Space laser communication method and system with automatic alignment function

Abstract

The application provides a space laser communication method and system with automatic alignment function, acquires image and point cloud data of a peer device, constructs a feature vector for the image according to the gray value, texture feature and contrast feature of each pixel point, acquires a binary image by using the feature vector, performs travel grouping on the binary image by taking a distance threshold as a judgment condition, fuses the centroid, contour, motor code disc angle corresponding to a beacon light of the peer device and distance information between the peer device and the device, performs spiral scanning, adjusts an antenna of a signal light of the device according to a position and an angle corresponding to the beacon light with a received power reaching a preset value, and further realizes automatic calibration. The application realizes automatic calibration based on the principle that the actual centroid, actual contour, actual angle of the motor code disc of the device and the beacon light communication link and the signal light communication link are parallel, does not need human intervention, improves the alignment efficiency under the premise of ensuring accuracy.

Description

Technical Field

[0001] This invention relates to the technical field of testing and verification of space laser terminals, and in particular to a space laser communication method and system with automatic alignment function. Background Technology

[0002] Space laser communication technology, with its abundant bandwidth resources, high transmission rate, large communication capacity, and strong anti-interference capability, is gradually becoming an important technical means in aerospace exploration activities. During the development and debugging process, space laser communication terminals need to undergo testing and verification, including achieving precise alignment between terminals in an indoor environment to establish a reliable communication link and ensure efficient and stable information transmission.

[0003] Precise alignment between space laser communication terminals requires micrometer- or even nanometer-level control precision. However, indoor environments are limited in space and are not ideal optical environments, filled with various optical obstacles that can cause laser beam scattering, refraction, or absorption. This makes the already delicate alignment process even more complex. Even highly skilled and experienced technicians must be extremely meticulous and cautious during the debugging process. Every step requires repeated confirmation and adjustment, and technicians need to perform a large number of repetitive manual operations, including but not limited to adjusting the angles and positions of the transmitter and receiver, as well as the parameters of the optical system. This prolonged repetitive operation is not only time-consuming and labor-intensive but also increases the risk of operational errors and the difficulty of debugging.

[0004] Therefore, there is a need to propose a space laser communication method and system with automatic alignment function to achieve rapid and accurate alignment in complex indoor environments, so as to improve efficiency and accuracy. Summary of the Invention

[0005] This invention provides a space laser communication method and system with automatic alignment function, which solves the problem that the efficiency and accuracy of automatic alignment between space laser communication terminals in indoor environments need to be improved in the existing technology.

[0006] The present invention solves the technical problem through the following technical solution:

[0007] A space laser communication method with automatic alignment function includes the following steps:

[0008] (1) Acquire the current image and point cloud data of the peer device;

[0009] (2) Calculate the gray value, texture feature and contrast feature of each pixel in the image; construct a feature vector by weighted summing of the gray value, texture feature and contrast feature; train an SVM model based on the feature vector to obtain a segmentation threshold; segment the image using the segmentation threshold to obtain a binary image;

[0010] For each ungrouped stroke in the binary image, a preset distance threshold is used as the adjacency judgment condition to group the strokes into groups; the centroid of each stroke group is calculated to obtain the contour of each stroke group; the centroid, contour, and motor encoder angle corresponding to the beacon light of the local device are used as the target position.

[0011] The distance between the local device and the current peer device is calculated using the point cloud data;

[0012] (3) Combine the centroid, contour, motor encoder angle and distance corresponding to the beacon light of the local device for each stroke group to determine the target area; emit beacon light to perform a spiral scan on the target area; once the power of the received beacon light reaches the preset value, record the current position of the received beacon light beam and the current angle of the motor encoder of the local device.

[0013] (4) Adjust the signal light antenna of the local device according to the current position and the current angle to achieve automatic alignment between the signal light transmitting antenna of the local device and the signal light receiving antenna of the current peer device.

