Control method, system and equipment for carrying satellite assembly and medium

Abstract

The invention discloses a control method, system, equipment and medium for carrying satellite components, and the method comprises the steps: obtaining a carrying instruction which comprises the number of the components, size data, an initial position and a target position, and generating minimum bounding rectangular box data according to the size data; inputting the minimum surrounding rectangular box data, the initial position and the target position corresponding to the to-be-carried assembly into a pre-trained path generation algorithm to obtain an initial carrying path corresponding to the to-be-carried assembly; the robot moves the to-be-carried assembly from the initial position to the target position according to the initial carrying path, point cloud data of the surrounding environment is obtained in real time in the carrying process, the safe distance on the carrying path is kept through calculation, if the safe distance is insufficient, the path is recalculated and planned, and then the to-be-carried assembly is moved to the target position from the initial position. The method has the advantages of being high in implementation degree, good in adaptability, reliable in safety and easy to integrate in a batch production line.

Description

Technical Field

[0001] This invention relates to the field of intelligent manufacturing technology, and more specifically, to a control method, system, device, and medium for transporting satellite components. Background Technology

[0002] In recent years, with the rapid development of satellite launch technology, the demand for large-scale mass production of low-Earth orbit satellites has been increasing. In traditional satellite manufacturing processes, component assembly and handling mainly rely on manual labor or simple automated equipment, such as rigid robotic arms and hoisting equipment. However, due to the characteristics of satellite components, such as large size, heavy weight, and diverse shapes, traditional handling methods have many problems. For example, manual handling places a high demand on operators, easily leading to fatigue and accident risks; if hoisting is used, it relies on manual path guidance, resulting in low handling efficiency and a lack of flexible adjustment capabilities. Furthermore, components are prone to collisions with surrounding equipment, the ground, and other components, posing higher safety requirements.

[0003] Therefore, there is an urgent need to study a robotic continuous handling method that can automatically plan paths and prevent collisions in response to changes in the size of satellite components, and can automatically return to the initial position after handling is completed, so as to improve production efficiency, safety and flexibility. Summary of the Invention

[0004] This specification provides a control method, system, device, and medium for transporting satellite components to overcome at least one technical problem existing in the related art.

[0005] According to a first aspect of the embodiments of this specification, a control method for transporting satellite components is provided, comprising: Obtain a transport instruction, which includes a preset number of components to be transported, size data, starting position, and target position. For a component to be transported, generate the outer contour shape of the component based on the component's size data to construct the corresponding minimum bounding rectangle data. The size data is obtained by scanning with a lidar and structured light depth camera in the production workshop where the component is located. The minimum bounding rectangle data, starting position, and target position of the component to be transported are input into a pre-trained path generation algorithm to obtain the initial transport path corresponding to the component to be transported. The path generation algorithm is trained by combining the kinematic model data of the robot arm that executes the transport command and the environmental map data of the production workshop that is pre-acquired. The initial transport path of the component from the starting position to the target position is calculated based on the input minimum bounding rectangle data. The robot moves the component to be transported from the starting position to the target position according to the initial transport path. The point cloud data of the surrounding environment of the initial transport path is acquired in real time by the lidar and structured light depth camera in the production workshop. The distance between the component to be transported and the point cloud data of the surrounding environment on the transport path is calculated and compared with a preset threshold. If the distance is not greater than the threshold, the transport path is regenerated and optimized according to the current position of the component to be transported by the path generation algorithm until the component to be transported is moved to the target position. The robot moves the next component to be moved until the preset number of components to be moved in the moving instruction are moved to the corresponding target position.

