Path acquisition method, system and device for ship sub-assembly welding robot

Abstract

The path acquisition method, system and device of the ship small assembly welding robot are provided, wherein the path acquisition method comprises the following steps: acquiring a welding instruction, the welding instruction comprising a motion instruction and a process instruction of the welding robot; acquiring a plurality of initial travel paths of the welding robot based on the motion instruction; screening the plurality of initial travel paths under a preset constraint condition to obtain a target travel path; debugging the welding robot in a virtual debugging environment based on the target travel path, moving the welding robot to a target position based on the target travel path, and performing welding work based on the process instruction. Through the path acquisition method provided by the present disclosure, the time required for robot debugging is greatly reduced, a large amount of manual cost is saved, and the efficiency of ship small assembly welding production is improved.

Description

Technical Field

[0001] This disclosure relates to the field of robot path planning, and in particular to a method, system and equipment for path acquisition of a ship assembly welding robot. Background Technology

[0002] In the shipbuilding industry, intelligent manufacturing and digital transformation are increasingly becoming the main trends in future manufacturing development. Major domestic shipyards are actively promoting the application of intelligent equipment. During the commissioning of this equipment, various factors need to be considered, including equipment accessibility / safety, electrical control accuracy / robustness, and human-machine interaction. Virtual commissioning technology can enable the simultaneous implementation of equipment design, control program development, and integrated commissioning, thereby shortening the actual commissioning time of the manufacturing system.

[0003] Currently, the application of virtual debugging is mainly concentrated in industries with a high degree of standardization, such as automobiles, CNC machine tools, and aerospace. Ship structural components have obvious customization characteristics and a high degree of dispersion. The traditional small group welding debugging process often requires a lot of time for trial and error, resulting in high cost and low efficiency. Furthermore, the existing technology does not consider the fine-tuning and real-time feedback mechanism of the welding robot's motion path and welding parameters, which affects the accuracy and efficiency of debugging.

[0004] The current virtual debugging differs from actual operation, making it difficult to directly apply the debugging results to actual welding work. Furthermore, the virtual debugging method lacks precise fine-tuning and real-time feedback mechanisms, making it difficult to achieve the required debugging accuracy. Therefore, adopting appropriate virtual debugging technology is crucial for the debugging efficiency and welding quality of ship assembly welding robots, and the existing manual on-site debugging methods need to be improved and upgraded. Summary of the Invention

[0005] The technical problem to be solved by this disclosure is to overcome the defect in the prior art that there is a difference between virtual debugging and actual operation, which makes it difficult to directly apply the debugging results to actual welding work, and to provide a path acquisition method, system and equipment for a ship group assembly welding robot.

[0006] This disclosure solves the above-mentioned technical problems through the following technical solution:

[0007] According to a first aspect of this disclosure, a path acquisition method for a ship assembly welding robot is provided, the path acquisition method comprising:

[0008] Obtain welding instructions, which include motion instructions and process instructions for the welding robot;

[0009] Several initial travel paths of the welding robot are obtained based on the motion commands;

[0010] The initial paths are filtered using preset constraints to obtain the target travel path;

[0011] The welding robot is debugged in a virtual debugging environment based on the target travel path;

[0012] In response to the presence of abnormal information during debugging, the target travel path is optimized to obtain a new initial travel path, and the step of optimizing the initial travel path with preset constraints is re-executed.

[0013] If no abnormal information is found during the debugging process, the welding robot moves to the target position based on the target travel path and performs welding work based on the process instructions.

[0014] Optionally, the step of filtering the initial travel paths according to preset constraints to obtain the target travel path includes:

[0015] Obtain the motion parameter information of the welding robot;

[0016] Based on the motion parameter information and the different initial travel paths, the corresponding motion time information is obtained;

[0017] If the motion time information is the minimum time, then the corresponding initial travel path is the target travel path.

[0018] Optionally, before the step of obtaining the target travel path after the response time information is the minimum time, the path acquisition method further includes:

[0019] The welding robot is subjected to continuous collision tests based on the target travel path.

