Pose adjustment method and device of spraying robot, electronic equipment and storage medium

Abstract

The application discloses a pose adjustment method and device of a spraying robot, electronic equipment and a storage medium. The method comprises the following steps: acquiring a spraying process of a spraying robot for spraying an unmanned aerial vehicle to be sprayed, and determining a current spraying node of the unmanned aerial vehicle to be sprayed; controlling the spraying robot to move to a working position corresponding to the current spraying node; acquiring a scene image collected by a camera arranged in a spraying scene, identifying an unmanned aerial vehicle workpiece contained in the scene image, and obtaining workpiece point cloud of a reference workpiece identified; acquiring image information containing the reference workpiece collected by an image acquisition device arranged on the spraying robot, constructing world coordinates according to the image information, and obtaining coordinate information of the reference workpiece; determining pose adjustment information of the spraying robot according to the workpiece point cloud and the coordinate information, and adjusting the pose of the spraying robot according to the pose adjustment information. The accuracy of the pose adjustment of the spraying robot is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of unmanned robot control adjustment, and particularly relates to a pose adjustment method and device of a spraying robot, an electronic device and a storage medium. BACKGROUND

[0002] With the development of society and technology, many industrial processes have gradually realized automation, and automated processing can effectively improve work efficiency.

[0003] At present, in the industrial production of unmanned machine spraying, the spraying process is usually completed manually. The operator inputs instructions to the related equipment according to experience, so that the related equipment sprays the unmanned machine according to the input control instructions. However, manual operation not only has the problem of poor spraying efficiency, but also has the problem of low spraying precision, such as inaccurate spraying position and uneven spraying, which are caused by inaccurate adjustment.

[0004] Therefore, there is an urgent need for a pose adjustment method of a spraying robot that improves spraying efficiency. SUMMARY

[0005] The purpose of the embodiments of the present application is to provide a pose adjustment method and device of a spraying robot, an electronic device and a storage medium, to solve the technical problem of poor spraying effect and efficiency of related technology.

[0006] In a first aspect, the embodiments of the present application provide a pose adjustment method of a spraying robot, comprising:

[0007] obtaining a spraying process of a spraying robot for spraying a to-be-sprayed unmanned machine, and determining a current spraying node of the to-be-sprayed unmanned machine, wherein the spraying process contains a plurality of spraying nodes, and there is a spraying sequence between each spraying node;

[0008] controlling the spraying robot to move to a work position corresponding to the current spraying node;

[0009] obtaining a scene image collected by a camera arranged in a spraying scene, and identifying an unmanned machine workpiece contained in the scene image to obtain a workpiece point cloud of an identified reference workpiece;

[0010] obtaining image information containing the reference workpiece collected by an image collection device arranged on the spraying robot, and constructing a world coordinate according to the image information to obtain coordinate information of the reference workpiece;

[0011] determining pose adjustment information of the spraying robot according to the workpiece point cloud and the coordinate information, and adjusting the pose of the spraying robot according to the pose adjustment information.

[0012] Secondly, embodiments of this application provide a pose adjustment device for a painting robot, comprising:

[0013] The node determination module is used to obtain the spraying process of the spraying robot to spray the drone to be sprayed, and to determine the current spraying node of the drone to be sprayed. The spraying process includes a number of spraying nodes, and there is a spraying sequence between the spraying nodes.

[0014] The movement control module is used to control the painting robot to move to the work position corresponding to the current painting node;

[0015] The first processing module is used to acquire scene images captured by cameras set in the spraying scene, and to identify the drone workpieces contained in the scene images to obtain the workpiece point cloud of the identified reference workpiece.

[0016] The second processing module is used to acquire image information containing the reference workpiece acquired by the image acquisition device set on the painting robot, and to construct world coordinates based on the image information to obtain the coordinate information of the reference workpiece.

[0017] The pose adjustment module is used to determine the pose adjustment information of the painting robot based on the workpiece point cloud and the coordinate information, and to adjust the pose of the painting robot according to the pose adjustment information.

[0018] Thirdly, embodiments of this application provide an electronic device, which includes a processor, a memory, and a computer program or embedded program stored in the internal storage of the memory and executable on the processor. When the processor executes the computer program or embedded program, it implements the steps in the pose adjustment method of the painting robot described above.

[0019] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program or embedded program, which, when executed by a processor, implements the steps in the pose adjustment method for the painting robot described above.

[0020] This application provides a method, apparatus, electronic device, and storage medium for adjusting the pose of a painting robot. The method involves acquiring the painting process of a drone to be painted and determining the current painting node of the drone. The painting process includes several painting nodes with a painting sequence. The method controls the painting robot to move to the work position corresponding to the current painting node. It acquires scene images captured by a camera set in the painting scene and identifies the drone workpieces included in the scene images to obtain a workpiece point cloud of the identified reference workpiece. It also acquires image information containing the reference workpiece captured by an image acquisition device set on the painting robot, constructs world coordinates based on the image information, and obtains the coordinate information of the reference workpiece. Based on the workpiece point cloud and coordinate information, it determines the pose adjustment information of the painting robot and adjusts the pose of the painting robot accordingly. By acquiring scene images in the painting scene and image information from the perspective of the painting robot, and analyzing and processing them to determine how to adjust the pose of the painting robot, the accuracy of the pose adjustment of the painting robot is improved. Attached Figure Description

