Moon leap trajectory planning method, system, device and medium

Abstract

The invention discloses a moon leap trajectory planning method, system and device and a medium. The method comprises the steps of obtaining target landing point data; based on the acquired data, establishing a moon surface leap device kinetic model by defining a specific coordinate system and a state variable; based on the kinetic model, discretizing the kinetic equation through a numerical integration algorithm, constructing and solving a leap trajectory convex optimization problem with optimal burnup, and obtaining an obstacle avoidance-free optimal trajectory; based on the obstacle avoidance-free optimal trajectory, performing obstacle avoidance iteration through an iteration obstacle increase strategy to obtain a static obstacle avoidance trajectory; and on the basis of the static obstacle avoidance trajectory, the flight trajectory is adjusted again by calculating the trajectory error band width and the maximum extension distance, and a dynamic safety enhanced trajectory is obtained. According to the method, safe, robust and efficient moon surface leap trajectory planning in a complex and uncertain environment is realized.

Description

Technical Field

[0001] This invention relates to the field of trajectory planning technology, and in particular to a method, system, device and medium for lunar flyby trajectory planning. Background Technology

[0002] The flyover moves by jumping across the surface of a celestial body. Compared to a rover, it is more flexible and less affected by terrain such as large craters and dense obstacles. It is expected to solve the problem of future exploration of the lunar polar regions.

[0003] The complex terrain of the lunar polar regions and the close-to-the-surface flight of the spacecraft place higher demands on the safety of the flyby trajectory. Trajectory planning methods achieve obstacle avoidance by setting obstacle avoidance constraints. To determine the obstacle avoidance range, terrain obstacles are often modeled as simplified geometries such as cylinders, convex cones, or semi-ellipsoids. While obstacle avoidance constraints based on these regular shapes are easy to process, they significantly exaggerate the extent of obstacles, reducing the probe's safety space and potentially making it impossible to solve feasible trajectories in complex environments. Furthermore, the trajectory planning process is affected by multiple types of errors: trajectory planning methods represent continuous trajectories with discrete trajectory points, resulting in discrete time step errors; the probe's position data contains errors, which, together with the discrete time step errors, constitute the trajectory error; terrain data also contains errors. The superposition of these multiple errors threatens the safety of the probe, placing even higher demands on obstacle avoidance safety. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a lunar flyby trajectory planning method, system, device, and medium to solve the problem that existing flyby trajectory planning methods are unable to obtain safe and effective probe flyby trajectories in complex terrain environments.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] In a first aspect, the present invention provides a method for planning lunar flyby trajectories, comprising:

[0008] Acquire target landing point data;

[0009] Based on the acquired data, a dynamic model of the lunar surface flyover was established by defining a specific coordinate system and state variables;

[0010] Based on the dynamic model, the dynamic equations are discretized using a numerical integration algorithm, and a convex optimization problem for the leap trajectory with optimal fuel consumption is constructed and solved to obtain the optimal trajectory without obstacle avoidance.

[0011] Based on the optimal trajectory without obstacle avoidance, an obstacle avoidance iteration is performed through an iterative obstacle addition strategy to obtain a static obstacle avoidance trajectory;

[0012] Based on the static obstacle avoidance trajectory, the flight trajectory is adjusted again by calculating the trajectory error band width and the maximum extension distance to obtain a dynamic safety enhancement trajectory.

[0013] As a preferred embodiment of the lunar flyby trajectory planning method described in this invention, the step of obtaining the obstacle-free optimal trajectory includes:

[0014] The optimization objective is set as minimizing the integral of thrust throughout the entire flight process;

[0015] Define and apply initial state constraints, target position constraints, and thrust constraints;

[0016] By introducing slack variables, the non-convex thrust constraint is transformed into a convex constraint;

[0017] The leap process is divided into multiple time periods, and a state vector is defined at each discrete time point;

[0018] The dynamic equations are discretized using a numerical integration algorithm, and the state evolution at each discrete time point is calculated recursively.

[0019] The discretized dynamic equations and the transformed convex constraints are combined to form a discrete convex optimization problem for solving the fly-off trajectory optimization problem.

[0020] The discrete convex optimization problem is solved using a convex optimization algorithm to obtain the optimal trajectory without obstacle avoidance.