[0020] In response to the presence of a collision, the target travel path is updated by spatial interpolation to obtain a new target travel path.

[0021] Optionally, the step of optimizing the target travel path to obtain a new initial travel path in response to an anomaly detected during debugging includes:

[0022] The target travel path is optimized based on the improved fast random tree algorithm to obtain a new initial travel path.

[0023] Optionally, prior to the step of obtaining welding instructions, the path acquisition method further includes:

[0024] Acquire shape and process information of ship machining parts, and structural and electrical control information of welding robots;

[0025] Obtain a virtual model library, which represents several model information included in the virtual debugging scenario;

[0026] A first association mapping model is constructed based on the shape information and the structural information. The first association mapping model is used to realize the driving of the welding robot on the ship processing part.

[0027] A second association mapping model is constructed based on the process information and the electrical control information. The second association mapping model is used to realize the logical control of the welding robot on the ship processing parts.

[0028] Based on the first association mapping model, the second association mapping model, and the virtual model library, the virtual model library is used to construct the virtual debugging environment.

[0029] Optionally, prior to the step of obtaining welding instructions, the path acquisition method includes:

[0030] Obtain the actual installation data and standard installation data of the welding robot;

[0031] The error installation data of the welding robot is obtained based on the actual installation data and the standard installation data;

[0032] Error compensation is performed on the welding robot based on the error installation data.

[0033] According to a second aspect of this disclosure, a path acquisition system for a ship assembly welding robot is provided, the path acquisition system comprising:

[0034] The instruction acquisition module is used to acquire welding instructions, which include the motion instructions and process instructions of the welding robot.

[0035] An initial path acquisition module is used to acquire several initial travel paths of the welding robot based on the motion commands;

[0036] The target path acquisition module is used to optimize several initial paths under preset constraints to obtain the target travel path;

[0037] A robot debugging module is used to debug the welding robot in a virtual debugging environment based on the target travel path;

[0038] The processing module is configured to, in response to an anomaly detected during the debugging process, optimize the target travel path to obtain a new initial travel path, and re-execute the optimization of the initial travel path with preset constraints.

[0039] The processing module is configured to respond that if the abnormal information is not present during the debugging process, the welding robot moves to the target position based on the target travel path and performs welding work based on the process instructions.

[0040] The target path acquisition module is specifically used to: acquire the motion parameter information of the welding robot;

[0041] Based on the motion parameter information and the different initial travel paths, the corresponding motion time information is obtained;

[0042] If the motion time information is the minimum time, then the corresponding initial travel path is the target travel path.

[0043] The target path acquisition module is also specifically used for:

[0044] The welding robot is subjected to continuous collision tests based on the target travel path.

[0045] In response to the presence of a collision, the target travel path is updated by spatial interpolation to obtain a new target travel path.

[0046] The processing module is further configured to: optimize the target travel path based on the improved fast random tree algorithm to obtain a new initial travel path.

[0047] The path acquisition system also includes a debugging environment construction module, which is used before acquiring the welding instructions:

[0048] Acquire shape and process information of ship machining parts, and structural and electrical control information of welding robots;

[0049] Obtain a virtual model library, which represents several model information included in the virtual debugging scenario;

[0050] A first association mapping model is constructed based on the shape information and the structural information. The first association mapping model is used to realize the driving of the welding robot on the ship processing part.

[0051] A second association mapping model is constructed based on the process information and the electrical control information. The second association mapping model is used to realize the logical control of the welding robot on the ship processing parts.

[0052] Based on the first association mapping model, the second association mapping model, and the virtual model library, the virtual model library is used to construct the virtual debugging environment.

[0053] The path acquisition system also includes an error compensation module, which is used before acquiring the welding command:

[0054] Obtain the actual installation data and standard installation data of the welding robot;

[0055] The error installation data of the welding robot is obtained based on the actual installation data and the standard installation data;

[0056] Error compensation is performed on the welding robot based on the error installation data.

[0057] According to a third aspect of this disclosure, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and for running on the processor, wherein the processor executes the computer program to implement the path acquisition method for a ship assembly welding robot according to the first aspect of this disclosure.

