Asteroid exploration heterogeneous robot group trajectory collaborative planning method based on convex optimization

Abstract

The invention discloses an asteroid exploration heterogeneous robot group trajectory collaborative planning method based on convex optimization. According to the method, a heterogeneous robot group trajectory collaborative planning problem is converted into a heterogeneous robot group collaborative control optimization problem, and the optimization problem comprises dynamics, state constraint, control constraint, boundary conditions and a target function. According to the method, the established heterogeneous robot group cooperative control optimization problem is subjected to convex processing. According to the method, the convex optimization problem of heterogeneous robot group cooperative control is solved by adopting sequence convex programming. According to the method, the group trajectory collaborative planning problem of the asteroid detection heterogeneous robot is solved under a convex optimization framework, the calculation speed is high, and therefore the requirement of airborne real-time calculation can be met, that is, the solved control variables of the first few times are used as maneuvering control, the trajectory is continuously calculated and updated in real time by using state information collected by a current sensor, and the calculation speed is high. And continuously rolling to update the control of the robot.

Description

Technical Field

[0001] The present application relates to the technical field of multi-target trajectory collaborative planning algorithms, and in particular to a method for collaborative trajectory planning of a group of heterogeneous robots for asteroid exploration based on convex optimization. Background Art

[0002] To carry out collaborative trajectory planning for a heterogeneous robot swarm, it is necessary to establish the dynamics of each part of the heterogeneous robot swarm, give state constraints to avoid collisions between robots and between robots and asteroids, as well as control constraints on the control capabilities of each robot. Taking the overall observation range or fuel consumption of the heterogeneous robot swarm on asteroids as the optimization index, an optimization problem of collaborative control of heterogeneous robot swarms for asteroid exploration is established, and then the motion trajectory and control quantity of each part of the heterogeneous robot swarm are solved.

[0003] However, the established asteroid exploration heterogeneous robot group collaborative control optimization problem is a complex multi-objective and multi-constraint problem, with equality constraints and inequality constraints, and each constraint may be highly nonlinear and non-convex. Solving such an optimization problem may not find the optimal solution, and the existing solution methods such as sequential quadratic programming have the problems of large computational complexity and slow solution speed. Therefore, it is necessary to propose an autonomous control planning algorithm with strong versatility, fast solution function and suitable for onboard real-time online computing, so as to realize the observation of asteroids using heterogeneous robot groups. Summary of the invention

[0004] The present application provides a method for collaborative trajectory planning of asteroid exploration heterogeneous robot groups based on convex optimization, which has good versatility and effectively improves the speed of solving multi-target trajectory collaborative planning problems.

[0005] In a first aspect, a method for collaborative trajectory planning of asteroid exploration heterogeneous robot groups based on convex optimization is provided, wherein the method is applied to an asteroid exploration heterogeneous robot system, the asteroid exploration heterogeneous robot system comprising a central robot and n observation robots, and the method comprises:

[0006] Convert the dynamic equations of the asteroid exploration heterogeneous robot system into the system state equation;

[0007] Convexify the state constraints of the heterogeneous robot system for asteroid exploration;

[0008] Converting control constraints into constraints of control variables, converting state constraints into constraints of state variables, and converting the objective function of the asteroid exploration heterogeneous robot system into a function of control variables and state variables, wherein the objective function includes height performance indicators and fuel performance indicators of all observation robots;

[0009] According to the system state equation, the state constraints after convexification, the constraints of the control variables and the constraints of the state variables, the objective function is optimized iteratively to obtain the group trajectory of the asteroid exploration heterogeneous robot system.

[0010] In combination with the first aspect, in some implementations of the first aspect, the system state equation satisfies:

[0011] x i (t) = [r i (t),v i (t)] T u i (t) = T i (t) T

[0012]

[0013]

[0014]

[0015] Where i represents the i-th robot, i = 1, 2, 3, ..., n, n is the total number of robots, when i = 1 it is the central robot; Ω is the rotation angular velocity of the asteroid, Ω = [Ω x ,Ω y ,Ω z ] T , r i (t) is the displacement of the ith robot, v i (t) is the speed of the ith robot, T i (t) is the thrust force on the ith robot, ▽U(r i ) is the asteroid gravitational force on the ith robot.

[0016] In combination with the first aspect, in some implementations of the first aspect, the state constraint after convexification satisfies:

[0017] or,

[0018]

[0019] in,

[0020]

[0021] Φ=[E3 03], For x i(t), k is the number of discrete time points, l represents the number of iterations of sequential convex programming, l = 1, 2, 3, ..., the trust region center of the kth discrete time point is located at is a virtual compensation item.