[0021] As a preferred embodiment of the lunar flyby trajectory planning method described in this invention, the step of obtaining the static obstacle avoidance trajectory includes:

[0022] For each terrain obstacle, a 3D convex hull is generated based on the original point cloud data;

[0023] Cluster analysis is performed on the normal vectors of the convex hull to obtain a fixed set of directions representing the main directions of the obstacle surface;

[0024] Based on the directions of the fixed direction set, construct the fixed-direction convex hull through a three-times error ellipsoid, and determine the plane equation of the fixed-direction convex hull;

[0025] Based on a fixed-direction convex hull, terrain error obstacle avoidance constraints are set;

[0026] By using an iterative obstacle-addition strategy, the optimal trajectory without obstacle avoidance is gradually corrected to obtain a static obstacle-avoidance trajectory.

[0027] The beneficial effect of this preferred technical solution is that by constructing a fixed-direction convex hull that takes into account terrain measurement errors and setting corresponding obstacle avoidance constraints, the safety margin of trajectory planning is effectively improved, ensuring that the vehicle can reliably avoid obstacles even when there is uncertainty in terrain data.

[0028] As a preferred embodiment of the lunar flyby trajectory planning method described in this invention, the iterative obstacle-addition strategy includes:

[0029] Use the optimal trajectory without obstacle avoidance as the reference trajectory;

[0030] Check the intersection of the trajectory with all obstacles;

[0031] For any trajectory segment that intersects with an obstacle, the terrain error obstacle avoidance constraint of the corresponding obstacle is added to the flyover trajectory optimization problem and solved again until a static obstacle avoidance trajectory is obtained.

[0032] As a preferred embodiment of the lunar flyby trajectory planning method described in this invention, the step of establishing a lunar surface flyby dynamic model includes:

[0033] Choose the target landing point of the leap mission as the origin of the coordinate system;

[0034] The direction perpendicular to the lunar surface and pointing into space is defined as the vertical axis;

[0035] The direction along the lunar surface pointing towards the lunar north pole is defined as the north pole axis;

[0036] A dynamic model of a lunar surface flyby is established based on position, velocity, and mass state variables, as well as lunar gravitational acceleration, thrust, and fuel consumption rate.

[0037] As a preferred embodiment of the lunar flyby trajectory planning method described in this invention, the method for obtaining a dynamically enhanced safety trajectory includes:

[0038] Determine discrete time points and their corresponding spacecraft positions;

[0039] Consider the critical case where the trajectory is tangent to an obstacle;

[0040] Use the static obstacle avoidance trajectory as a reference trajectory;

[0041] The error band width of the trajectory segment is calculated based on the major axis of the position error ellipsoid of the aircraft in the reference trajectory, the distance between adjacent points, and the ratio of Minkowski distance to Euclidean distance.

[0042] Based on geometric relationships, the maximum extension distance of the terrain boundary is derived, and the maximum extension distance of the terrain boundary is superimposed on the plane constant term of the fixed-direction convex hull to form a dual error obstacle avoidance constraint.

[0043] An iterative obstacle-addition strategy is adopted, incorporating the dual-error obstacle avoidance constraint into the leap trajectory optimization problem for re-solving until a dynamically enhanced safety trajectory is obtained.

[0044] The beneficial effects of this preferred technical solution are that by comprehensively considering terrain measurement errors and aircraft trajectory uncertainties, constructing dual error obstacle avoidance constraints and iteratively optimizing them, the robustness of the final trajectory and flight safety in complex and uncertain environments are significantly improved.

[0045] As a preferred embodiment of the lunar flyby trajectory planning method described in this invention, the dual error obstacle avoidance constraint is expressed as:

[0046]

[0047] Where m is a constant, c i It is a binary variable, r k The position of the jump vehicle is h, the number of fixed surfaces is b. i b′ is a plane constant term. i For the enhanced plane constant term, It is the transpose of the normal vector, Δb max It is the maximum extension distance of the terrain boundary.

[0048] Secondly, the present invention provides a lunar flyby trajectory planning system, comprising:

[0049] The acquisition module is used to acquire target landing point data;

[0050] The dynamics modeling module is used to build a dynamics model of a lunar surface flyby based on the acquired data by defining a specific coordinate system and state variables;

[0051] The obstacle-free trajectory optimization module is used to discretize the dynamic equations based on the dynamic model using a numerical integration algorithm, construct and solve the convex optimization problem of the leap trajectory with optimal fuel consumption, and obtain the optimal trajectory without obstacle avoidance.

[0052] The static obstacle avoidance optimization module is used to perform obstacle avoidance iteration based on the optimal trajectory without obstacle avoidance, and obtain the static obstacle avoidance trajectory by using an iterative obstacle addition strategy.

[0053] The dynamic safety enhancement module is used to adjust the flight trajectory based on the static obstacle avoidance trajectory by calculating the trajectory error band width and the maximum extension distance, so as to obtain a dynamic safety enhancement trajectory.

