Methods, devices, and electronic equipment for spraying paths

Abstract

This disclosure provides a method, apparatus, and electronic device for planning a spraying path. The method includes: offline planning of an optimal spraying path that meets optimization criteria using a graph theory algorithm under multiple constraints; converting the optimal spraying path into control commands, and controlling a robot and a spray gun to move synchronously in the same time sequence based on the control commands to spray the surface of the object to be sprayed; the control commands include pause coordinates; when the robot moves to the pause coordinates, controlling the robot and the spray gun to pause synchronously in the same time sequence, and using sensors installed on the robot's end flange to measure the actual effect parameters; and dynamically adjusting the spraying parameters online based on the error between the preset target parameters and the actual effect parameters to obtain an adjusted spraying path. This disclosure can derive the optimal spraying path, achieve global optimal path planning, and dynamically adjust the spraying parameters online to improve the optimal spraying path and achieve better spraying results.

Description

Technical Field

[0001] This disclosure relates to the field of spraying technology, and more specifically to a method, apparatus, and electronic device for planning a spraying path. Background Technology

[0002] In robotic painting applications, for complex, large objects to be painted, current painting path planning methods can only plan locally optimal paths offline, and cannot improve locally optimal paths or achieve globally optimal path planning.

[0003] There is currently no effective technical solution to the problem that spraying path planning methods in related technologies cannot improve local optimal paths or achieve global optimal path planning. Summary of the Invention

[0004] The main objective of this disclosure is to provide a method, apparatus, and electronic device for planning spraying paths, in order to solve the problem that spraying path planning methods in related technologies cannot improve locally optimal paths or achieve globally optimal path planning.

[0005] To achieve the above objectives, a first aspect of this disclosure provides a method for planning a spraying path, comprising:

[0006] Under multiple constraints, the optimal spraying path that meets the optimization index is planned offline using graph theory algorithms, where the graph theory algorithm includes the shortest path algorithm in graph theory.

[0007] The optimal spraying path is converted into control commands, and the robot and spray gun are controlled to move synchronously in the same time sequence based on the control commands to spray the surface of the object to be sprayed. The control commands include pause coordinates.

[0008] When the robot reaches the pause coordinate, the robot and spray gun are synchronously paused at the same time sequence, and the actual effect parameters are measured using sensors mounted on the robot's end flange; and

[0009] Based on the error between the preset target parameters and the actual effect parameters, the spraying parameters are dynamically adjusted online to obtain the adjusted spraying path.

[0010] Optionally, under multiple constraints, an optimal spraying path that meets the optimization criteria is planned offline using graph theory algorithms, including:

[0011] Define nodes and edges, where nodes represent spray points in the spraying process, and edges connect two spray points to represent the spraying path;

[0012] Under multiple constraints, a graph structure is constructed using nodes and edges. These constraints include coating process constraints, robot space constraints, and dual-arm collision avoidance constraints.

[0013] Based on the optimization metrics, weights are added to the edges of the graph structure. The optimization metrics include the number of color changes, the length of the spraying path, the coverage of the spraying area, and the amount of end pose adjustment.

[0014] Using the shortest path algorithm in graph theory, the optimal spraying path from the starting spraying point to the target spraying point is determined based on the weight of each edge. The shortest path algorithm is either Dijkstra's algorithm or Bellman-Ford's algorithm.

[0015] Optionally, sensors mounted on the robot's end flange are used to measure actual performance parameters, including:

[0016] Multiple sensors mounted on the end flange of the robot are used to measure the actual effect parameters, including the actual coating thickness, the amount of color block overlap, and the current pose of the end.

[0017] Furthermore, the preset target parameters include the preset target coating thickness and the target end pose;

[0018] The spraying parameters include spraying distance and spraying position, where spraying distance is the distance between the spray gun and the surface to be sprayed, and spraying position is the angle between the spray gun and the surface to be sprayed.

