Air-ground combined space debris batch rendezvous elimination method

Abstract

The invention discloses an air-ground combined space debris batch rendezvous elimination method, and relates to the technical field of air-ground mixed multi-space debris removal. In order to solve the technical problems in the prior art that several existing mature space debris cleaning technologies subjected to on-orbit verification strike multiple satellites based on a single satellite, and an optimization upper limit exists, the technical scheme provided by the invention comprises the following steps of: acquiring an optimization target of an algorithm; setting parameters of a simulated annealing algorithm according to the optimization target; a step of collecting a randomly generated initial solution as a starting point of the simulated annealing algorithm; and iteratively optimizing the initial solution until an orbital transfer parameter combination of a preset condition is obtained. And on the basis that only one space debris is removed from each satellite, matching the satellite individuals with the space debris individuals according to the orbital transfer parameter combination. The air-ground mixed multi-space debris removing device is suitable for air-ground mixed multi-space debris removing work.

Description

Technical Field

[0001] The invention relates to the technical field of air-ground mixed multi-space debris removal, and in particular to air-ground mixed multi-space debris removal task planning based on a simulated annealing algorithm. Background Art

[0002] With the development of science and technology and the progress of the times, large-scale satellite constellations have been paid more and more attention by countries around the world. Large-scale satellite constellations in planning and in orbit continue to emerge, and more and more small satellites are launched into space. Among them, the more prominent large-scale constellations include Musk's Starlink and China's BeiDou. Space debris is space garbage in outer space generated by human activities. Space debris may collide with satellites in orbit and interfere with the normal operation of satellite constellations. More and more satellites are launched into outer space, and the space debris generated by these satellites is also increasing, which has a great impact on the normal operation of satellites. Suitable orbital resources are therefore more precious. Therefore, it is extremely important to use appropriate methods to remove space debris.

[0003] There have been many studies on space debris removal technology. Now there are several relatively mature and in-orbit verified space debris removal technologies, such as space rope net capture technology, robotic arm capture technology, etc. These technologies require satellites equipped with removal devices to be launched into space. In order to save resources, each satellite can perform multiple orbit changes and multiple capture missions until the fuel carried by the satellite is exhausted. Therefore, saving satellite fuel and allowing a single satellite to clean up multiple space debris has become a top priority for cleaning up more space debris. Many scholars have discussed the issue of saving fuel for a single satellite. However, there is an optimization upper limit for a single satellite to attack multiple satellites. When the difference between the dihedral angles of the two orbits is too large or the initial positions are far apart, even the best result after optimization will consume more fuel. Summary of the invention

[0004] In order to solve the technical problem that several existing relatively mature and in-orbit verified space debris cleaning technologies in the prior art are based on a single satellite attacking multiple satellites, and have their optimization upper limit. When the difference between the dihedral angles of two orbits is too large or the initial positions are far apart, even the best result after optimization will consume more fuel, the technical solution provided by the present invention is:

[0005] The orbit change parameter calculation method for space debris removal based on the annealing algorithm includes:

[0006] Collect the steps of the space debris strike mission;

[0007] Steps to simulate satellites in a constellation;

[0008] The step of obtaining an optimization target of an algorithm according to the space debris strike mission and the satellites in the constellation;

[0009] The step of setting simulated annealing algorithm parameters according to the optimization target;

[0010] A step of collecting a randomly generated initial solution as a starting point of the simulated annealing algorithm;

[0011] Iteratively optimizing the initial solution to obtain a combination of trajectory change parameters under preset conditions;

[0012] On the basis that each satellite only clears one piece of space debris, a step of matching individual satellites with individual space debris according to the orbit change parameter combination is provided.

[0013] Furthermore, a preferred implementation is provided, wherein the algorithm is a Lambert algorithm.

[0014] Furthermore, a preferred implementation is provided, wherein the optimization objectives include track change time, rendezvous time and number of rendezvous circles.

[0015] Furthermore, a preferred embodiment is provided, wherein the parameters include an initial temperature, an end temperature, a cooling rate, and the number of iterations at each temperature.

