Method for optimizing constellation configuration of service constellation facing space multi-target rendezvous

Abstract

The application discloses a service constellation configuration optimization method for space multi-target rendezvous and belongs to the technical field of spacecraft system configuration design. The application aims at the minimum scale configuration optimization problem of a heterogeneous constellation in multi-target rendezvous. The application comprises the following steps: constructing a near-earth orbit target set to be rendezvoused; explicitly defining the maximum velocity increment constraint and the response time constraint of a satellite in a service constellation; modeling the service constellation as a combination of multiple walker constellations, determining the configuration rules and distribution modes of each walker constellation; extracting the to-be-optimized configuration parameters and the configuration parameter value range of the service constellation; setting the optimization variables and the target function of the service constellation based on the to-be-optimized configuration parameters, taking the reachability constraint condition as a nonlinear constraint function, and iteratively optimizing the to-be-optimized configuration parameters by using a genetic algorithm until the convergence condition is met, so that the optimal constellation configuration parameters are obtained. The application can minimize the total number of constellation satellites on the premise of ensuring the rendezvous capability for all targets.

Description

Technical Field

[0001] This invention relates to a service constellation configuration optimization method for multi-target rendezvous in space, belonging to the field of spacecraft system configuration design technology. Background Technology

[0002] With the increasing number of missions such as space debris cleanup, on-orbit servicing, and space situational awareness, how to achieve rapid and low-cost rendezvous and access to the numerous and widely distributed space targets in low Earth orbit (LEO) has become a major challenge in aerospace system design.

[0003] Existing research often employs single-serving spacecraft or small-scale homogeneous satellite constellations for target rendezvous. The former is limited by fuel and response time, making it difficult to handle multiple targets simultaneously. Traditional homogeneous constellations (such as the Walker constellation) are typically designed for continuous coverage, and when performing on-demand rendezvous for discrete, sparse target groups, they often suffer from excessive constellation size or insufficient rendezvous capabilities. In recent years, heterogeneous constellations have attracted attention due to their flexibility and scalability, such as large-scale heterogeneous constellations composed of multiple Walker sub-constellations with different configuration parameters. However, determining the number of sub-constellations, orbital altitude, inclination, phase factor, and other parameters to ensure the constellation can meet the rendezvous requirements of a given target set under fuel and rendezvous transfer time constraints with the minimum total number of satellites is a complex combinatorial optimization problem. This problem involves numerous reachability judgments between targets and candidate orbits, which are difficult to solve directly using traditional analytical methods. Summary of the Invention

[0004] To address the problem of minimizing the configuration of heterogeneous constellations in multi-target rendezvous, this invention provides a service constellation configuration optimization method for space multi-target rendezvous.

[0005] The present invention provides a service constellation configuration optimization method for multi-target rendezvous in space, comprising:

[0006] Construct a set of near-Earth orbit targets to be rendezvoused; clarify the maximum velocity increment constraints and response time constraints of satellites in the service constellation; model the service constellation as a combination of multiple walker constellations, determine the configuration rules and distribution of each walker constellation; extract the configuration parameters to be optimized and the range of values ​​for the configuration parameters of the service constellation;

[0007] Based on the configuration parameters to be optimized, the optimization variables and objective function of the service constellation are set. The reachability constraint is used as a nonlinear constraint function. A genetic algorithm is used to iteratively optimize the configuration parameters to be optimized until the convergence condition is met, and the optimal constellation configuration parameters are obtained.

[0008] According to the service constellation configuration optimization method for multi-target rendezvous in space of the present invention, the optimization variables are expressed as follows: :

[0009] ,

[0010] In the formula For the number of orbital surfaces, For phase factor, Number of satellites in the plane It is the average fraction of the inclination angle. The average height score.

[0011] According to the service constellation configuration optimization method for multi-target rendezvous in space of the present invention, the objective function is defined as the total number of satellites in the service constellation:

[0012] ,

[0013] In the formula The objective function is denoted as .

[0014] According to the service constellation configuration optimization method for multi-target rendezvous in space of the present invention, the reachability constraint condition is that at least one satellite in the service constellation satisfies the response time constraint:

[0015] ,

[0016] In the formula The velocity increment required for the satellite to rendezvous with the target. This represents the satellite's maximum speed increment.