[0019] Furthermore, based on the error between the preset target parameters and the actual effect parameters, the spraying parameters are dynamically adjusted online, including:

[0020] The spraying distance is dynamically adjusted online based on the difference between the target coating thickness and the actual coating thickness.

[0021] When the actual coating thickness is greater than the target coating thickness, raise the spray gun and lengthen the spraying distance to make the coating sprayed on the surface thinner.

[0022] When the actual coating thickness is less than the target coating thickness, lower the spray gun and shorten the spraying distance to make the coating sprayed on the surface thicker.

[0023] Furthermore, based on the error between the preset target parameters and the actual effect parameters, the spraying parameters are dynamically adjusted online, including:

[0024] When the spray gun sprays color blocks on adjacent paths, it detects whether there is any overlap between the color blocks sprayed on adjacent paths.

[0025] If there is color block overlap, increase the spraying interval between adjacent paths to reduce the color block overlap.

[0026] Furthermore, based on the error between the preset target parameters and the actual effect parameters, the spraying parameters are dynamically adjusted online, including:

[0027] The spraying posture is dynamically adjusted online based on the error between the target posture and the current posture of the end effector.

[0028] A second aspect of this disclosure provides a spraying path planning device, comprising:

[0029] The planning unit is used to plan the optimal spraying path that meets the optimization index offline using graph theory algorithms under multiple constraints. The graph theory algorithm includes the shortest path algorithm in graph theory.

[0030] The spraying unit is used to convert the optimal spraying path into control commands, and based on the control commands, control the robot and the spray gun to move synchronously in the same time sequence to spray the surface of the object to be sprayed. The control commands include pause coordinates.

[0031] The measurement unit is used to control the robot and the spray gun to pause synchronously at the same time when the robot moves to the pause coordinate, and to measure the actual effect parameters using sensors mounted on the robot's end flange; and

[0032] The adjustment unit is used to dynamically adjust the spraying parameters online based on the error between the preset target parameters and the actual effect parameters, so as to obtain the adjusted spraying path after adjusting the spraying parameters.

[0033] A third aspect of this disclosure provides a computer-readable storage medium storing computer instructions for causing a computer to execute a method for planning a spraying path provided in any of the first aspects.

[0034] A fourth aspect of this disclosure provides an electronic device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to cause the at least one processor to perform a method for planning a spraying path provided in any of the first aspects.

[0035] In the spraying path planning method provided in this embodiment, by constraining the conditions and optimizing the offline planning using graph theory algorithms, the optimal spraying path is obtained, which can achieve global optimal path planning. By adjusting the error between the preset target parameters and the actual effect parameters, the spraying parameters are dynamically adjusted online to improve the optimal spraying path and obtain the adjusted spraying path, thereby achieving a better spraying effect. This solves the problem that spraying path planning methods in related technologies cannot improve local optimal paths or achieve global optimal path planning. Attached Figure Description

[0036] To more clearly illustrate the technical solutions in the specific embodiments or related technologies of this disclosure, the accompanying drawings used in the description of the specific embodiments or related technologies will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0037] Figure 1 A schematic flowchart illustrating the spraying path planning method provided in this embodiment of the disclosure;

[0038] Figure 2 A block diagram of a spraying path planning device provided in an embodiment of this disclosure;

[0039] Figure 3 A block diagram of an electronic device provided in an embodiment of this disclosure. Detailed Implementation

[0040] To enable those skilled in the art to better understand the present disclosure, the technical solutions of the present disclosure will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of the present disclosure, and not all embodiments. Based on the embodiments of the present disclosure, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present disclosure.

[0041] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this disclosure are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate for the embodiments of this disclosure described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0042] In this disclosure, the terms "upper," "lower," "left," "right," "front," "rear," "top," "bottom," "inner," "outer," "middle," "vertical," "horizontal," "lateral," and "longitudinal" indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings. These terms are primarily for the purpose of better describing this disclosure and its embodiments, and are not intended to limit the indicated devices, elements, or components to having a specific orientation, or to be constructed and operated in a specific orientation.