[0016] Based on the same inventive concept, the present invention also provides a device for calculating orbit change parameters for space debris removal by air-ground hybrid based on an annealing algorithm, comprising:

[0017] A module for collecting space debris strike missions;

[0018] Modules for simulating satellites in a constellation;

[0019] A module for obtaining an optimization target of an algorithm according to the space debris strike mission and the satellites in the constellation;

[0020] A module for setting simulated annealing algorithm parameters according to the optimization goal;

[0021] A module for collecting randomly generated initial solutions as the starting point of the simulated annealing algorithm;

[0022] Iteratively optimize the initial solution to obtain a module for a combination of orbit change parameters under preset conditions;

[0023] A module for matching individual satellites with individual space debris based on the combination of orbit change parameters on the basis that each satellite only clears one space debris.

[0024] Based on the same inventive concept, the present invention also provides a space-ground joint batch rendezvous elimination method for space debris, comprising:

[0025] The step of collecting the matching results of the satellite individuals and the space debris individuals;

[0026] A step of clearing space debris is performed according to the matching result.

[0027] Based on the same inventive concept, the present invention also provides an air-ground combined space debris batch rendezvous elimination device, comprising:

[0028] A module for collecting matching results of satellite individuals and space debris individuals;

[0029] A module for clearing space debris according to the matching result.

[0030] Based on the same inventive concept, the present invention also provides a computer storage medium for storing a computer program. When the computer program is read by a computer, the computer executes the method described.

[0031] Based on the same inventive concept, the present invention also provides a computer, including a processor and a storage medium. When the processor reads a computer program stored in the storage medium, the computer executes the method described.

[0032] Based on the same inventive concept, the present invention also provides a computer program product, which is a computer program. When the computer program is executed, the method described above is implemented.

[0033] Compared with the prior art, the technical solution provided by the present invention is beneficial in that:

[0034] The introduction of the simulated annealing algorithm effectively solves the global optimization problem of the Lambert orbit change problem. Specifically, the simulated annealing algorithm avoids the limitation of the traditional Lambert orbit change method that is prone to fall into the local optimal solution by gradually reducing the temperature and accepting suboptimal solutions. This improvement can significantly reduce the speed pulse during the orbit change process, thereby reducing fuel consumption. Compared with the direct use of the Lambert method, the optimized effect is more significant.

[0035] The dynamic programming algorithm optimizes the matching problem between satellites and space debris. By properly designing the state transition equations and boundary conditions, dynamic programming ensures that the total removal cost is minimized under the constraint that each satellite can only remove one piece of debris. Compared with the simple heuristic matching method, this optimization method is more systematic and efficient, and ultimately significantly reduces the overall removal cost of the mission.

[0036] With the combination of simulated annealing algorithm and dynamic programming, the entire technical solution not only solves the dual problems of orbit change and debris removal, but also achieves the optimal allocation of resources. Compared with the existing single optimization method, this combined solution provides stronger global optimization capabilities, especially when facing multiple variables and complex constraints, it can provide better solutions and significantly improve the efficiency and success rate of mission execution.

[0037] It is suitable for clearing mixed multi-space debris in open space. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 This is a schematic diagram of the satellite removal system;

[0039] Figure 2 A schematic diagram of a simulated space debris constellation;

[0040] Figure 3 It is the flow chart of the simulation algorithm;

[0041] Figure 4 It is a schematic diagram of the average speed pulse size;

[0042] Figure 5 This is a schematic diagram of the number of emergency satellites that need to be launched;

[0043] Figure 6 A schematic diagram of a preset satellite approaching a target space debris;

[0044] Figure 7 A schematic diagram of an emergency satellite approaching target debris;

[0045] Figure 8 Schematic diagram of emergency satellite clearing target debris. DETAILED DESCRIPTION

[0046] In order to make the advantages and benefits of the technical solution provided by the present invention more clearly reflected, the technical solution provided by the present invention is now further described in detail with reference to the accompanying drawings, specifically:

[0047] Embodiment 1: This embodiment provides a method for matching space debris removal schemes by mixing air and ground, including:

[0048] Brief description

[0049] Step 1: Select the spacecraft trajectory change method and determine the optimization strategy.

