Cluster space debris active protection task planning method

Abstract

The invention discloses a cluster space debris active protection task planning method. The method comprises the following steps: 1, completing mathematical modeling of a space debris active protection task planning problem; 2, considering energy consumption, time constraint and a clearing effect, performing construction of a revenue function of a single service spacecraft, and completing construction of an overall revenue function of a plurality of service spacecrafts on the basis; 3, a particle swarm optimization algorithm is used for completing design of a sequence planning problem solving method in single-star fragment removal; and 4, based on a double-layer integrated solving thought, completing design of a multi-satellite debris removal task planning problem solving method on the basis of single-row sequence planning, and completing solving of cluster space debris active protection task planning.

Description

Technical Field

[0001] The invention relates to a cluster space debris active protection mission planning method and relates to the field of aerospace technology. Background Art

[0002] With the increase of human space activities, the space debris generated is increasing, and the space environment is deteriorating, which has seriously affected the normal operation of existing spacecraft and the normal development of space activities. At present, the research on space debris can be divided into the following four aspects: debris detection, which is to detect, track and identify space debris through space-based, ground-based optical or radar detection; passive protection, that is, to enhance the resistance of spacecraft to debris impact through material and structural design of the external structure of the spacecraft; debris removal, which uses contact and non-contact methods to clean, remove or control the debris in the earth orbit; debris avoidance, which is to adjust the orbital state of the spacecraft by maneuvering and actively avoid the incoming space debris. However, these methods still have shortcomings. For example, it is difficult to passively protect special structures such as large antennas and solar panels, which will affect their structure and function; debris avoidance does not fundamentally eliminate the threat of debris, which will not only consume the spacecraft's own fuel, but also affect the normal operation of spacecraft with special orbital positions; currently, debris removal does not take into account the spacecraft that needs to be guarded, and for large-scale space debris, it is difficult to achieve the ideal debris removal effect by limited service spacecraft alone. Therefore, under the condition of limited resources, it is necessary to carry out more effective active protection technology research for key large spacecraft, especially those with special structures, special orbits, and limited maneuverability. Summary of the invention

[0003] The technical problem to be solved by the present invention is to overcome the deficiencies of the prior art and provide a task planning method for solving the problem of multiple spacecraft clearing multiple debris during active protection of space debris.

[0004] The purpose of the present invention is achieved through the following technical solutions:

[0005] A cluster space debris active protection mission planning method, comprising:

[0006] S1. Mathematical modeling of the active space debris protection mission planning problem;

[0007] S2. Construct a single service spacecraft benefit function based on energy consumption, time constraints, and cleaning effects, and then construct an overall benefit function for multiple service spacecraft;

[0008] S3, using particle swarm optimization algorithm to solve the sequence planning problem in single-star debris removal;

[0009] S4. Solve the multi-star debris removal mission planning problem based on single-star sequence planning.

[0010] Compared with the prior art, the present invention has the following beneficial effects:

[0011] (1) Compared with traditional methods, the active space debris protection technology involved in the present invention can be applied to debris protection tasks of spacecraft of different sizes and types.

[0012] (2) Compared with other traditional methods, when solving the mission planning problem of a single service spacecraft for multiple debris, it is necessary to solve two-level optimization problems of clearing order and clearing time. The single-star sequence planning algorithm proposed in the present invention uses time as the optimization parameter, realizes the simultaneous optimization of the clearing order and clearing time of a single service spacecraft for multiple debris, and converts the original two-level optimization problem into a single-level optimization problem.

[0013] (3) The present invention uses a particle swarm optimization algorithm to find a better planning result while comprehensively considering the completion of many-to-many tasks and the fuel consumption of each satellite. Compared with other methods, it is more suitable for realizing many-to-many active protection debris removal task planning in a shorter time.

[0014] (4) The many-to-many double-layer integrated solution method proposed in the present invention introduces a dynamic set, so that each service spacecraft can adjust the set of debris it needs to remove in real time according to the new mission when planning its mission. Compared with other traditional algorithms, it avoids falling into local optimality and can find a better solution. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is a schematic diagram of the active protection scenario for space debris;

[0016] Figure 2 A hierarchical diagram for many-to-many problems;

[0017] Figure 3 This is the flow chart of the single-star sequence planning algorithm;

[0018] Figure 4 It is a flow chart of the double-layer integrated solution method;

[0019] Figure 5 The figure is a flow chart of the steps of the method of the present invention. DETAILED DESCRIPTION

[0020] To make the objectives, technical solutions and advantages of the present invention more clear, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0021] A cluster space debris active protection mission planning method, such as Figure 5 As shown, including:

[0022] S1, mathematical modeling of cluster protection scenarios for multiple space debris attacks;

[0023] S2, design the benefit function of the space debris active protection mission planning problem: complete the construction of the benefit function of a single service spacecraft, and on this basis, complete the construction of the overall benefit function of multiple service spacecraft;

[0024] S3, using particle swarm optimization algorithm to solve the sequence planning problem in single-star debris removal;

[0025] S4, based on the double-layer integrated solution idea, completes the solution of multi-star debris removal mission planning problem on the basis of single-star sequence planning.