[0058] According to a fourth aspect of this disclosure, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the path acquisition method for a ship assembly welding robot as described in the first aspect of this disclosure.

[0059] According to a fifth aspect of this disclosure, a computer program product is provided, comprising a computer program that, when executed by a processor, implements the path acquisition method for a ship assembly welding robot as described in the first aspect of this disclosure.

[0060] Based on common knowledge in the field, the above-mentioned preferred conditions can be combined arbitrarily to obtain various preferred embodiments of this disclosure.

[0061] The positive and progressive effects of this disclosure are as follows:

[0062] The path acquisition method for the ship assembly welding robot provided in this disclosure enables the association mapping mechanism between ship processing parts and the virtual operating state of the ship assembly welding robot, thereby constructing a high-fidelity virtual debugging environment for the ship assembly welding robot. In this virtual debugging environment, the mechanical motion system of the assembly robot under spatial constraints is virtually debugged, further improving the accuracy of the debugging and ensuring that the welding path of the robot is optimal in actual applications. This significantly reduces the time required for robot debugging, saves a lot of labor costs, and improves the efficiency of ship assembly welding production. Attached Figure Description

[0063] Figure 1 This is a flowchart illustrating the path acquisition method provided in Embodiment 1 of this disclosure;

[0064] Figure 2This is a schematic diagram of the process for constructing a virtual debugging environment provided in Embodiment 1 of this disclosure;

[0065] Figure 3 This is a flowchart illustrating the specific process of constructing a virtual debugging environment as provided in Embodiment 1 of this disclosure;

[0066] Figure 4 This is a schematic diagram of the error compensation process provided in Embodiment 1 of this disclosure;

[0067] Figure 5 This is a flowchart illustrating the path acquisition process in a specific application provided in Embodiment 1 of this disclosure;

[0068] Figure 6 This is a flowchart illustrating the path acquisition system provided in Embodiment 2 of this disclosure;

[0069] Figure 7 This is a schematic diagram of the structure of the electronic device provided in Embodiment 3 of this disclosure. Detailed Implementation

[0070] The present disclosure is further illustrated below by way of embodiments, but the present disclosure is not limited to the scope of the embodiments described herein.

[0071] The prefixes such as "first" and "second" used in this disclosure are merely for distinguishing different descriptive objects and do not limit the position, order, priority, quantity, or content of the described objects. The use of ordinal numbers and other prefixes used to distinguish descriptive objects in this disclosure does not constitute a limitation on the described objects. The description of the described objects is given in the claims or the context of the embodiments, and should not be construed as an unnecessary limitation. Furthermore, in the description of this embodiment, unless otherwise stated, "multiple" means two or more.

[0072] Example 1

[0073] like Figure 1 As shown, the path acquisition method in this embodiment includes:

[0074] S11: Obtain welding instructions, which include motion instructions and process instructions for the welding robot;

[0075] S12: Obtain several initial travel paths of the welding robot based on motion commands;

[0076] S13: Filter several initial paths using preset constraints to obtain the target travel path;

[0077] S14: Debug the welding robot in the virtual debugging environment based on the target travel path; if there is abnormal information during the debugging process, optimize the target travel path to obtain a new initial travel path, and re-execute the step of optimizing the initial travel path with preset constraints; if there is no abnormal information during the debugging process, the welding robot moves to the target position based on the target travel path and performs welding work based on the process instructions.

[0078] The path acquisition method in this embodiment is based on Sim2Real (Simulation-to-Reality).

[0079] The preset constraints include physical factors such as velocity, acceleration, and collision constraints.

[0080] Abnormal information includes whether the welding robot's limbs are not in motion, and whether there are any conflicts with the motion trajectories of other welding robots during the motion process.

[0081] Step S13 includes:

[0082] Obtain motion parameter information of the welding robot;

[0083] Based on motion parameter information and different initial travel paths, obtain the corresponding motion time information;

[0084] If the minimum time is obtained in response to the motion time information, then the corresponding initial travel path is the target travel path.