[0014] Furthermore, in step (2), the formula for calculating the grayscale value is:

[0015] Y = 0.299*R + 0.587*G + 0.114*B, where Y is the grayscale value, R is the value of the red component, G is the value of the green component, and B is the value of the blue component.

[0016] Furthermore, in step (2), the steps for calculating texture features are as follows:

[0017] 1) Define a 3x3 texture window;

[0018] 2) Take pixels in the non-boundary pixel areas of the image according to the texture window size;

[0019] 3) Using the gray value of the center pixel in the texture window as the gray value threshold, traverse the 8 pixels outside the center pixel in the texture window in a clockwise or counterclockwise direction. If the gray value of the current pixel is greater than the gray value threshold, mark the current pixel as 1, otherwise mark it as 0, and obtain an 8-bit binary number.

[0020] 4) Traverse the non-boundary pixel regions in the image that have not undergone texture feature calculation, and execute steps 2) and 3);

[0021] 5) Convert each of the obtained 8-bit binary numbers into decimal numbers. Each decimal number is the texture feature of the image.

[0022] Furthermore, in step (2), the step of calculating the contrast features is as follows:

[0023] 1') Define a 3x3 contrast window;

[0024] 2') Take pixels in the non-boundary pixel region of the image according to the size of the contrast window;

[0025] 3') In the contrast window, for each pixel, calculate the contrast between the pixel and other pixels. The contrast measurement formula is: i and j are the coordinates of the 8 pixels surrounding the center pixel of the contrast window, Pij| is the gray value of the 8 pixels surrounding the center pixel of the contrast window; q is the pixel value of the center pixel of the contrast window, I(q) is the gray value of the center pixel of the contrast window, and ε is a constant.

[0026] 4') Traverse the non-boundary pixel regions in the image that have not undergone contrast feature calculation, and execute steps 2') and 3') to obtain one or more contrast measurement values, each of which is the contrast feature of the image.

[0027] Further, in step (2), the feature vector is F = 0.25 × Y + 0.5 × L + 0.25 × C, where Y is the gray value of the image, L is the texture feature of the image, and C is the contrast feature of the image.

[0028] Further, in step (2), the step of grouping the binary image is as follows:

[0029] a) Preset distance threshold;

[0030] b) Create a trip group to store an ungrouped trip;

[0031] Iterate through all other ungrouped trips as the current trip;

[0032] If the distance between the current trip and the last trip in the trip group is less than or equal to the distance threshold, the current trip is stored in the trip group and the current trip is updated as the last trip in the trip group; otherwise, it is not stored in the trip group.

[0033] c) Iterate through the next ungrouped trip and execute step b) until all trips are stored in the corresponding trip group.

[0034] Further, in step (2), the centroid of each travel group is calculated: Where n is the number of trips in the trip group, (x i ,y i ) represents the starting position of the i-th trip, w i h is the width of the i-th stroke. i Let be the height of the i-th stroke.

[0035] Furthermore, in step (3), the spiral scanning step is as follows:

[0036] a') Using the geometric center of the target area as the initial scanning point, set the initial radius and initial rotation step of the spiral scan based on the principle of covering the contour of each stroke group;

[0037] b') Generate the spiral trajectory using the spiral equation r = a + b × Δθ in polar coordinates, where r is the scanning radius, a is the distance between the initial scanning point and the center of the polar coordinates, b is the spacing of the spiral lines, and Δθ is the step angle.

[0038] Furthermore, a space laser communication system with automatic alignment function that operates the above-mentioned communication method includes one local device and at least one remote device; the local device and each remote device establish a communication link in a time-division manner.

[0039] The local device includes a local two-axis servo turntable control module, a local DSP processor, a local lidar module, a local camera module, a local beacon light transmitting module, a local beacon light receiving module, a local signal light transmitting module, and a local signal light receiving module.

