Multi-joint industrial robot 3D printing path optimization method

Abstract

The invention discloses a 3D printing path optimization method for a multi-joint industrial robot, and relates to the field of 3D printing path optimization. The method comprises the steps that a normalized torque load index is generated according to the joint torque of the mechanical arm, and a layer cutting thickness sequence is generated in combination with the model surface curvature; a vibration risk index is constructed through the joint angular acceleration, the attitude interpolation weight is dynamically adjusted, and an attitude interpolation trajectory is generated; according to the distribution characteristics of the temperature field around the path point, calculating a thermal coupling factor, generating a thermal avoidance path offset, and correcting the path point; calculating a collision risk score based on the minimum Euclidean distance between the path point and the obstacle model; and fusing the cutting layer thickness, the vibration risk, the thermal coupling factor and the collision risk score, and constructing a global path scoring function. Through comprehensive consideration of mechanical arm load, vibration risk, heat accumulation and collision risk, printing precision and quality are effectively improved, path smoothness is optimized, vibration and heat influences are reduced, material deformation is avoided, and stability of the printing process is ensured.

Description

Technical Field

[0001] The present invention relates to the field of 3D printing path optimization, and in particular to a 3D printing path optimization method for a multi-joint industrial robot. Background Art

[0002] With the widespread adoption of industrial robots in high-precision manufacturing, 3D printing technology is gaining increasing adoption across multiple industries, such as aerospace, automotive, and medical devices. However, existing path planning technologies for 3D printing still face numerous challenges, particularly in areas such as precision, vibration control, heat buildup, and collision avoidance.

[0003] Existing Technology Issues: Current path planning methods primarily focus on geometric optimization or the shortest path, ignoring dynamic factors that may arise during the printing process. This results in printing accuracy and quality that are difficult to meet high standards. Key issues include: Inadequate vibration control: During robot motion, especially during sharp turns or accelerations, vibrations often occur, affecting printing accuracy; Heat accumulation: During 3D printing, nozzle heating and material melting can cause localized temperature increases, leading to heat accumulation and printing defects; and Poor collision avoidance: Existing technologies primarily rely on offline planning, ignoring the changing dynamics of obstacles in a 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 the shortest path or optimizing path geometry, while ignoring factors such as vibration, thermal effects, and collisions. As a result, they cannot meet the high precision and quality requirements of complex 3D printing tasks.

[0005] Technical Challenges: Path smoothness: Traditional methods fail to effectively address path vibration and heat accumulation, resulting in reduced printing accuracy; Multi-objective optimization: Existing technologies typically only consider single factors such as path length and speed, and fail to comprehensively consider vibration, thermal control, and collision risks; Dynamic adaptability: Existing methods are unable to adjust the path in real time when faced with dynamically changing environmental conditions.

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

[0007] Based on the above-mentioned shortcomings of the prior art, the purpose of the present invention is to provide a multi-joint industrial robot 3D printing path optimization method 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 a 3D printing path of a multi-joint industrial robot, comprising: S1. Generate a normalized torque load index based on the manipulator joint torque and combine it with the model surface curvature to generate the slice thickness values ​​through inverse proportional mapping, and construct a slice thickness sequence. S2. Build a vibration risk index based on the joint angular acceleration during the manipulator's motion. Use this index to dynamically adjust the weight distribution during posture interpolation, generating a posture interpolation trajectory sequence that minimizes vibration risk. S3. Calculate the thermal coupling factor and generate a thermal avoidance path offset based on the temperature distribution characteristics around the path point. Modify the path point based on the thermal avoidance path offset. S4. Calculate the collision risk score based on the minimum three-dimensional Euclidean distance between the path point and the obstacle model using an exponential decay function; S5. Construct a heterogeneously weighted global path scoring function by integrating slice thickness, vibration risk index, thermal coupling factor, and collision risk score.

[0009] The present invention is further configured such that S1 comprises: Generate a path through the path planning algorithm; The torque value of each joint is obtained by inverse solution of the robotic arm, and then normalized with the maximum allowable torque of each joint to eliminate the dimensional difference and obtain the normalized torque load index.

[0010] The present invention is further configured to perform local quadratic surface fitting on discrete points on the surface of the three-dimensional model, calculate the principal curvature and obtain the curvature by combining it with Gaussian filtering, and fuse the curvature with the normalized torque load index through a nonlinear function to obtain the slice thickness; Traverse all discrete points on the surface of the 3D model, calculate the slice thickness point by point, and form a slice thickness sequence that matches the geometric features and the robot arm load.