[0054] Thirdly, the present invention provides an electronic device, comprising:

[0055] Memory, used to store programs;

[0056] A processor for executing the computer-executable instructions, which, when executed by the processor, implement the steps of the lunar flyby trajectory planning method.

[0057] Fourthly, the present invention provides a computer-readable storage medium, comprising: when the program is executed by a processor, the steps of implementing the lunar flyby trajectory planning method.

[0058] The beneficial effects of this invention are as follows: By using a fixed-direction convex hull to represent terrain obstacles and introducing a three-fold error ellipsoid for boundary expansion, this invention effectively encompasses the uncertainty of terrain measurement and constructs a static obstacle avoidance constraint with a basic safety margin, thereby ensuring the initial obstacle avoidance safety of the flight trajectory in a known obstacle environment. By analyzing the geometric characteristics of the reference trajectory, combining the ratio of the aircraft position error ellipsoid to the Minkowski distance to calculate the trajectory segment error band width, and deriving the maximum expansion distance of the terrain boundary, this invention achieves quantitative modeling of the aircraft's own navigation and control uncertainty (trajectory error), transforming trajectory uncertainty into a calculable geometric safety boundary. By superimposing the maximum expansion distance of the terrain boundary onto the plane constant term of the fixed-direction convex hull, an enhanced obstacle avoidance constraint that simultaneously considers both terrain and trajectory errors is constructed. Combined with an iterative obstacle augmentation strategy for trajectory re-optimization, this invention ultimately achieves dynamic safety-enhanced trajectory planning with high robustness and sufficient safety margin in a comprehensive uncertainty environment. Attached Figure Description

[0059] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein:

[0060] Figure 1 This is a basic flowchart illustrating a lunar flyby trajectory planning method according to an embodiment of the present invention.

[0061] Figure 2 A diagram illustrating the influence of trajectory and terrain errors on obstacle avoidance constraint boundaries in a lunar flyby trajectory planning method provided in one embodiment of the present invention.

[0062] Figure 3 Monte Carlo verification diagram of a lunar flyby trajectory planning method provided in one embodiment of the present invention. Detailed Implementation

[0063] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0064] Example 1, referring to Figure 1 As an embodiment of the present invention, a lunar flyby trajectory planning method is provided, comprising:

[0065] S100: Acquire target landing point data;

[0066] S200: Based on the acquired data, a dynamic model of the lunar surface spacecraft is established by defining a specific coordinate system and state variables;

[0067] S300: Based on the dynamic model, the dynamic equations are discretized through a numerical integration algorithm, and a convex optimization problem of the leap trajectory with optimal fuel consumption is constructed and solved to obtain the optimal trajectory without obstacle avoidance.

[0068] S400: Based on the optimal trajectory without obstacle avoidance, an obstacle avoidance iteration is performed through an iterative obstacle addition strategy to obtain a static obstacle avoidance trajectory;

[0069] S500: Based on the static obstacle avoidance trajectory, the flight trajectory is readjusted by calculating the trajectory error band width and the maximum extension distance to obtain a dynamic safety enhancement trajectory.

[0070] It should be noted that existing flight trajectory planning methods face a series of severe challenges during actual operation, including insufficient handling of terrain measurement errors. Most methods directly rely on the original point cloud or its precise convex hull for obstacle avoidance, failing to fully consider the uncertainties brought by sensor measurements. This results in insufficient safety margins for the planned trajectories in real-world environments, making collisions more likely. The neglect of the aircraft's own trajectory uncertainties, i.e., failing to incorporate the risk of actual flight trajectories deviating from the planned path due to navigation deviations and control errors into the constraints, means that seemingly safe trajectories may intrude into obstacle areas during execution. The construction of obstacle avoidance constraints is complex or conservative; for example, using spherical or axial bounding boxes (AABB) may lead to excessive space waste, while precise point-by-point collision detection requires enormous computational resources, making it difficult to meet real-time requirements. There is a lack of effective iterative optimization mechanisms, making it difficult to efficiently handle complex and dense obstacle environments while ensuring optimality. Furthermore, the failure to hierarchically, quantify, and fuse terrain and trajectory errors, and the lack of a systematic method to simultaneously address these two types of key uncertainties, results in poor robustness of the planned trajectories in complex dynamic environments, making it difficult to balance safety, efficiency, and reliability.