[0043] Furthermore, in addition to indicating location or positional relationship, some of the aforementioned terms may also have other meanings. For example, the term "above" may also be used in certain circumstances to indicate a dependency or connection. Those skilled in the art can understand the specific meaning of these terms in this disclosure according to the specific circumstances.

[0044] Furthermore, the terms "installation," "setup," "equipped with," "connection," "linking," and "socketing" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral structure; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium, or an internal connection between two devices, components, or parts. Those skilled in the art can understand the specific meaning of the above terms in this disclosure according to the specific circumstances.

[0045] It should be noted that, unless otherwise specified, the embodiments and features described in this disclosure can be combined with each other. This disclosure will now be described in detail with reference to the accompanying drawings and embodiments.

[0046] For complex, large objects to be sprayed, current spraying path planning methods can only plan locally optimal paths offline, and cannot improve locally optimal paths or achieve globally optimal path planning.

[0047] To address the aforementioned problems, embodiments of this disclosure provide a method for planning spraying paths, such as... Figure 1 As shown, the method includes the following steps S11 to S14:

[0048] Step S11: Under multiple constraints, the optimal spraying path that meets the optimization index is planned offline using graph theory algorithms. The graph theory algorithms include the shortest path algorithm in graph theory. Multiple conditions are used for constraints, a three-dimensional spraying model is adopted, and the optimal spraying path that meets the optimization index is planned offline using graph theory algorithms. The optimal spraying path is obtained, which can realize the global optimal path planning.

[0049] In one optional embodiment of this disclosure, step S11 includes:

[0050] Define nodes and edges, where nodes represent spray points in the spraying process, and edges connect two spray points to represent the spraying path;

[0051] Under multiple constraints, a graph structure is constructed using nodes and edges. These constraints include coating process constraints, robot space constraints, and dual-arm collision avoidance constraints.

[0052] Based on the optimization indicators, weights are added to the edges of the graph structure. The optimization indicators include the number of color changes, the length of the spraying path, the coverage of the spraying area, and the amount of end pose adjustment. The purpose of the optimization indicators is to minimize the number of color changes, the shortest spraying path, the best coverage of the spraying area, and the least amount of end pose adjustment, thereby improving the spraying effect.

[0053] Using the shortest path algorithm in graph theory, the optimal spraying path from the starting spraying point to the target spraying point is determined based on the weight of each edge. The shortest path algorithm is either Dijkstra's algorithm or Bellman-Ford's algorithm.

[0054] Step S12: Convert the optimal spraying path into control commands, and control the robot and spray gun to move synchronously in the same time sequence based on the control commands to spray the surface of the object to be sprayed. The control commands include pause coordinates. The spray gun is installed on the end flange of the robot and moves synchronously with the robot. The movement of the spray gun is the opening and closing movement of the spray gun, including the spray gun on state and the spray gun off state. When the spray gun is on state, the surface of the object to be sprayed is sprayed.

[0055] In addition, the control commands also include spraying coordinates and robot pose. The pause coordinates and spraying coordinates are both three-dimensional coordinates of the robot, and the robot pose is the angle between the robot and the three coordinate axes respectively. Based on the control commands, the robot and the spray gun are synchronized and move in coordination at the same time sequence. The robot is controlled to move to the spraying coordinates according to the robot pose, while the spray gun is controlled to spray the surface of the object to be sprayed.

[0056] Step S13: When the robot moves to the pause coordinate, control the robot and the spray gun to pause synchronously in the same time sequence, and use the sensor installed on the end flange of the robot to measure the actual effect parameters; when the robot moves to the pause coordinate, the robot pauses, and the spray gun is in the off state.