[0021] Figure 1 This is a flowchart illustrating the steps of the pose adjustment method for a painting robot provided in an embodiment of this application;

[0022] Figure 2 This is a schematic diagram of a painting robot movement control provided in an embodiment of this application;

[0023] Figure 3 This is a flowchart illustrating one step of pose adjustment provided in an embodiment of this application;

[0024] Figure 4 This is a schematic diagram of a pose adjustment device for a painting robot provided in an embodiment of this application;

[0025] Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application;

[0026] Figure 6 This is another structural schematic diagram of the electronic device provided in the embodiments of this application. Detailed Implementation

[0027] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0028] It should be understood that the steps described in the method embodiments disclosed in this application may be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of this application is not limited in this respect.

[0029] The term "comprising" and its variations as used herein are open-ended inclusions, meaning "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Definitions of other terms will be given in the description below.

[0030] In the industrial production of drone painting, the painting process is usually done manually. Operators input instructions to the relevant equipment based on their experience, so that the equipment can paint the drone according to the input control instructions. However, manual operation not only has the problem of poor painting efficiency, but also the problem of low painting accuracy, such as inaccurate painting position and uneven painting, all of which are caused by insufficient adjustment.

[0031] To address the technical problems existing in related technologies, this application provides a method for adjusting the pose of a painting robot. Please refer to [link to relevant documentation]. Figure 1 , Figure 1 This is a flowchart illustrating the steps of a pose adjustment method for a painting robot provided in this application embodiment, which includes steps 101 to 105.

[0032] Step 101: Obtain the spraying process of the spraying robot to spray the drone to be sprayed, and determine the current spraying node of the drone to be sprayed. The spraying process includes several spraying nodes, and there is a spraying sequence between the spraying nodes.

[0033] In practice, when using a painting robot to paint a drone, the drone is moved to the appropriate painting scene, and then the painting robot, according to the specific requirements, performs the painting process. During the actual painting process, the painting robot adjusts its position and posture based on the specific painting needs to meet different requirements. Therefore, in the actual painting process, the painting robot needs to be adjusted in real time based on the actual painting requirements and the actual painting situation of the drone, including adjustments to its position and posture, to complete the painting operation more intelligently and conveniently.

[0034] In one embodiment, when painting a drone, the painting robot first determines the painting process and the current painting node of the drone. Specifically, the painting robot needs to paint several locations on the drone, and when painting different locations, it needs to control the painting robot to be in different states, including different positions and different postures, to meet different painting requirements at different locations.

[0035] For example, when controlling a painting robot to perform painting, it is necessary to spray corresponding images or text on one or more locations of the drone to be painted. Therefore, before the painting process, the position of the painting robot is adjusted to a suitable position, and its posture is adjusted to ensure the painting accuracy. For example, the angle and distance of the spray gun of the painting robot are finely adjusted to ensure the clarity and accuracy of the image or text.

[0036] Furthermore, determining the current spraying node of the drone to be painted will be used to initially adjust the position of the painting robot, adjusting the painting robot to the working position corresponding to the current spraying node. The working position can be a position area. Then, the position and attitude of the painting robot will be further fine-tuned according to the actual painting requirements. Specifically, when obtaining the painting process and determining the current spraying node, the process includes: acquiring the first image of the drone to be painted captured by the image acquisition device, and performing recognition and analysis processing on the first image to obtain the drone identifier of the drone to be painted; querying and matching the drone identifier in the painting process list to obtain the painting process of the drone to be painted; and determining the current spraying node for the painting robot to paint the drone to be painted based on the painting process.

[0037] Specifically, the first image of the drone to be painted is acquired by the image acquisition device, and the first image is identified and analyzed to obtain the drone identifier of the drone to be painted. Then, the drone identifier is used for query and matching to obtain the painting process for painting the drone to be painted, and the current painting node is accurately determined based on the process.

[0038] For example, after the drone to be painted enters the painting area, an image acquisition device, such as a camera, set in the painting area will acquire a first image containing the drone to be painted. Then, through analysis and processing of the first image, the specific model and identification information of the drone are identified. Subsequently, the system automatically searches for the corresponding painting process in the painting process list to ensure that each painting operation is strictly carried out in accordance with the preset process, thereby ensuring the quality and efficiency of painting.

[0039] When determining the current spraying node of the drone to be painted, it is necessary to consider the actual status of the drone being painted, such as which node it has reached. Therefore, when determining the current spraying node based on the spraying process, the current spraying node can be accurately locked by acquiring the recorded spraying progress information of the drone to be painted and combining it with real-time status feedback.

[0040] Furthermore, when processing and analyzing the first image to obtain the drone identifier of the drone to be painted, the identifier can be determined through image comparison, similarity calculation, feature recognition, and coded information recognition. Specifically, this includes: calculating the similarity between the first image and each image in the drone image library, and using the identifier information of the image with the highest similarity exceeding a preset threshold as the drone identifier of the drone to be painted; or, identifying the drone feature information contained in the first image, and processing it according to the drone feature information to obtain the drone identifier of the drone to be painted; or, identifying the coded information contained in the first image, and processing it to obtain the drone identifier of the drone to be painted.