[0071] Therefore, addressing the problem that existing fly-through trajectory planning methods struggle to obtain safe and effective detector fly-through trajector trajectories in complex terrain environments, this paper proposes a safe and accurate obstacle avoidance constraint mechanism through steps S100-S500. Based on a mixed-integer convex programming method, it uses a fixed-direction convex hull model to represent the obstacle range and considers the influence of trajectory error and terrain error. This mechanism employs an iterative obstacle addition method to identify obstacles to be avoided, progressively adding obstacle avoidance constraints to improve algorithm efficiency.

[0072] Example 2, refer to Figure 2 As an embodiment of the present invention, a lunar flyby trajectory planning method is provided based on the previous embodiment, comprising:

[0073] In this embodiment, step S200, establishing the lunar surface jump vehicle dynamic model, includes taking the target landing point of the jump mission as the origin O, the Z-axis perpendicular to the lunar surface pointing outwards, the Y-axis along the lunar surface pointing towards the lunar north pole, and the X-axis forming a spatial rectangular coordinate system with the other two axes, thus establishing the jump coordinate system O-XYZ. The dynamic equations in this coordinate system are:

[0074]

[0075] Where r, v, and m are the position, velocity, and mass of the spacecraft, respectively; g is the gravitational acceleration of the moon; T is the thrust applied by the thrusters on the spacecraft; and I... sp α is the thrust specific impulse, and α is the fuel consumption rate.

[0076] In this embodiment of the application, obtaining the optimal trajectory without obstacle avoidance in step S300 includes:

[0077] The optimization objective is set as minimizing the integral of thrust throughout the entire flight process;

[0078] Define and apply initial state constraints, target position constraints, and thrust constraints;

[0079] By introducing slack variables, the non-convex thrust constraint is transformed into a convex constraint;

[0080] The leap process is divided into multiple time periods, and a state vector is defined at each discrete time point;

[0081] The dynamic equations are discretized using a numerical integration algorithm, and the state evolution at each discrete time point is calculated recursively.

[0082] The discretized dynamic equations and the transformed convex constraints are combined to form a discrete convex optimization problem for solving the fly-off trajectory optimization problem.

[0083] The discrete convex optimization problem is solved using a convex optimization algorithm to obtain the optimal trajectory without obstacle avoidance.

[0084] In this embodiment of the application, the numerical integration algorithm in step S300 includes using the Runge-Kutta algorithm to discretize and solve the dynamic equations, and gradually calculate the position and velocity of the aircraft at each time point.

[0085] In an optional implementation, the numerical integration algorithm in step S300 can also use a small number of initial time point state values ​​as starting values, and employ a multi-step numerical integration algorithm (Adams-Bashforth) to discretize and solve the dynamic equations, progressively recursively calculating the aircraft state variables at each time point.

[0086] In another optional implementation, the numerical integration algorithm in step S300 can also use the Trapezoidal Method to discretize the dynamic equations. By solving the implicit equations containing the current and next time states at each time step, the aircraft state variables at each segmented time point are recursively calculated.

[0087] In this embodiment of the application, obtaining the optimal trajectory without obstacle avoidance in step S300 includes taking the minimum fuel consumption as the optimization objective for the leap transfer trajectory optimization design, that is:

[0088]

[0089] In the formula, t f This indicates the time taken for the entire leap.

[0090] During the leap, the leap vehicle is subject to the following constraints: initial state, target position, and thrust:

[0091]

[0092] In the formula, r0, v0, and m0 are the initial position, velocity, and mass of the jump vehicle, respectively. f It is the target landing location for the leap mission, T min and T max These are the minimum and maximum thrust values ​​of the thruster, respectively.

[0093] Introduce a slack variable Γ, let p = ln m can convert a non-convex thrust constraint into a convex constraint. The entire leap process is discretized into N time periods, and the state variable of the leap device at each discrete time point is defined as X. k =[r k T ,v k T ,p k ] T (k = 0, 1, ..., N). Based on the initial state variable X0 = [r0] of the jump device... T v0 T[p0] T Based on the dynamic equations, the state variables of the flyover at each time point are recursively calculated using the Runge-Kutta algorithm:

[0094]

[0095] In the formula, U k =[u k T ,σ k ] T These are the control variables for the flyover at each segmented time node. Let be the gravitational acceleration experienced by the flyer at each discrete time point.

[0096] Therefore, the discrete form of the leap trajectory optimization problem is as follows:

[0097]

[0098] In the formula, M = [I3O 3×4 ], p0(t)=ln(m0-αT max t).

[0099] The trajectory is optimized to obtain the optimal trajectory without obstacle avoidance when obstacle avoidance is not considered.