[0017] The service constellation configuration optimization method for multi-objective rendezvous in space according to the present invention includes the following calculations to satisfy reachability constraints:

[0018] For each orbital plane in the service constellation, calculate the planar distance from the target to the orbital plane. Radial difference :

[0019] ,

[0020] ,

[0021] In the formula Let be the position vector of the target in the geocentric inertial frame. Let be the unit normal vector of the orbital plane. For the semi-major axis of the track;

[0022] ,

[0023] In the formula For the track inclination angle, Right ascension of the ascending node;

[0024] track inclination According to the actual inclination list Select:

[0025] ,

[0026] In the formula The basic dip angle list consists of dip angle intervals. Divided into equal parts The first value Composed of individual values; For height indexing, This represents the tilt angle step size corresponding to the height step size factor;

[0027] ;

[0028] Right ascension of ascending node The method for obtaining the value is as follows:

[0029] ,

[0030] In the formula Let be the right ascension of the initial ascending node of each orbital plane. The step length of the right ascension of the ascending node. For the tilt angle index,

[0031] ,

[0032] ;

[0033] like and ,

[0034] The current orbital plane is retained as a candidate orbital plane; where For the preset planar tolerance, This is the preset radial tolerance.

[0035] The service constellation configuration optimization method for multi-objective rendezvous in space according to the present invention further includes the calculation of reachingability constraints as follows:

[0036] For each candidate orbital plane, calculate the target phase angle within the candidate orbital plane. :

[0037] Target phase angle for:

[0038] ,

[0039] Let Y be the position vector of the target in the orbital coordinate system. The X-axis position vector of the target in the orbital coordinate system;

[0040] ,

[0041] In the formula Let be the position vector of the target in the orbital coordinate system. Let be the rotation matrix from the geocentric inertial frame to the orbital coordinate system;

[0042] Calculate the ideal initial phase of the satellite :

[0043] ,

[0044] In the formula The orbital angular velocity, To meet response time constraints;

[0045] ,

[0046] In the formula The gravitational constant of Earth;

[0047] Select the true perimeter angle of the satellite Ideal initial phase of the satellite The satellites that are closest to the set number are selected as candidate satellites.

[0048] According to the service constellation configuration optimization method for multi-target rendezvous in space of the present invention, for each candidate orbital plane, the true perigee angle of the satellite is... The calculation method is as follows:

[0049] ,

[0050] In the formula For the satellite's fundamental true anomaly angle, For phase shift;

[0051] ,

[0052] ,

[0053] In the formula , The number of candidate orbital surfaces.

[0054] The service constellation configuration optimization method for multi-objective rendezvous in space according to the present invention further includes the calculation of reachingability constraints as follows:

[0055] Calculate the velocity increment required for the candidate satellite to rendezvous with the target. :

[0056] Based on the current position of the candidate satellite Current speed The target's position vector in the geocentric inertial frame and response time constraints Solve the Lambert problem to obtain the velocity increment required for the candidate satellite to rendezvous with the target. .

[0057] According to the service constellation configuration optimization method for space multi-target rendezvous of the present invention, Encapsulated as a nonlinear constraint function And satisfy:

[0058] ;

[0059] ,

[0060] in This is a marker indicating the reachability of all objectives; when all objectives are reachable... ,otherwise .

[0061] According to the service constellation configuration optimization method for multi-target rendezvous in space of the present invention, the process of iteratively optimizing the configuration parameters to be optimized using a genetic algorithm includes:

[0062] Step 1: Initialize the genetic algorithm parameters and randomly generate an initial population. Each individual in the initial population corresponds to an optimization variable. ;

[0063] Step 2: For each individual, calculate the objective function. And calculate the nonlinear constraint function. ;

[0064] Step 3: Based on the objective function and nonlinear constraint functions Calculate fitness, and based on fitness, select, crossover, and mutate all individuals to generate a new generation of population;

[0065] Repeat steps two and three until the termination condition is met;

[0066] Output optimization variables optimal solution and objective function optimal solution .

[0067] The beneficial effects of this invention are as follows: The method of this invention takes near-Earth orbit targets that are randomly and uniformly distributed within a certain altitude range as the service targets, uses multiple Walker constellations to form a heterogeneous constellation, and, under the constraints of fuel and response time, uses a fast search algorithm as the underlying reachability criterion and a genetic algorithm to globally optimize the outer constellation configuration parameters. In this way, while ensuring that all targets have the ability to rendezvous, the total number of constellation satellites is minimized, so that the service constellation has fast reachability to multiple targets distributed within a certain altitude range in space.