[0022] In combination with the first aspect, in some implementations of the first aspect, the state constraint after convexification satisfies:

[0023] or,

[0024]

[0025] in,

[0026]

[0027]

[0028] x(t)=[x1(t) T ,...,x i (t) T ,...,x j (t) T ,...,x n (t) T ] T , P ij is a coefficient matrix of n×n blocks, each block is a 6×6 square matrix, for the (i,i)th block Block (j,j) The remaining blocks are all zero square matrices; is the reference trajectory of x(t), is a virtual compensation term, k is the number of discrete time points, l represents the number of iterations of sequential convex programming, l = 1, 2, 3, ..., the trust region center of the kth discrete time point is located at is the virtual compensation term, and d is the radius of the safety sphere.

[0029] In combination with the first aspect, in some implementations of the first aspect, the state constraint after convexification satisfies:

[0030]

[0031] or,

[0032] in,

[0033]

[0034] β is the half cone angle, Hi is a coefficient matrix of n×n blocks, each block is a 6×6 square matrix, for the (i,i)th block The remaining blocks are all zero matrices, i = 1, 2, ..., n, k is the number of discrete time points, l represents the number of iterations of the sequential convex programming, l = 1, 2, 3, ..., the trust region center of the kth discrete time point is located at is a virtual compensation item.

[0035] In conjunction with the first aspect, in some implementations of the first aspect, the constraints of the control variables satisfy:

[0036] ‖u i (t)‖ ∞ ≤T max ,i=1,2,3,...,n

[0037] Among them, T max is the maximum thrust of the thruster.

[0038] In conjunction with the first aspect, in some implementations of the first aspect, the constraints of the state variables satisfy:

[0039] ||Φx i (t)||2≤R i ,i=1,2,3,...,n

[0040] Among them, R i The highest observation height of each robot during observation.

[0041] In combination with the first aspect, in some implementations of the first aspect, the objective function is and Function, ω r and ω m are the weight coefficients of the overall observation range term and fuel consumption term of the heterogeneous robot group, ω r >0,ω m >0, I sp is the vacuum specific impulse of the thruster, g0 is the standard gravity constant of the earth, t f The terminal time.

[0042] In conjunction with the first aspect, in some implementations of the first aspect, the objective function satisfies:

[0043]

[0044] l represents the number of iterations of sequential convex programming, l = 1, 2, 3, ...; is the state deviation of two sequence iterations, is the square of the trust region radius, and the trust region center of each discrete point is located at is the penalty function of the trust region, ω δ is the trust region weight coefficient; and They are virtual compensation items, and is the penalty function of the virtual compensation term, ω b ,ω e and ω c is the weight coefficient of the virtual compensation term,

[0045] In combination with the first aspect, in some implementations of the first aspect, the iterative optimization of the objective function includes:

[0046] According to the system state equation, the state constraints after convexification, the constraints of the control variables and the constraints of the state variables, and the initial reference trajectory containing all the robot state variables, the objective function is solved, and the current solution is used as the reference trajectory for the next solution, and it is continuously iterated until When the value is less than a given threshold or the number of iterations is reached, the trajectory containing state variables and control variables obtained at the last time is the group trajectory of the asteroid exploration heterogeneous robot system.

[0047] Compared with the prior art, the solution provided by this application includes at least the following beneficial technical effects:

[0048] By constructing a convex optimization problem for the collaborative control of heterogeneous robot groups, the trajectory collaborative planning problem of heterogeneous robot groups is solved in a convex optimization framework, which solves the problem of poor universality of existing solution methods. This framework provides a method to transform non-convex problems into convex problems that can ensure the convergence of the solution, and in this framework, constraints can be inserted or increased or decreased according to the needs of the observation task and the number of robot groups, ensuring that the solution of the trajectory collaborative planning problem will not be increased when the heterogeneous robot group system changes.