[0041] In practical applications, when determining the drone identification of a drone to be painted, the first image acquired is analyzed from multiple dimensions. By employing techniques such as image recognition, feature extraction, or code parsing, accurate identification of the drone identification is ensured, thus laying a solid foundation for the efficient execution of subsequent painting processes. Therefore, during processing, the acquired first image can be compared with a pre-set drone image library. Combined with real-time status feedback, the drone identification can be accurately located. Furthermore, drone feature information contained in the first image, such as color, shape, and size, can be identified. By comprehensively analyzing these features, the specific model and identification of the drone can be further verified and confirmed. Additionally, encoded information contained in the first image, such as QR codes and barcodes, can be identified. Decoding technology can be used to parse the specific encoded content, thereby accurately determining the drone model and identification.

[0042] It should be noted that in order to determine the drone identification based on the above method, corresponding processing is required in advance. For example, the images, feature information and encoding data of each drone should be stored or set in advance, a detailed drone image library should be built, and reasonable similarity thresholds and feature recognition algorithms should be set to ensure that the specific model and identification of the drone to be painted can be quickly and accurately identified in practical applications.

[0043] When determining the current spraying node based on the spraying process, the system combines the pre-set spraying process flow with real-time collected data. For example, when the drone to be sprayed just enters the spraying area, the first spraying node in the spraying process is taken as the current spraying node. When the drone to be sprayed is undergoing spraying, the actual spraying situation needs to be considered. Specifically, this includes: determining whether there are any completed processes recorded by the spraying robot for spraying the drone to be sprayed; if there are completed processes recorded, the current spraying node for spraying the drone to be sprayed is obtained from the spraying process based on the completed processes and the spraying sequence; if there are no completed processes recorded, the first spraying node in the spraying process is taken as the current spraying node for spraying the drone to be sprayed.

[0044] Specifically, when determining the current spraying node, it is determined whether the spraying results of the drone to be sprayed are recorded, including completed processes. If it is determined that the completed processes of the drone to be sprayed are recorded, it means that the drone to be sprayed has not just started the spraying scene. At this time, the current spraying node will be determined according to the completed processes and the spraying order in the spraying process. If it is determined that no completed processes are recorded, it will default to starting from the first spraying node in the spraying process, that is, the first spraying node will be determined as the current spraying node.

[0045] Based on the above description, it can be seen that the painting status of the drone to be painted can be monitored and recorded in real time. Therefore, the painting process can also include: recording the painting status of the painting robot painting the drone to be painted, wherein the painting status includes the completion status of each painting node in the painting process, and the completion status includes completed and uncompleted.

[0046] In other words, a painting robot is used to paint a drone, recording the completion status of each painting step, including completed and incomplete. Furthermore, each drone undergoing painting can be assigned a unique number to accurately distinguish between drones of the same model.

[0047] Step 102: Control the painting robot to move to the work position corresponding to the current painting node.

[0048] In one embodiment, after determining the current painting node of the drone to be painted, in order to complete the subsequent painting operation, it is necessary to control the painting drone to move to the working position corresponding to the current painting node. At this time, when moving the painting robot, only its position can be moved, moving it to a working position where the next painting operation can be completed, so as to facilitate subsequent fine-tuning of the pose of the painting robot.

[0049] like Figure 2 As shown, after the painting robot completes the painting operation corresponding to painting node 1 at position A, the current painting node is determined to be painting node 2, and painting node 2 is the next adjacent node of painting node 1. The painting robot needs to move from position A to position B corresponding to painting node 2. At this time, control commands are sent to the painting robot to make it move to position B according to the preset path and speed. The preset path and speed can be set and calculated according to actual needs.

[0050] Step 103: Obtain scene images captured by cameras set in the spraying scene, and identify the drone workpieces contained in the scene images to obtain the workpiece point cloud of the identified reference workpieces.

[0051] In one embodiment, when adjusting the pose of the painting robot, a scene image is acquired by a camera set in the painting scene, and the scene image contains a drone to be painted. Then, the drone workpiece to be painted contained in the colonoscopy image is identified and processed to obtain the workpiece point cloud of the identified reference workpiece through point cloud construction.

[0052] For example, after an image acquisition device such as a camera acquires a scene image, it identifies and constructs a point cloud of a set reference workpiece to obtain the workpiece point cloud of the reference workpiece.

[0053] Step 104: Obtain image information containing the reference workpiece from the image acquisition device set on the painting robot, and construct world coordinates based on the image information to obtain the coordinate information of the reference workpiece.

[0054] In one embodiment, when adjusting the pose of the painting robot, in addition to analyzing the scene images captured by the camera in the painting scene, the images captured by the painting robot itself are also analyzed and processed. Specifically, the images captured by the image acquisition device set on the painting robot are obtained, and the acquired images contain a reference workpiece of the drone to be painted. The world coordinates are constructed based on the image information to obtain the coordinate information of the reference workpiece.

[0055] For example, before adjusting the pose of the painting robot, the painting robot is moved to the working position corresponding to the current painting node, and then the position and attitude are finely adjusted so that the painting robot is in a suitable pose to complete the painting operation.