[0006] Optionally, the step of inputting the minimum bounding rectangle data, starting position, and target position of the component to be transported into a pre-trained path generation algorithm to obtain the initial transport path corresponding to the component to be transported includes: The planar area of ​​the workshop where the component to be transported is located is divided into a grid, and each grid node is denoted as N{i,j}. The actuator center of the robot that executes the transport command is set as the motion point. The grid node occupied by the minimum bounding rectangle corresponding to the component to be transported and the location of the obstacle in the environmental map of the production workshop are set as the obstacle bounding box. Exclude grid nodes that are in direct contact with the obstacle bounding box. Calculate the heuristic function value from the remaining grid nodes to the moving point using a preset heuristic function. Filter the remaining grid nodes based on the heuristic function value, and select grid nodes whose heuristic function value falls within a preset range as path nodes. The path nodes are connected in the direction from the starting position to the target position to obtain the path curve. The generated path curve is then smoothed by spline curve processing to obtain the initial transport path corresponding to the component to be transported.

[0007] Optionally, the heuristic function is: f(N) = g(N) + h(N), Where g(N) is the cost of the path already taken, and h(N) = d(N, target) / v + α*V box +β*d safe , Where d(N, target) represents the Euclidean distance from the current node to the target node, v represents the robot's maximum speed, and V box V represents the volume function of the minimum bounding rectangle. box =L*W*H, where L is the length of the bounding box (along the X-axis), W is the width of the bounding box (along the Y-axis), and H is the height of the bounding box (along the Z-axis); d safeThis represents the safety gap distance. α and β are adjustment factors, and the values ​​of the adjustment factors are set according to the preset range of the selected grid nodes.

[0008] Optionally, the maximum speed v is 0.5 to 1.5 meters per second, and the grid node accuracy range is 10 to 50 millimeters.

[0009] Optionally, the step of acquiring point cloud data of the surrounding environment of the initial transport path in real time using LiDAR and structured light depth cameras in the production workshop, calculating the distance between the component to be transported and the point cloud data of the surrounding environment on the transport path, comparing the distance with a preset threshold, and if the distance is not greater than the threshold, then regenerating an optimized transport path based on the current position of the component to be transported using the path generation algorithm includes: The OBBTree algorithm is used to calculate whether the minimum bounding rectangle of the real-time position of the component to be transported intersects with the bounding rectangle of the obstacle in the environmental map of the production workshop. If the bounding rectangles intersect, the transport path is replanned in the local area where the intersection exists according to the path generation algorithm. The point cloud data of the surrounding environment along the transport path, acquired in real time by a structured light depth camera, is used to calculate the shortest distance D on the surface of the minimum bounding rectangle. sur When the shortest distance D sur Not greater than the preset safe distance threshold d thresh At that time, the robot is controlled to reduce its speed and replan the transport path according to the path generation algorithm.

[0010] Optionally, after the step of moving the component to be transported to the target location, the method further includes: The robot calculates its return path using a path generation algorithm, and then enters a standby state after arriving at the starting position of the next component to be transported.

[0011] According to a second aspect of the embodiments of this specification, a control system for transporting satellite components is provided, including a component data acquisition module, a path generation module, a path optimization module, and an execution return module, wherein... The component data acquisition module is configured to acquire a handling instruction. The handling instruction includes a preset number of components to be handled, size data, starting position, and target position. For a component to be handled, the outer contour shape of the component is generated based on the component's size data to construct the corresponding minimum bounding rectangle data. The size data is obtained by scanning with a lidar and structured light depth camera in the production workshop where the component is located. The path generation module is configured to input the minimum bounding rectangle data, starting position, and target position of the component to be transported into a pre-trained path generation algorithm to obtain the initial transport path corresponding to the component to be transported. The path generation algorithm is trained by combining the kinematic model data of the robot arm that executes the transport command and the environmental map data of the production workshop that is pre-acquired. Based on the input minimum bounding rectangle data, the initial transport path of the component from the starting position to the target position is calculated. The path optimization module is configured to move the component to be transported from the starting position to the target position by the robot according to the initial transport path, and to acquire point cloud data of the surrounding environment of the initial transport path in real time by the lidar and structured light depth camera in the production workshop, calculate the distance between the component to be transported on the transport path and the point cloud data of the surrounding environment, compare the distance with a preset threshold, and if the distance is not greater than the threshold, then regenerate the optimized transport path according to the current position of the component to be transported by the path generation algorithm until the component to be transported is moved to the target position; The execution return module is configured to use a robot to move the next component to be moved until the preset number of components to be moved in the moving instruction are moved to the corresponding target position.