[0026] Step S1 is specifically as follows:

[0027] like Figure 1 , the triangle represents the reference spacecraft; the circles c1, c2…c n represents a service spacecraft (i.e., a spacecraft used to perform debris removal missions); squares g1, g2…g m represents the space debris flying towards the reference spacecraft; the circle surrounded by the service spacecraft represents the safety range of the reference spacecraft, that is, the service spacecraft needs to remove the debris before it enters the safety range. The problem is simplified to a many-to-many task planning problem with constraints such as time and maneuverability. In order to facilitate problem analysis and solution, the problem is now divided into the following Figure 2 The two-level planning problem of "fragment allocation + sequence planning" shown in the figure is:

[0028]

[0029] In formula (1), J i represents the individual benefit value of each service spacecraft; J total Represents the total benefit value of all serviced spacecraft, i is the ordinal number, and n is the number of serviced spacecraft.

[0030] Debris allocation is used to determine the set of debris that each service spacecraft needs to clear; sequence planning is used to determine the order and time for clearing debris by each service spacecraft based on the set of debris of each service spacecraft.

[0031] The motion of each spacecraft adopts two-body dynamics, and the mission time, speed, and distance constraints of each service spacecraft are as follows:

[0032]

[0033] In formula (2) Respectively represent c i , g j Position vector in the Earth's inertial coordinate system; Respectively represent c i , g j Velocity vector in the Earth's inertial coordinate system; ρ min 、v min is a fixed value, and ρ is set in the present invention min =0, v min = 0, which means that removal can only be achieved when the service spacecraft and the debris have the same position and speed.

[0034]

[0035] In formula (3) Indicates service spacecraft c i Arrival Fragment G i The moment; t0 represents the initial moment; Indicates fragment g j The transfer time required to reach the boundary of the reference spacecraft's safety range from the initial position.

[0036]

[0037] In formula (4) Indicates c i The maximum single speed increment amplitude; Indicates c i Total velocity increment constraint; Respectively represent c i Clear debris k The magnitude of the velocity increment applied at the time of departure and arrival; Indicates that c i The cumulative consumption speed increment size;

[0038] Step S2 is specifically as follows:

[0039] S2.1, design and complete the construction of a single service spacecraft benefit function by analyzing the one-to-many debris removal task problem;

[0040] S2.2, based on the benefit function of a single service spacecraft, design and complete the construction of the overall benefit function of multiple service spacecraft in the active debris protection mission;

[0041] S2.1, for any service spacecraft i, given a set of debris, considering the constraints of single pulse size, clearing time, and total fuel, it may not be possible to clear all the assigned debris during the actual mission execution. Therefore, it is necessary to define the following n×m matrix A, where n is equal to the number of service spacecraft and m is equal to the number of debris, to record the final clearance of debris by each service spacecraft, where the i-th row represents the i-th service spacecraft ci The clearing vector:

[0042]

[0043] Any element a in A ij represents the clearance of the jth debris by the ith service spacecraft (i∈{1, 2, ..., n}, J∈{1, 2, ..., m}), and its value is 0 or 1. i Able to successfully remove debris g j hour, Time c i With g j Satisfy the distance and speed constraints and reach g j When the total pulse constraint is satisfied, then a ij =1, otherwise a ij = 0. For any service spacecraft i, since different removal sequences will lead to changes in the debris set that can be successfully removed in the end, under the given debris set, a ij It can be viewed as a function of a given set of shards and the time it takes to clear each shard.

[0044] For any service spacecraft c i , during the entire debris removal task, its individual benefit function can be constructed as follows:

[0045]

[0046] Indicates service spacecraft c i Given the set of cleanup debris and the cleaning sequence, the speed increment consumed during the entire cleaning task; Indicates c i The total pulse increment at the initial moment; a ij ∈{0, 1} represents the service spacecraft c i For fragments g j The clearance situation, when a ij =1 indicates fragment g j Successfully c i Cleared, otherwise not cleared i Clear; Indicates the entire clearing process c i The number of debris successfully removed; ξ1, ξ2∈[0,1] are weight coefficients. When ξ1=1, ξ2=1, the overall meaning of the above formula can be understood as: obtaining the greatest possible benefit while consuming the least possible fuel.