[0100] In this embodiment of the application, obtaining the static obstacle avoidance trajectory in step S400 includes:

[0101] For each terrain obstacle, a 3D convex hull is generated based on the original point cloud data;

[0102] Cluster analysis is performed on the normal vectors of the convex hull to obtain a fixed set of directions representing the main directions of the obstacle surface;

[0103] Based on the directions of the fixed direction set, construct the fixed-direction convex hull through a three-times error ellipsoid, and determine the plane equation of the fixed-direction convex hull;

[0104] Based on a fixed-direction convex hull, terrain error obstacle avoidance constraints are set;

[0105] By using an iterative obstacle-addition strategy, the optimal trajectory without obstacle avoidance is gradually corrected to obtain a static obstacle-avoidance trajectory.

[0106] In this embodiment of the application, the iterative obstacle-addition strategy in step S400 includes:

[0107] Use the optimal trajectory without obstacle avoidance as the reference trajectory;

[0108] Check the intersection of the trajectory with all obstacles;

[0109] For any trajectory segment that intersects with an obstacle, the terrain error obstacle avoidance constraint of the corresponding obstacle is added to the flyover trajectory optimization problem and solved again until a static obstacle avoidance trajectory is obtained.

[0110] In an optional implementation, the iterative obstacle-addition strategy in step S400 can also use the start and end points of the optimal trajectory without obstacle avoidance as boundaries, and use algorithms such as RRT* to randomly sample in the state space and construct a search tree. During the expansion process, collisions with the convex hull of obstacles that take into account terrain errors are detected in real time. The path is continuously optimized by rewire until an asymptotically optimal leap trajectory that connects the start and end points and satisfies obstacle avoidance constraints is generated.

[0111] In another optional implementation, the iterative obstacle-addition strategy in step S400 can also use the optimal trajectory without obstacle avoidance as the initial path, construct a composite potential field that includes the attraction of the target and the repulsive force of the obstacle considering terrain error, and guide the iterative update of the position by calculating the resultant force on the aircraft at each discrete time point, gradually optimizing the path until all obstacles are completely avoided and converged to a safe final trajectory.

[0112] In this embodiment, obtaining the static obstacle avoidance trajectory in step S400 includes generating a three-dimensional convex hull based on the original point cloud data for each terrain obstacle, performing K-means clustering analysis on the convex hull surface normal vectors to obtain h normal vectors a i (i = 1, 2, ..., h) constitute a fixed orientation set, where h is the number of fixed faces specified according to task requirements and computational conditions. Based on the orientations in the fixed orientation set, a fixed-orientation convex hull (considering a safety boundary for topographic surveying errors) is constructed that can exactly enclose the obstacle point cloud at three times the mean error ellipsoid. The plane equation a of the fixed-orientation convex hull is then determined. i T P+b i =0 (i=1,2,...,h), where P represents any point in three-dimensional space, b i This is a plane constant term.

[0113] In this embodiment, a three-dimensional convex hull is generated based on the original point cloud data. The normal vectors of each face of the convex hull are calculated and extracted. The K-means algorithm is applied to cluster these normal vectors to obtain a fixed direction set representing the main directions of the obstacle surface. This fixed direction set is used to construct a fixed-direction convex hull that takes into account terrain measurement errors, and corresponding obstacle avoidance constraints are set.

[0114] In an optional implementation, in step S400, the cluster analysis can also extract the normal vectors of all faces of the obstacle after generating the three-dimensional convex hull, apply the DBSCAN density clustering algorithm to automatically identify the main directional clusters and filter out noise, take the center direction of each cluster as a representative fixed direction set, and then construct a fixed-direction convex hull that takes into account terrain measurement errors and set obstacle avoidance constraints.

[0115] In another optional implementation, in step S400, after generating the three-dimensional convex hull of the obstacle, the cluster analysis can also extract the normal vectors of all its faces, use an agglomerative hierarchical clustering algorithm to gradually merge similar directions to form a tree structure, and cut the tree diagram according to the preset number of clusters or distance threshold to obtain a fixed direction set representing the main directions of the obstacle surface, which is then used to construct a fixed-direction convex hull that takes into account terrain measurement errors and set obstacle avoidance constraints.

[0116] In this embodiment of the application, the construction of the fixed-direction convex hull in step S400 includes applying a three-fold error ellipsoid to each original point to encompass the topographic measurement error, constructing a fixed-direction convex hull that can cover all error ellipsoids using these fixed directions, and setting obstacle avoidance constraints accordingly.