[0012] Optionally, the system further includes a return path generation module, wherein The return path generation module is configured to calculate the robot's return path using a path generation algorithm, and then enter a standby state after arriving at the starting position of the next component to be transported according to the return path.

[0013] According to a third aspect of the embodiments of this specification, a computing device is provided, including a storage device and a processor, the storage device being used to store a computer program, and the processor running the computer program to cause the computing device to perform the steps of the control method for transporting satellite components.

[0014] According to a fourth aspect of the embodiments of this specification, a storage medium is provided that stores a computer program used in the computing device, which, when executed by a processor, implements the steps of the control method for transporting satellite components.

[0015] The beneficial effects of the embodiments in this specification are as follows: This specification provides a control method, system, device, and medium for transporting satellite components. The method uses the size data of the component to be transported and the workshop environment data to calculate a transport path that can be implemented by a robot through an algorithm. During the transport process, bounding box collision prediction is performed by combining real-time data of the surrounding environment and obstacles on the path. It has the adaptability to continuously optimize the path according to the real-time situation, and can achieve the purpose of automatically, efficiently, and safely moving the component to be transported to the target location. It not only improves the transport efficiency, but also avoids the high-intensity labor and potential safety risks of manual transport of satellite components.

[0016] The innovative aspects of the embodiments in this specification include: 1. In this specification, by using component dimensions and online environmental information, and through the calculation and filtering of grid nodes, the transportation path is planned, and the robot is controlled to autonomously perform transportation tasks without human intervention. The high degree of automation is one of the innovative points of the embodiments in this specification.

[0017] 2. In this specification, based on component bounding boxes and multi-sensor point cloud data, real-time collision prediction and obstacle avoidance are achieved, which greatly reduces the risk of equipment collision. While ensuring safety, efficiency is improved and safety and reliability are enhanced. It can not only generate transport paths but also optimize the paths as necessary based on real-time data during the transport process. This is one of the innovative points of the embodiments in this specification.

[0018] 3. In this specification, the bounding box and critical safety factor can be adjusted online for satellite components of different sizes and shapes. It has good versatility and scalability, and strong adaptability, which is one of the innovative points of the embodiments in this specification.

[0019] Attached image caption To more clearly illustrate the technical solutions in the embodiments or related technologies of this specification, the drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a flowchart illustrating a control method for transporting satellite components according to an embodiment of this specification. Figure 2 A schematic diagram of a control system for transporting satellite components, provided as an embodiment of this specification; Figure 3 This is a schematic diagram of the structure of a computing device provided in one embodiment of this specification; Figure 4 This is a schematic diagram of the structure of a storage medium provided in one embodiment of this specification. Detailed Implementation

[0021] The technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] It should be noted that the terms "comprising" and "having," and any variations thereof, in the embodiments and drawings of this specification are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the steps or units listed, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or devices.

[0023] This specification discloses a control method, system, device, and medium for transporting satellite components, which will be described in detail below.

[0024] Figure 1 This is a flowchart illustrating a control method for transporting satellite components according to an embodiment of this specification, as shown below. Figure 1 As shown, a control method for transporting satellite components includes: S110. Obtain a transport instruction. The transport instruction includes a preset number of components to be transported, size data, starting position, and target position. For a component to be transported, generate the outer contour shape of the component based on the component's size data to construct the corresponding minimum bounding rectangle data. The size data is obtained by scanning with a lidar and structured light depth camera in the production workshop where the component is located.

[0025] The lidar and structured light depth camera in the production workshop acquire three-dimensional dimensional data of satellite components based on preset or online scanning, thereby constructing component bounding boxes and marking them in the production line environment model.