[0050] Step 2: Initialize the simulated annealing algorithm parameters.

[0051] Step 3: Generate an initial solution, calculate and evaluate the objective function value.

[0052] Step 4: Iteratively optimize the trajectory change parameters to find the optimal solution.

[0053] Step 5: Use dynamic programming to optimize the matching scheme between satellites and space debris.

[0054] Step 6: Integrate the optimization results and execute the spacecraft orbit change and debris removal tasks.

[0055] Detailed Description

[0056] Step 1: Select the spacecraft trajectory change method and determine the optimization strategy

[0057] Input: orbit change method options (Hohmann orbit change, three-pulse orbit change, Lambert orbit change)

[0058] Processing: Among the many orbit change methods, the Lambert orbit change method was selected for spacecraft orbit change. Since it is difficult to obtain the best results by directly using the Lambert algorithm, the simulated annealing algorithm is used for global optimization. The simulated annealing algorithm will optimize the three key variables of the Lambert problem (orbit change time, rendezvous time, and number of rendezvous circles) to minimize the velocity pulse and achieve the purpose of fuel saving.

[0059] Output: The selected optimization strategy and objective function (minimizing velocity pulse) will serve as the basis for subsequent steps.

[0060] Step 2: Initialize simulated annealing algorithm parameters

[0061] Input: Optimization strategy and objective function

[0062] Processing: Determine the initial parameters of the simulated annealing algorithm, including the initial temperature (100.0), the end temperature (0.001), the cooling rate, and the number of iterations at each temperature (100 times). The setting of these parameters will directly affect the convergence speed of the algorithm and the quality of the final solution.

[0063] Output: The initialized simulated annealing algorithm is ready for the optimization process.

[0064] Step 3: Generate an initial solution, calculate and evaluate the objective function value

[0065] Input: Initialized simulated annealing algorithm

[0066] Processing: Generate an initial solution randomly or obtain it through heuristic methods. The solution includes the initial values ​​of the track change time, the rendezvous time, and the number of rendezvous circles. Then calculate the objective function value of the solution, that is, the speed pulse size, and evaluate its quality.

[0067] Output: The initial solution and its objective function value will serve as the starting point for the simulated annealing algorithm.

[0068] Step 4: Iteratively optimize the trajectory change parameters to find the optimal solution

[0069] Input: initial solution and its objective function value

[0070] Processing: Iterative optimization is performed using a simulated annealing algorithm. In each iteration, new candidate solutions are generated and their objective function values ​​are calculated. According to the Metropolis criterion, poor solutions are accepted with a certain probability to prevent falling into a local optimum. As the temperature gradually decreases, the optimization process gradually converges and eventually finds the optimal combination of orbit change parameters.

[0071] Output: optimal trajectory change parameters (trajectory change time, rendezvous time, number of rendezvous circles) and minimized velocity pulse value.

[0072] Step 5: Use dynamic programming to optimize the matching scheme between satellites and space debris

[0073] Input: Optimized orbit change parameters

[0074] Processing: Apply the optimized orbit change parameters to the matching problem between satellites and space debris. Use the dynamic programming algorithm to construct a two-dimensional array to represent the state and determine the state transition equation to ensure that each satellite can only remove one piece of space debris and the total removal cost is minimized. According to the state transition equation and boundary conditions, gradually optimize the matching scheme between satellites and debris.

[0075] Output: The optimal satellite and space debris matching solution will be used to perform the actual removal mission.

[0076] Step 6: Integrate the optimization results and execute the spacecraft orbit change and debris removal tasks

[0077] Input: Optimal orbit change parameters and satellite debris matching solution

[0078] Processing: Integrate orbit change parameters and debris matching schemes to plan the actual orbit change and debris removal mission of the spacecraft. Ensure that in actual operations, orbit change and debris removal are carried out as planned to achieve optimal fuel consumption and mission efficiency.