[0047] S2.2, according to S2.1, the group benefit function of all service spacecraft during the entire mission can be constructed as follows:

[0048]

[0049] Among them J total represents the group benefit of the entire service spacecraft; J i Represents each service spacecraft c i The individual benefit function value of m is the total number of fragments.

[0050] Step S3 is specifically as follows:

[0051] By analyzing the one-to-many space debris protection problem, a service sequence planning algorithm for a single service spacecraft to multiple debris targets based on particle swarm optimization algorithm (hereinafter referred to as the single-spacecraft service sequence planning algorithm) is designed and proposed.

[0052] like Figure 3 , with any service spacecraft c i For example, suppose the set of fragments to be cleared is {g1, g2, ..., g k}, the specific process of the single-star service sequence planning algorithm based on the particle swarm optimization algorithm can be described as follows:

[0053] (1) According to the number of fragments k to be cleared, the dimension of the optimization variable X is set to k, where the jth dimension of the optimization variable corresponds to the value x j , represents the debris removal by the service spacecraft g j time; and according to the initial time t0 and the fragment transfer time Set the upper and lower limits of the values ​​of each dimension of the optimization variable: Where α is a fixed constant with a value in the interval (1, 2).

[0054] (2) Determine the number of individuals N in the particle swarm according to the problem scale and initialize the position X of each particle i and speed V i ;

[0055] (3) Calculate the fitness function value f corresponding to each particle i , and the individual's historical optimal fitness function value Compare, if Update the historical optimal value And update the position P corresponding to the optimal value of the particle i =X i , otherwise jump to step (4);

[0056] (4) Update the optimal value of the group g best : If f i > best, then let g best =f i , and update the particle group optimal value corresponding position G = X i , otherwise jump to step (5);

[0057] (5) Update the particle position and speed based on the particle's current position and speed, the particle's optimal position, and the particle group's optimal position to obtain a new position and speed X i 、V i , and perform boundary processing on the new position and velocity;

[0058] (6) Determine whether the algorithm meets the termination condition. If so, terminate; otherwise, go to step (3).

[0059] The specific solution rules for the fitness function value are as follows:

[0060] (3.1) Assume that the set of fragments to be cleared is {g1, g2, ..., g k}, then the time it takes for the spacecraft to clear the debris is used as a variable to construct any particle in Indicates service spacecraft c i Arrival Fragment G j moment;

[0061] (3.2) Preliminary determination of the clearing sequence. i Each component in Corresponding to the service spacecraft c i Arrival fragment {g1, g2, ..., g k}, so to satisfy the clearing time constraint ( in Indicates fragment g j The debris with the required time from the initial position to the reference spacecraft safety range boundary) is sorted from small to large according to the arrival time, and the order of clearing the debris by the service spacecraft can be preliminarily obtained. Assuming that the debris is sorted from small to large according to the clearing time, the clearing sequence obtained is:

[0062] [g2,g k ,…,g1](8)

[0063] After reordering, the clearing times for each fragment are:

[0064]

[0065] In order to facilitate the subsequent expression needs, [g2, g k ,...,g1] is represented as [g′1,g′2,…,g′ k ], that is, g′1=g2, g′2=gk ,…,g′ k =g1. Similarly, let make

[0066] (3.3) Record the clearing result. Define vector A i =[a i1 , a i2 , …, a ik ] and initialized to 0 to record the service spacecraft c i In the order [g′1, g′2, ..., g′ k ],time When the final clearing of each fragment, only fragment g j Can be successfully serviced spacecraft c i When clearing a ij =1, otherwise a ij =0.

[0067] ① Assume that spacecraft c i From the initial position in the order [g′0, g′1, g′2, ..., g′ k ] clears the target, where g′0 represents c i Initial position, p mark =0, p=1;

[0068] ②Then calculate the spacecraft by Time has come moment, from to g p 'Speed ​​consumption

[0069]

[0070] ③If g p ′ corresponds to {g1, g2, ..., g k The g in} j , and when the above constraints are met, a ij =1, p mark = p, otherwise a ij =0, p=p+1;

[0071] ④Judge if p>k, end if yes, otherwise go to step ②.

[0072] Then the final spacecraft c i By Particle The single star benefit when performing debris removal (i.e. particle X j The fitness function value of is:

[0073]

[0074] Step S4 is specifically as follows:

[0075] Based on the single-satellite service sequence planning algorithm, a two-level integrated solution method is further proposed to solve the mission planning problem of multiple service spacecraft removing multiple space debris.