[0026] S120. Input the minimum bounding rectangle data, starting position and target position of the component to be transported into a pre-trained path generation algorithm to obtain the initial transport path corresponding to the component to be transported. The path generation algorithm is trained by combining the kinematic model data of the robot arm that executes the transport command and the environmental map data of the production workshop that is pre-acquired. The initial transport path of the component from the starting position to the target position is calculated based on the input minimum bounding rectangle data.

[0027] Based on the kinematic model of the robotic arm and the production line environment map, an algorithm combined with a minimum bounding box collision detection module is used to dynamically generate the optimal transport path from the starting position to the target position.

[0028] In a specific embodiment, step S120, which involves inputting the minimum bounding rectangle data, starting position, and target position of the component to be transported into a pre-trained path generation algorithm to obtain the initial transport path for the component to be transported, includes: S122. Divide the planar area of ​​the workshop where the component to be transported is located into a grid, and denot each grid node as N{i, j}. Set the actuator center of the robot that executes the transport command as the motion point, and set the grid node occupied by the minimum bounding rectangle corresponding to the component to be transported and the location of the obstacle in the environmental map of the production workshop as the obstacle bounding box.

[0029] S124. Exclude grid nodes that are in direct contact with the bounding box of obstacles. Calculate the heuristic function value from the remaining grid nodes to the moving point using a preset heuristic function. Filter the remaining grid nodes based on the heuristic function value and select grid nodes whose heuristic function value falls within a preset range as path nodes.

[0030] The heuristic function is: f(N) = g(N) + h(N), Where g(N) is the cost of the path already taken, and h(N) = d(N, target) / v + α*V box +β*d safe , Where d(N, target) represents the Euclidean distance from the current node to the target node, v represents the robot's maximum speed, and V box V represents the volume function of the minimum bounding rectangle. box =L*W*H, where L is the length of the bounding box (along the X-axis), W is the width of the bounding box (along the Y-axis), and H is the height of the bounding box (along the Z-axis); d safe This represents the safety gap distance. α and β are adjustment factors, and the values ​​of the adjustment factors are set according to the preset range of the selected grid nodes.

[0031] By introducing a component bounding box volume adjustment factor and a safety gap coefficient into the heuristic function, a balance between path length and safety is achieved. safe The preferred range for the safety clearance distance is 20~50mm, and the adjustment factors α and β range from 0.1 to 1.0. After excluding nodes that are in direct contact with the obstacle bounding box, the mesh node selection prioritizes the node with the lowest heuristic value, selecting path nodes from the starting position to the target position.

[0032] The maximum speed v is 0.5~1.5 meters per second, and the grid node accuracy range is 10~50 millimeters.

[0033] S126. Connect the path nodes in the direction from the starting position to the target position to obtain the path curve. Perform spline curve smoothing on the generated path curve to obtain the initial transport path corresponding to the component to be transported.

[0034] Furthermore, during the handling process, LiDAR or depth cameras are used to collect point cloud data of the surrounding environment in real time. Based on the distance calculation between the bounding box and the point cloud, multi-level collision prediction is performed. If a potential collision risk is detected, path replanning or local obstacle avoidance strategies are triggered.

[0035] S130. The robot moves the component to be transported from the starting position to the target position according to the initial transport path. The point cloud data of the surrounding environment of the initial transport path is acquired in real time by the lidar and structured light depth camera in the production workshop. The distance between the component to be transported and the point cloud data of the surrounding environment on the transport path is calculated. The distance is compared with a preset threshold. If the distance is not greater than the threshold, the optimized transport path is regenerated according to the current position of the component to be transported by the path generation algorithm until the component to be transported is moved to the target position.

[0036] In a specific embodiment, S130, which involves acquiring point cloud data of the surrounding environment of the initial transport path in real time using the lidar and structured light depth camera in the production workshop, calculating the distance between the component to be transported on the transport path and the point cloud data of the surrounding environment, comparing the distance with a preset threshold, and if the distance is not greater than the threshold, then regenerating an optimized transport path based on the current position of the component to be transported using the path generation algorithm, includes: The OBBTree algorithm is used to calculate whether the minimum bounding rectangle of the real-time position of the component to be transported intersects with the bounding rectangle of the obstacle in the environmental map of the production workshop. If an intersection is detected, the transport path is replanned in the local area where the intersection exists according to the path generation algorithm.