[0079] Output: Successfully completed spacecraft orbit change and space debris removal mission, ultimately achieving safe and efficient mission execution.

[0080] This summary method ensures the completeness and logical order of the technical solution, which helps to understand and implement each step in practical applications.

[0081] Implementation Method 2: Combination Figure 1-8 This embodiment is described in detail by using specific examples to further describe the technical solution provided above. Specifically:

[0082] The technical solution provided in this embodiment uses the optimization algorithm module in the SpaceSim software developed by the School of Aerospace Engineering of Harbin Institute of Technology to calculate the service satellite's orbit change velocity pulse and directly display the orbit change process in the software, so that the optimization results of the removal task are clear. For the satellite disintegration scenario, the multi-loop Lambert algorithm is optimized by simulated annealing algorithm to obtain the velocity pulse size required for each service satellite to clear each target space debris, thereby establishing a velocity pulse matrix. For this matrix, a dynamic programming algorithm is used for processing, and when the position of some space debris is relatively special, the method of emergency launching satellites to the target space debris position is used to clear the space debris.

[0083] This implementation method proposes a new method, which is to establish a satellite constellation for clearing space debris and combine it with temporary launch to supplement it, and conduct cluster research on the problem of multiple satellites clearing space debris. For each piece of space debris, dynamic programming and other methods are used to find the most suitable satellite to clear it, so that a single satellite can save fuel and clear more space debris. When the location of some space debris is relatively special, the space debris is cleared by emergency satellite launch to the target space debris location, ensuring low fuel consumption. There are many studies on space debris removal technology. Now there are several relatively mature space debris cleaning technologies that have been verified in orbit, such as space rope net capture technology, etc. These mechanisms can effectively capture invalid space debris.

[0084] The simulated annealing algorithm is a heuristic algorithm that simulates the cooling process of metals in real life. As the temperature decreases, the state of the algorithm solution tends to stabilize. Finally, when the temperature reaches the lowest point, it stops calculating and outputs the optimal solution. During the search process, the difference between the current solution and the new solution and the current temperature are used to decide whether to accept the new solution. At the beginning of the algorithm, the temperature is high, allowing poor solutions to be accepted. As the iteration proceeds, the temperature gradually decreases, and the acceptance probability also gradually decreases, making the algorithm gradually tend to find a better solution in the global scope. The simulated annealing algorithm is widely used in aerospace planning research. For example, Ding Yinan and others from the Beijing Institute of Control Engineering established a multi-satellite and multi-observation target agile imaging satellite mission planning model. Further, taking the imaging time as the optimization object, a multi-objective variable neighborhood simulated annealing algorithm that uses the cooling process to adjust the search range is proposed. The algorithm has achieved good results.

[0085] Dynamic programming is a method for solving optimization problems in multi-stage decision-making processes. It can effectively reduce repeated calculations and improve algorithm efficiency. For problems with higher complexity, dynamic programming can effectively reduce computational complexity through recursion and memoization search, making the problem solvable. Due to its wide applicability and high efficiency, dynamic programming has been widely used in algorithm design and practical problem solving.

[0086] This implementation method studies the problem of space-ground mixed multi-space debris removal mission planning for multiple satellites to clear multiple space debris. A complete mission planning module for multiple space debris problems is established in the domestic space simulation software SpaceSim, and the results can be visualized more clearly. The multi-loop Lambert algorithm is optimized by simulated annealing algorithm to establish a velocity pulse matrix, and when the position of some space debris is relatively special, the space debris is cleared by emergency satellite launch to the target space debris position. The results show that the mission planning results for multiple space debris problems are good, which can effectively reduce the total pulse of the mission and the fuel consumed by a single satellite, effectively saving satellite costs while playing a role in eliminating more space debris.

[0087] Specific:

[0088] Space debris strike mission planning problem

[0089] The scenario is that a satellite suddenly disintegrates, and the fragments are distributed on multiple orbital planes to form a debris cluster. It is urgent to clear the debris cluster in orbit to prevent these fragments from colliding with other satellites and causing greater impact.