[0068] This invention provides a method for optimizing service constellation configurations for multi-objective space optimization, balancing engineering constraints with optimization efficiency. It solves the constellation configuration optimization problem with rapid multi-objective rendezvous capabilities, achieving constellation size minimization. It has broad application potential. Attached Figure Description

[0069] Figure 1 This is a flowchart of the service constellation configuration optimization method for multi-target rendezvous in space as described in this invention;

[0070] Figure 2 This is a schematic diagram illustrating the selection of orbital surfaces during the calculation of reachability constraints;

[0071] Figure 3 This is a schematic diagram illustrating the selection of satellites based on their ideal initial phase.

[0072] Figure 4 This is a schematic diagram of the target location;

[0073] Figure 5 It is the objective function The graph shows the change with the number of iterations;

[0074] Figure 6 It is a service constellation configuration orbital diagram. Detailed Implementation

[0075] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0076] Specific Implementation Method 1: Combination Figures 1 to 3 As shown, this invention provides a service constellation configuration optimization method for multi-target rendezvous in space, including,

[0077] Construct a set of near-Earth orbit targets to be rendezvoused; clarify the maximum velocity increment constraints and response time constraints of satellites in the service constellation; model the service constellation as a combination of multiple walker constellations, determine the configuration rules and distribution of each walker constellation; extract the configuration parameters to be optimized and the range of values ​​for the configuration parameters of the service constellation;

[0078] Based on the configuration parameters to be optimized, the optimization variables and objective function of the service constellation are set. The reachability constraint is used as a nonlinear constraint function. A genetic algorithm is used to iteratively optimize the configuration parameters to be optimized until the convergence condition is met, and the optimal constellation configuration parameters are obtained.

[0079] In this embodiment, the targets are distributed within a certain altitude range, and the maximum velocity increment constraint and response time constraint for each serving satellite in the constellation are preset. The number of satellites, orbital parameters, and distribution pattern are designed for the Walker constellation.

[0080] To determine whether the reachability constraints of the service constellation for all targets are satisfied: For a given service constellation, design a fast search algorithm to check each target one by one to see if there is at least one satellite that satisfies the reachability requirement for that target. If all targets satisfy the requirement, then the constellation satisfies the reachability constraints.

[0081] The maximum velocity increment refers to the upper limit of the velocity increment required for the serving satellite to transfer from its initial orbit to the target rendezvous orbit via pulse maneuvers. This value is determined by the amount of fuel carried by the satellite's propulsion system and the fuel allocation strategy during the mission's lifetime. Response time refers to the time required from the issuance of ground commands or the appearance of the target until the serving satellite completes rendezvous with the target.

[0082] Walker constellations are constellations with circular orbits and evenly distributed orbital planes. The constellation includes five basic parameters: total number of satellites, number of orbital planes, phase factor, orbital altitude, and orbital inclination.

[0083] A fast search algorithm is used to calculate the reachability constraints, quickly selecting the satellite with the lowest rendezvous cost from the constellation. Reachability means that at least one satellite in the constellation can successfully rendezvous with the target under given maximum velocity impulse and maximum response time constraints. The nonlinear constraint function refers to the reachability of the constellation to all targets, calculated through configuration parameter optimization variables. If all targets are reachable, the nonlinear constraint function is satisfied; otherwise, it is not.

[0084] The genetic algorithm refers to a branch of evolutionary computation, which is a random search algorithm that simulates the biological evolution process in nature.

[0085] The configuration of a service constellation is determined by the following key parameters: the number of orbital planes. The phase factor is F, and the number of satellites in the plane is The mean fraction of the inclination angle is The average height score is .

[0086] Furthermore, the optimization variables are expressed as :

[0087] ,

[0088] In the formula For the number of orbital planes of a constellation, For phase factor, Number of satellites in the plane It is the average fraction of the inclination angle. The average height score.

[0089] All of the above variables are positive integers, and their search range is preset by the task requirements.

[0090] Service constellation configuration initialization: Assume the service constellation consists of... Composed of several Walker constellations; orbital altitude range As specified by the task. The tilt angle range is... The range of right ascension of the ascending node is .

[0091] orbital height range Divided into equal parts Each value yields a list of heights. ;

[0092] To minimize launch and maintenance costs while ensuring accessibility to all space targets, the constellation size should be as small as possible. Therefore, the objective function is defined as the total number of satellites in the service constellation:

[0093] ,

[0094] In the formula Let be the objective function, and the optimization objective is to minimize .