[0040] Each peer device includes a peer two-axis servo turntable control module, a peer DSP processor, a peer lidar module, a peer camera module, a peer beacon light transmitting module, a peer beacon light receiving module, a peer signal light transmitting module, and a peer signal light receiving module.

[0041] The local camera module provides images to the local DSP processor; the local lidar module provides point cloud data to the local DSP processor; the local beacon light transmitting module transmits beacon light under the control of the local DSP processor; the local beacon light receiving module receives beacon light from the peer device in a time-division manner and inputs it to the local DSP processor; the local signal light transmitting module transmits signal light under the control of the local DSP processor; the local signal light receiving module receives the signal and inputs it to the local DSP processor and external optical fiber; the local DSP signal processor performs steps (1), (2), and (3); the local two-axis servo turntable control module performs step (4).

[0042] The structure and working principle of the peer device are the same as those of the local device.

[0043] The advantages and effects of this invention are:

[0044] The invention acquires images of the peer device via a camera module and point cloud data of the peer device via a lidar module. Feature vectors are constructed for each pixel based on its grayscale value, texture features, and contrast features. A segmentation threshold is obtained from these feature vectors, and the image is segmented to obtain binary images. These binary images are then grouped based on a distance threshold. The centroid, contour, and motor encoder angle corresponding to the beacon light of the local device are then obtained for each travel group. Combining this with the distance information between the local and peer devices provided by the point cloud data, a spiral scan is performed. The antenna of the local device's signal light is adjusted based on the position and angle of the beacon light with received power reaching a preset value, thus achieving automatic calibration. This invention employs an adaptive multi-feature fusion threshold segmentation method and a travel grouping coding algorithm to detect targets. Automatic calibration is achieved based on the actual centroid and contour of the peer device, the actual angle of the motor encoder of the local device, and the principle that the beacon light communication link and the signal light communication link are parallel. This eliminates the need for human intervention and improves alignment efficiency while ensuring accuracy. Attached Figure Description

[0045] Figure 1 This is a block diagram illustrating the principle of the automatic alignment method of the present invention.

[0046] Figure 2 A schematic diagram of the spiral scanning trajectory.

[0047] Figure 3 This is a block diagram illustrating the structural principle of establishing a communication link between one local device and one remote device according to the present invention. Detailed Implementation

[0048] The present invention will be further described below with reference to the embodiments, but the present invention is not limited to these embodiments.

[0049] A space laser communication method with automatic alignment function includes the following steps:

[0050] (1) Acquire the current image and point cloud data of the peer device;

[0051] (2) Calculate the gray value, texture feature and contrast feature of each pixel in the image; construct a feature vector by weighted summing of the gray value, texture feature and contrast feature; train an SVM model based on the feature vector to obtain a segmentation threshold; segment the image using the segmentation threshold to obtain a binary image;

[0052] For each ungrouped stroke in the binary image, a preset distance threshold is used as the adjacency judgment condition to group the strokes into groups; the centroid of each stroke group is calculated to obtain the contour of each stroke group; the centroid, contour, and motor encoder angle corresponding to the beacon light of the local device are used as the target position.

[0053] The distance between the local device and the current peer device is calculated using the point cloud data;

[0054] (3) Combine the centroid, contour, motor encoder angle and distance corresponding to the beacon light of the local device for each stroke group to determine the target area; emit beacon light to perform a spiral scan on the target area; once the power of the received beacon light reaches the preset value, record the current position of the received beacon light beam and the current angle of the motor encoder of the local device.

[0055] (4) Adjust the signal light antenna of the local device according to the current position and the current angle to achieve automatic alignment between the signal light transmitting antenna of the local device and the signal light receiving antenna of the current peer device.

[0056] The structural and operational principles of the local device and each peer device are the same. One space laser communication terminal is regarded as the local device, and the other space laser communication terminals are regarded as peer devices.