[0090] The parameters and variables are defined as follows:

[0091] Y preset satellite set, Y = {y1, y2, ...y Ny}Where N y The number of preset satellites.

[0092] P emergency satellite set, P = {p1, p2, ... p Np}Where N p is the number of emergency satellites.

[0093] K space fragments set, K = {k1, k2, ...k Nk}Where N k is the amount of space debris.

[0094] t1 is the time when our satellite changes orbit.

[0095] t2 is the rendezvous time between the satellite and the space debris.

[0096] n is the number of orbits our satellite makes before rendezvous.

[0097] Space Debris Strike Mission Modeling:

[0098] For the space debris removal mission, a removal equivalent simulation model is established as the design constraint for the pre-set constellation and the emergency constellation. The removal satellite deployment methods include emergency deployment and dedicated constellation pre-set deployment. Co-orbit deployment is used as the main form of emergency deployment.

[0099] Clearance model: The clearance system is simplified using beams, and the weapon system is Figure 1 As shown in the figure, the beam pointing represents the clearing direction of our satellite. When our satellite beam points to a space debris, it means that our satellite will clear the space debris, assuming that the clearing will be successful and a single satellite can only clear a single space debris.

[0100] Since the satellites in the Walker constellation are in different orbital planes and are relatively evenly distributed, the display effect is good and can effectively play a simulation role. In this implementation, the Walker constellation is used as space debris to simulate the space debris group that we want to remove. The specific orbital six numbers of the generated space debris are calculated by the SpaceSim software and displayed inside the software, such as Figure 2 Show.

[0101] The emergency constellation is deployed for rapid launch when the number of pre-set constellation satellites or fuel is insufficient to strike all space debris in the debris cluster. It is necessary to design the launch point, number of launch satellites, etc. The orbit design principle of the emergency satellite is based on the rapid realization of the strike target.

[0102] At the initial moment, the satellite begins to erect and reaches the expected position on the territory of my country. After the satellite is erected, it first reaches the seven gathering points located on the territory of my country that are closest to its expected launch position. After the satellite is erected, it will maneuver at a constant speed toward the target launch point based on its own geographical location. After the satellite maneuvers to the launch point, it will stay at the launch point for a period of time to prepare for the launch.

[0103] After a period of simulation, our satellites will be launched one after another. The launch of our satellites is based on the relative position of our satellites and space debris. When the relative position is at a minimum value within a period of time, the satellite will be launched immediately. If the minimum value has not appeared within 3000s, the satellite will no longer wait for the launch window and will be launched immediately. The time it takes for the satellite to be launched to the expected position is related to the distance between our satellite and the space debris at the launch time. The shorter the distance between our satellite and the space debris at the launch time, the shorter the flight time after launch.

[0104] Spacecraft trajectory change model:

[0105] Commonly used orbit change methods for spacecraft orbit change include Hohmann orbit change, three-pulse orbit change, Lambert orbit change, etc. This embodiment adopts the Lambert algorithm as the orbit change algorithm, and uses the simulated annealing algorithm to optimize the Lambert problem. For the Lambert method, it is difficult to get a good result by directly using the Lambert orbit change. Usually, the optimized Lambert orbit change method is used to change the orbit to save fuel. This embodiment uses the simulated annealing algorithm to optimize the Lambert problem. The simulated annealing algorithm is a global optimization algorithm based on the principles of physics. It was originally used to solve the crystal structure optimization problem in the metal annealing process. This algorithm finds the minimum value of a function by simulating the process in which solid materials become more uniform and stable at high temperatures. For the multi-turn Lambert problem, the variables to be optimized are the orbit change time, the rendezvous time, and the number of rendezvous circles. The minimum value of the function refers to the minimum value of the velocity pulse.

[0106] In the simulated annealing algorithm, the system is initially in a high temperature state, in which it accepts poor solutions, and gradually lowers the temperature over time. As the temperature decreases, the system will be more and more inclined to accept solutions that are better than the current solution. This can avoid being trapped in a local optimal solution and being unable to jump out.

