A space spin type rope net antenna deployment test method

Abstract

This invention discloses a method for deploying a space-based spin-type rope-net antenna. It employs a hexagonal parabolic rope-net structure, with six corner points tractioned by a cluster of unmanned aerial vehicles (UAVs). The antenna achieves hovering, synchronous spinning, and dynamic stabilization via a pre-set trajectory. A rope-net spin dynamics model is established, and tension distribution is adjusted using UAV attitude feedback to ensure the rope-net shape conforms to the pre-set parabolic accuracy during deployment. Multiple reflective targets are deployed on the rope-net, and deformation data is collected in real-time by an independent UAV equipped with a vision system. Three-dimensional coordinates are extracted using image processing algorithms to verify the deployment dynamics and shape retention capabilities of the space-based rope-net antenna. The experimental method includes a spin angular velocity calculation formula, a tension balance equation, and a target positioning algorithm, enabling efficient simulation and verification of the deployment dynamics of the space-based spin-type rope-net antenna. This invention is low-cost, highly repeatable, and suitable for ground-based testing and verification of space-based spin-type deployable antennas.

Description

Technical Field

[0001] This invention relates to the field of ground testing technology for space structure mechanisms, and in particular to a method for deploying a space spin-type rope net antenna, used to verify the spin deployment dynamics and stability of the space spin-type rope net antenna. Background Technology

[0002] Space-based rope-net antennas, with their advantages of lightweight design, large deployment ratio, and reconfigurability, have become a key technology for deep space exploration communication. However, their space spin deployment process involves complex multibody dynamics coupling problems, including rope-net elastic vibration, UAV cooperative attitude control, and simulation of microgravity environments. Traditional ground tests of space-deployable antennas often rely on air-bearing platforms or high-tower suspension systems, which suffer from high costs and limited space. While air-bearing bearings are used to simulate microgravity, it is difficult to achieve three-dimensional motion of large-scale structures, especially for large parabolic antennas with diameters exceeding 10 meters, where insufficient normal stiffness of the air-bearing platform leads to a rope-net morphological distortion rate of over 12%. Tower crane methods are limited by the number of suspension points and cannot accurately simulate distributed tension. Furthermore, existing technologies have not yet solved the problem of simulating the Coriolis effect during the spin deployment process, and the monitoring of the dynamic characteristics of the spin process is insufficient, relying heavily on contact sensors, which affects measurement accuracy. This invention innovatively adopts UAV formation cooperative control combined with non-contact visual measurement to accurately reproduce the space spin deployment dynamics process under gravity, overcoming the limitations of traditional methods. Summary of the Invention

[0003] To address the issues of high cost, complexity, and poor flexibility associated with traditional air-floating platforms or high-tower suspension, this invention provides a method for deploying a space spin-type rope-net antenna. By employing a ground-based test method based on UAV swarm collaborative control, it solves the problem of accurately simulating the spin deployment dynamics of the space rope-net antenna, enabling visualization of the deployment process under multi-physics coupling, measurable parameters, and diagnosable faults.

[0004] To achieve the aforementioned objectives of the invention, the technical solution adopted to solve its technical problems is as follows:

[0005] A method for deploying a space-based spin-type rope-net antenna includes the following steps:

[0006] Step 1: Construct a hexagonal parabolic rope-net antenna experimental system, including the rope-net structure, UAV swarm, and data acquisition system, wherein:

[0007] The rope net structure adopts an equal hexagonal parabolic topology and is woven from high-strength ultralight fibers. High-precision force sensors and inertial measurement units are set at the hexagonal nodes.

[0008] The drone swarm consists of six high-precision multi-rotor drones, equipped with a distributed collaborative control module and a visual navigation system.

[0009] The data acquisition system includes an airborne camera on a drone, a ground-based high-speed motion capture system, and a central data processing unit.

[0010] Step 2: Connect the hexagonal nodes of the rope net to the drones through a connecting mechanism. The drone swarm hovers in a hexagonal formation at a height H above the ground, forming an initial planar hexagonal layout.

[0011] Step 3: The drone swarm initiates the collaborative control algorithm spin program, synchronously spinning around the central axis at a preset angular velocity ω; the drone thrust vector is dynamically adjusted by real-time feedback of rope and net tension data.

[0012] Step 4: After the rope net spins into a stable state, the seventh observation drone enters the observation position to collect multi-view image data of N reflective targets on the surface of the rope net, and simultaneously records the drone flight control data, rope net node mechanical data and environmental parameters.