[0049] The sequential convex programming method is used to solve the trajectory collaborative planning problem of heterogeneous robot groups with a faster solution speed and better convergence, solving the problem of difficulty in solving the multi-objective trajectory collaborative planning problem and slow calculation speed. Sequential convex programming is not very sensitive to the initial reference trajectory. Even if the solution obtained is not the global optimal solution, it has good local optimality. It can use a custom solver to find the optimal solution in polynomial time, give the motion trajectory and corresponding control quantity of each part of the heterogeneous robot group, have a faster solution speed, and can meet the needs of onboard real-time computing and trajectory planning. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 Schematic diagram of a collaborative trajectory planning method for asteroid exploration heterogeneous robot groups based on convex optimization. DETAILED DESCRIPTION

[0051] The present application is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0052] The currently proposed methods for collaborative trajectory planning of heterogeneous robot groups have poor versatility. The constraints designed to ensure flight safety and communication of heterogeneous robot groups should be universal, and should try to avoid major changes caused by changes in the configuration of the heterogeneous robot group. It only needs to add or subtract constraints in the constructed optimization problem to ensure the integrity of the entire problem framework and avoid increasing the difficulty of solving the problem.

[0053] The currently proposed method for collaborative trajectory planning of heterogeneous robot groups is difficult to solve and slow. The multi-objective and multi-constraint planning problem, especially considering the communication and collision avoidance between the various parts of the heterogeneous robot group, is complex and redundant. It is difficult to quickly find the optimal solution and ensure convergence using the existing solution method, and the computing power of the existing onboard computers cannot meet the needs of online real-time computing.

[0054] To solve the above problems, Figure 1 As shown, the embodiment of the present application provides a method for collaborative trajectory planning of asteroid exploration heterogeneous robots based on convex optimization. The method can be applied to asteroid exploration heterogeneous robot systems. Assume that the asteroid exploration heterogeneous robot system consists of a central robot and n observation robots. The dynamics of each robot is established in the asteroid fixed coordinate system. The dynamics can be expressed as

[0055]

[0056] Where i represents the i-th robot, i = 1, 2, 3, ..., n, n is the total number of robots, when i = 1 it is the central robot; Ω is the rotation angular velocity of the asteroid, r i (t) is the displacement of the ith robot, v i (t) is the speed of the ith robot, T i (t) is the thrust force on the ith robot, ▽U(r i ) is the asteroid gravitational force on the ith robot.

[0057] The thrust output of the thruster will cause the mass of the robot to change. Assuming that the rate of fuel consumption is proportional to the magnitude of the thrust vector, the mass change can be expressed as

[0058]

[0059] Among them, I sp is the vacuum specific impulse of the thruster, and g0 is the standard gravity constant of the earth.

[0060] Since asteroids generally have irregular shapes and fast spin speeds, an ellipsoid state constraint is used to ensure that the robot does not collide with the surface of the asteroid during observation. It is required that all robots are outside the escape ellipsoid surrounding the asteroid. This ellipsoid constraint can be described as

[0061] 1-r i (t) T Θr i (t)≤0,i=1,2,3,...,n (3)

[0062] in, (a, b, c) are the semi-major axes of the ellipsoid.

[0063] In order to ensure that there is no collision between robots, a safety sphere with its center of mass as the center is set for each robot, and all other robots are required to be outside the safety sphere. This sphere constraint can be described as

[0064] d 2 ≤[r i (t)-r j (t)] T [r i (t)-r j (t)],i≠j,i=1,2,3,...,n,j=1,2,3,...,n (4)

[0065] Where d is the radius of the safety sphere.

[0066] In order to ensure that all observation robots can communicate with the central robot, it is required that all observation robots are in a cone with the axis of the line connecting the central robot and the asteroid's center of mass as the axis and β as the semi-cone angle. This cone constraint can be expressed as

[0067]

[0068] In addition, the maneuverability of the robot is determined by the thrust output by the thruster, and the thrust size is limited. The control constraint can be given as

[0069] ‖T i (t)‖ ∞ ≤T max ,i=1,2,3,...,n (6)

[0070] Among them, T maxis the maximum thrust of the thruster.

[0071] Assuming that the observation range angles of all observation robot cameras remain unchanged, the higher the observation position of the robot, the larger the observation range of the asteroid surface. Therefore, the height of all observation robots can be used as a performance indicator, and its expression is:

[0072]

[0073] In addition, since the observation distance of the robot camera is limited by its working range, that is, in equation (7), ||r i (t)||2 cannot be infinite, and the observation distance of each robot needs to be limited. Therefore, the distance state constraint can be given as

[0074] ||r i (t)||2≤R i ,i=1,2,3,...,n (8)

[0075] Among them, R i The highest observation height of each robot during observation.

[0076] In addition, the robot's orbit transfer or maintenance during asteroid observation will consume fuel. In order to minimize the fuel consumed by the robot while completing the task, the fuel consumed by all robots can be used as the performance indicator, and its expression is:

[0077]

[0078] in, t f Terminal time, which indicates the end time of the entire flight process.