[0011] The present invention is further configured such that S2 includes: The vibration risk index is obtained by calculating the square sum of the angular accelerations of each joint of the robotic arm to quantify the vibration intensity; The interpolation weight is obtained by dynamically adjusting the posture interpolation ratio according to the endpoint's vibration risk index and time parameters.

[0012] The present invention is further configured to generate a smooth posture interpolation trajectory by quaternion weighted interpolation; The posture interpolation trajectory sequence is obtained by connecting the generated posture interpolation trajectories in time sequence.

[0013] The present invention is further configured such that S3 includes: The thermal coupling factor is obtained by quantifying the degree of temperature accumulation in the local area around the path point.

[0014] 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 in combination with the offset sensitivity coefficient: The original path point is superimposed with the thermal avoidance path offset to obtain a new corrected path point.

[0015] The present invention is further configured such that the generation of the collision risk score included in S4 includes: The planned path generated by the path planning algorithm is discretized to obtain the path discrete point set of the end effector of the robot arm on the path segment; Use LiDAR and RGB-D cameras to collect real-time location information of printed parts, robotic arms, and environmental obstacles to build an obstacle model. The minimum three-dimensional Euclidean distance between the discrete point set of the path and the obstacle model is extracted, and the cubed value of the minimum three-dimensional Euclidean distance is nonlinearly mapped using an exponential decay function to generate a normalized collision risk score sequence.

[0016] The present invention is further configured such that the global path score included in S5 is a weighted fusion of the inverse of the slice thickness, vibration risk index, thermal coupling factor, and collision risk score of each path point to obtain a path score for a single path point, and then the path scores of all path points are summed to obtain a global path score.

[0017] The present invention is further configured such that the method further comprises a path determination module: The global path score is calculated for the generated path. If the global path score is greater than the threshold, it is judged as an invalid path, and the feedback system regenerates the path. If it is less than the threshold, all parameters contained in the path are fed back to each execution module based on this path to complete the 3D model printing.

[0018] The present invention provides a method for optimizing the 3D printing path of a multi-jointed industrial robot. The method comprises the following steps: S1. generating a normalized torque load index based on the joint torque of the manipulator and combining it with the surface curvature of the model to generate slice thickness values ​​through inverse proportional mapping, thereby constructing a slice thickness sequence; S2. constructing a vibration risk index based on the joint angular acceleration during the manipulator's motion, dynamically adjusting the weight distribution during posture interpolation using the vibration risk index, and generating a posture interpolation trajectory sequence that minimizes vibration risk; S3. calculating a thermal coupling factor based on the distribution characteristics of the temperature field around the path point and generating a thermal avoidance path offset, and correcting the path point based on the thermal avoidance path offset; S4. calculating a collision risk score based on the minimum three-dimensional Euclidean distance between the path point and the obstacle model using an exponential decay function; and S5. fusing the slice thickness, vibration risk index, thermal coupling factor, and collision risk score to construct a heterogeneously weighted global path scoring function. The beneficial effects produced include: Improve printing accuracy and quality: This invention performs multi-objective optimization by comprehensively considering multiple factors such as robot arm joint load, vibration risk, heat accumulation and collision risk, ensuring that each path meets the printing accuracy requirements while avoiding printing defects caused by vibration, heat impact or collision, thereby significantly improving the overall accuracy and quality of 3D printing.

[0019] Optimizing path smoothness and vibration control: By dynamically adjusting the weight distribution of posture interpolation and combining it with a vibration risk index to minimize vibration, the present invention effectively reduces micro-vibrations or errors caused by sudden posture changes in the path, ensuring smooth movement of the nozzle during printing, thereby improving printing accuracy, especially in applications requiring high precision.

[0020] Reducing Thermal Impact and Material Deformation: This system calculates the thermal coupling factor and generates a thermal avoidance path offset, effectively preventing material warping, deformation, and poor interlayer adhesion caused by excessive heat accumulation during printing. This intelligent path adjustment avoids thermal impacts from high-temperature areas, improving print stability and quality.

[0021] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. 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 inventive efforts. In the drawings: Figure 1 The present invention is a flowchart of a method for optimizing a multi-joint industrial robot 3D printing path, showing an exemplary embodiment of the present invention. DETAILED DESCRIPTION

[0023] The following describes the embodiments of the present invention with reference to the accompanying drawings and preferred embodiments. Those skilled in the art will readily appreciate the other advantages and benefits of the present invention from the disclosure herein. The present invention may also be implemented or applied through various other specific embodiments, and the various details in this specification may be modified or altered based on different viewpoints and applications without departing from the spirit of the present invention. It should be understood that the preferred embodiments are intended only to illustrate the present invention and are not intended to limit the scope of protection of the present invention.