[0057] In one optional embodiment of this disclosure, step S13 involves measuring actual effect parameters using sensors mounted on the robot's end effector flange, including:

[0058] Multiple sensors mounted on the robot's end effector flange are used to measure actual effect parameters, including actual coating thickness, color block overlap, and the current end effector pose. Following the actual spraying path of the spray gun, these same sensors are used to measure the actual coating thickness, color block overlap, and the current end effector pose.

[0059] Step S14: Based on the error between the preset target parameters and the actual effect parameters, dynamically adjust the spraying parameters online to obtain the adjusted spraying path.

[0060] By comparing the pre-set target parameter sensors with the measured actual effect parameters, the optimal spraying path planned offline is dynamically adjusted online. During the robot's movement, the spraying parameters are dynamically adjusted based on the error obtained from the comparison. Finally, an adjusted spraying path is obtained after adjusting the optimal spraying path planned offline, achieving a better spraying effect. This solves the problem that spraying path planning methods in related technologies cannot improve local optimal paths or achieve global optimal path planning.

[0061] In a preferred embodiment of this disclosure, the preset target parameters include a preset target coating thickness and a preset target end pose;

[0062] The spraying parameters include spraying distance and spraying position, where spraying distance is the distance between the spray gun and the surface to be sprayed, and spraying position is the angle between the spray gun and the surface to be sprayed.

[0063] In a preferred embodiment of this disclosure, the spraying parameters are dynamically adjusted online based on the error between the preset target parameters and the actual effect parameters, including:

[0064] The spraying distance is dynamically adjusted online based on the difference between the target coating thickness and the actual coating thickness.

[0065] When the actual coating thickness is greater than the target coating thickness, raise the spray gun and lengthen the spraying distance to make the coating on the surface to be sprayed thinner; at this time, the coating on the surface to be sprayed is relatively thick. When the actual coating thickness is greater than the target coating thickness by a certain distance, raise the spray gun by the corresponding distance and lengthen the spraying distance to reduce the coating thickness.

[0066] When the actual coating thickness is less than the target coating thickness, the spray gun is lowered and the spraying distance is shortened to make the coating on the surface to be sprayed thicker. At this time, the coating on the surface to be sprayed is relatively thin. When the actual coating thickness is less than the target coating thickness by a certain distance, the spray gun is lowered by the corresponding distance to shorten the spraying distance and increase the coating thickness.

[0067] This disclosure dynamically adjusts the spraying distance online based on the difference between the target coating thickness and the actual coating thickness, thereby dynamically adjusting the sprayed coating thickness online and effectively improving the uniformity of the coating thickness.

[0068] In a preferred embodiment of this disclosure, the spraying parameters are dynamically adjusted online based on the error between the preset target parameters and the actual effect parameters, including:

[0069] When the spray gun sprays color blocks on adjacent paths, it detects whether there is any overlap between the color blocks sprayed on adjacent paths.

[0070] If there is color block overlap, increase the spraying interval between adjacent paths to reduce the color block overlap.

[0071] When detecting the overlap of color blocks, if the overlap between two color blocks on two adjacent spraying paths is large, the overlap can be reduced and the spraying effect improved by increasing the spraying interval between adjacent paths.

[0072] In a preferred embodiment of this disclosure, the spraying parameters are dynamically adjusted online based on the error between the preset target parameters and the actual effect parameters, including:

[0073] Based on the error between the end-target pose and the current end-target pose, the spraying pose is dynamically adjusted online. The spraying pose is the angle between the spray gun and the surface to be sprayed. When the angle between the spray gun and the surface to be sprayed is 90 degrees, the spray gun is perpendicular to the surface, and the spray gun is spraying directly at the surface. When the angle between the spray gun and the surface to be sprayed is any angle other than 90 degrees, the spray gun is tilted at a certain angle to spray the surface. Different spraying poses are adjusted to produce different spraying effects.

[0074] As can be seen from the above description, this disclosure achieves the following technical effects:

[0075] This disclosure uses multiple constraints and a graph theory algorithm to plan the optimal spraying path offline that meets the optimization index, thus obtaining the optimal spraying path and achieving global optimal path planning.