[0057] The principle block diagram of the automatic alignment method of the present invention is as follows: Figure 1 As shown. After the local device and each peer device are powered on, the local device performs a 360-degree rotation scan, as described in step (1) to acquire the image and point cloud data of the current peer device; as described in step (2) to extract the position and outline of the current peer device, the motor encoder angle corresponding to the beacon light of the local device, and the distance information between the local device and the current peer device; as described in steps (3) and (4) to fuse the data, and emit beacon light in the target area for spiral scanning, and when the received beacon light reaches the preset power, automatically adjust the antenna of the signal light according to the position and angle of the current beacon light to achieve automatic calibration.

[0058] The image in step (1) shows the position and outline of the current peer device; the point cloud data in step (1) refers to a set of vectors in a three-dimensional coordinate system. The distance between the local device and the current peer device can be calculated through the point cloud data.

[0059] In step (2), the formula for calculating the gray value is Y = 0.299*R + 0.587*G + 0.114*B, where Y is the gray value, R is the value of the red component, G is the value of the green component, and B is the value of the blue component.

[0060] In step (2), the steps for calculating texture features are as follows:

[0061] 1) Define a 3x3 texture window;

[0062] 2) Take pixels in the non-boundary pixel areas of the image according to the texture window size;

[0063] 3) Using the gray value of the center pixel in the texture window as the gray value threshold, traverse the 8 pixels outside the center pixel in the texture window in a clockwise or counterclockwise direction. If the gray value of the current pixel is greater than the gray value threshold, mark the current pixel as 1, otherwise mark it as 0, and obtain an 8-bit binary number.

[0064] 4) Traverse the non-boundary pixel regions in the image that have not undergone texture feature calculation, and execute steps 2) and 3);

[0065] 5) Convert each of the obtained 8-bit binary numbers into decimal numbers. Each decimal number is the texture feature of the image.

[0066] In step (2), the steps for calculating the contrast features are as follows:

[0067] 1') Define a 3x3 contrast window;

[0068] 2') Take pixels in the non-boundary pixel region of the image according to the size of the contrast window;

[0069] 3') In the contrast window, for each pixel, calculate the contrast between the pixel and other pixels. The contrast measurement formula is: Where i and j are the coordinates of the 8 pixels surrounding the center pixel of the contrast window, Pij| is the gray value of the 8 pixels surrounding the center pixel of the contrast window; q is the pixel value of the center pixel of the contrast window, I(q) is the gray value of the center pixel of the contrast window, and ε is a constant.

[0070] 4') Traverse the non-boundary pixel regions in the image that have not undergone contrast feature calculation, and execute steps 2') and 3') to obtain one or more contrast measurement values, each of which is the contrast feature of the image.

[0071] In step (2), the feature vector is F = 0.25 × Y + 0.5 × L + 0.25 × C, where Y is the gray value of the image, L is the texture feature of the image, and C is the contrast feature of the image.

[0072] Support Vector Machine (SVM) is a supervised machine learning algorithm that classifies data by finding the optimal line or hyperplane, thereby maximizing the distance between each class in N-dimensional space. In step (2), the SVM training model is a current technique; the feature vector is input into the SVM training model to obtain the desired segmentation threshold. When segmenting the image, pixels greater than the segmentation threshold are set to 255, and pixels less than the segmentation threshold are set to 0, resulting in a binary image.

[0073] Run-length grouping (LTG) coding algorithms are commonly used in image compression. This invention applies the algorithm to the analysis of binary images. After run-length grouping, centroid and contour information are extracted, which can extract the effective features of the peer device to the greatest extent possible, improve the accuracy of target detection, and thus improve the accuracy of automatic alignment. It can also achieve accurate detection of multiple peer devices.

[0074] In step (2), the step of grouping the binary image is as follows:

[0075] a) Preset distance threshold;

[0076] b) Create a trip group to store an ungrouped trip;

[0077] Iterate through all other ungrouped trips as the current trip;

[0078] If the distance between the current trip and the last trip in the trip group is less than or equal to the distance threshold, the current trip is stored in the trip group and the current trip is updated as the last trip in the trip group; otherwise, it is not stored in the trip group.