[0117] In an optional implementation, the construction of the fixed-direction convex hull in step S400 can also be achieved by directly solving for the minimum volume ellipsoid containing all points after obtaining the original point cloud data of the terrain obstacles to accommodate measurement errors. The fixed-direction set obtained through cluster analysis is used to construct a fixed-direction convex hull that exactly surrounds the minimum volume ellipsoid, and the plane equation is determined and the terrain error obstacle avoidance constraint is set accordingly.

[0118] In another optional implementation, the construction of the fixed-direction convex hull in step S400 can also be achieved by directly applying the α-shape algorithm to generate a non-convex polyhedron that can accurately fit the point cloud contour and contain measurement errors after obtaining the original point cloud data of the terrain obstacles, in order to characterize the obstacle boundary. The fixed direction set is obtained by cluster analysis of the surface normal vector of the α-shape, and then the fixed-direction convex hull surrounding the non-convex polyhedron is constructed. Based on this, the plane equation is determined and the terrain error obstacle avoidance constraint is set.

[0119] In this embodiment, during step S400, the jumper should always be located outside the fixed-direction convex hull of the obstacle. It is defined that the normal vector of each facet within the convex hull points outwards. If any point in space is outside the convex hull, then there exists at least one facet such that the scalar product of the vector from a point on that facet to that point and the normal vector of that facet is greater than 0. Therefore, at discrete time point k, the jumper position r... k The obstacle avoidance constraint (terrain error obstacle avoidance constraint) outside the convex hull of a certain obstacle in a fixed direction is as follows:

[0120]

[0121] In the formula, m is a very large constant, and c i It is a binary variable.

[0122] In this embodiment, step S400 uses an iterative obstacle-addition method to determine the necessary obstacle avoidance constraints. Using the optimal fuel consumption jump trajectory without considering obstacle avoidance as a reference trajectory, the intersection of this trajectory with obstacles is detected. For trajectory segments that intersect with obstacles, terrain error obstacle avoidance constraints for the corresponding obstacles are added to the jump trajectory optimization problem in step S300, and the obstacle avoidance jump trajectory is calculated. It is then checked whether the newly generated jump trajectory collides with obstacles; if so, terrain error obstacle avoidance constraints are added until the trajectory does not collide with any obstacles, thus obtaining the static obstacle avoidance trajectory.

[0123] In this embodiment of the application, obtaining the dynamic safety enhancement trajectory in step S500 includes recording the aircraft positions corresponding to discrete time points k and k+1 as r, respectively. k and r k+1 Consider the critical case where the trajectory is tangent to an obstacle, along r k r k+1 Connect the lines to create a cross-section diagram as shown below. Figure 2 As shown.

[0124] In the figure, Δr is r k and r k+1 Distance between points, θ is r k r k+1 The angle between the connecting line and boundary C, ε is the trajectory segment error width, and Δb is the distance between boundary B and boundary C. In the diagram, the trajectory error band (blue area) is tangent to the obstacle error range (green area), and the aircraft is in a safe state where it will not collide with the obstacle. Using the flight trajectory considering only terrain error as a reference trajectory, the error band width of the trajectory segment is calculated:

[0125]

[0126] In the formula, e l Let be the major axis of the aircraft error ellipsoid, and p be the ratio of the Minkowski distance to the Euclidean distance between two trajectory points in the reference trajectory.

[0127] When r k r k+1 The line connecting the midpoint and the vertex of boundary B is perpendicular to r. k r k+1 At that time, Δb reaches its maximum value. According to geometric relations, we have when Sometimes, The obstacle avoidance constraint that simultaneously considers terrain and trajectory error obstacles (dual error obstacle avoidance constraint) is as follows:

[0128]

[0129] In this embodiment of the application, the iterative obstacle-addition strategy is also used in step S500, and the dual error obstacle avoidance constraint is added to the leap trajectory optimization problem in step S300 to solve the dynamic safety-enhanced trajectory.

[0130] Example 3, referring to Figure 3 This is one embodiment of the present invention, which provides a lunar flyby trajectory planning method. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through specific implementation methods and implementation effects.

[0131] The specific details of this embodiment are as follows:

[0132] To verify the feasibility and robustness of the proposed flyby trajectory planning method, this embodiment conducted a Monte Carlo simulation with 100 planning iterations. The standard deviation of the three axes of the flyby position was set to 1m, and the standard deviation of the three axes of the obstacle point cloud data was set to 5cm. 100 sets of initial flyby position and obstacle point cloud data were randomly generated to simulate the simultaneous existence of errors in both flyby position and terrain data. The flyby exploration of the lunar polar region was used as the simulation background, and the parameter settings are shown in Table 1. The fixed direction number of the obstacle fixed-direction convex hull model was set to 10. The flyby trajectory planning was performed, and the resulting 100 trajectories are shown in Table 1. Figure 3 As shown by the red curve, the blue area in the figure represents the actual extent of the obstacle.