[0095] Determine whether the reachability constraints of the service constellation for all targets are satisfied:

[0096] The reachability constraint is satisfied by at least one satellite in the service constellation under the response time constraint:

[0097] ,

[0098] In the formula The velocity increment required for the satellite to rendezvous with the target. This represents the satellite's maximum speed increment.

[0099] For each target, let its position vector in the geocentric inertial frame be... The service constellation needs to determine whether there is a satellite that can intersect with the target within the allowable velocity increment range.

[0100] Furthermore, combining Figure 2 As shown, the calculation to satisfy the reachability constraint includes orbital plane screening:

[0101] For each orbital plane in the service constellation, calculate the planar distance from the target to the orbital plane. Radial difference :

[0102] ,

[0103] ,

[0104] In the formula Let be the position vector of the target in the geocentric inertial frame. Let be the unit normal vector of the orbital plane. For the semi-major axis of the track;

[0105] ,

[0106] In the formula For the track inclination angle, Right ascension of the ascending node;

[0107] Generate constellations by iterating through the altitudes and inclinations. For each altitude... , Indexed by current height List of calculated actual tilt angles .

[0108] track inclination According to the actual inclination list Select:

[0109] ,

[0110] In the formula The basic dip angle list consists of dip angle intervals. Divided into equal parts The first value Composed of individual values; For height indexing, This represents the tilt angle step size corresponding to the height step size factor;

[0111] ;

[0112] For each inclination value, perform the following operation to obtain the right ascension of the ascending node. The possible values ​​of:

[0113] Add offsets related to height and tilt:

[0114] ,

[0115] In the formula Let be the right ascension of the initial ascending node of each orbital plane. The step length of the right ascension of the ascending node. For the tilt angle index,

[0116] ,

[0117] ;

[0118] like and ,

[0119] The current orbital plane is retained as a candidate orbital plane; where For the preset planar tolerance, This is the preset radial tolerance.

[0120] The target's position vector in the geocentric inertial frame The calculation method is as follows:

[0121] ,

[0122] ,

[0123] In the formula The distance from the Earth's center. Zenith angle, It is the azimuth angle;

[0124] , The radius of the Earth; exist Randomly select values ​​within the range. exist Randomly select a value from the range.

[0125] Figure 2 In the coordinate system, XYZ represents the orbital coordinate system, with O being the origin.

[0126] Combination Figure 3 As shown, the calculation to satisfy the reachability constraint also includes phase filtering:

[0127] For each candidate orbital plane, calculate the target phase angle within the candidate orbital plane. :

[0128] Target phase angle for:

[0129] ,

[0130] Let Y be the position vector of the target in the orbital coordinate system. The X-axis position vector of the target in the orbital coordinate system;

[0131] ,

[0132] In the formula Let be the position vector of the target in the orbital coordinate system. Let be the rotation matrix from the geocentric inertial frame to the orbital coordinate system;

[0133] Let the current time be After receiving the command, the satellite waits for a fixed period of time. Arrival Time .

[0134] Calculate the ideal initial phase of the satellite :

[0135] ,

[0136] In the formula The orbital angular velocity, To meet response time constraints;

[0137] ,

[0138] In the formula The gravitational constant of Earth;

[0139] For each satellite in the candidate orbital plane, select the satellite's true anomaly angle. Ideal initial phase of the satellite The satellites that are closest to the set number are selected as candidate satellites.

[0140] For each candidate orbital plane, the satellite's true perimeter angle The calculation method is as follows:

[0141] ,

[0142] In the formula For the satellite's fundamental true anomaly angle, For phase shift;

[0143] ,

[0144] ,

[0145] In the formula , The number of candidate orbital surfaces.

[0146] For each satellite, store its constellation index, satellite index, semi-major axis, eccentricity (given value), orbital inclination, right ascension of the ascending node, argument of perigee (given value), and mean perigee.

[0147] When the inclination angle is 0, only one orbital plane is generated to avoid repetition of the equatorial orbital plane.

[0148] Through the above process, the initial values ​​of the orbital elements of all satellites are generated, forming the complete initialization data for the service constellation.

[0149] The calculation of satisfying reachability constraints also includes:

[0150] Calculate the velocity increment required for the candidate satellite to rendezvous with the target. :

[0151] Based on the current position of the candidate satellite Current speed (Obtained through orbital recursion) The target's position vector in the geocentric inertial frame. and response time constraints Solve the Lambert problem to obtain the velocity increment required for the candidate satellite to rendezvous with the target. .