[0079] Then the objective function that considers both the overall observation range of the heterogeneous robot group and the composite index of fuel consumption can be given as

[0080] minJ=ω r J r +ω m J m (10)

[0081] Among them, ω r and ω m are the weight coefficients of the overall observation range term and fuel consumption term of the heterogeneous robot group, ω r >0,ω m >0.

[0082] In addition, for an optimization problem, the boundary conditions of all state quantities must be given. For asteroid exploration heterogeneous robot groups, the boundary conditions are:

[0083] r i (0) = r i0 ,v i (0) = v i0 ,i=1,2,3,...,n (11)

[0084] r1(t f )=r 1tf (12)

[0085] Combining the dynamics of equations (1) and (2), the state constraints of equations (3)-(5) and (8), the control constraints of equation (6), the boundary conditions of equations (11) and (12), and the objective function of equation (10), the trajectory collaborative control optimization problem of heterogeneous robots for asteroid exploration is established.

[0086] Let the position, velocity and mass of all robots constitute the state variables of the heterogeneous robot system, and let the thrust vectors of all robots constitute the control variables of the heterogeneous robot system.

[0087] x i (t) = [r i (t) T ,v i (t) T ] T u i (t) = T i (t) T (13)

[0088] In order to solve the optimization problem in the convex optimization framework, we first transform the dynamics of equation (1) into the system state equation, which is expressed as

[0089]

[0090]

[0091] Where, Ω=[Ω x ,Ω y ,Ω z ] T .

[0092] In addition, since equations (3), (4) and (5) are non-convex state constraints, they need to be convexified to transform them into convex constraints. The convexification method uses direct linearization.

[0093] By linearizing equation (3), we can obtain

[0094]

[0095]

[0096] Where, Φ = [E3 03], For x i (t) is the reference state.

[0097] By linearizing equation (4), we can obtain

[0098]

[0099]

[0100] Among them, ij is a coefficient matrix of n×n blocks, each block is a 6×6 square matrix, for the (i,i)th block Block (j,j) The remaining blocks are all zero square matrices; is the reference trajectory of x(t).

[0101] By linearizing equation (5), we can obtain

[0102]

[0103]

[0104]

[0105] Among them, H i is a coefficient matrix of n×n blocks, each block is a 6×6 square matrix, for the (i,i)th block The remaining blocks are all zero square matrices, i = 1, 2, ..., n.

[0106] Rewrite equation (8) and express it in terms of state variables:

[0107] ||Φx i (t)||2≤R i ,i=1,2,3,...,n (22)

[0108] Rewrite equation (6) and express it using control variables:

[0109] ‖u i (t)‖ ∞ ≤T max ,i=1,2,3,...,n (23)

[0110] Using state variables to represent the state equation, equations (11) and (12) can be expressed as

[0111] x i (0) = x i0 ,x1(t f )=x 1tf,i=1,2,3...,n (24)

[0112] Using state variables and control variables to represent performance indicators, we have

[0113]

[0114]

[0115] The convex optimization problem of trajectory collaborative control of heterogeneous robots for asteroid exploration is constructed by combining the objective function (10), the system state equation (14), the state constraints (16)(18)(20)(22) and the control constraints (23), as well as the boundary conditions (24).

[0116] Sequential convex programming is used to solve the convex optimization problem of trajectory collaborative control of heterogeneous robots for asteroid exploration. Firstly, the continuous-time infinite-dimensional time problem is transformed into a discrete-time finite-dimensional optimization problem by discretizing it. Then, trust region and virtual compensation are introduced to give a convex constraint subproblem of collaborative control of heterogeneous robots for asteroid exploration.

[0117] The convex constraint subproblem of trajectory coordination control for heterogeneous robots in asteroid exploration is:

[0118]

[0119]

[0120]

[0121]

[0122]

[0123]

[0124]

[0125] ‖u i (k)‖ ∞ ≤T max ,i=1,2,3,...,n (34)

[0126]

[0127]

[0128] Where l represents the number of iterations of sequential convex programming, l = 1, 2, 3, ...; in formula (36) is the state deviation of two sequence iterations, is the square of the trust region radius, and the trust region center at the kth discrete time point is located at In formula (21), is the penalty function of the trust region, ω δ is the trust region weight coefficient; and They are virtual compensation terms introduced after linearizing the non-convex state constraints to eliminate the artificial infeasibility caused by linearization. These virtual compensation terms are also introduced into the objective function (21). and is the penalty function of the virtual compensation term, ω b ,ω e and ω c is the weight coefficient of the virtual compensation item.