[0133] Table 1 Boundary Conditions and Parameter Settings

[0134] parameter value parameter value <![CDATA[Initial position r0]]> (800,600,0)m <![CDATA[Thrust upper limit T max > 900N <![CDATA[Initial velocity v0]]> (0,0,0)m <![CDATA[Flyover transfer time t f > 35s <![CDATA[Initial mass m0]]> 200kg Number of discrete time periods N 40 <![CDATA[Target position r f > (0,0,0)m Fuel consumption rate α <![CDATA[3.4×10 -4 s / m]]>

[0135] As can be seen, none of the trajectories collided with obstacles, and all maintained a safe distance from them, indicating that the invention has good robustness even when the positions of the lander and obstacles are uncertain. The small minimum distance between the trajectory and the obstacle suggests that the obstacle avoidance constraints of the invention do not excessively exaggerate the obstacle range, leaving flexible flight space for the aircraft.

[0136] Example 4 is an embodiment of the present invention. This embodiment differs from the first embodiment in that it provides a lunar flyby trajectory planning system.

[0137] It should be noted that the technical solution of the lunar flyby trajectory planning system and the technical solution of the lunar flyby trajectory planning method described above belong to the same concept. For details not described in detail in the technical solution of the lunar flyby trajectory planning system in this embodiment, please refer to the description of the technical solution of the lunar flyby trajectory planning method described above.

[0138] This embodiment provides a lunar flyby trajectory planning system, comprising:

[0139] The acquisition module is used to acquire target landing point data;

[0140] The dynamics modeling module is used to build a dynamics model of a lunar surface flyby based on the acquired data by defining a specific coordinate system and state variables;

[0141] The obstacle-free trajectory optimization module is used to discretize the dynamic equations based on the dynamic model using a numerical integration algorithm, construct and solve the convex optimization problem of the leap trajectory with optimal fuel consumption, and obtain the optimal trajectory without obstacle avoidance.

[0142] The static obstacle avoidance optimization module is used to perform obstacle avoidance iteration based on the optimal trajectory without obstacle avoidance, and obtain the static obstacle avoidance trajectory by using an iterative obstacle addition strategy.

[0143] The dynamic safety enhancement module is used to adjust the flight trajectory based on the static obstacle avoidance trajectory by calculating the trajectory error band width and the maximum extension distance, so as to obtain a dynamic safety enhancement trajectory.

[0144] This embodiment also provides an electronic device applicable to a lunar flyby trajectory planning method, including:

[0145] The system includes a memory and a processor. The memory stores computer-executable instructions, and the processor executes these instructions to implement a lunar flyby trajectory planning method as described in the above embodiments.

[0146] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements a lunar flyby trajectory planning method as proposed in the above embodiments.

[0147] The storage medium proposed in this embodiment belongs to the same inventive concept as the lunar flyby trajectory planning method proposed in the above embodiments. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0148] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0149] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for planning lunar flyby trajectories, characterized in that, include: Acquire target landing point data; Based on the acquired data, a dynamic model of the lunar surface flyover was established by defining a specific coordinate system and state variables; Based on the dynamic model, the dynamic equations are discretized using a numerical integration algorithm, and a convex optimization problem for the leap trajectory with optimal fuel consumption is constructed and solved to obtain the optimal trajectory without obstacle avoidance. Based on the optimal trajectory without obstacle avoidance, an obstacle avoidance iteration is performed through an iterative obstacle addition strategy to obtain a static obstacle avoidance trajectory; Based on the static obstacle avoidance trajectory, the flight trajectory is adjusted again by calculating the trajectory error band width and the maximum extension distance to obtain a dynamic safety enhancement trajectory.

2. The lunar flyby trajectory planning method as described in claim 1, characterized in that: The process of obtaining the optimal obstacle-free trajectory includes: The optimization objective is set as minimizing the integral of thrust throughout the entire flight process; Define and apply initial state constraints, target position constraints, and thrust constraints; By introducing slack variables, the non-convex thrust constraint is transformed into a convex constraint; The leap process is divided into multiple time periods, and a state vector is defined at each discrete time point; The dynamic equations are discretized using a numerical integration algorithm, and the state evolution at each discrete time point is calculated recursively. The discretized dynamic equations and the transformed convex constraints are combined to form a discrete convex optimization problem for solving the fly-off trajectory optimization problem. The discrete convex optimization problem is solved using a convex optimization algorithm to obtain the optimal trajectory without obstacle avoidance.