[0085] In this embodiment, before the step of obtaining the target travel path after responding to the minimum time of the motion time information, the path acquisition method further includes:

[0086] Continuous collision testing of the welding robot based on the target travel path;

[0087] In response to the presence of a collision, the target's path is updated by spatial interpolation to obtain a new target path.

[0088] In this embodiment, the target travel path is optimized based on the improved fast random tree algorithm to obtain a new initial travel path.

[0089] like Figure 2 As shown, before the step of obtaining the welding instructions, the path acquisition method also includes:

[0090] S21: Obtain shape and process information of ship processing parts, and structural and electrical control information of welding robots;

[0091] S22: Obtain the virtual model library, which represents several model information included in the virtual debugging scenario;

[0092] S23: Construct a first association mapping model based on shape information and structural information. The first association mapping model is used to realize the driving of the welding robot on the ship processing parts.

[0093] S24: Construct a second association mapping model based on process information and electrical control information. The second association mapping model is used to realize the logical control of the welding robot on the ship processing parts.

[0094] S25: Based on the first association mapping model, the second association mapping model, and the virtual model library, a virtual debugging environment is constructed using the virtual model library.

[0095] By implementing a correlation mapping mechanism between ship machining parts and the virtual operating status of ship assembly welding robots, a high-fidelity virtual debugging environment for ship assembly welding robots is constructed. Virtual debugging of the mechanical motion system of the small assembly robot oriented towards spatial constraints is carried out in the virtual debugging environment, further improving the accuracy of debugging.

[0096] In one specific implementation, such as Figure 3 As shown, the virtual debugging method for a ship assembly welding robot based on Sim2Real includes the following specific steps:

[0097] This embodiment provides a virtual debugging method for a ship assembly welding robot based on Sim2Real.

[0098] First, the geometric dimensions, spatial posture, and technological features of the structural components are described parametrically to extract structural shape attribute information and construct a structural component shape attribute model. Then, multibody dynamics modeling methods are integrated to construct a high-precision robot motion model that includes multibody displacement, compound joint motion, and nonlinear response characteristics. This establishes a mapping model between the structural component shape attributes and the equipment mechanical system, enabling dynamic matching and driving of structural component attributes and equipment actions.

[0099] Subsequently, by combining the PLC (Power Logic Controller) control logic diagram and I / O (input / output) signal modeling, state machine expression, and logic unit partitioning, an electrical system control logic model is constructed. Based on the structural component process attributes, input variables that can be used for control are formed, and a structural component process attribute model is constructed. Based on the structural component process attribute model and the electrical system control logic model, the process requirements of the structural component are automatically converted into electrical system control commands to establish a correlation mapping model between the structural component process attributes and the equipment electrical system, thereby realizing parameter-driven control logic response.

[0100] Specifically, a mapping relationship between structural component attributes and equipment control systems can be established through rule-based reasoning, enabling the automatic conversion of structural component process attributes into electrical control commands.

[0101] Finally, by combining a 3D virtual model library with environmental information, a high-fidelity virtual debugging environment for the ship assembly welding robot is constructed.

[0102] The above steps can be used to reconstruct the real scene through platforms such as RoboGSim (a platform for providing robot programming) and 3DGS (a 3D scene representation and rendering technology based on 3D Gaussian distribution) to obtain a virtual debugging environment.

[0103] like Figure 4 As shown, due to installation errors in the welding robot's installation environment, such as the robot's base not being perfectly level, or manufacturing errors in the welding robot's parts, the path acquisition method includes the following steps before obtaining the welding instructions:

[0104] S41: Obtain the actual installation data and standard installation data of the welding robot;

[0105] S42: Obtain error installation data for the welding robot based on actual installation data and standard installation data;

[0106] S43: Perform error compensation on the welding robot based on error installation data.

[0107] By compensating for the errors in the welding robot, the welding program that has been perfectly verified in the virtual debugging environment can be reproduced by the real robot with high precision and without errors, thereby improving the accuracy of virtual debugging.