[0129] Combining equations (27)-(36), and giving the initial reference trajectory containing all robot state variables, the convex constraint subproblem of trajectory cooperative control of heterogeneous robots for asteroid exploration is solved, and the current solution is used as the reference trajectory for the next solution, and it is continuously iterated until If it is less than a given threshold or reaches the number of iterations, the trajectory containing state variables and control variables obtained last time is the solution to the collaborative trajectory planning of the heterogeneous robot group for asteroid exploration.

[0130] The collaborative trajectory planning problem of asteroid exploration heterogeneous robot groups is solved under a convex optimization framework with a fast calculation speed, so it can meet the needs of onboard real-time computing, that is, the control variables solved for the first few times are used as maneuver control, and the status information collected by the current sensor is used to continuously calculate and update the trajectory in real time, and continuously update the control of the robot.

[0131] The idea of ​​the asteroid exploration heterogeneous robot swarm trajectory collaborative planning method based on convex optimization proposed in the present invention is divided into three aspects: construction of the heterogeneous robot swarm collaborative control optimization problem and convex processing of the optimization problem, and solving it by sequential convex programming.

[0132] 1. Transform the trajectory collaborative planning problem of heterogeneous robot swarm into a collaborative control optimization problem of heterogeneous robot swarm, which includes: dynamics, state constraints, control constraints, boundary conditions and objective function.

[0133] 1.1. The dynamics part includes the dynamics of all robots in the heterogeneous robot group. Since the exploration object is an asteroid, the dynamics established is an expression in the asteroid fixed coordinate system, that is, the rotation and gravity of the asteroid must be considered. In addition, considering the fuel consumption, the dynamics also includes the mass change of each part of the robot group.

[0134] 1.2. State constraints are constraints on the flight path of the robot group, and are also conditions to ensure communication and flight safety between each part, including: constraints to ensure that each part of the heterogeneous robot group does not collide with asteroids, constraints to ensure that there is no collision between robots, constraints to ensure that robots can communicate with each other, etc.

[0135] 1.3. The maneuverability of the robot is determined by the output control force of the thruster. The control constraint mainly refers to the constraint on the thrust size of each part of the robot group.

[0136] 1.4. Boundary conditions are the prerequisite for the optimization problem to have a definite solution. Here, the initial and terminal states of each part of the heterogeneous robot group must be given as boundary conditions.

[0137] 1.5. The objective function represents the goal of the optimization model. Maximizing or minimizing a given objective function can obtain the optimal solution to the optimization problem. In the present invention, the observation range of all observation robots in the robot group to the asteroid and the fuel consumption of the robot group together constitute a composite index as the objective function.

[0138] 2. Convexify the established optimization problem of cooperative control of heterogeneous robot groups. First, construct state variables containing the position, velocity and mass of all robots in the asteroid fixed coordinate system, as well as control variables containing the thrust vectors of all robots; rewrite the dynamics into the form of system state equations; rewrite the control constraints into the form of convex function constraints represented by control variables; linearize some nonlinear and non-convex state constraints, and then rewrite the linearized constraints and other state constraints into the form of convex function constraints represented by state variables, so that the feasible domain set where the solution is located is a convex set; similarly, the non-convex function part in the objective function can also be linearized to obtain a new objective function in the form of convex function, and finally give the convex optimization problem of cooperative control of heterogeneous robot groups for asteroid exploration.

[0139] 3. Sequential convex programming is used to solve the convex optimization problem of collaborative control of heterogeneous robot groups. Sequential convex programming is a sequential iterative process for solving a series of convex constraint sub-problems. First, the convex optimization problem of collaborative control of heterogeneous robot groups is discretized. Then, for the discretized problem, virtual compensation is introduced in the state constraint to eliminate the artificial infeasibility caused by linearization, and the trust region constraint composed of the state deviation of the two iteration results is introduced to construct the convex constraint sub-problem of collaborative control of heterogeneous robot groups. The initial reference trajectory is given, and the current solved trajectory is used as the reference trajectory for the next solution for sequential iteration until the state variable difference of the two solved trajectories is less than the allowable error or the number of iterations is reached. The last solved trajectory is the optimal solution to the trajectory collaborative planning problem of heterogeneous robot groups for asteroid exploration.

[0140] Although the present invention is 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 without departing from the spirit and scope of the present invention. Therefore, the scope of protection of the present invention shall be based on the scope defined by the claims of the present invention.