[0024] It should be noted that the illustrations provided in the following embodiments are merely schematic illustrations of the basic concept of the present invention. Therefore, the illustrations only show components related to the present invention and are not drawn according to the number, shape, and size of components in actual implementation. In actual implementation, the type, quantity, and proportion of each component may be changed arbitrarily, and the component layout may also be more complex.

[0025] In the following description, numerous details are discussed to provide a more thorough explanation of the embodiments of the present invention. However, it will be apparent to those skilled in the art that the embodiments of the present 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 the embodiments of the present invention.

[0026] Example 1 A multi-joint industrial robot 3D printing path optimization method, such as Figure 1 Shown, including: S1. Generate a normalized torque load index based on the manipulator joint torque and combine it with the model surface curvature to generate the slice thickness values ​​through inverse proportional mapping, and construct a slice thickness sequence. S2. Build a vibration risk index based on the joint angular acceleration during the manipulator's motion. Use this index to dynamically adjust the weight distribution during posture interpolation, generating a posture interpolation trajectory sequence that minimizes vibration risk. S3. Calculate the thermal coupling factor and generate a thermal avoidance path offset based on the temperature distribution characteristics around the path point. Modify the path point based on the thermal avoidance path offset. S4. Calculate the collision risk score based on the minimum three-dimensional Euclidean distance between the path point and the obstacle model using an exponential decay function; S5. Construct a heterogeneously weighted global path scoring function by integrating slice thickness, vibration risk index, thermal coupling factor, and collision risk score.

[0027] The present invention is further configured such that S1 comprises: Generate a path through the path planning algorithm; The torque value of each joint is obtained by inverse solution of the manipulator, and then normalized with the maximum allowable torque of each joint to eliminate the dimension difference and obtain the normalized torque load index. Specifically, the initial path is generated by the rapid exploration random tree algorithm in the path planning algorithm, and the B-spline algorithm is used to output it as a continuous curve. Then, the smoothness is adjusted by combining the trajectory optimization algorithm to obtain the path. , the path contains the path points This method is an existing technology and will not be described in detail here. The normalized torque load index is a standardized parameter that comprehensively reflects the load status of each joint of the robotic arm. The specific calculation logic is: ,in, For waypoints The index of , indicating the number of path points; For each joint of the robotic arm at the path point Normalized torque load index at ; is the joint index, indicating the joint number in the multi-joint robotic arm; For the joints at waypoints The real-time torque at the position is collected in real time by sensors; is the maximum allowable torque of the joint, provided by the robot manufacturer; is the total number of joints; the real-time torque of each joint is compared with its maximum allowable value, and the overall load state of the robotic arm is comprehensively evaluated through the square sum operation.

[0028] The present invention is further configured to perform local quadratic surface fitting on discrete points on the surface of the three-dimensional model, calculate the principal curvature and obtain the curvature by combining it with Gaussian filtering, and fuse the curvature with the normalized torque load index through a nonlinear function to obtain the slice thickness; Traverse all discrete points on the 3D model surface and calculate the slice thickness point by point to form a slice thickness sequence that matches the geometric features and the robot arm load. Specifically, directly reading the vertex coordinates of the STL or other mesh model as discrete points for the 3D digital model and calculating the curvature based on these discrete points is an existing technology and will not be elaborated on here. The slice thickness is used to control the printing accuracy based on the curvature of the model surface. The greater the local curvature, the smaller the layer thickness and the finer the print. In areas with smoother surfaces, the curvature is smaller, so a larger layer thickness is used to speed up the printing. The slice thickness calculation logic is as follows: ,in, is the slice thickness, indicating the The slice thickness at each path point is is the maximum layer thickness that can be set. is the curvature, specifically expressed as The curvature of the model surface at each path point. The curvature calculation is a prior art and will not be described in detail here. is the normalized torque load index; 、 To adjust the exponential factor of curvature and normalized torque load index, the value range is between 1.5 and 2.5 and can be adjusted according to different usage scenarios; It is a numerical stability term used to prevent errors caused by the denominator being zero, and is usually set to The slice thickness set is to include the slice thickness of each path point calculated into the set, and finally obtain the slice thickness set including the slice thickness of all path points.