[0079] c) Iterate through the next ungrouped trip and execute step b) until all trips are stored in the corresponding trip group.

[0080] In step (2), the centroid of each travel group is calculated: Where n is the number of trips in the trip group, (x i ,y i ) represents the starting position of the i-th trip, w i h is the width of the i-th stroke. i Let be the height of the i-th stroke.

[0081] In step (2), the outline of the travel group, namely the length and width of the travel group, refers to the number of pixels between the maximum and minimum values ​​in the x-direction of the travel group, and the number of pixels between the maximum and minimum values ​​in the y-direction of the travel group.

[0082] In step (2), the angle of the motor encoder corresponding to the beacon light of the local device is directly read from the local two-axis servo turntable.

[0083] In step (3), the centroid, contour, motor encoder angle and distance corresponding to the beacon light of the local device for each stroke group are unified into the same coordinate system for fusion. The fused target information is a collection of data.

[0084] In step (3), the spiral scanning process is as follows:

[0085] a') Using the geometric center of the target area as the initial scanning point, set the initial radius and initial rotation step of the spiral scan based on the principle of covering the contour of each stroke group;

[0086] b') The spiral trajectory is generated using the spiral equation r = a + b × Δθ in polar coordinates, where r is the scanning radius, a is the distance between the initial scanning point and the center of the polar coordinates, b is the spacing of the control spiral lines, and Δθ is the step angle. A schematic diagram of the spiral scanning trajectory of this invention is shown below. Figure 2 As shown, M is the current centroid position of the opposite end device, c is the polar coordinate center point, and A and B represent the two points corresponding to △θ on the spiral trajectory.

[0087] In step (3), taking one local device and one remote device as an example, once the power of the beacon light received by the local device reaches the preset value and the power of the beacon light received by the remote device also reaches the preset value, the local device records the current position of the beacon light beam emitted by the local device and the current angle of the motor encoder, and the remote device records the current position of the beacon light beam emitted by the remote device and the current angle of the motor encoder.

[0088] The automatic alignment in step (4) includes the acquisition process and the precise alignment process. In this device, once the beacon laser beam is received, the position, angle and intensity information of the beacon light are immediately recorded. At this time, the intensity is usually at least -35dBm. After successful acquisition, the antenna of the signal light can be adjusted according to the position and angle information to achieve precise alignment.

[0089] After automatic alignment is completed, the optical axes of the emitted light and the beacon laser beam are parallel.

[0090] A space laser communication system with automatic alignment function that performs the above steps includes one local device and at least one remote device; the local device and each remote device establish a communication link in a time-division manner.

[0091] The local device includes a local two-axis servo turntable control module, a local DSP processor, a local lidar module, a local camera module, a local beacon light transmitting module, a local beacon light receiving module, a local signal light transmitting module, and a local signal light receiving module.

[0092] Each peer device includes a peer two-axis servo turntable control module, a peer DSP processor, a peer lidar module, a peer camera module, a peer beacon light transmitting module, a peer beacon light receiving module, a peer signal light transmitting module, and a peer signal light receiving module; the structure and working principle of the peer device are the same as those of the local device.

[0093] The local camera module provides images to the local DSP processor; the local lidar module provides point cloud data to the local DSP processor; the local beacon light transmitting module transmits beacon light under the control of the local DSP processor; the local beacon light receiving module receives beacon light from the peer device in a time-division manner and inputs it to the local DSP processor; the local signal light transmitting module transmits signal light under the control of the local DSP processor; the local signal light receiving module receives the signal and inputs it to the local DSP processor and external optical fiber; the local DSP signal processor operates steps (1), (2), and (3); the local two-axis servo turntable control module operates step (4). The structural principle block diagram of establishing a communication link between one local device and one peer device is shown below. Figure 3 As stated above.