[0076] like Figure 4 , a two-layer integrated solution method based on the single-satellite service sequence planning algorithm, the algorithm flow is as follows:

[0077] (1) Initialize the total set of fragments to be allocated G = {g1, g2, ..., g m}(The debris set G here has a different meaning from the debris set mentioned in 3.1 of S3. The debris set mentioned in 3.1 is only used to illustrate the single-star service sequence planning algorithm process, which is a subset of the total debris set). The dynamic set C of debris removed by each service spacecraft i ={}, single unit maximum benefit maxJ i =0, total benefit J total =0;

[0078] (2) Initialize the overflow fragment set G′ i = {}, the remaining fragment set G′ = {}, the profit change ΔJ i =-inf, -inf means negative infinity, mark = 0;

[0079] (3) Each time, a fragment g is taken out from the allocation fragment set G without duplication. j , which are injected into the service spacecraft c in sequence i , i = 1, 2, ..., n cleared dynamic set of debris, get the set {C i , g j}, based on the single-star sequence planning method, calculate the service spacecraft c i In the fragment collection {C i , g j The set of fragments C′ that can be successfully cleared under i ;

[0080] (4) According to the service spacecraft set C i and set C′ i Determine whether there is a service spacecraft c i Successfully cleared the debris j , if it exists, calculate c i For the fragment set C′ i The single unit profit maxJ′ i , the change in monomer revenue ΔJ i =(maxJ′ i-maxJ i ), overflow fragment set G′ i ; Otherwise, let G′={G′, g j} and go to step (3);

[0081] (5) Record the maximum monomer benefit max(ΔJ i ) corresponds to the service spacecraft subscript mark, let J total =J total +ΔJ mark ,max(J mark )=max(J mark )+ΔJ mark , C i =C′ i , G′={G′, G′ mark};

[0082] (6) Determine whether the remaining fragment set G′ is empty. If so, terminate the calculation. Otherwise, further determine whether the elements in G are the same as the elements in G′. If so, terminate the calculation. If not, set G = G′ and go to step (2).

[0083] The contents not described in detail in the specification of the present invention belong to the common knowledge of those skilled in the art.

[0084] Although the present invention has been disclosed as above in the form of a preferred embodiment, it is not intended to limit the present invention. Any person skilled in the art may make possible changes and modifications to the technical solution of the present invention by using the methods and technical contents disclosed above without departing from the spirit and scope of the present invention. Therefore, any simple modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall fall within the protection scope of the technical solution of the present invention.

Claims

1. A cluster space debris active protection task planning method, characterized in that: include: S1. Mathematical modeling of the active space debris protection mission planning problem; S2. Construct a single service spacecraft benefit function based on energy consumption, time constraints, and cleaning effects, and then construct an overall benefit function for multiple service spacecraft; S3, using particle swarm optimization algorithm to solve the sequence planning problem in single-star debris removal; S4. Solve the multi-star debris removal mission planning problem based on single-star sequence planning.

2. The cluster space debris active protection mission planning method according to claim 1 is characterized in that: The space debris active protection mission planning problem is split into a two-level planning problem of debris allocation and sequence planning. Debris allocation, which determines the set of debris that each serviced spacecraft needs to remove; Sequence planning determines the order and time for clearing debris by each service spacecraft based on the debris set of each service spacecraft.

3. The cluster space debris active protection task planning method according to claim 1 is characterized in that: For any service spacecraft c i , during the entire debris removal task, its individual benefit function can be constructed as follows: Indicates service spacecraft c i Given the set of cleanup debris and the cleaning sequence, the speed increment consumed during the entire cleaning task; Indicates c i The total pulse increment at the initial moment; a ij ∈{0,1} represents the service spacecraft c i For fragments g j The clearance situation, when a ij =1 indicates fragment g j Successfully c i Cleared, otherwise not cleared i Clear; Indicates the entire clearing process c i The number of successfully cleared debris; ξ1, ξ2∈[0,1] are weight coefficients, and m is the total number of debris; when ξ1=1, ξ2=1, the overall meaning of the above formula can be understood as: obtaining the greatest possible benefit while consuming the least possible fuel.

4. The cluster space debris active protection mission planning method according to claim 3 is characterized in that: The group profit function of all service spacecraft is as follows: Among them J total represents the group benefit of the entire service spacecraft; J i Represents each service spacecraft c i The value of the individual profit function.