[0152] Reachability determination:

[0153] If at least one satellite exists that makes the required velocity increment If the condition is met, the reachability constraint of the service constellation for that target is satisfied; otherwise, it is not satisfied. The above process is executed in parallel and independently for each target point, and the overall reachability requirement of the service constellation is that the reachability constraint conditions are satisfied for all targets.

[0154] Reachability constraint: For a given constellation configuration parameter, the constellation must be able to cover all preset space targets. For each target, determine whether there exists a satellite that can rendezvous with it within the allowable velocity increment range. If all targets can rendezvous, the constellation configuration is feasible; otherwise, it is not.

[0155] In this embodiment, Encapsulated as a nonlinear constraint function And satisfy:

[0156] ;

[0157] ,

[0158] in This is a marker indicating the reachability of all objectives; when all objectives are reachable... ,otherwise .

[0159] Furthermore, the process of iteratively optimizing the configuration parameters using a genetic algorithm includes:

[0160] Step 1: Initialize the genetic algorithm parameters and randomly generate an initial population. Each individual in the initial population corresponds to an optimization variable. ;

[0161] Step 2: For each individual, calculate the objective function. And calculate the nonlinear constraint function. ;

[0162] Step 3: Based on the objective function and nonlinear constraint functions Calculate fitness, and based on fitness (considering both the objective function and the degree of constraint violation), select, crossover, and mutate all individuals to generate a new generation of population;

[0163] Repeat steps two and three until the termination condition is met;

[0164] Output optimization variables optimal solution and objective function optimal solution .

[0165] Through the above optimization process, the optimal configuration parameters that satisfy all target reachability requirements and minimize constellation size can be obtained.

[0166] Simulation verification:

[0167] The service constellation is designed to rendezvous with 10 targets, distributed at orbital altitudes between 300km and 400km. Figure 4 This is a schematic diagram of the target location. The service constellation's orbital altitude ranges from 350 km to 450 km, its inclination ranges from 0° to 180°, and its right ascension of the ascending node ranges from 0 to 2π. The satellite's initial orbit is a circular orbit, and the Earth's gravitational constant is taken as... Earth's radius is taken The maximum permissible speed increment for the satellite is set to... The planar tolerance is set at 800 km, and the radial tolerance is set at 500 km. The configuration of the service constellation is optimized with the minimum constellation size as the optimization criterion.

[0168] Optimize variables F , , The values ​​of are integers within the ranges of [2,12], [1,4], [4,20], [1,10], and [1,6]. The objective function is the total number of constellation satellites. The constraint is that all target points are reachable. The genetic algorithm population size is set to 100, the maximum number of generations is set to 200, and integer encoding is used. The optimized constellation configuration parameters are: 3 orbital planes, 1 phase factor, 7 satellites per orbital plane, 6 average inclination fraction, and 5 average altitude fraction, with a total of 630 satellites. The curve showing the change of the objective function (total number of satellites) with the number of iterations during the optimization process is shown below. Figure 5 As shown. Figure 6 This is a diagram of the orbital configuration of a constellation.

[0169] As can be seen, the method of the present invention can solve the service constellation configuration optimization problem with the goal of minimizing the scale, effectively solve the constellation accessibility constraint problem of space targets, and is suitable for constellation design for rapid response tasks.

[0170] While the invention has been described herein with reference to specific embodiments, it should be understood that these embodiments are merely examples of the principles and applications of the invention. Therefore, it should be understood that many modifications can be made to the exemplary embodiments, and other arrangements can be designed without departing from the spirit and scope of the invention as defined by the appended claims. It should be understood that different dependent claims and features described herein can be combined in ways different from those described in the original claims. It is also understood that features described in conjunction with individual embodiments can be used in other described embodiments.

Claims

1. A service constellation configuration optimization method for multi-objective rendezvous in space, characterized in that... include, Construct a set of near-Earth orbit targets to be rendezvoused; clarify the maximum velocity increment constraints and response time constraints of satellites in the service constellation; model the service constellation as a combination of multiple walker constellations, determine the configuration rules and distribution of each walker constellation; extract the configuration parameters to be optimized and the range of values ​​for the configuration parameters of the service constellation; Based on the configuration parameters to be optimized, the optimization variables and objective function of the service constellation are set. The reachability constraint is used as a nonlinear constraint function. A genetic algorithm is used to iteratively optimize the configuration parameters to be optimized until the convergence condition is met, and the optimal constellation configuration parameters are obtained.