[0076] Based on the difference between the target coating thickness and the actual coating thickness, the spraying distance is dynamically adjusted online, thereby dynamically adjusting the coating thickness online, which can effectively improve the uniformity of the coating thickness.

[0077] By dynamically adjusting the spraying parameters online based on the error between the preset target parameters and the actual effect parameters, the optimal spraying path is improved, resulting in an adjusted spraying path with better spraying effect. This solves the problem that spraying path planning methods in related technologies cannot improve the local optimal path or achieve global optimal path planning.

[0078] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0079] This disclosure also provides a device for planning spraying paths for implementing the above-described method embodiments, such as... Figure 2 As shown, the planning device 20 includes:

[0080] Planning unit 21 is used to plan the optimal spraying path that meets the optimization index offline using graph theory algorithms under multiple constraints. The graph theory algorithm includes the shortest path algorithm in graph theory.

[0081] The spraying unit 22 is used to convert the optimal spraying path into control commands, and control the robot and the spray gun to move synchronously in the same time sequence based on the control commands to spray the surface of the object to be sprayed. The control commands include pause coordinates.

[0082] Measurement unit 23 is used to control the robot and spray gun to pause synchronously at the same time when the robot moves to the pause coordinate, and to measure the actual effect parameters using sensors mounted on the robot's end flange; and

[0083] The adjustment unit 24 is used to dynamically adjust the spraying parameters online based on the error between the preset target parameters and the actual effect parameters, so as to obtain the adjusted spraying path after adjusting the spraying parameters.

[0084] The specific methods of execution of each unit in the above device embodiments have been described in detail in the embodiments related to the method, and will not be elaborated here.

[0085] This disclosure also provides an electronic device, such as... Figure 3 As shown, the electronic device includes one or more processors 31 and a memory 32. Figure 3 Take a processor 31 as an example.

[0086] The controller may also include an input device 33 and an output device 34.

[0087] The processor 31, memory 32, input device 33, and output device 34 can be connected via a bus or other means. Figure 3 Taking the example of a connection between China and Israel via a bus.

[0088] Processor 31 can be a central processing unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or combinations of the above types of chips. The general-purpose processor can be a microprocessor or any conventional processor.

[0089] The memory 32, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs, non-transitory computer-executable programs, and modules, such as the program instructions / modules corresponding to the control method in this embodiment. The processor 31 executes various functional applications and data processing of the server by running the non-transitory software programs, instructions, and modules stored in the memory 32, thereby implementing the spraying path planning method of the above method embodiment.

[0090] The memory 32 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created by the use of the processing device operated by the server. Furthermore, the memory 32 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, the memory 32 may optionally include memory remotely located relative to the processor 31, and these remote memories can be connected to a network connection device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0091] Input device 33 can receive input numerical or character information, and generate key signal inputs related to user settings and function control of the server's processing device. Output device 34 may include display devices such as a display screen.

[0092] One or more modules are stored in memory 32, and when executed by one or more processors 31, they perform actions such as... Figure 1 The method shown.

[0093] Those skilled in the art will understand that all or part of the processes in the above method embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes as described in the above method embodiments. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), random access memory (RAM), flash memory (FM), hard disk drive (HDD), or solid-state drive (SSD), etc.; the storage medium can also include combinations of the above types of memory.