[0056] In determining how to perform fine-tuning, the obtained workpiece point cloud and coordinate information are compared, and the specific requirements of the spraying process can be considered to determine how to adjust the pose of the spraying robot. During the actual adjustment process, the scene image captured by the camera set in the spraying scene is fixed. The position and posture of the spraying robot need to be adjusted so that the reference workpiece in the image information captured by the spraying robot matches the workpiece point cloud of the reference workpiece in the scene image. This confirms that the pose adjustment of the spraying robot is complete.

[0057] Of course, there is a certain transformation relationship between point cloud coordinates and world coordinate system coordinates. The difference between the reference workpiece and the two can be determined by the transformation relationship, and then the pose adjustment of the painting robot can be determined.

[0058] Step 105: Determine the pose adjustment information of the painting robot based on the workpiece point cloud and coordinate information, and adjust the pose of the painting robot according to the pose adjustment information.

[0059] In one embodiment, after obtaining the workpiece point cloud and coordinate information of the reference workpiece, the pose adjustment information for fine-tuning the pose of the painting robot is determined based on the workpiece point cloud and coordinate information, and then the pose of the painting robot is fine-tuned based on the obtained pose adjustment information.

[0060] For example, when determining the pose adjustment information for the painting robot, the position deviation and attitude deviation of the reference workpiece between the two are determined by analyzing and processing the workpiece point cloud and coordinate information, and then the pose adjustment information for adjusting the painting robot is calculated, including relevant parameters such as translation and rotation, and then the pose adjustment of the painting robot is performed based on the pose adjustment information.

[0061] Specifically, refer to Figure 3 , Figure 3 This is a flowchart illustrating a pose adjustment step provided in an embodiment of this application, wherein the step includes steps 301 to 303.

[0062] Step 301: Obtain the coordinate transformation rules between point cloud coordinates and world coordinates;

[0063] Step 302: Calculate the pose deviation of the workpiece point cloud and coordinate information according to the coordinate transformation rules to obtain the position deviation and attitude deviation of the painting robot.

[0064] Step 303: Obtain the pose adjustment information for adjusting the painting robot based on the position deviation and posture deviation, and adjust the pose of the painting robot based on the pose adjustment information.

[0065] For example, during the adjustment process, the pose deviation between the workpiece point cloud and coordinate information is determined according to the coordinate transformation rules between point cloud coordinates and world coordinates, and the position deviation and attitude deviation of the painting robot are obtained. The corresponding pose adjustment information is then calculated based on the position deviation and attitude deviation, and the pose of the painting robot is adjusted according to the pose adjustment information.

[0066] When calculating pose deviation, the coordinate transformation rules between point cloud coordinates and world coordinates are known. Then, through coordinate transformation, such as converting the workpiece point cloud into the corresponding world coordinates, and comparing the coordinate information with it, the specific value of pose deviation can be determined. That is, using the workpiece point cloud as a reference, the difference between the coordinate information and the workpiece point cloud is determined to obtain the specific deviation. After the painting robot makes adjustments based on the obtained pose adjustment information, it will execute the painting operation of the current painting node, and then complete the execution of the entire painting process through continuous loop processing.

[0067] Furthermore, based on Figure 2 It is known that after determining the current spraying node, it is necessary to control the spraying robot to move to the corresponding working position of the current spraying node in order to facilitate subsequent fine-tuning. Therefore, during the spraying process, relevant information in the spraying process is obtained as a reference for adjusting the spraying robot. Specifically, the spraying process also includes: obtaining the working position of the robot to be sprayed at each spraying node in the spraying process; when it is determined that the drone to be sprayed is at the first spraying node in the spraying process, controlling the spraying robot to move to the working position corresponding to the first spraying node; when it is determined that the spraying node of the drone to be sprayed moves from the first spraying node to the second spraying node, controlling the spraying robot to move from the first working position to the second working position. Here, the first spraying node is not the first spraying node, the first working position is the working position corresponding to the spraying robot under the first spraying node, and the second working position is the working position corresponding to the spraying robot under the second spraying node.

[0068] In practical applications, the execution of each spraying node in the spraying process requires controlling the spraying robot to be in the corresponding working position. Therefore, the resulting spraying process records the working position corresponding to each spraying node, and then the position of the spraying robot is adjusted in a timely manner according to the actual spraying situation during the spraying operation. Furthermore, when initially adjusting the position of the spraying robot, it is possible to refer to... Figure 2 When the current spraying node is the first spraying node in the spraying process, the spraying robot can be controlled to move directly to the working position corresponding to the first spraying node. As the spraying operations of different nodes are continuously completed, the spraying nodes will also switch sequentially according to the predetermined spraying sequence. At the same time, the position of the spraying robot can be continuously adjusted, for example, by combining...Figure 2 When spraying node 1 is the first spraying node and spraying node 2 is the second spraying node, after the spraying robot completes the spraying operation corresponding to spraying node 1, it obtains the position B corresponding to spraying node 2, so as to perform path planning processing based on the position A and position B corresponding to spraying node 1, and control the spraying robot to move based on the obtained planned path.