[0037] The point cloud data of the surrounding environment along the transport path, acquired in real time by a structured light depth camera, is used to calculate the shortest distance D on the surface of the minimum bounding rectangle. sur When the shortest distance D sur Not greater than the preset safe distance threshold d thresh At that time, the robot is controlled to reduce its speed and replan the transport path according to the path generation algorithm.

[0038] The OBBTree algorithm described above calculates whether bounding boxes intersect, which is a coarse-grained bounding box collision detection method. On the other hand, calculating distance based on point cloud data is a fine-grained point cloud surface contact analysis method. The planned path is optimized through these two methods.

[0039] In one specific embodiment, after the step of moving the component to be transported to the target location, the method further includes: The robot calculates its return path using a path generation algorithm, and then enters a standby state after arriving at the starting position of the next component to be transported.

[0040] After the robot smoothly places the satellite component into the target position, it calls the return control module to return the robotic arm to the preset initial working position in the shortest time path, ready for the next handling task.

[0041] S140. The robot moves the next component to be moved until the preset number of components to be moved in the moving instruction are moved to the corresponding target position.

[0042] The aforementioned path planning, collision detection, and return control modules are executed cyclically within the control system until all components have been transported. For satellite components of different sizes and shapes, the bounding box, critical safety factor, and adjustment factor can be adjusted online, demonstrating good versatility and scalability across different components and production environments.

[0043] In this embodiment, by acquiring satellite component size data, constructing a bounding box, generating a path through an algorithm, and optimizing the path based on real-time data, the satellite component is safely and efficiently transported from the starting position to the target position. After the task is completed, it automatically returns to the initial working position. This method improves the automation level of the production line, reduces the risk of collisions, and has significant technical effects and application prospects.

[0044] Figure 2 This is a schematic diagram of a control system for transporting satellite components according to an embodiment of this specification, as shown below. Figure 2 As shown, a control system 200 for transporting satellite components includes a component data acquisition module 210, a path generation module 220, a path optimization module 230, and an execution return module 240, wherein... The component data acquisition module 210 is configured to acquire a handling instruction. The handling instruction includes a preset number of components to be handled, size data, starting position, and target position. For a component to be handled, the outer contour shape of the component is generated based on the component's size data to construct the corresponding minimum bounding rectangle data. The size data is obtained by scanning with a lidar and structured light depth camera in the production workshop where the component is located.

[0045] The path generation module 220 is configured to input the minimum bounding rectangle data, starting position, and target position of the component to be transported into a pre-trained path generation algorithm to obtain the initial transport path corresponding to the component to be transported. The path generation algorithm is trained by combining the kinematic model data of the robot arm that executes the transport command and the environmental map data of the production workshop that is pre-acquired. Based on the input minimum bounding rectangle data, the initial transport path of the component from the starting position to the target position is calculated.

[0046] The path optimization module 230 is configured to move the component to be transported from the starting position to the target position by the robot according to the initial transport path, and to acquire point cloud data of the surrounding environment of the initial transport path in real time by the lidar and structured light depth camera in the production workshop, calculate the distance between the component to be transported on the transport path and the point cloud data of the surrounding environment, compare the distance with a preset threshold, and if the distance is not greater than the threshold, then regenerate the optimized transport path according to the current position of the component to be transported by the path generation algorithm until the component to be transported is moved to the target position.

[0047] The execution return module 240 is configured to use a robot to move the next component to be moved until the preset number of components to be moved in the moving instruction are moved to the corresponding target position.

[0048] In one specific embodiment, the system further includes a return path generation module, wherein The return path generation module is configured to calculate the robot's return path using a path generation algorithm, and then enter a standby state after arriving at the starting position of the next component to be transported according to the return path.