[0094] In this local device, the local beacon light transmitting module is used to generate the beacon light to be tracked and aligned, the local beacon light receiving module is used to capture and track the beacon light generated by the remote device, the local signal light transmitting module is used to generate signal light that can transmit information, the local signal light receiving module is used to receive the transmitted optical signal, and the local two-axis servo turntable performs scanning, tracking, and antenna alignment functions.

[0095] Furthermore, the local device also includes a local two-axis servo turntable body. The local two-axis servo turntable control module, local DSP processor, local lidar module, local camera module, local beacon light transmitting module, local beacon light receiving module, local signal light transmitting module, and local signal light receiving module are mounted on the local two-axis servo turntable body and rotate together with the local two-axis servo turntable body. In addition, the local two-axis servo turntable body can control the antennas of the local beacon light transmitting module, local beacon light receiving module, local signal light transmitting module, and local signal light receiving module in two-axis directions.

[0096] Furthermore, in each peer device, the peer device includes a peer two-axis servo turntable body, and each peer device has the same structural principle and working principle as the local device.

[0097] The two-axis servo turntable mainly consists of the turntable body and the control module. The structural and operational principles of the two-axis servo turntable are existing technologies. The turntable body comprises two mutually perpendicular rotation axes, responsible for precise angle adjustment in the horizontal and vertical directions, respectively. High-precision position, velocity, and acceleration control is achieved through a closed-loop control system using the FOC (Field-Oriented Control) algorithm. Once the local device successfully captures the position of the beacon light emitted by the remote device, it converts this position into a feedback signal from the encoder on the two-axis servo turntable. The deviation angle is then calculated to control the azimuth and pitch direction of the turntable body, thereby aligning it with the transmitting and receiving beams.

Claims

1. A space laser communication method with automatic alignment function, characterized in that, Includes the following steps: (1) Acquire the current image and point cloud data of the peer device; (2) Calculate the gray value, texture feature and contrast feature of each pixel in the image; construct a feature vector by weighted summing of the gray value, texture feature and contrast feature; train an SVM model based on the feature vector to obtain a segmentation threshold; segment the image using the segmentation threshold to obtain a binary image; For each ungrouped route in the binary image, a preset distance threshold is used as the adjacency criterion to group the routes, resulting in various route groups. Calculate the centroid of each stroke group and obtain the contour of each stroke group; use the centroid, contour, and motor encoder angle corresponding to the beacon light of the local device as the target position for each stroke group. The distance between the local device and the current peer device is calculated using the point cloud data; (3) Combine the centroid, contour, motor encoder angle and distance corresponding to the beacon light of the local device for each stroke group to determine the target area; emit beacon light to perform a spiral scan on the target area; once the power of the received beacon light reaches the preset value, record the current position of the received beacon light beam and the current angle of the motor encoder of the local device. (4) Adjust the signal light antenna of the local device according to the current position and the current angle to achieve automatic alignment between the signal light transmitting antenna of the local device and the signal light receiving antenna of the current peer device.

2. The space laser communication method with automatic alignment function according to claim 1, characterized in that: In step (2), the formula for calculating the gray value is Y = 0.299*R + 0.587*G + 0.114*B, where Y is the gray value, R is the value of the red component, G is the value of the green component, and B is the value of the blue component.

3. A space laser communication method with automatic alignment function according to claim 2, characterized in that: In step (2), the steps for calculating texture features are as follows: 1) Define a 3x3 texture window; 2) Take pixels in the non-boundary pixel areas of the image according to the texture window size; 3) Using the gray value of the center pixel in the texture window as the gray value threshold, traverse the 8 pixels outside the center pixel in the texture window in a clockwise or counterclockwise direction. If the gray value of the current pixel is greater than the gray value threshold, mark the current pixel as 1, otherwise mark it as 0, and obtain an 8-bit binary number. 4) Traverse the non-boundary pixel regions in the image that have not undergone texture feature calculation, and execute steps 2) and 3); 5) Convert each of the obtained 8-bit binary numbers into decimal numbers. Each decimal number is the texture feature of the image.