[0069] It should be noted that when painting drones, there is no limit to the number of painting robots in the painting scene, and each painting robot can perform painting operations simultaneously. When at least two painting robots are combined to complete the painting of a drone to be painted, different painting operations can be set for each painting robot, and each painting robot completes the painting of the drone to be painted after completing its own painting operation.

[0070] When multiple painting robots work collaboratively, precise control of each robot is required, taking into account its working position and status. This includes preventing collisions. Therefore, when multiple robots are working together, after determining the current painting node for each robot, proper path planning is necessary to move them to their corresponding positions. This ensures that collisions are avoided, and also prevents collisions between robots and the drones being painted. In multi-robot collaborative operations, the path planning algorithm needs to be updated in real time to ensure dynamic obstacle avoidance and efficient completion of the painting task.

[0071] In summary, the above embodiments provide a pose adjustment method for a painting robot. The method involves acquiring the painting process of a drone to be painted and determining the current painting node of the drone. The painting process includes several painting nodes with a painting sequence. The method controls the painting robot to move to the work position corresponding to the current painting node. Scene images captured by cameras set in the painting scene are acquired, and drone workpieces included in the scene images are identified to obtain a workpiece point cloud of the identified reference workpiece. Image information containing the reference workpiece is acquired by an image acquisition device set on the painting robot, and world coordinates are constructed based on the image information to obtain the coordinate information of the reference workpiece. Based on the workpiece point cloud and coordinate information, pose adjustment information of the painting robot is determined, and the pose of the painting robot is adjusted according to the pose adjustment information. By acquiring scene images in the painting scene and image information from the perspective of the painting robot, and analyzing and processing them to determine how to adjust the pose of the painting robot, the accuracy of pose adjustment for the painting robot is improved.

[0072] Based on the method described in the above embodiments, this embodiment will further describe the pose adjustment device of the painting robot from the perspective of the pose adjustment device of the painting robot. The pose adjustment device of the painting robot can be implemented as an independent entity or integrated into an electronic device, such as a terminal, which may include a mobile phone, a tablet computer, etc.

[0073] Please see Figure 4 , Figure 4 This is a schematic diagram of a pose adjustment device for a painting robot provided in an embodiment of this application, as shown below. Figure 4 As shown in the embodiment of this application, the pose adjustment device 400 for a painting robot includes:

[0074] The node determination module 401 is used to obtain the spraying process of the spraying robot to spray the drone to be sprayed, and to determine the current spraying node of the drone to be sprayed. The spraying process includes several spraying nodes, and there is a spraying sequence between the spraying nodes.

[0075] The mobile control module 402 is used to control the painting robot to move to the work position corresponding to the current painting node;

[0076] The first processing module 403 is used to acquire scene images captured by a camera set in the spraying scene, and to identify the drone workpieces contained in the scene images to obtain the workpiece point cloud of the identified reference workpiece.

[0077] The second processing module 404 is used to acquire image information containing a reference workpiece acquired by the image acquisition device set on the spraying robot, and to construct world coordinates based on the image information to obtain the coordinate information of the reference workpiece.

[0078] The pose adjustment module 405 is used to determine the pose adjustment information of the painting robot based on the workpiece point cloud and coordinate information, and to adjust the pose of the painting robot according to the pose adjustment information.

[0079] In one embodiment, the node determination module 401 is further configured to:

[0080] The first image of the drone to be painted is acquired by the image acquisition device, and the first image is identified and analyzed to obtain the drone identification of the drone to be painted.

[0081] The painting process for the drone to be painted is obtained by querying and matching the drone's identifier in the painting process list.

[0082] Based on the spraying process, determine the current spraying node where the spraying robot will spray the drone to be sprayed.

[0083] In one embodiment, the node determination module 401 is further configured to:

[0084] The first image is compared with each image in the drone image library for similarity, and the identification information of the image with the highest similarity score exceeding a preset threshold is used as the drone identification for the drone to be painted; or

[0085] Identify the drone feature information contained in the first image, and analyze and process the drone feature information to obtain the drone identification mark of the drone to be painted; or

[0086] The encoded information contained in the first image is identified and processed to obtain the drone logo to be painted.

[0087] In one embodiment, the node determination module 401 is further configured to:

[0088] Determine whether there are records of completed processes where a painting robot has performed painting on a drone to be painted.

[0089] When it is determined that there are completed processes, the current spraying node in the spraying process is obtained by the spraying robot to spray the drone to be sprayed, based on the completed processes and the spraying sequence.

[0090] When it is determined that no completed process is recorded, the first spraying node in the spraying process is taken as the current spraying node for the spraying robot to perform spraying treatment on the robot to be sprayed.

[0091] In one embodiment, the pose adjustment device 400 of the painting robot further includes a moving module for:

[0092] Obtain the working position of the robot to be sprayed at each spraying node in the spraying process;

[0093] When it is determined that the drone to be painted is at the first painting node in the painting process, control the painting robot to move to the working position corresponding to the first painting node;

[0094] When it is determined that the painting node of the drone to be painted moves from the first painting node to the second painting node, the painting robot is controlled to move from the first working position to the second working position. Here, the first painting node is not the first painting node, the first working position is the working position corresponding to the painting robot under the first painting node, and the second working position is the working position corresponding to the painting robot under the second painting node.