[0094] Although embodiments of the present disclosure have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the present disclosure, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A method for planning a spraying path, characterized in that, include: Under multiple constraints, the optimal spraying path that meets the optimization index is planned offline using graph theory algorithms, wherein the graph theory algorithms include the shortest path algorithm in graph theory; The optimal spraying path is converted into control commands, and the robot and spray gun are controlled to move synchronously in the same time sequence based on the control commands to spray the surface of the object to be sprayed. The control commands include pause coordinates. When the robot moves to the pause coordinate, the robot and the spray gun are controlled to pause synchronously at the same time sequence, and the actual effect parameters are measured using sensors mounted on the robot's end flange; and Based on the error between the preset target parameters and the actual effect parameters, the spraying parameters are dynamically adjusted online to obtain the adjusted spraying path after adjusting the spraying parameters; The step of offline planning of the optimal spraying path that meets the optimization index using graph theory algorithms under multiple constraints includes: Define nodes and edges, where nodes represent spray points in the spraying process, and edges connect two spray points to represent the spraying path; Under multiple constraints, a graph structure is constructed using nodes and edges, wherein the multiple constraints include spraying process constraints, robot space constraints, and dual-arm collision avoidance constraints. According to the optimization indicators, weights are added to the edges of the graph structure, wherein the optimization indicators include the number of color changes, the length of the spraying path, the coverage of the spraying area, and the amount of end pose adjustment. Using the shortest path algorithm in graph theory, the optimal spraying path from the starting spraying point to the target spraying point is determined according to the weight of each edge. The shortest path algorithm is either Dijkstra's algorithm or Bellman-Ford's algorithm.

2. The method according to claim 1, characterized in that, The method of measuring actual effect parameters using sensors installed on the robot's end flange includes: Multiple sensors mounted on the end flange of the robot are used to measure actual effect parameters, including the actual coating thickness, color block overlap, and the current pose of the end.

3. The method according to claim 2, characterized in that, The preset target parameters include the preset target coating thickness and the target end pose; The spraying parameters include spraying distance and spraying position, wherein the spraying distance is the distance between the spray gun and the surface to be sprayed, and the spraying position is the angle between the spray gun and the surface to be sprayed.

4. The method according to claim 3, characterized in that, The step of dynamically adjusting the spraying parameters online based on the error between the preset target parameters and the actual effect parameters includes: The spraying distance is dynamically adjusted online based on the difference between the target coating thickness and the actual coating thickness. When the actual thickness of the coating is greater than the target thickness of the coating, the spray gun is raised and the spraying distance is lengthened so that the coating sprayed by the spray gun on the surface to be sprayed becomes thinner; When the actual thickness of the coating is less than the target thickness, the spray gun is lowered to shorten the spraying distance, so that the coating sprayed by the spray gun on the surface to be sprayed becomes thicker.

5. The method according to claim 3, characterized in that, The step of dynamically adjusting the spraying parameters online based on the error between the preset target parameters and the actual effect parameters includes: When the spray gun sprays color blocks on adjacent paths, it detects whether there is any overlap between the color blocks sprayed by the spray gun on adjacent paths. If there is color block overlap, increase the spraying interval between adjacent paths to reduce the color block overlap.

6. The method according to claim 3, characterized in that, The step of dynamically adjusting the spraying parameters online based on the error between the preset target parameters and the actual effect parameters includes: The spraying posture is dynamically adjusted online based on the error between the target posture of the end and the current posture of the end.

7. A spraying path planning device, characterized in that, The method for planning the spraying path according to claim 1, the planning device comprising: The planning unit is used to plan the optimal spraying path that meets the optimization index offline using graph theory algorithms under multiple constraints, wherein the graph theory algorithm includes the shortest path algorithm in graph theory; The spraying unit is used to convert the optimal spraying path into control commands, and control the robot and the spray gun to move synchronously in the same time sequence based on the control commands to spray the surface of the object to be sprayed, wherein the control commands include pause coordinates; A measurement unit is used to control the robot and the spray gun to pause synchronously at the same time when the robot moves to the pause coordinate, and to measure the actual effect parameters using sensors mounted on the robot's end flange; and The adjustment unit is used to dynamically adjust the spraying parameters online based on the error between the preset target parameters and the actual effect parameters, so as to obtain the adjusted spraying path after adjusting the spraying parameters.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing a computer to execute the spraying path planning method according to any one of claims 1 to 6.

9. An electronic device, characterized in that, The electronic device includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to cause the at least one processor to perform the spraying path planning method according to any one of claims 1 to 6.

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