[0049] Figure 3 This is a schematic diagram of the structure of a computing device provided in one embodiment of this specification, such as... Figure 3 As shown, a computing device 300 includes a storage device 310 and a processor 320. The storage device 310 stores a computer program, and the processor 320 runs the computer program to enable the computing device 300 to perform the steps of the control method for transporting satellite components.

[0050] Figure 4 This is a schematic diagram of the structure of a storage medium provided in one embodiment of this specification, such as... Figure 4 As shown, a storage medium 400 stores a computer program 410 used in the computing device, which, when executed by a processor, implements the steps of the control method for transporting satellite components.

[0051] In summary, the embodiments of this specification provide a control method, system, device, and medium for transporting satellite components. This method can automatically plan paths and perform effective collision avoidance control in response to changes in the size of the satellite components. It can also execute a continuous robotic transport method that automatically returns to the initial position after transport is completed, thereby improving production efficiency, safety, and flexibility.

[0052] Those skilled in the art will understand that the accompanying drawings are merely schematic diagrams of one embodiment, and the modules or processes shown in the drawings are not necessarily essential for implementing the present invention.

[0053] Those skilled in the art will understand that the modules in the apparatus of the embodiments can be distributed in the apparatus of the embodiments as described in the embodiments, or they can be located in one or more devices different from this embodiment with corresponding changes. The modules of the above embodiments can be combined into one module, or they can be further divided into multiple sub-modules.

[0054] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A control method for transporting satellite components, characterized in that, include: Obtain a transport instruction, which includes a preset number of components to be transported, size data, starting position, and target position. For a component to be transported, generate the outer contour shape of the component based on the component's size data to construct the corresponding minimum bounding rectangle data. The size data is obtained by scanning with a lidar and structured light depth camera in the production workshop where the component is located. The minimum bounding rectangle data, starting position, and target position of the component to be transported are input into a pre-trained path generation algorithm to obtain the initial transport path corresponding to the component to be transported. The path generation algorithm is trained by combining the kinematic model data of the robot arm that executes the transport command and the environmental map data of the production workshop that is pre-acquired. The initial transport path of the component from the starting position to the target position is calculated based on the input minimum bounding rectangle data. The robot moves the component to be transported from the starting position to the target position according to the initial transport path. The point cloud data of the surrounding environment of the initial transport path is acquired in real time by the lidar and structured light depth camera in the production workshop. The distance between the component to be transported and the point cloud data of the surrounding environment on the transport path is calculated and compared with a preset threshold. If the distance is not greater than the threshold, the transport path is regenerated and optimized according to the current position of the component to be transported by the path generation algorithm until the component to be transported is moved to the target position. The robot moves the next component to be moved until the preset number of components to be moved in the moving instruction are moved to the corresponding target position.

2. The method according to claim 1, characterized in that, The step of inputting the minimum bounding rectangle data, starting position, and target position of the component to be transported into a pre-trained path generation algorithm to obtain the initial transport path for the component to be transported includes: The planar area of ​​the workshop where the component to be transported is located is divided into a grid, and each grid node is denoted as N{i,j}. The actuator center of the robot that executes the transport command is set as the motion point. The grid node occupied by the minimum bounding rectangle corresponding to the component to be transported and the location of the obstacle in the environmental map of the production workshop are set as the obstacle bounding box. Exclude grid nodes that are in direct contact with the obstacle bounding box. Calculate the heuristic function value from the remaining grid nodes to the moving point using a preset heuristic function. Filter the remaining grid nodes based on the heuristic function value, and select grid nodes whose heuristic function value falls within a preset range as path nodes. The path nodes are connected in the direction from the starting position to the target position to obtain the path curve. The generated path curve is then smoothed by spline curve processing to obtain the initial transport path corresponding to the component to be transported.