[0095] In one embodiment, the pose adjustment module 405 is further configured to:

[0096] Obtain the coordinate transformation rules between point cloud coordinates and world coordinates;

[0097] Based on the coordinate transformation rules, the pose deviation of the workpiece point cloud and coordinate information is calculated to obtain the position deviation and attitude deviation of the painting robot.

[0098] The position and posture deviations are used to obtain the pose adjustment information for adjusting the painting robot, and the pose of the painting robot is adjusted according to the pose adjustment information.

[0099] In one embodiment, the pose adjustment device 400 of the painting robot further includes a recording module for:

[0100] The system records the spraying status of the painting robot during the painting process on the drone to be painted. The spraying status includes the completion status of each spraying node in the spraying process, with completion status categorized as "completed" and "incomplete".

[0101] Additionally, please see Figure 5 , Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. For example... Figure 5 As shown, the electronic device 500 includes a processor 501 and a memory 502. The processor 501 and the memory 502 are electrically connected.

[0102] The processor 501 is the control center of the electronic device 500. It connects various parts of the electronic device through various interfaces and circuits. It runs or loads the application program stored in the internal flash memory of the memory 502, which can be an embedded program, and calls the data stored in the memory 502 to complete the pose adjustment of the painting robot using the pose adjustment method of the painting robot described above.

[0103] In this embodiment, the processor 501 in the electronic device 500 will load the instructions corresponding to the process of one or more applications into the memory 502 according to the steps in the above-described method for adjusting the pose of the painting robot, and the processor 501 will run the applications stored in the memory 502 to realize the pose adjustment and analysis of the painting robot.

[0104] The electronic device 500 can implement the steps of any embodiment of the pose adjustment method for the painting robot provided in this application. Therefore, it can achieve the beneficial effects that any pose adjustment method for the painting robot provided in this application can achieve. For details, please refer to the previous embodiments, which will not be repeated here.

[0105] Please see Figure 6 , Figure 6 This is another structural schematic diagram of the electronic device provided in the embodiments of this application, such as... Figure 6 As shown, Figure 6A specific structural block diagram of the electronic device provided in the embodiments of this application is shown. The electronic device can be used to implement the pose adjustment method of the painting robot provided in the above embodiments.

[0106] RF circuit 610 is used to receive and transmit electromagnetic waves, converting electromagnetic waves into electrical signals and vice versa, thereby enabling communication with communication networks or other devices. RF circuit 610 may include various existing circuit elements used to perform these functions, such as antennas, radio frequency transceivers, digital signal processors, encryption / decryption chips, subscriber identity modules (SIM cards), memory, etc. RF circuit 610 can communicate with various networks such as the Internet, corporate intranets, and wireless networks, or communicate with other devices via wireless networks. The aforementioned wireless networks may include cellular telephone networks, wireless local area networks (WLANs), or metropolitan area networks (MANs). The aforementioned wireless networks may use various communication standards, protocols, and technologies, including but not limited to Global System for Mobile Communication (GSM), Enhanced Data GSM Environment (EDGE), Wideband Code Division Multiple Access (WCDMA), Code Division Multiple Access (CDMA), Time Division Multiple Access (TDMA), Wireless Fidelity (Wi-Fi) (such as IEEE 802.11a, IEEE 802.11b, IEEE 802.11g, and / or IEEE 802.11n), Voice over Internet Protocol (VoIP), Worldwide Interoperability for Microwave Access (Wi-Max), other protocols for email, instant messaging, and short messages, and any other suitable communication protocols, including those that have not yet been developed.

[0107] The memory 620 can be used to store software programs and modules, such as the program instructions / modules corresponding to the pose adjustment method of the painting robot in the above embodiment. The processor 680 executes various functional applications to realize the pose adjustment method of the painting robot by running the dual-mode module and the program for controlling the dual-mode module stored in the internal flash of the memory 620. The program for controlling the dual-mode module can be an embedded program and stored in the internal flash of the memory 620.

[0108] Memory 620 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, memory 620 may further include memory remotely located relative to processor 680, which can be connected to electronic device 600 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.

[0109] The input unit 630 can be used to receive uploaded digital or character information, and to generate keyboard, mouse, joystick, optical, or trackball signal inputs related to user settings and function control. Specifically, the input unit 630 may include a touch-sensitive surface 631 and other input devices 632. The touch-sensitive surface 631, also known as a touch display or touchpad, can collect touch operations performed by the user on or near it (such as operations performed by the user using a finger, stylus, or any suitable object or accessory on or near the touch-sensitive surface 631), and drive the corresponding connection device according to a pre-set program. Optionally, the touch-sensitive surface 631 may include two parts: a touch detection device and a touch controller. The touch detection device detects the user's touch position and the signal generated by the touch operation, and transmits the signal to the touch controller; the touch controller receives touch information from the touch detection device, converts it into touch point coordinates, sends it to the processor 680, and can receive and execute commands sent by the processor 680. In addition, the touch-sensitive surface 631 can be implemented using various types such as resistive, capacitive, infrared, and surface acoustic wave. In addition to the touch-sensitive surface 631, the input unit 630 may also include other input devices 632. Specifically, other input devices 632 may include, but are not limited to, one or more of the following: physical keyboard, function keys (such as volume control buttons, power buttons, etc.), trackball, mouse, joystick, etc.