4. A space laser communication method with automatic alignment function according to claim 3, characterized in that: In step (2), the steps for calculating the contrast features are as follows: 1') Define a 3x3 contrast window; 2') Take pixels in the non-boundary pixel region of the image according to the size of the contrast window; 3') In the contrast window, for each pixel, calculate the contrast between the pixel and other pixels. The contrast measurement formula is: i and j are the coordinates of the 8 pixels surrounding the center pixel of the contrast window, Pij| is the gray value of the 8 pixels surrounding the center pixel of the contrast window; q is the pixel value of the center pixel of the contrast window, I(q) is the gray value of the center pixel of the contrast window, and ε is a constant. 4') Traverse the non-boundary pixel regions in the image that have not undergone contrast feature calculation, and execute steps 2') and 3') to obtain one or more contrast measurement values, each of which is the contrast feature of the image.

5. A space laser communication method with automatic alignment function according to claim 4, characterized in that: In step (2), the feature vector is F = 0.25 × Y + 0.5 × L + 0.25 × C, where Y is the gray value of the image, L is the texture feature of the image, and C is the contrast feature of the image.

6. A space laser communication method with automatic alignment function according to claim 1, characterized in that: In step (2), the step of grouping the binary image is as follows: a) Preset distance threshold; b) Create a trip group to store an ungrouped trip; Iterate through all other ungrouped trips as the current trip; If the distance between the current trip and the last trip in the trip group is less than or equal to the distance threshold, the current trip is stored in the trip group and the current trip is updated as the last trip in the trip group; otherwise, it is not stored in the trip group. c) Iterate through the next ungrouped trip and execute step b) until all trips are stored in the corresponding trip group.

7. A space laser communication method with automatic alignment function according to claim 6, characterized in that: In step (2), the centroid of each travel group is calculated: Where n is the number of trips in the trip group, (x i ,y i ) represents the starting position of the i-th trip, w i h is the width of the i-th stroke. i Let be the height of the i-th stroke.

8. A space laser communication method with automatic alignment function according to claim 7, characterized in that: In step (3), the spiral scanning process is as follows: a') Using the geometric center of the target area as the initial scanning point, set the initial radius and initial rotation step of the spiral scan based on the principle of covering the contour of each stroke group; b') Generate the spiral trajectory using the spiral equation r = a + b × Δθ in polar coordinates, where r is the scanning radius, a is the distance between the initial scanning point and the center of the polar coordinates, b is the spacing of the spiral lines, and Δθ is the step angle.

9. A space laser communication system with automatic alignment function, operating the space laser communication method with automatic alignment function according to any one of claims 1-8, characterized in that: It includes one local device and at least one peer device; the local device and each peer device establish a communication link in a time-division multiplexing manner; The local device includes a local two-axis servo turntable control module, a local DSP processor, a local lidar module, a local camera module, a local beacon light transmitting module, a local beacon light receiving module, a local signal light transmitting module, and a local signal light receiving module. Each peer device includes a peer two-axis servo turntable control module, a peer DSP processor, a peer lidar module, a peer camera module, a peer beacon light transmitting module, a peer beacon light receiving module, a peer signal light transmitting module, and a peer signal light receiving module. The local camera module provides images to the local DSP processor; the local lidar module provides point cloud data to the local DSP processor; the local beacon light transmitting module transmits beacon light under the control of the local DSP processor; the local beacon light receiving module receives beacon light from the peer device in a time-division manner and inputs it to the local DSP processor; the local signal light transmitting module transmits signal light under the control of the local DSP processor; the local signal light receiving module receives the signal and inputs it to the local DSP processor and external optical fiber; the local DSP processor runs steps (1), (2), and (3); the local two-axis servo turntable control module runs step (4); The structure and working principle of the peer device are the same as those of the local device.

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