[0013] Step 5: Reconstruct the three-dimensional shape of the rope net based on visual data, calculate the parabolic surface error and vibration frequency, and verify the accuracy and stability of the antenna deployment surface.

[0014] Furthermore, in step one, the distributed collaborative control module and the visual navigation system's distributed collaborative control communication delay compensation algorithm is as follows:

[0015]

[0016] Among them, u i (t) The control output of the i-th UAV, K p =2.5 is the proportional gain coefficient, T i =0.8s is the integration time constant, T d =0.2s is the differential time constant, τ d =0.015s is the communication delay constant, e(t) is the UAV formation position error, and s is the Laplace operator.

[0017] Furthermore, in step three, the angular velocity is determined according to the following plan:

[0018] ω(t)=ω c (1-e (-t / τ) )

[0019] Where, ω c Let τ be the target angular velocity, and τ be the time to reach the target angular velocity, where τ = 3s;

[0020] Maximum angular acceleration α of the drone max =0.15rad / s2 ;

[0021] Adjust the drone's thrust vector to ensure the net deployment process meets the following requirements:

[0022]

[0023] Among them, T i Let be the tension of the i-th side rope, m be the equivalent mass of the rope net, R be the side length of the hexagon, and θ be the inclination angle of the parabolic target.

[0024] The calculation of the UAV's spin angular velocity ω must satisfy the equilibrium condition between centrifugal force and gravity:

[0025]

[0026] Where g is the acceleration due to gravity.

[0027] Furthermore, in step four, the rope-net antenna maintains an angular velocity ω. c = 0.78 rad / s, corresponding to centrifugal acceleration a c =3g, duration 25s, during which the dynamic compensation algorithm adjusts the drone's thrust in real time:

[0028] F i =F0+ΔF i

[0029] ΔF i =m i (r i ×a+ω×r i ×ω)

[0030] Where, m i For equivalent quality, r i It is a position vector;

[0031] When the maximum deformation error of the parabolic surface of the rope net is ≤3% and the attitude angle fluctuation of the UAV is <0.5°, it is determined that it has entered a stable state;

[0032] The seventh observation drone entered the observation position and collected multi-view image data of N reflective targets on the surface of the rope net. The targets are distributed according to the Fibonacci spiral, and their polar coordinate positions are determined by the following formula:

[0033]

[0034] Where a is the proportionality coefficient and k is the target number.

[0035] Furthermore, in step five, a binocular vision localization algorithm is used to solve for the target's three-dimensional coordinates by matching the pixel coordinates (u1, v1) and (u2, v2) of the target in the two cameras.

[0036]

[0037] Where f is the focal length and B is the baseline distance;

[0038] Reconstructing the 3D morphology of the rope net based on visual data, and calculating the RMS error of the parabolic shape:

[0039]

[0040] Among them, z i The position of the i-th target, z ref Target reference position.

[0041] By employing the above technical solutions, this invention has the following advantages and positive effects compared with the prior art:

[0042] This invention addresses the problem of accurately simulating the spatial spin deployment dynamics of a rope-net antenna through a ground-based testing method based on UAV swarm collaborative control. It achieves visualization of the deployment process under multi-physics coupling, parameter measurability, and fault diagnosis. It features low cost and high repeatability, making it suitable for ground-based testing and verification of space-based spin-deployable antennas. Attached Figure Description

[0043] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In the drawings:

[0044] Figure 1 This is a schematic diagram of a space spin-type rope net antenna deployment test system according to an embodiment of the present invention;

[0045] Figure 2 This is a diagram of the control force curve of an unmanned aerial vehicle according to an embodiment of the present invention;

[0046] Figure 3 This is a spin velocity curve of a rope-net antenna according to an embodiment of the present invention;

[0047] Figure 4 This is a three-dimensional topographic image of a space spin-type rope net antenna reconstructed from data of an embodiment of the present invention;

[0048] Figure 5 This is a flowchart of the spatial spin rope net deployment test of the present invention.

[0049] [Explanation of Key Symbols]

[0050] 1- Rope and net antenna; 2- Drone; 3- Camera; 4- Adapter mechanism; 5- Target. Detailed Implementation

[0051] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. 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.