[0107] Specifically, the simulated annealing algorithm includes the following steps:

[0108] 1. Initialization: determine parameters such as initial temperature, end temperature, cooling rate, etc.

[0109] 2. Generate an initial solution randomly or obtain an initial approximate solution from a known heuristic method.

[0110] 3. In each iteration step, a small perturbation is made to the current solution to generate a new candidate solution. The perturbation decreases as the temperature decreases, and the objective function value is calculated.

[0111] 4. According to the Metropolis criterion (probability acceptance of differential solution), choose whether to accept the new candidate solution as the next search point or update the current search point with a certain probability.

[0112] 5. Repeat steps 3-4 until the termination condition is met (such as reaching the maximum number of iterations).

[0113] For the Lambert problem, there are three variables, namely, the time t1 of our satellite changing orbit, the time t2 of the satellite rendezvous with the space debris, and the number of our satellite's orbits before the rendezvous, n. The initial conditions are set as follows

[0114] t1=0.2t,t2=0.8t,

[0115] Wherein, a is the semi-major axis of our satellite's original orbit, 0.6t=t2-t1, that is, the flight time of our satellite after changing its orbit, and n is the integer number of flight circles from time t1 to time t2 assuming that our satellite does not change its orbit.

[0116] The main restrictions are as follows:

[0117] The constraint condition for time t is 0 <t1<t2<t

[0118] The limit condition for the number of running cycles n is 0 <n

[0119] The orbit cannot pass through the earth during the orbit change process

[0120] For the annealing algorithm solution, the initial temperature is set to 100.0, the end temperature is set to 0.001, the number of iterations at each temperature is set to 100 times, and the temperature is reduced after every 100 iterations. The simulation algorithm flow chart is as follows: Figure 3 shown.

[0121] For the debris clustering problem of our n pre-set satellites clearing n pieces of space debris, assuming that each satellite can only clear one piece of space debris, and each piece of space debris can only be cleared by one satellite, the corresponding relationship between satellites clearing space debris can affect the final result of the clearance plan. Using the annealing algorithm, each pre-set satellite can get an optimization result for each piece of space debris. Finally, an n*n matrix can be obtained. The problem can be simplified to taking a value for each row, and their column values ​​cannot be repeated, and the sum of the n numbers finally obtained must be the smallest. The dynamic programming algorithm can solve this problem well.

[0122] The dynamic programming algorithm to solve this problem is as follows:

[0123] 1. Define the state: Design a state representation, which is usually related to the solution of the problem. In this problem, you can design a two-dimensional array dp, a selection order p, and a final selection order q. In dp, dp[i][j] stores the minimum sum of the array starting from the jth column after planning i rows, p[i][j] stores the selection order of each row, and q is the selection order with the best result in p.

[0124] 2. State transition equation: Determine the state transition equation, that is, how to update the current state based on the previous state. In this problem, the state transition equation is expressed as: dp[i][j] = min(dp[i-1][k] + matrix[i][j]), where k≠j and satisfies the constraints (i.e., the number of columns selected in the i-1th row cannot be the same as the number of columns previously selected in p).

[0125] 3. Boundary conditions: Determine the boundary conditions, i.e. the initial state. In this problem, the state of the first row can be directly determined by the values ​​in the matrix.

[0126] Finally, the results analysis:

[0127] Classification of space-ground mixed multi-space debris removal tasks

[0128] For us N y Preset satellites clear N k The debris clustering problem of N space debris. Assuming that each satellite can only clear one space debris, and each space debris can only be cleared by one satellite, the corresponding relationship between satellites and space debris can affect the final clearance planning result. Using the annealing algorithm, each preset satellite can get an optimization result for each space debris. Finally, we can get an N y *N k There are three cases.