[0029] The present invention is further configured such that S2 includes: The vibration risk index is obtained by calculating the square sum of the angular accelerations of each joint of the robotic arm to quantify the vibration intensity; The interpolation weight is obtained by dynamically adjusting the posture interpolation ratio according to the vibration risk index of the endpoint and the time parameter. Specifically, in order to prevent the robot arm from moving from the current path point to the next path point, 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 posture change rate is slowed down by reducing the weight, the transition time is extended, and a smooth posture sequence is generated to reduce the impact on the robot arm joints; the vibration risk index reflects the potential threat of mechanical vibration to accuracy and stability during movement. The angular acceleration increases during sudden acceleration, sudden stop or high curvature path segments. The greater the joint angular acceleration, the higher the vibration risk index; the vibration risk index calculation logic: ,in, For waypoints The index of , which indicates the path point. is the shock risk index, For the The joint angles of the joints, For the The angular acceleration of each joint is obtained by performing a cubic spline fitting on the joint angles in the path and then taking the second-order derivative. is the total number of joints. Interpolation weight calculation logic: ,in, is the interpolation weight; is the interpolation time variable, defined in Between, it indicates the interpolation progress from the starting posture to the ending posture; is the difference endpoint, Indicates the starting point, Indicates the end point; The vibration risk index of the starting point and the ending point of the current path point, that is, the vibration risk index of the current path point and the next path point; The vibration risk growth control factor is used to control the speed at which the weight of the high-risk segment decreases. The value range is between 1.0 and 2.0, and the default setting is 1.5. Represents the interpolation time variable Applying nonlinear transformations makes interpolation weights more sensitive to progress; is the vibration risk index scheduling item, representing the interpolation endpoint Corresponding vibration risk index For the current interpolation time variable The intensity of the impact.

[0030] The present invention is further configured to generate a smooth posture interpolation trajectory by quaternion weighted interpolation; The generated posture interpolation trajectories are connected in chronological order to obtain a posture interpolation trajectory sequence. Specifically, compared with traditional linear or SLERP interpolation, a nonlinear weight combination is directly performed on the start and end postures. The vibration risk index of the path endpoint is introduced to modulate the weight, making the posture transition more inclined to the low vibration risk point. The posture interpolation calculation logic is as follows: , where i represents the path segment, i.e. the path point To waypoint The path range, For the path segment at time The interpolated quaternion at is the spatial posture of the print head. is the starting posture, indicating that the print head is at the path point The posture, The end posture indicates that the print head is at the next path point The starting and ending poses are used to define the initial and target orientations of the end effector of the robot arm. 、 is the time weight factor related to vibration risk, used to meet the heterogeneous scheduling strategy, Used to normalize the denominator to ensure that the output attitude quaternion is on the unit sphere and remains legal.

[0031] The present invention is further configured such that S3 includes: The thermal coupling factor is obtained by quantifying the temperature accumulation degree of the local area around the path point. Specifically, the thermal coupling factor is used to quantify the temperature accumulation degree of the local area around the path point, reflecting the risk of material melting and collapse due to heat. The specific calculation formula is: ,in, is the thermal coupling factor, is the heat affected radius, indicating that the calculation needs to consider m adjacent path points before and after. is the estimated temperature of the nearby path point, obtained by measuring the infrared sensor. is a waypoint, is the adjacent waypoint, is the Euclidean distance between a path point and its neighboring path points, used to weight the temperature contribution. The closer the distance, the more significant the temperature impact. When the temperature is high and the distance is close in a high-temperature dense area, the thermal coupling factor increases significantly, indicating a high risk of melt collapse. When in a low-temperature sparse area, points with low temperatures or long distances contribute less to the thermal coupling factor, indicating that the risk is controllable.

[0032] 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 in combination with the offset sensitivity coefficient: 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 that the path point needs to move toward the low temperature area to adjust the path point to avoid local overheating. The specific calculation logic is: ,in, is the thermal avoidance path offset, which is used to indicate the distance required for displacement. The offset sensitivity coefficient is used to control the degree to which the thermal gradient amplifies the offset. The value range is between 0.1 and 0.5, and the default setting is 0.3. It can be modified according to the actual scenario. is the spatial thermal gradient vector, which is estimated by the ratio of the temperature difference and distance between adjacent path points. It represents the rate of change of the temperature distribution around the path point. The spatial thermal gradient vector reflects the spatial variation of the temperature in the local area and affects the adjustment of the path. The specific calculation formula is: ,in, is the spatial thermal gradient vector, Indicates the distance between the two path points before and after the current path point. Indicates the thermal coupling factor of the two waypoints before and after the current waypoint. The corrected waypoint is the waypoint adjusted based on the thermal coupling factor, which is obtained by adding the thermal avoidance path offset to the waypoint. The specific calculation formula is: ,in, To correct the path point.