[0052] like Figure 1-5 As shown, this embodiment discloses a method for deploying a space spin-type rope-net antenna, including the following steps:

[0053] Step 1: Constructing a hexagonal parabolic rope-net antenna experimental system (see...) Figure 1 This includes a rope net structure, a drone swarm, and a data acquisition system, among which:

[0054] The rope net structure adopts an equal hexagonal parabolic topology and is woven from high-strength ultralight fibers. The rope net material is Dyneema SK78 fiber with a warp modulus of 120 GPa and a weaving angle of 45°±2°. The parabolic equation z=(x) is designed based on a focal length f=9.375m. 2 +y 2 ) / 37.5, with a high-precision force sensor and inertial measurement unit (IMU) set at the hexagonal node.

[0055] The drone swarm consists of six high-precision multi-rotor drones (positioning accuracy ±1cm, payload ≥5kg, modified from DJI Matrice 600Pro), equipped with a distributed collaborative control module and a visual navigation system. The distributed collaborative control communication latency compensation algorithm is as follows:

[0056]

[0057] Among them, u i (t) The control output of the i-th UAV, K p =2.5 is the proportional gain coefficient, T i =0.8s is the integration time constant, T d =0.2s is the differential time constant, τ d =0.015s is the communication delay constant, e(t) is the UAV formation position error, and s is the Laplace operator.

[0058] The data acquisition system includes an airborne camera 3 (120Hz frame rate) for the UAV, a ground high-speed motion capture system (Vicon MX series), and a central data processing unit.

[0059] Step 2: Connect the hexagonal nodes of the rope net to the UAV 2 through the adapter mechanism 4. The UAV cluster hovers in a hexagonal formation at a height H above the ground (H≥10m to avoid ground effect interference), forming an initial planar hexagonal layout.

[0060] Specifically, the hexagonal nodes of the rope net are connected to the drones 2 via the adapter mechanism 4, and the six drones 2 are arranged according to the coordinates of the hexagonal vertices: The drone swarm hovers in a hexagonal formation at a height H (H≥10m to avoid ground effect interference), forming an initial planar hexagonal layout with an initial tension F0=40N, and is held for t=30s to eliminate creep.

[0061] Step 3: The UAV cluster initiates the collaborative control algorithm spin program, synchronously spinning around the central axis at a preset angular velocity ω; by real-time feedback of rope and net tension data (obtained by nodal force sensors), the thrust vector of the UAV is dynamically adjusted.

[0062] Specifically, in step three, the angular velocity is determined according to the following plan:

[0063] ω(t)=ω c (1-e (-t / τ) )

[0064] Where, ω c Let τ be the target angular velocity, and τ be the time to reach the target angular velocity, where τ = 3s;

[0065] Maximum angular acceleration α of UAV 2 max =0.15rad / s 2 ;

[0066] Adjust the drone's thrust vector to ensure the net deployment process meets the following requirements:

[0067]

[0068] Among them, T i Let be the tension of the i-th side rope, m be the equivalent mass of the rope net, R be the side length of the hexagon, and θ be the inclination angle of the parabolic target.

[0069] The calculation of the UAV's spin angular velocity ω must satisfy the equilibrium condition between centrifugal force and gravity:

[0070]

[0071] Where g is the acceleration due to gravity.

[0072] Step 4: Once the rope net spins into a stable state, the seventh observation drone 2 (equipped with a 4K infrared / visible dual-mode camera) enters the observation position to collect multi-view image data of N reflective targets on the surface of the rope net, and simultaneously record drone flight control data, rope net node mechanical data and environmental parameters (wind speed, humidity).

[0073] Specifically, in step four, the rope-net antenna 1 maintains an angular velocity ω. c = 0.78 rad / s, corresponding to centrifugal acceleration a c =3g, duration 25s, during which the dynamic compensation algorithm adjusts the drone's thrust in real time:

[0074] F i =F0+ΔF i

[0075] ΔF i =m i (r i ×a+ω×r i ×ω)

[0076] Where, m i For equivalent quality, r i It is a position vector;

[0077] When the maximum deformation error of the parabolic surface of the rope net is ≤3% (calculated in real time by the Vicon system) and the attitude angle fluctuation of the UAV is <0.5°, it is determined that it has entered a stable state;

[0078] The seventh observation drone 2 (equipped with a 4K infrared / visible dual-mode camera) entered the observation position and collected multi-view image data of N reflective targets on the surface of the rope net. Target 5 is distributed according to the Fibonacci spiral, and its polar coordinate position is determined by the following formula:

[0079]

[0080] Where a is the proportionality coefficient and k is the target number.

[0081] Step 5: Reconstruct the 3D shape of the rope net based on visual data. Using a binocular vision positioning algorithm, the 3D coordinates of target 5 are solved by matching the pixel coordinates (u1, v1) and (u2, v2) of the target in the two cameras.