[0129] Case 1: N y =N k

[0130] At this point, the number of our satellites is equal to the number of space debris, and N y *N k The matrix is ​​calculated using the original dynamic programming algorithm to obtain the selection order q. Assuming that the value of qn is a, it means that in the plan, our satellite numbered n will change orbit to the space debris numbered a to clear it. A set of parameters is selected, and an example of our 5 satellites clearing 5 space debris is used. The matrix is ​​shown in Table 1. q is [5, 1, 2, 3, 4], the total speed pulse size is 1189.8 m / s, and the single speed pulse does not exceed 300 m / s. The total speed pulse is small, and the clearing effect is good.

[0131] Table 1 Case 1

[0132]

[0133] Case 2: N y >N k

[0134] At this time, the number of our satellites is greater than the number of space debris, and some of our satellites will not be assigned to clear targets. y *N k Matrix flip to N k *N yThe matrix is ​​still calculated using the original dynamic programming algorithm. The original algorithm intends to give priority to finding a space debris with the best clearance effect of our satellite. After flipping, since the number of columns is greater than the number of rows, the number of columns represents the number of our satellites, so that our satellite resources can be fully utilized. Taking our 5 satellites clearing 4 space debris as an example, the matrix is ​​shown in Table 2. q is [2,3,4,5], the total speed pulse size is 958.4m / s, and the single speed pulse does not exceed 300m / s. The total speed pulse is small and the clearance effect is good.

[0135] Table 2 Case 2

[0136]

[0137] Case 3: N y <N k

[0138] At this time, the number of our satellites is less than the number of space debris, and N y *N k The matrix is ​​calculated using the original dynamic programming algorithm to obtain the selection order q. Assuming q n The value of is a, which means that the nth satellite of our side will change orbit to the space debris numbered a in the plan to clear it. Taking the example of our 4 satellites clearing 5 space debris, the matrix is ​​shown in Table 3. q is [5,1,2,3], the total speed pulse size is 918.8m / s, and the single speed pulse does not exceed 300m / s. The total speed pulse is small, and the clearing effect is good, but one space debris is not cleared. In this case, an emergency satellite will be launched to clear the space debris that is not assigned to the satellite for clearing. The emergency satellite is uniformly set to a cost of 400m / s. In addition, if the speed pulse of the preset satellite to clear the space debris is greater than 400m / s, the space debris will also be cleared by launching an emergency satellite. Therefore, the total speed pulse in this case is 1318.8m / s.

[0139] Table 3 Case 3

[0140]

[0141] The impact of the number of satellites on the total speed pulse size:

[0142] Take situation 1 as an example for analysis. We continuously increase the number of our satellites and draw a curve showing the change of the average velocity pulse size as the number of our satellites increases. Figure 4As shown in the figure, analysis shows that the average speed pulse size decreases with the increase in the number of satellites, and it will drop sharply at some points. This is because adding new satellites at these points will have a greater impact on the overall dynamic planning path selection, while most points will not have a significant impact after adding, thus causing the average speed pulse to rise. When the number of satellites is small, the average speed pulse decreases sharply with the increase in the number of satellites. After exceeding 10, a smoother dynamic planning path can be selected, causing the speed pulse size to decrease slowly. Therefore, it is recommended to preset at least 10 satellites, and these satellites are preferably located in different orbital planes to reduce the average speed pulse.

[0143] The number of emergency satellites that need to be launched, that is, the number of space debris that needs to be cleared when the speed pulse exceeds 400m / s, and the number of emergency satellites that need to be launched to clear the space debris changes with the preset satellites as shown in the following figure: Figure 5 As shown, it can be seen that under normal circumstances, the number of emergency satellites that need to be launched is relatively small, and emergency satellites are mainly used when the number of our satellites is less than the number of space debris.

[0144] The results in Spacesim show:

[0145] The calculation results are visualized in Spacesim. Taking the scenario where our satellites are less than space debris as an example, the Spacesim visualization window is displayed. Our preset satellites are green, and the space debris that has not been cleared is a white ball. After being cleared, it becomes a red ball. Our emergency launch satellite is a blue ball. Figure 6 As shown, we can see that our preset satellites have approached the target space debris, overlapped with it and cleared it. Since the number of our preset satellites is less than the number of space debris, there are still white balls in the scene. Our satellite aims the beam at the white balls to clear them, but fails because the distance is too far.