[0110] Display unit 640 can be used to display information input by the user or information provided to the user, as well as various graphical user interfaces of electronic device 600. These graphical user interfaces can be composed of graphics, text, icons, video, and any combination thereof. Display unit 640 may include display panel 641, optionally configured as LCD (Liquid Crystal Display), OLED (Organic Light-Emitting Diode), or other similar forms. Further, touch-sensitive surface 631 may cover display panel 641. When touch-sensitive surface 631 detects a touch operation on or near it, it transmits the information to processor 680 to determine the type of touch event. Subsequently, processor 680 provides corresponding visual output on display panel 641 according to the type of touch event. Although in the figures, touch-sensitive surface 631 and display panel 641 are implemented as two separate components to achieve input and output functions, in some embodiments, touch-sensitive surface 631 and display panel 641 can be integrated to achieve input and output functions.

[0111] The electronic device 600 may also include at least one sensor 650, such as a light sensor, a motion sensor, and other sensors. Specifically, the light sensor may include an ambient light sensor and a proximity sensor, wherein the ambient light sensor can adjust the brightness of the display panel 641 according to the ambient light level, and the proximity sensor can generate an interruption when the flip is closed or shut down. As a type of motion sensor, a gravity acceleration sensor can detect the magnitude of acceleration in various directions (generally three axes), and can detect the magnitude and direction of gravity when stationary. It can be used for applications that recognize the phone's posture (such as landscape / portrait switching, related games, magnetometer posture calibration), vibration recognition related functions (such as pedometer, tapping), etc. Other sensors that may be configured in the electronic device 600, such as gyroscopes, barometers, hygrometers, thermometers, and infrared sensors, will not be described in detail here.

[0112] Audio circuitry 660, speaker 661, and microphone 662 provide an audio interface between the user and electronic device 600. Audio circuitry 660 converts received audio data into electrical signals, which are then transmitted to speaker 661, where they are converted into sound signals for output. Conversely, microphone 662 converts collected sound signals into electrical signals, which are received by audio circuitry 660, converted back into audio data, and then processed by processor 680 before being transmitted via RF circuitry 610 to, for example, another terminal, or output to memory 620 for further processing. Audio circuitry 660 may also include an earphone jack to facilitate communication between external headphones and electronic device 600.

[0113] Electronic device 600, through transmission module 670 (e.g., Wi-Fi module), can help users receive requests, send information, etc., providing users with wireless broadband internet access. Although transmission module 670 is shown in the figure, it is understood that it is not an essential component of electronic device 600 and can be omitted as needed without changing the essence of the invention.

[0114] The processor 680 is the control center of the electronic device 600. It connects to various parts of the phone via various interfaces and lines, and performs various functions and processes data of the electronic device 600 by running or executing software programs and / or modules stored in the memory 620, and by calling data stored in the memory 620, thereby providing overall monitoring of the electronic device. Optionally, the processor 680 may include one or more processing cores; in some embodiments, the processor 680 may integrate an application processor and a modem processor, wherein the application processor mainly handles the operating system, user interface, and applications, and the modem processor mainly handles wireless communication. It is understood that the modem processor may also not be integrated into the processor 680.

[0115] The electronic device 600 also includes a power supply 690 (such as a battery) that supplies power to the various components. In some embodiments, the power supply may be logically connected to the processor 680 through a power management system, thereby enabling functions such as managing charging, discharging, and power consumption through the power management system. The power supply 690 may also include one or more DC or AC power supplies, recharging systems, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components.

[0116] Specifically, in this embodiment, the display unit of the electronic device is a touch screen display, and the mobile terminal also includes a memory and one or more programs, wherein one or more programs are stored in the memory and configured to be executed by one or more processors to implement any step of the pose adjustment method of the painting robot provided in the above embodiment.

[0117] In practice, the above modules can be implemented as independent entities or combined in any way to be implemented as the same or several entities. For the specific implementation of the above modules, please refer to the previous method implementation examples, which will not be repeated here.

[0118] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by instructions, or by instructions controlling related hardware. These instructions can be stored in a computer-readable storage medium and loaded and executed by a processor. Therefore, this application provides a storage medium storing multiple instructions that, when executed by a processor, can implement any step in the pose adjustment method for the painting robot provided in the above embodiments.

[0119] The storage medium may include: read-only memory (ROM), random access memory (RAM), disk, or optical disk, etc. Specifically, the storage medium may be internal flash memory, that is, the embedded program that controls the pose adjustment method of the painting robot described above can be stored in the internal flash memory.

[0120] Since the instructions stored in the storage medium can execute the steps in any embodiment of the pose adjustment method for the painting robot provided in this application, the beneficial effects that any pose adjustment method for the painting robot provided in this application can achieve can be realized. For details, please refer to the previous embodiments, which will not be repeated here.