3. The lunar flyby trajectory planning method as described in claim 1 or 2, characterized in that: The obtained static obstacle avoidance trajectory includes: For each terrain obstacle, a 3D convex hull is generated based on the original point cloud data; Cluster analysis is performed on the normal vectors of the convex hull to obtain a fixed set of directions representing the main directions of the obstacle surface; Based on the directions of the fixed direction set, construct the fixed-direction convex hull through a three-times error ellipsoid, and determine the plane equation of the fixed-direction convex hull; Based on a fixed-direction convex hull, terrain error obstacle avoidance constraints are set; By using an iterative obstacle-addition strategy, the optimal trajectory without obstacle avoidance is gradually corrected to obtain a static obstacle-avoidance trajectory.

4. The lunar flyby trajectory planning method as described in claim 3, characterized in that: The iterative obstacle-addition strategy includes: Use the optimal trajectory without obstacle avoidance as the reference trajectory; Check the intersection of the trajectory with all obstacles; For any trajectory segment that intersects with an obstacle, the terrain error obstacle avoidance constraint of the corresponding obstacle is added to the flyover trajectory optimization problem and solved again until a static obstacle avoidance trajectory is obtained.

5. The lunar flyby trajectory planning method as described in claim 4, characterized in that: The establishment of the lunar surface flyby dynamics model includes: Choose the target landing point of the leap mission as the origin of the coordinate system; The direction perpendicular to the lunar surface and pointing into space is defined as the vertical axis; The direction along the lunar surface pointing towards the lunar north pole is defined as the north pole axis; A dynamic model of a lunar surface flyby is established based on position, velocity, and mass state variables, as well as lunar gravitational acceleration, thrust, and fuel consumption rate.

6. The lunar flyby trajectory planning method as described in claim 5, characterized in that: The obtained dynamic security enhancement trajectory includes: Determine discrete time points and their corresponding spacecraft positions; Consider the critical case where the trajectory is tangent to an obstacle; Use the static obstacle avoidance trajectory as a reference trajectory; The error band width of the trajectory segment is calculated based on the major axis of the position error ellipsoid of the aircraft in the reference trajectory, the distance between adjacent points, and the ratio of Minkowski distance to Euclidean distance. Based on geometric relationships, the maximum extension distance of the terrain boundary is derived, and the maximum extension distance of the terrain boundary is superimposed on the plane constant term of the fixed-direction convex hull to form a dual error obstacle avoidance constraint. An iterative obstacle-addition strategy is adopted, incorporating the dual-error obstacle avoidance constraint into the leap trajectory optimization problem for re-solving until a dynamically enhanced safety trajectory is obtained.

7. The lunar flyby trajectory planning method as described in claim 6, characterized in that: The dual-error obstacle avoidance constraint is expressed as follows: Where m is a constant, c i It is a binary variable, r k The position of the jump vehicle is h, the number of fixed surfaces is b. i b′ is a plane constant term. i For the enhanced plane constant term, It is the transpose of the normal vector, Δb max It is the maximum extension distance of the terrain boundary.

8. A lunar flyby trajectory planning system, using the method described in any one of claims 1-7, characterized in that, include: The acquisition module is used to acquire target landing point data; The dynamics modeling module is used to build a dynamics model of a lunar surface flyby based on the acquired data by defining a specific coordinate system and state variables; The obstacle-free trajectory optimization module is used to discretize the dynamic equations based on the dynamic model using a numerical integration algorithm, construct and solve the convex optimization problem of the leap trajectory with optimal fuel consumption, and obtain the optimal trajectory without obstacle avoidance. The static obstacle avoidance optimization module is used to perform obstacle avoidance iteration based on the optimal trajectory without obstacle avoidance, and obtain the static obstacle avoidance trajectory by using an iterative obstacle addition strategy. The dynamic safety enhancement module is used to adjust the flight trajectory based on the static obstacle avoidance trajectory by calculating the trajectory error band width and the maximum extension distance, so as to obtain a dynamic safety enhancement trajectory.

9. An electronic device, characterized in that, include: Memory, used to store programs; A processor for loading the program to perform the steps of the method as claimed in any one of claims 1-7.

10. A computer-readable storage medium storing a program, characterized in that, When the program is executed by a processor, it implements the steps of the method as described in any one of claims 1-7.

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