5. The cluster space debris active protection mission planning method according to claim 1 is characterized in that: The use of particle swarm optimization algorithm to solve the sequence planning problem in single-star debris removal includes: For any service spacecraft c i , assuming that the set of fragments to be cleared is {g1,g2,…,g k }; (1) According to the number of fragments k to be cleared, the dimension of the optimization variable X is set to k, where the jth dimension of the optimization variable corresponds to the value x j , represents the debris removal by the service spacecraft g j time; and according to the initial time t0 and the fragment transfer time Set the upper and lower limits of the values ​​of each dimension of the optimization variable: Where α is a fixed constant with a value in the interval (1,2); (2) Determine the number of individuals N in the particle swarm according to the problem scale and initialize the position X of each particle i and speed V i ; (3) Calculate the fitness function value f corresponding to each particle i , and the individual's historical optimal fitness function value Compare, if Update the historical optimal value And update the position P corresponding to the optimal value of the particle i =X i , otherwise jump to step (4); (4) Update the optimal value of the group g best : If f i > best , then let g best =f i , and update the particle group optimal value corresponding position G = X i , otherwise jump to step (5); (5) Update the particle position and speed based on the particle's current position and speed, the particle's optimal position, and the particle group's optimal position to obtain a new position and speed X i 、V i , and perform boundary processing on the new position and velocity; (6) Determine whether the algorithm meets the termination condition. If so, terminate; otherwise, go to step (3).

6. The cluster space debris active protection mission planning method according to claim 5 is characterized in that: The solution to the fitness function value is: (3.1) Assume that the set of fragments to be cleared is {g1,g2,…,g k }, then the time it takes for the spacecraft to clear the debris is used as a variable to construct any particle in Indicates service spacecraft c i Arrival Fragment G j moment; (3.2) Preliminary determination of the clearing sequence: Since particle X i Each component in Corresponding to the service spacecraft c i Arrival fragment {g1,g2,…,g k } time, so for each debris that meets the clearing time constraint, sort it by arrival time from small to large, and the clearing order of each debris by the service spacecraft can be preliminarily obtained. The time constraint is in Indicates fragment g j The time required to reach the boundary of the reference spacecraft safety range from the initial position; assuming that the debris is sorted from small to large in terms of clearance time, the clearance sequence is: [g2,g k ,…,g1] After reordering, the clearing times for each fragment are: Sequentially change [g2,g k ,…,g1] is represented by [g ′ 1,g ′ 2,…,g ′ k ], i.e. g ′ 1=g2,g ′ 2 = g k ,…,g ′ k =g1; Similarly, let make (3.3) Record the clearing result: define vector A i =[a i1 ,a i2 ,…,a ik ] and initialized to 0 to record the service spacecraft c i In order [g ′ 1,g ′ 2,…,g ′ k ],time When the final clearing of each fragment, only fragment g j Can be successfully serviced spacecraft c i When clearing a ij =1, otherwise a ij =0.

7. The cluster space debris active protection mission planning method according to claim 6 is characterized in that: Solving the multi-satellite debris removal mission planning problem includes: (1) Initialize the total set of fragments to be allocated G = {g1, g2, ..., g m }, dynamic set C of debris removed by each service spacecraft i ={}, single unit maximum benefit maxJ i =0, total benefit J total =0; (2) Initialize the overflow fragment set G′ i = {}, remaining fragment set G ′ ={}, income change ΔJ i =-inf, -inf means negative infinity, mark = 0; (3) Each time, a fragment g is taken out from the allocation fragment set G without duplication. j , which are injected into the service spacecraft c in sequence i ,i=1,2,…,n dynamic set of cleared fragments, and obtain the set {C i ,g j }, based on the single-star sequence planning method, calculate the service spacecraft c i In the fragment collection {C i ,g j The set of fragments C′ that can be successfully cleared under i ; (4) According to the service spacecraft set C i and set C i ′ Determine whether there is a service spacecraft c i Successfully cleared the debris j , if it exists, calculate c i For the fragment set C i ′ The single unit income maxJ i ′ , the change in monomer revenue ΔJ i =(maxJ i ′ -maxJ i ), overflow fragment set G i ′ Otherwise, let G ′ = {G ′ ,g j } and go to step (3); (5) Record the maximum single unit benefit max(ΔJ i ) corresponds to the service spacecraft subscript mark, let J total =J total +ΔJ mark ,max(J mark )=max(J mark )+ΔJ mark , C i =C i ′ , G ′ = {G ′ ,G ′ mark }; (6) Determine whether the remaining fragment set G′ is empty. If so, terminate the calculation. Otherwise, further determine whether the elements in G are the same as the elements in G′. If so, terminate the calculation. If not, set G = G′ and go to step (2).

8. A computer-readable storage medium having computer program instructions stored thereon, wherein when the computer program instructions are loaded and executed by a processor, the processor is caused to execute the method according to any one of claims 1 to 7.

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