3. The method according to claim 2, characterized in that, The heuristic function is: f(N) = g(N) + h(N), Where g(N) is the cost of the path already taken, and h(N) = d(N, target) / v + α*V box +β*d safe , Where d(N, target) represents the Euclidean distance from the current node to the target node, v represents the robot's maximum speed, and V box V represents the volume function of the minimum bounding rectangle. box =L*W*H, where L is the length of the bounding box (along the X-axis), W is the width of the bounding box (along the Y-axis), and H is the height of the bounding box (along the Z-axis); d safe This represents the safety gap distance. α and β are adjustment factors, and the values ​​of the adjustment factors are set according to the preset range of the selected grid nodes.

4. The method according to claim 3, characterized in that, The maximum speed v is 0.5~1.5 meters per second, and the grid node accuracy range is 10~50 millimeters.

5. The method according to claim 2, characterized in that, The step of acquiring point cloud data of the surrounding environment of the initial transport path in real time using LiDAR and structured light depth cameras in the production workshop, calculating the distance between the component to be transported on the transport path and the point cloud data of the surrounding environment, comparing the distance with a preset threshold, and if the distance is not greater than the threshold, then regenerating an optimized transport path based on the current position of the component to be transported using the path generation algorithm includes: The OBBTree algorithm is used to calculate whether the minimum bounding rectangle of the real-time position of the component to be transported intersects with the bounding rectangle of the obstacle in the environmental map of the production workshop. If the bounding rectangles intersect, the transport path is replanned in the local area where the intersection exists according to the path generation algorithm. The point cloud data of the surrounding environment along the transport path, acquired in real time by a structured light depth camera, is used to calculate the shortest distance D on the surface of the minimum bounding rectangle. sur When the shortest distance D sur Not greater than the preset safe distance threshold d thresh At that time, the robot is controlled to reduce its speed and replan the transport path according to the path generation algorithm.

6. The method according to claim 1, characterized in that, Following the step of moving the component to be transported to the target location, the method further includes: The robot calculates its return path using a path generation algorithm, and then enters a standby state after arriving at the starting position of the next component to be transported.

7. A control system for transporting satellite components, characterized in that, It includes a component data acquisition module, a path generation module, a path optimization module, and an execution return module, among which... The component data acquisition module is configured to acquire a handling instruction. The handling instruction includes a preset number of components to be handled, size data, starting position, and target position. For a component to be handled, the outer contour shape of the component is generated based on the component's size data to construct the corresponding minimum bounding rectangle data. The size data is obtained by scanning with a lidar and structured light depth camera in the production workshop where the component is located. The path generation module is configured to input the minimum bounding rectangle data, starting position, and target position of the component to be transported into a pre-trained path generation algorithm to obtain the initial transport path corresponding to the component to be transported. The path generation algorithm is trained by combining the kinematic model data of the robot arm that executes the transport command and the environmental map data of the production workshop that is pre-acquired. Based on the input minimum bounding rectangle data, the initial transport path of the component from the starting position to the target position is calculated. The path optimization module is configured to move the component to be transported from the starting position to the target position by the robot according to the initial transport path, and to acquire point cloud data of the surrounding environment of the initial transport path in real time by the lidar and structured light depth camera in the production workshop, calculate the distance between the component to be transported on the transport path and the point cloud data of the surrounding environment, compare the distance with a preset threshold, and if the distance is not greater than the threshold, then regenerate the optimized transport path according to the current position of the component to be transported by the path generation algorithm until the component to be transported is moved to the target position; The execution return module is configured to use a robot to move the next component to be moved until the preset number of components to be moved in the moving instruction are moved to the corresponding target position.

8. The system according to claim 7, characterized in that, The system also includes a return path generation module, wherein The return path generation module is configured to calculate the robot's return path using a path generation algorithm, and then enter a standby state after arriving at the starting position of the next component to be transported according to the return path.

9. A computing device, characterized in that, The device includes a storage device and a processor, the storage device being used to store a computer program, and the processor running the computer program to cause the computing device to perform the steps of the method according to any one of claims 1-6.

10. A storage medium, characterized in that, It stores a computer program used in the computing device of claim 9, which, when executed by a processor, implements the steps of the method of any one of claims 1-6.

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