[0121] The foregoing has provided a detailed description of the pose adjustment method, apparatus, electronic device, and storage medium for a painting robot according to embodiments of this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the embodiments above are merely for the purpose of helping to understand the method and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application. Moreover, those skilled in the art can make several improvements and modifications without departing from the principles of this application, and these improvements and modifications are also considered within the scope of protection of this application.

Claims

1. A method for adjusting the pose of a painting robot, characterized in that, include: The painting process of the painting robot to paint the drone to be painted is obtained, and the current painting node of the drone to be painted is determined. The painting process includes a number of painting nodes, and there is a painting sequence between the painting nodes. Control the painting robot to move to the work position corresponding to the current painting node; The scene image captured by the camera set in the spraying scene is obtained, and the drone workpiece contained in the scene image is identified to obtain the workpiece point cloud of the identified reference workpiece. The image information containing the reference workpiece is acquired by the image acquisition device set on the painting robot, and the world coordinates are constructed based on the image information to obtain the coordinate information of the reference workpiece. Based on the workpiece point cloud and the coordinate information, the pose adjustment information of the painting robot is determined, and the pose of the painting robot is adjusted according to the pose adjustment information.

2. The method as described in claim 1, characterized in that, The step of obtaining the spraying process of the spraying robot spraying the drone to be sprayed, and determining the current spraying node of the drone to be sprayed, includes: The first image of the drone to be painted is acquired by the image acquisition device, and the first image is identified and analyzed to obtain the drone identifier of the drone to be painted. The painting process for the drone to be painted is obtained by querying and matching the drone identifier in the painting process list. Based on the spraying process, the current spraying node for the spraying robot to spray the drone to be sprayed is determined.

3. The method as described in claim 2, characterized in that, The step of recognizing and analyzing the first image to obtain the drone identification of the drone to be painted includes: The first image is compared with each image in the drone image library for similarity calculation, and the identification information of the image with the highest similarity score exceeding a preset threshold is used as the drone identifier of the drone to be painted; or Identify the drone feature information contained in the first image, and analyze and process the drone feature information to obtain the drone identification of the drone to be painted; or The encoded information contained in the first image is identified and processed to obtain the drone identification of the drone to be painted.

4. The method as described in claim 2, characterized in that, The step of determining the current spraying node for the spraying robot to spray the drone to be sprayed, based on the spraying process, includes: Determine whether the completed process of the painting robot painting the drone to be painted is recorded; When it is determined that there is a completed process, the current spraying node in the spraying process is obtained by the spraying robot spraying the drone to be sprayed, according to the completed process and the spraying sequence. When it is determined that no completed process is recorded, the first spraying node in the spraying process is taken as the current spraying node for the spraying robot to perform spraying treatment on the robot to be sprayed.

5. The method as described in claim 1, characterized in that, The method further includes: Obtain the working position of the robot to be sprayed at each spraying node in the spraying process; When it is determined that the drone to be painted is at the first painting node in the painting process, the painting robot is controlled to move to the working position corresponding to the first painting node; When it is determined that the painting node of the drone to be painted moves from the first painting node to the second painting node, the painting robot is controlled to move from the first working position to the second working position. Here, the first painting node is not the first painting node, the first working position is the working position corresponding to the painting robot under the first painting node, and the second working position is the working position corresponding to the painting robot under the second painting node.

6. The method as described in claim 1, characterized in that, The step of determining the pose adjustment information of the painting robot based on the workpiece point cloud and the coordinate information, and adjusting the pose of the painting robot based on the pose adjustment information, includes: Obtain the coordinate transformation rules between point cloud coordinates and world coordinates; The positional deviation of the painting robot is calculated based on the coordinate transformation rule of the workpiece point cloud and the coordinate information. Based on the position deviation and the posture deviation, position adjustment information is obtained for adjusting the painting robot, and the position adjustment of the painting robot is performed based on the position adjustment information.

7. The method as described in claim 1, characterized in that, The method further includes: The spraying status of the spraying robot spraying the drone to be sprayed is recorded, wherein the spraying status includes the completion status of each spraying node in the spraying process, and the completion status includes completed and incomplete.

8. A pose adjustment device for a painting robot, characterized in that, include: The node determination module is used to obtain the spraying process of the spraying robot to spray the drone to be sprayed, and to determine the current spraying node of the drone to be sprayed. The spraying process includes a number of spraying nodes, and there is a spraying sequence between the spraying nodes. The movement control module is used to control the painting robot to move to the work position corresponding to the current painting node; The first processing module is used to acquire scene images captured by cameras set in the spraying scene, and to identify the drone workpieces contained in the scene images to obtain the workpiece point cloud of the identified reference workpiece. The second processing module is used to acquire image information containing the reference workpiece acquired by the image acquisition device set on the painting robot, and to construct world coordinates based on the image information to obtain the coordinate information of the reference workpiece. The pose adjustment module is used to determine the pose adjustment information of the painting robot based on the workpiece point cloud and the coordinate information, and to adjust the pose of the painting robot according to the pose adjustment information.

9. An electronic device, characterized in that, The electronic device includes a processor, a memory, and a computer program or embedded program stored in the internal storage of the memory and executable on the processor, wherein the processor executes the computer program or embedded program to implement the steps of the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program or embedded program that, when executed by a processor, implements the steps of the method as described in any one of claims 1 to 7.

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