[0108] The following specific example illustrates the implementation principle of the path acquisition method for the ship assembly welding robot in this embodiment:

[0109] like Figure 5 As shown, during the virtual debugging phase, a high-precision 3D environment model of the ship group assembly welding robot is constructed based on the 3D model library. The model is then calibrated based on measured data and error compensation is performed to ensure simulation accuracy.

[0110] Based on this, the welding path of the welding robot is automatically generated according to the process attributes of the structural components, and the path is optimized based on physical constraints such as speed, acceleration, and collision, so as to generate control commands and joint motion commands according to the final optimized path.

[0111] The robot's motion is virtually synchronized and debugged using a virtual simulation platform to assess the coordination of welding actions and the feasibility of the path. If path conflicts or incoordination exist, an improved Rapid Random Tree (RRT) algorithm is invoked to iteratively optimize the welding path until all constraints are met. Finally, the debugged control logic and path instructions are exported as actual robot operation instructions, achieving a seamless transition from virtual debugging to real-world applications.

[0112] The path acquisition method for the ship assembly welding robot provided in this disclosure enables the association mapping mechanism between ship processing parts and the virtual operating state of the ship assembly welding robot, thereby constructing a high-fidelity virtual debugging environment for the ship assembly welding robot. In this virtual debugging environment, the mechanical motion system of the assembly robot under spatial constraints is virtually debugged, further improving the accuracy of the debugging and ensuring that the welding path of the robot is optimal in actual applications. This significantly reduces the time required for robot debugging, saves a lot of labor costs, and improves the efficiency of ship assembly welding production.

[0113] Example 2

[0114] like Figure 6 As shown, in this embodiment, a path acquisition system for a ship assembly welding robot is provided. The path acquisition system includes:

[0115] The instruction acquisition module 100 is used to acquire welding instructions, which include motion instructions and process instructions of the welding robot.

[0116] The initial path acquisition module 200 is used to acquire several initial travel paths of the welding robot based on motion commands;

[0117] The target path acquisition module 300 is used to optimize several initial paths under preset constraints to obtain the target travel path;

[0118] The robot debugging module 400 is used to debug the welding robot in a virtual debugging environment based on the target travel path;

[0119] The processing module 500 is used to respond to abnormal information during the debugging process, optimize the target travel path to obtain a new initial travel path, and re-execute the optimization of the initial travel path with preset constraints.

[0120] The processing module 500 is used to respond to the absence of abnormal information during the debugging process, in which case the welding robot moves to the target position based on the target travel path and performs welding work based on the process instructions.

[0121] The target path acquisition module 300 is specifically used to: acquire motion parameter information of the welding robot;

[0122] Based on motion parameter information and different initial travel paths, obtain the corresponding motion time information;

[0123] If the minimum time is obtained in response to the motion time information, then the corresponding initial travel path is the target travel path.

[0124] The target path acquisition module 300 is also specifically used for:

[0125] Continuous collision testing of the welding robot based on the target travel path;

[0126] In response to the presence of a collision, the target's path is updated by spatial interpolation to obtain a new target path.

[0127] The processing module 500 is also used to: optimize the target travel path based on the improved fast random tree algorithm to obtain a new initial travel path.

[0128] The path acquisition system also includes a debug environment construction module 600, which is used before acquiring welding instructions:

[0129] Acquire shape and process information of ship machining parts, and structural and electrical control information of welding robots;

[0130] Obtain the virtual model library, which represents several model information included in the virtual debugging scenario;

[0131] A first association mapping model is constructed based on shape and structural information. This first association mapping model is used to realize the driving of the welding robot on the ship processing parts.

[0132] A second correlation mapping model is constructed based on process information and electrical control information. The second correlation mapping model is used to realize the logical control of the welding robot on the ship processing parts.

[0133] Based on the first association mapping model, the second association mapping model, and the virtual model library, a virtual debugging environment is constructed.

[0134] The path acquisition system also includes an error compensation module 700, which is used before acquiring welding instructions:

[0135] Obtain actual installation data and standard installation data for the welding robot;

[0136] Error installation data for the welding robot is obtained based on actual installation data and standard installation data;

[0137] Error compensation for welding robots is performed based on installation error data.