[0082]

[0083] Where f is the focal length and B is the baseline distance;

[0084] Reconstructing the three-dimensional morphology of the rope net based on visual data (see...) Figure 4 ), calculate the RMS error of the parabolic surface:

[0085]

[0086] Among them, z i The position of the i-th target, z ref Target reference position.

[0087] The experimental verification procedure for deploying a space spin-type rope net antenna is as follows (see...). Figure 5 The antenna deployment accuracy and stability were verified by measuring the surface error and vibration frequency of the rope net antenna.

[0088] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for testing deployment of a space spun rope net antenna, characterized by, Includes the following steps: Step 1: Construct a hexagonal parabolic rope-net antenna experimental system, including the rope-net structure, UAV swarm, and data acquisition system, wherein: The rope net structure adopts an equal hexagonal parabolic topology and is woven from high-strength ultralight fibers. High-precision force sensors and inertial measurement units are set at the hexagonal nodes. The drone swarm consists of six high-precision multi-rotor drones, equipped with a distributed collaborative control module and a visual navigation system. The data acquisition system includes an airborne camera on a drone, a ground-based high-speed motion capture system, and a central data processing unit. Step 2: Connect the hexagonal nodes of the rope net to the drones through a connecting mechanism. The drone swarm hovers in a hexagonal formation at a height H above the ground, forming an initial planar hexagonal layout. Step 3: The drone swarm initiates the collaborative control algorithm spin program, synchronously spinning around the central axis at a preset angular velocity ω; the drone thrust vector is dynamically adjusted by real-time feedback of rope and net tension data. Step 4: After the rope net spins into a stable state, the seventh observation drone enters the observation position to collect multi-view image data of N reflective targets on the surface of the rope net, and simultaneously records the drone flight control data, rope net node mechanical data and environmental parameters. Step 5: Reconstruct the three-dimensional shape of the rope net based on visual data, calculate the parabolic surface error and vibration frequency, and verify the accuracy and stability of the antenna deployment surface.

2. The method of claim 1, wherein, In step one, the distributed collaborative control module and the visual navigation system's distributed collaborative control communication delay compensation algorithm is as follows: Among them, u i (t) The control output of the i-th UAV, K p =2.5 is the proportional gain coefficient, T i =0.8s is the integration time constant, T d =0.2s is the differential time constant, τ d =0.015s is the communication delay constant, e(t) is the UAV formation position error, and s is the Laplace operator.

3. The method of claim 1, wherein the method further comprises: In step three, the angular velocity is determined according to the following plan: ω(t) = ω c (1-e (-t / τ) ) where ω c is the target angular velocity, and τ is the time to reach the target angular velocity, τ = 3 s. Maximum angular acceleration of the drone α max = 0.15 rad / s 2 ; Adjust the drone's thrust vector to ensure the rope net deployment process meets the following requirements: Among them, T i Let be the tension of the i-th side rope, m be the equivalent mass of the rope net, R be the side length of the hexagon, and θ be the inclination angle of the parabolic target. The calculation of the UAV's spin angular velocity ω must satisfy the equilibrium condition between centrifugal force and gravity: Where g is the acceleration due to gravity.

4. The experimental method for deploying a space spin-type rope net antenna according to claim 1, characterized in that, In step four, the rope antenna maintains an angular velocity ω c = 0.78 rad / s, corresponding to centrifugal acceleration a c =3g, duration 25s, during which the dynamic compensation algorithm adjusts the drone's thrust in real time: F i = F0+ AF i ΔF i = m i (r i × a + ω × r i × ω) where m i is the equivalent mass, r i is the position vector; When the maximum deformation error of the parabolic surface of the rope net is ≤3% and the attitude angle fluctuation of the UAV is <0.5°, it is determined that it has entered a stable state; The seventh observation drone entered the observation position and collected multi-view image data of N reflective targets on the surface of the rope net. The targets are distributed according to the Fibonacci spiral, and their polar coordinate positions are determined by the following formula: Where a is the proportionality coefficient and k is the target number.

5. The experimental method for deploying a space spin-type rope-net antenna according to claim 1, characterized in that, In step five, a binocular vision localization algorithm is used to solve for the target's three-dimensional coordinates by matching the pixel coordinates (u1, v1) and (u2, v2) of the target in the two cameras. Where f is the focal length and B is the baseline distance; Reconstructing the 3D morphology of the rope net based on visual data, and calculating the RMS error of the parabolic shape: wherein z i the ith target position, z ref target reference position.

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