[0033] The present invention is further configured such that the generation of the collision risk score included in S4 includes: The planned path generated by the path planning algorithm is discretized to obtain the path discrete point set of the end effector of the robot arm on the path segment; Use LiDAR and RGB-D cameras to collect real-time location information of printed parts, robotic arms, and environmental obstacles to build an obstacle model. The minimum three-dimensional Euclidean distance between the discrete points of the path and the obstacle model is extracted. The cubed value of the minimum three-dimensional Euclidean distance is nonlinearly mapped 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 operation, it maps the safe distance in physical space to a risk value in the interval [0,1], providing a computable safety indicator for path optimization. The collision risk score calculation logic is as follows: ,in, scoring collision risk; is a set of discrete points on the path, which is the end effector of the robot arm on the path The set of trajectory points on the path is a discrete point set generated by sampling at a fixed or adaptive step size on the path. This method is a prior art and will not be described in detail here. The obstacle set is constructed by using LiDAR and RGB-D cameras to collect point cloud data of the printed part, the robot body, and environmental obstacles in real time. The robot body surface point cloud is calculated based on the robot's kinematic model. The future position of dynamic obstacles is predicted using Kalman filtering or particle filtering. This method is based on existing technology and will not be elaborated on here. is the path point of the discrete point set of the path; is the obstacle point in the obstacle set; The risk sensitivity coefficient is used to control the steepness of the risk change with distance. The value needs to be adjusted according to the movement speed of the robot arm and the dynamics of the obstacle. It needs to be increased in high dynamic scenes. To enhance security sensitivity, usually in Risk sensitivity coefficient The larger the value, the higher the collision risk score at the same distance. The closer it is to 1; is an exponential function, which is used to compress the calculation results into between; Used to calculate the minimum safe distance, traverse all points in the path segment, calculate the Euclidean distance to all points in the obstacle set, and find the minimum distance of the path segment; Indicates the cubic operation of the minimum distance, which is used to represent nonlinear risk mapping. The purpose is to amplify the collision risk of the distance area; finally, the risk value is compressed by the exponential function, and the output result is compressed in When the risk of collision is extremely high , when the risk of collision is extremely low and completely safe .

[0034] The present invention further configures the global path score included in S5 to be a weighted fusion of the inverse of the slice thickness, vibration risk index, thermal coupling factor, and collision risk score for each path point to obtain a path score for each path point, and then sums the path scores of all path points to obtain a global path score. Specifically, the global path score scores multiple performance indicators for each path point, and through weighted combination, constructs a global path cost function, which is used to drive subsequent path selection or optimizer to find the optimal printing path. The specific calculation formula is: ,in, Score the global path; is an exponential scaling factor used to control the compression speed of the mapping. The larger the value, the more sensitive the path score is. The value range is Between, the default is ; is 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 a higher time cost. By taking the reciprocal, the thinner the slice, the greater the cost, and the thicker the slice, the lower the cost. scoring collision risk; is the thermal coupling factor; is a vibration risk indicator; 、 、 、 is a weight adjustment parameter used to control the relative influence of each scoring item in the global path scoring function. It is used to control the layer thickness, which affects the printing efficiency and details. The value range is between 0.1-0.4. Used to control obstacle avoidance risk, the value range is between 0.2-0.4, Used to control thermal deformation, the value range is between 0.1-0.3, Used to control vibration stability, the value range is between 0.1-0.3, which needs to be satisfied .

[0035] The present invention is further configured such that the method further comprises a path determination module: A global path score is calculated for the generated path. If the global path score is greater than a threshold, it is determined to be an invalid path, and the feedback system regenerates the path. If it is less than the threshold, all parameters contained in the path are fed back to each execution module based on this path to complete the 3D model printing. Specifically, the path judgment module realizes path decision-making and execution control in complex scenarios through multi-objective quantitative scoring, threshold judgment, parameter mapping and closed-loop feedback. It is the decision-making unit of the 3D printing path optimization system, which is used to convert discrete path planning results into executable, verifiable and adjustable industrial control processes. The lower the global path score, the smaller the total cost of the path. A threshold is set. The default threshold is 0.45. The higher the threshold requirement for path planning accuracy, the path less than the threshold is set as the optimal path. The printing point position coordinates and slice thickness data corresponding to the optimal path are converted into control instructions that can be recognized by the execution module; finally, the control instructions are sent to the execution terminal through a real-time communication protocol.