2. The service constellation configuration optimization method for multi-target rendezvous in space according to claim 1, characterized in that, Let the optimization variable be represented as : ， In the formula For the number of orbital surfaces, For phase factor, Number of satellites in the plane It is the average fraction of the inclination angle. The average height score.

3. The service constellation configuration optimization method for multi-target rendezvous in space according to claim 2, characterized in that, The objective function is defined as the total number of satellites in the service constellation: ， In the formula The objective function is denoted as .

4. The service constellation configuration optimization method for multi-target rendezvous in space according to claim 3, characterized in that, The reachability constraint is satisfied by at least one satellite in the service constellation under the response time constraint: ， In the formula The velocity increment required for the satellite to rendezvous with the target. This represents the satellite's maximum speed increment.

5. The service constellation configuration optimization method for multi-target rendezvous in space according to claim 4, characterized in that, The calculation of satisfying reachability constraints includes: For each orbital plane in the service constellation, calculate the planar distance from the target to the orbital plane. Radial difference : ， ， In the formula Let be the position vector of the target in the geocentric inertial frame. Let be the unit normal vector of the orbital plane. For the semi-major axis of the track; ， In the formula For the track inclination angle, Right ascension of the ascending node; track inclination According to the actual inclination list Select: ， In the formula The basic dip angle list consists of dip angle intervals. Divided into equal parts The first value Composed of individual values; For height indexing, This represents the tilt angle step size corresponding to the height step size factor; ； Right ascension of ascending node The method for determining the value is as follows: ， In the formula Let be the right ascension of the initial ascending node of each orbital plane. The step length of the right ascension of the ascending node. For the tilt angle index, ， ； like and , The current orbital plane is retained as a candidate orbital plane; where For the preset planar tolerance, This is the preset radial tolerance.

6. The service constellation configuration optimization method for multi-target rendezvous in space according to claim 5, characterized in that, The calculation of satisfying reachability constraints also includes: For each candidate orbital plane, calculate the target phase angle within the candidate orbital plane. : Target phase angle for: ， Let Y be the position vector of the target in the orbital coordinate system. The X-axis position vector of the target in the orbital coordinate system; ， In the formula Let be the position vector of the target in the orbital coordinate system. Let be the rotation matrix from the geocentric inertial frame to the orbital coordinate system; Calculate the ideal initial phase of the satellite : ， In the formula The orbital angular velocity, To meet response time constraints; ， In the formula The gravitational constant of Earth; Select the true perimeter angle of the satellite Ideal initial phase of the satellite The satellites that are closest to the set number are selected as candidate satellites.

7. The service constellation configuration optimization method for multi-target rendezvous in space according to claim 6, characterized in that, For each candidate orbital plane, the satellite's true perimeter angle The calculation method is as follows: ， In the formula For the satellite's fundamental true anomaly angle, For phase shift; ， ， In the formula , The number of candidate orbital surfaces.

8. The service constellation configuration optimization method for multi-target rendezvous in space according to claim 7, characterized in that, The calculation of satisfying reachability constraints also includes: Calculate the velocity increment required for the candidate satellite to rendezvous with the target. : Based on the current position of the candidate satellite Current speed The target's position vector in the geocentric inertial frame and response time constraints Solve the Lambert problem to obtain the velocity increment required for the candidate satellite to rendezvous with the target. .

9. The service constellation configuration optimization method for multi-target rendezvous in space according to claim 4, characterized in that, Will Encapsulated as a nonlinear constraint function And satisfy: ； ， in This is a marker indicating the reachability of all objectives; when all objectives are reachable... ,otherwise .

10. The service constellation configuration optimization method for multi-target rendezvous in space according to claim 9, characterized in that, The process of iteratively optimizing the configuration parameters using a genetic algorithm includes: Step 1: Initialize the genetic algorithm parameters and randomly generate an initial population. Each individual in the initial population corresponds to an optimization variable. ; Step 2: For each individual, calculate the objective function. And calculate the nonlinear constraint function. ; Step 3: Based on the objective function and nonlinear constraint functions Calculate fitness, and based on fitness, select, crossover, and mutate all individuals to generate a new generation of population; Repeat steps two and three until the termination condition is met; Output optimization variables optimal solution and objective function optimal solution .

Images