[0146] After a period of time, an emergency satellite is launched to the space debris that has not been cleared. The emergency satellite approaches the target debris. Figure 7 As shown, the emergency satellite successfully cleared the target debris. Figure 8 As shown in the figure, it can be seen that our emergency satellite successfully approached and cleared the target debris, the scene demonstration was successful, and Spacesim can demonstrate the space debris removal scene more intuitively.

[0147] This implementation method studies the problem of space-ground mixed multi-space debris removal mission planning based on simulated annealing algorithm for multiple service satellites. The simulation results show that the establishment of a large-scale pre-set constellation can effectively remove space debris groups, the average velocity pulse size decreases with the number of satellites, and the launch of emergency satellites can well supplement the pre-set satellites. Spacesim software has strong analysis and visualization capabilities and supports simulation of large-scale scenarios.

[0148] The technical solution provided by the present invention is further described in detail above through several specific implementation modes in order to highlight the advantages and benefits of the technical solution provided by the present invention. However, the several specific implementation modes described above are not intended to be used as limitations on the present invention. Any reasonable modification and improvement of the present invention, combination of implementation modes and equivalent substitution within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. Calculation method of orbit change parameters for space debris removal based on annealing algorithm. It is characterized in that include: Collect the steps of the space debris strike mission; Steps to simulate satellites in a constellation; The step of obtaining an optimization target of an algorithm according to the space debris strike mission and the satellites in the constellation; The step of setting simulated annealing algorithm parameters according to the optimization target; A step of collecting a randomly generated initial solution as a starting point of the simulated annealing algorithm; Iteratively optimizing the initial solution to obtain a combination of trajectory change parameters under preset conditions; On the basis that each satellite only clears one piece of space debris, a step of matching individual satellites with individual space debris according to the orbit change parameter combination is provided.

2. According to the method for calculating orbit change parameters for space debris removal based on annealing algorithm in air-to-ground hybrid space debris removal, It is characterized in that The algorithm is the Lambert algorithm.

3. According to the method for calculating orbit change parameters of space debris removal based on annealing algorithm in air-ground hybrid space ... It is characterized in that The optimization objectives include track change time, rendezvous time and number of rendezvous circles.

4. According to the method for calculating orbit change parameters for space debris removal based on annealing algorithm for air-ground hybrid space ... It is characterized in that The parameters include initial temperature, final temperature, cooling rate and number of iterations at each temperature.

5. A device for calculating orbital parameters for space debris removal based on annealing algorithm. It is characterized in that include: A module for collecting space debris strike missions; Modules for simulating satellites in a constellation; A module for obtaining an optimization target of an algorithm according to the space debris strike mission and the satellites in the constellation; A module for setting simulated annealing algorithm parameters according to the optimization goal; A module for collecting randomly generated initial solutions as the starting point of the simulated annealing algorithm; Iteratively optimize the initial solution to obtain a module for a combination of orbit change parameters under preset conditions; A module for matching individual satellites with individual space debris based on the combination of orbit change parameters on the basis that each satellite only clears one space debris.

6. A space-ground joint batch rendezvous elimination method for space debris, It is characterized in that include: The step of collecting the matching results of the satellite individuals and the space debris individuals obtained in claim 1; A step of clearing space debris is performed according to the matching result.

7. A space-ground combined space debris batch rendezvous elimination device, It is characterized in that include: A module for collecting matching results of satellite individuals and space debris individuals obtained in claim 1; A module for clearing space debris according to the matching result.

8. Computer storage media for storing computer programs, It is characterized in that When the computer program is read by a computer, the computer executes the method of claim 1 .

9. A computer, including a processor and a storage medium, It is characterized in that When the processor reads the computer program stored in the storage medium, the computer executes the method of claim 1 .

10. Computer program product, being a computer program, It is characterized in that When the computer program is executed, the method of claim 1 is implemented.

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