[0036] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. 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 computer-readable storage medium. 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 a wired (e.g., infrared, wireless, microwave, etc.) method. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0037] It should be understood that the term "and / or" as used herein simply describes a relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A alone, A and B together, or B alone. A and B can be singular or plural. Furthermore, the character " / " as used herein generally indicates an "or" relationship between the associated objects, but it may also indicate an "and / or" relationship. For specific understanding, please refer to the context.

[0038] In this application, "at least one" means one or more, and "plurality" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural 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 plural.

[0039] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean 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 the present application.

[0040] Those skilled in the art will appreciate that the units and algorithm steps of each example 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 performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel 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.

[0041] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0042] 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 schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0043] The units described as separate components may or may not be physically separate, and 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 these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0044] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0045] If the functions are implemented in the form of 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 the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0046] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A method for optimizing the 3D printing path of a multi-joint industrial robot, characterized in that: include: S1. Generate a normalized torque load index based on the manipulator joint torque and combine it with the model surface curvature to generate the slice thickness values ​​through inverse proportional mapping and construct a slice thickness sequence. S2. Build a vibration risk index based on the joint angular acceleration during the manipulator's motion. Use this index to dynamically adjust the weight distribution during posture interpolation, generating a posture interpolation trajectory sequence that minimizes vibration risk. S3. Calculate the thermal coupling factor and generate a thermal avoidance path offset based on the temperature distribution characteristics around the path point. Modify the path point based on the thermal avoidance path offset. S4. Calculate the collision risk score based on the minimum three-dimensional Euclidean distance between the path point and the obstacle model using an exponential decay function; S5. Construct a heterogeneously weighted global path scoring function by integrating slice thickness, vibration risk index, thermal coupling factor, and collision risk score.

2. A multi-joint industrial robot 3D printing path optimization method according to claim 1, characterized in that: S1 contains: Generate a path through the path planning algorithm; The torque value of each joint is obtained by inverse solution of the robotic arm, and then normalized with the maximum allowable torque of each joint to eliminate the dimensional difference and obtain the normalized torque load index.

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

4. A multi-joint industrial robot 3D printing path optimization method according to claim 1, characterized in that: S2 contains: The vibration risk index is obtained by calculating the square sum of the angular accelerations of each joint of the robotic arm to quantify the vibration intensity; The weight is obtained by dynamically adjusting the posture interpolation ratio according to the endpoint's vibration risk index and time parameters.

5. A multi-joint industrial robot 3D printing path optimization method according to claim 4, characterized in that: Generate smooth pose interpolation trajectory through quaternion weighted interpolation; The posture interpolation trajectory sequence is obtained by connecting the generated posture interpolation trajectories in time sequence.

6. A multi-joint industrial robot 3D printing path optimization method according to claim 1, characterized in that: S3 contains: The thermal coupling factor is obtained by quantifying the degree of temperature accumulation in the local area around the path point.

7. A multi-joint industrial robot 3D printing path optimization method 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 the offset sensitivity coefficient: The original path point is superimposed with the thermal avoidance path offset to obtain a new corrected path point.

8. The method for optimizing a 3D printing path of a multi-joint industrial robot according to claim 1, wherein: The collision risk score generation included in S4 includes: The planned path generated by the path planning algorithm is discretized to obtain the path discrete point set of the end effector of the robot arm on the path segment; Use LiDAR and RGB-D cameras to collect real-time location information of printed parts, robotic arms, and environmental obstacles to build an obstacle model. The minimum three-dimensional Euclidean distance between the discrete point set of the path and the obstacle model is extracted, and the cubed value of the minimum three-dimensional Euclidean distance is nonlinearly mapped using an exponential decay function to generate a normalized collision risk score sequence.

9. A multi-joint industrial robot 3D printing path optimization method according to claim 1, characterized in that: The global path score included in S5 is a weighted fusion of 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, and then the path scores of all path points are summed to obtain the global path score.

10. The method for optimizing a 3D printing path of a multi-joint industrial robot according to claim 1, wherein: This method also includes a path judgment module: The global path score is calculated for the generated path. If the global path score is greater than the threshold, it is judged as an invalid path, and the feedback system regenerates the path. If it is less than the threshold, all parameters contained in the path are fed back to each execution module based on this path to complete the 3D model printing.

Images