Claims

1. A method for collaborative trajectory planning of heterogeneous robot groups for asteroid exploration based on convex optimization, characterized in that: The method is applied to a heterogeneous robot system for asteroid exploration, which includes a central robot and n observation robots. The method includes: Convert the dynamic equations of the asteroid exploration heterogeneous robot system into the system state equation; Convexify the state constraints of the heterogeneous robot system for asteroid exploration; Converting control constraints into constraints of control variables, converting state constraints into constraints of state variables, and converting the objective function of the asteroid exploration heterogeneous robot system into a function of control variables and state variables, wherein the objective function includes height performance indicators and fuel performance indicators of all observation robots; According to the system state equation, the state constraints after convexification, the constraints of the control variables and the constraints of the state variables, the objective function is optimized iteratively to obtain the group trajectory of the asteroid exploration heterogeneous robot system.

2. The method according to claim 1, characterized in that The system state equation satisfies: x i (t)=[r i (t),v i (t)] T u i (t)=T i (t) T Where i represents the i-th robot, i = 1, 2, 3, ..., n, n is the total number of robots, when i = 1 it is the central robot; Ω is the rotation angular velocity of the asteroid, Ω = [Ω x ,Ω y ,Ω z ] T , r i (t) is the displacement of the ith robot, v i (t) is the speed of the ith robot, T i (t) is the thrust force on the ith robot, ▽U(r i ) is the asteroid gravitational force on the ith robot.

3. The method according to claim 2, characterized in that The state constraints after convexification satisfy: or, in, Φ=[E3 03], For x i (t), k is the number of discrete time points, l represents the number of iterations of sequential convex programming, l = 1, 2, 3, ..., the trust region center of the kth discrete time point is located at is a virtual compensation item.

4. The method according to claim 2, characterized in that: The state constraints after convexification satisfy: or, in, x(t)=[x1(t) T ,...,x i (t) T ,...,x j (t) T ,...,x n (t) T ] T , P ij is a coefficient matrix of n×n blocks, each block is a 6×6 square matrix, for the (i,i)th block Block (j,j) The remaining blocks are all zero square matrices; is the reference trajectory of x(t), is a virtual compensation term, k is the number of discrete time points, l represents the number of iterations of sequential convex programming, l = 1, 2, 3, ..., the trust region center of the kth discrete time point is located at is the virtual compensation term, and d is the radius of the safety sphere.

5. The method according to claim 2, characterized in that: The state constraints after convexification satisfy: or, in, β is the half cone angle, H i is a coefficient matrix of n×n blocks, each block is a 6×6 square matrix, for the (i,i)th block The remaining blocks are all zero matrices, i = 1, 2, ..., n, k is the number of discrete time points, l represents the number of iterations of the sequential convex programming, l = 1, 2, 3, ..., the trust region center of the kth discrete time point is located at is a virtual compensation item.

6. The method according to claim 2, characterized in that The constraints on the control variables satisfy: ‖u i (t)‖ ∞ ≤T max ,i=1,2,3,...,n Among them, T max is the maximum thrust of the thruster.

7. The method according to claim 2, characterized in that The constraints on the state variables satisfy: ||Φx i (t)||2≤R i ,i=1,2,3,...,n Where, Φ = [E303], R i The highest observation height of each robot during observation.

8. The method according to claim 2, characterized in that: The objective function is and Function, ω r and ω m are the weight coefficients of the overall observation range term and fuel consumption term of the heterogeneous robot group, ω r >0,ω m >0, I sp is the vacuum specific impulse of the thruster, g0 is the standard gravity constant of the earth, t f The terminal time.

9. The method according to claim 8, characterized in that The objective function satisfies: l represents the number of iterations of sequential convex programming, is the state deviation of two sequence iterations, is the square of the trust region radius, and the trust region center of each discrete point is located at is the penalty function of the trust region, ω δ is the trust region weight coefficient; and They are virtual compensation items, and is the penalty function of the virtual compensation term, ω b ,ω e and ω c is the weight coefficient of the virtual compensation term, 10. The method according to claim 9, characterized in that The iterative optimization of the objective function comprises: According to the system state equation, the state constraints after convexification, the constraints of the control variables and the constraints of the state variables, and the initial reference trajectory containing all the robot state variables, the objective function is solved, and the current solution is used as the reference trajectory for the next solution, and it is continuously iterated until When the value is less than a given threshold or the number of iterations is reached, the trajectory containing state variables and control variables obtained at the last time is the group trajectory of the asteroid exploration heterogeneous robot system.

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