajectories in chronological order.

5. The method for optimizing the 3D printing path of a multi-joint industrial robot according to claim 1, characterized in that, S3 includes: The thermal coupling factor is obtained by quantifying the cumulative temperature in the local area around the path point.

6. The method for optimizing the 3D printing path of a multi-joint industrial robot according to claim 1, characterized in that, The spatial thermal gradient vector is estimated by the ratio of the temperature difference to the distance between adjacent path points, and the thermal avoidance path offset is obtained by combining it with the offset sensitivity coefficient. The original path point is superimposed with the thermal avoidance path offset to obtain a new corrected path point.

7. The method for optimizing the 3D printing path of a multi-joint industrial robot according to claim 1, characterized in that, This method also includes a path determination module: The generated path is scored globally. If the global path score is greater than the threshold, it is determined to be an invalid path and the system is fed back to regenerate the path. If the score is less than the threshold, all parameters contained in the path are fed back to each execution module based on the current path to complete the 3D model printing.

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