[0138] For the system embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The system embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separate, and the components 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 modules can be selected to achieve the purpose of this disclosure according to actual needs.

[0139] The path acquisition system for the ship assembly welding robot provided in this disclosure enables a mapping mechanism between ship machining parts and the virtual operating state of the ship assembly welding robot. This allows for the construction of a high-fidelity virtual debugging environment for the ship assembly welding robot. Virtual debugging of the mechanical motion system of the assembly robot, oriented towards spatial constraints, is performed in this virtual debugging environment. This further improves the accuracy of the debugging and ensures that the welding path of the robot is optimal in actual applications. Consequently, the time required for robot debugging is significantly reduced, saving a large amount of labor costs and improving the efficiency of ship assembly welding production.

[0140] Example 3

[0141] like Figure 7 As shown, Figure 7 This is a schematic diagram of the structure of an electronic device according to Embodiment 3 of this disclosure. The electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the methods described in the above embodiments. Figure 7 The electronic device 30 shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments disclosed herein.

[0142] like Figure 7 As shown, the electronic device 30 can be represented in the form of a general computing device, such as a server device. The components of the electronic device 30 may include, but are not limited to: at least one processor 31, at least one memory 32, and a bus 33 connecting different system components (including memory 32 and processor 31).

[0143] Bus 33 includes a data bus, an address bus, and a control bus.

[0144] The memory 32 may include volatile memory, such as random access memory (RAM) 321 and / or cache memory 322, and may further include read-only memory (ROM) 323.

[0145] The memory 32 may also include a program / utility 325 having a set (at least one) of program modules 324, including but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of these examples may include an implementation of a network environment.

[0146] The processor 31 performs various functional applications and data processing, such as the methods described in the above embodiments of this disclosure, by running computer programs stored in the memory 32.

[0147] Electronic device 30 can also communicate with one or more external devices 34 (e.g., keyboard, pointing device, etc.). This communication can be performed via input / output (I / O) interface 35. Furthermore, the model-generating device 30 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 36. ​ As shown, network adapter 36 communicates with other modules of the model-generated device 30 via bus 33. It should be understood that, although not shown in the figure, other hardware and / or software modules can be used in conjunction with the model-generated device 30, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID (disk array) systems, tape drives, and data backup storage systems.

[0148] It should be noted that although several units / modules or sub-units / modules of the electronic device have been mentioned in the detailed description above, this division is merely exemplary and not mandatory. In fact, according to embodiments of this disclosure, the features and functions of two or more units / modules described above can be embodied in one unit / module. Conversely, the features and functions of one unit / module described above can be further divided and embodied by multiple units / modules.

[0149] Example 4

[0150] This disclosure also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the path acquisition method for a ship assembly welding robot provided in any of the above embodiments.

[0151] The readable storage medium may be more specifically adopted, including but not limited to: portable disk, hard disk, random access memory, read-only memory, erasable programmable read-only memory, optical storage device, magnetic storage device, or any suitable combination thereof.

[0152] Example 5

[0153] This disclosure also provides a computer program product, including a computer program that, when executed by a processor, implements the path acquisition method for a ship assembly welding robot provided in any of the above embodiments.

[0154] The program code for executing the computer program product of this disclosure can be written in any combination of one or more programming languages, and the program code can be executed entirely on a user device, partially on a user device, as a stand-alone software package, partially on a user device and partially on a remote device, or entirely on a remote device.

[0155] While specific embodiments of this disclosure have been described above, those skilled in the art should understand that these are merely illustrative examples, and the scope of protection of this disclosure is defined by the appended claims. Those skilled in the art can make various changes or modifications to these embodiments without departing from the principles and essence of this disclosure, but all such changes and modifications fall within the scope of protection of this disclosure.

Claims

1. A path acquisition method for a ship assembly welding robot, characterized in that, The path acquisition method includes: Obtain welding instructions, which include motion instructions and process instructions for the welding robot; Several initial travel paths of the welding robot are obtained based on the motion commands; The initial travel paths are filtered using preset constraints to obtain the target travel path; The welding robot is debugged in a virtual debugging environment based on the target travel path; In response to the presence of abnormal information during the debugging process, the target travel path is optimized to obtain a new initial travel path, and the step of optimizing the initial travel path with preset constraints is re-executed. If no abnormal information is found during the debugging process, the welding robot moves to the target position based on the target travel path and performs welding work based on the process instructions. Prior to the step of obtaining welding instructions, the path acquisition method further includes: Acquire shape and process information of ship machining parts, and structural and electrical control information of welding robots; Obtain a virtual model library, which represents several model information included in the virtual debugging scenario; A first association mapping model is constructed based on the shape information and the structural information. The first association mapping model is used to realize the driving of the welding robot on the ship processing part. A second association mapping model is constructed based on the process information and the electrical control information. The second association mapping model is used to realize the logical control of the welding robot on the ship processing parts. The virtual debugging environment is constructed based on the first association mapping model, the second association mapping model, and the virtual model library.

2. The path acquisition method for the ship assembly welding robot according to claim 1, characterized in that, The step of filtering the initial travel paths using preset constraints to obtain the target travel path includes: Obtain the motion parameter information of the welding robot; Based on the motion parameter information and the different initial travel paths, obtain the corresponding motion time information; If the motion time information is the minimum time, then the corresponding initial travel path is the target travel path.

3. The path acquisition method for the ship assembly welding robot according to claim 2, characterized in that, Before the step of obtaining the target travel path after responding to the minimum time of the motion time information, the path acquisition method further includes: The welding robot is subjected to continuous collision tests based on the target travel path. In response to the presence of a collision, the target travel path is spatially interpolated and updated to obtain a new target travel path.

4. The path acquisition method for the ship assembly welding robot according to claim 1, characterized in that, The step of optimizing the target travel path to obtain a new initial travel path in response to an anomaly detected during debugging includes: The target travel path is optimized based on the improved fast random tree algorithm to obtain a new initial travel path.

5. The path acquisition method for a ship assembly welding robot according to any one of claims 1-4, characterized in that, Prior to the step of obtaining welding instructions, the path acquisition method includes: Obtain the actual installation data and standard installation data of the welding robot; The error installation data of the welding robot is obtained based on the actual installation data and the standard installation data; Error compensation is performed on the welding robot based on the error installation data.

6. A path acquisition system for a ship assembly welding robot, used to implement the path acquisition method according to any one of claims 1-5, characterized in that, The path acquisition system includes: The instruction acquisition module is used to acquire welding instructions, which include the motion instructions and process instructions of the welding robot. An initial path acquisition module is used to acquire several initial travel paths of the welding robot based on the motion commands; The target path acquisition module is used to optimize several initial travel paths under preset constraints to obtain the target travel path. A robot debugging module is used to debug the welding robot in a virtual debugging environment based on the target travel path; The processing module is used to respond to abnormal information during debugging by optimizing the target travel path to obtain a new initial travel path, and re-executing the optimization of the initial travel path with preset constraints. The processing module is configured to respond that if the abnormal information is not present during the debugging process, the welding robot moves to the target position based on the target travel path and performs welding work based on the process instructions; The path acquisition system also includes a debugging environment construction module, which is used before acquiring the welding instructions: Acquire shape and process information of ship machining parts, and structural and electrical control information of welding robots; Obtain a virtual model library, which represents several model information included in the virtual debugging scenario; A first association mapping model is constructed based on the shape information and the structural information. The first association mapping model is used to realize the driving of the welding robot on the ship processing part. A second association mapping model is constructed based on the process information and the electrical control information. The second association mapping model is used to realize the logical control of the welding robot on the ship processing parts. The virtual debugging environment is constructed based on the first association mapping model, the second association mapping model, and the virtual model library.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and for running on the processor, characterized in that, When the processor executes the computer program, it implements the path acquisition method of the ship assembly welding robot as described in any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the path acquisition method of the ship assembly welding robot as described in any one of claims 1 to 5.

9. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the path acquisition method for the ship assembly welding robot as described in any one of claims 1 to 5.

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