A method and system for planning trajectory of stratospheric aircraft across day and night missions in a dynamic wind environment

Abstract

The present application provides a method and system for planning the trajectory of a stratospheric aircraft's cross-day and night mission in a dynamic wind field environment, the method comprising: obtaining mission objectives and mission constraints; obtaining external wind field information; constructing a motion model and cost function of a stratospheric aircraft under a dynamic wind field; using a level set method combined with particle swarm optimization to output the minimum feasibility requirements of a cross-day and night mission under dynamic wind field conditions and the optimal trajectory when the mission is completed. The advantages of the present application are: by innovatively proposing a combined framework of a level set algorithm and a particle swarm algorithm, the problem of the level set algorithm's strong dependence on initial conditions is solved, while reducing the dimension of the search space and reducing the probability of the algorithm falling into a local optimal solution; by flexibly designing a fitness function, the speed constraints, energy consumption constraints, mission feasibility and other conditions of the stratospheric aircraft's cross-day and night arrival or stay missions can be comprehensively considered to achieve the globality and constraint compatibility of trajectory optimization.

Description

Technical Field

[0001] The present application belongs to the field of trajectory planning, and specifically relates to a method and system for trajectory planning of a stratospheric aircraft for a cross-day and night mission in a dynamic wind environment. Background Art

[0002] As a type of near-space vehicle (high-altitude pseudo-satellite platform), the stratospheric airship is designed to operate at altitudes far above the jet stream and in areas with relatively low prevailing winds (usually at altitudes of 18.3 to 22.9 kilometers). In recent years, with the emergence of new materials, the improvement of solar cell endurance, and the significant improvement in weather forecast accuracy, the advantages of stratospheric airships have received increasing attention. Compared with other near-space vehicles operating at the same altitude (such as solar aircraft and balloons), stratospheric airships provide greater payload capacity and cost-effectiveness. They can maintain a longer regional presence with less energy consumption by utilizing buoyancy, while having a certain maneuverability to cruise or reach a designated area. By utilizing the strategy of obtaining enough solar energy during the day to store energy for nighttime electricity, it also provides a guarantee for the continuous energy supply of stratospheric airships across day and night missions. These characteristics give it a significant advantage in long-duration, long-distance (wide-area) stratospheric observation missions, and have been used in scenarios such as remote sensing, aerial photography, and communication relay.

[0003] However, due to the complexity of the stratospheric environment, the mission completion capability and sustainability of stratospheric airships in day-and-night missions still face major challenges. The wind speed and direction of the stratospheric wind field vary significantly in time and space, and the average wind speed often exceeds the maximum flight speed of the airship. In addition, the large size, high wind resistance and low power characteristics of stratospheric airships make them very susceptible to wind field effects, and they need to consume a lot of energy during mission execution. However, the existing energy system still cannot ensure that the airship obtains sufficient energy supply at night, resulting in the risk of losing maneuverability in strong winds due to insufficient thrust. In order to improve the long-duration arrival and stay capabilities of stratospheric airships, the mission trajectory of stratospheric airships must comprehensively consider the dynamic wind field during the mission time, minimize the energy consumption during the mission, meet the day and night energy constraints, and consider the dynamic changes in the external flight environment and the constraints it brings.

[0004] At present, a variety of path planning methods are used in various fields and tasks. Some algorithms, such as A*, artificial potential field (APF), probabilistic roadmap (PRM) and rapidly explored random tree (RRT) algorithm, are considered to be real-time path planning algorithms. However, they consider the problem at a limited scale and lack global optimization capabilities. For stratospheric airships, it is necessary to consider the changes in the external environment in larger scale areas and time spans globally. Reinforcement learning algorithms are also often used in trajectory planning for stratospheric airships. Liu et al. proposed a trajectory planning method based on the PPO algorithm, which enables the airship to achieve long-term regional residence in time-varying wind fields. Zheng et al. developed a path planning model based on dual deep Q network (DDQN) that takes into account dynamic wind fields and energy cycle characteristics. However, the unexplainable and uncertain problems of learning methods cannot be solved, which may have disastrous consequences for flight missions. In contrast, other algorithms, such as particle swarm optimization (PSO), ant colony optimization (ACO), genetic algorithm (GA), grey wolf optimization (GWO) and bat swarm optimization (BSO), can easily obtain the global optimal path in static scenarios. However, the efficiency of the algorithm is still hindered by complexity in dynamic environments, especially in environments where wind field data changes. In this case, high-dimensional states may hinder the effectiveness and feasibility of the algorithm. Summary of the invention

[0005] The purpose of this application is to provide a trajectory planning method for the day-night regional arrival and regional stay missions of stratospheric aircraft under dynamic wind fields, which comprehensively considers the impact of stratospheric dynamic wind fields, nighttime energy constraints and mission completion progress.

[0006] This application proposes a level set-particle swarm trajectory planning algorithm for multi-constrained complex trajectory planning tasks of stratospheric aircraft with long flight time across day and night under dynamic wind fields. The particle swarm algorithm is used to optimize the speed sequence of the aircraft to meet the constraints of speed, energy consumption and feasibility of the task, while the level set algorithm simulates the impact of the wind field on the trajectory of the aircraft through the evolution process, perceives the global wind field distribution within the mission time, and realizes trajectory calculation in a dynamic environment. In the arrival and residence tasks, different particle swarm fitness functions and the evolution end points of the level set can be designed respectively to obtain trajectories that meet the mission objectives.

[0007] The present application provides a method for planning a trajectory of a stratospheric aircraft across day and night missions in a dynamic wind environment, comprising:

[0008] Obtain mission constraints for stratospheric aircraft to arrive at or stay in the stratospheric spacecraft during the day and night; obtain external wind field information;

[0009] Construct the motion model and cost function of stratospheric aircraft in dynamic wind fields;

[0010] The level set method is combined with particle swarm optimization to output the optimal trajectory for day-night missions under dynamic wind field conditions.

[0011] As an improvement of the above method, the method specifically includes:

[0012] Step A0: Obtain task objectives and task constraints;

[0013] Step A1: Initialize the aircraft motion space and wind field environment data;

[0014] Step A2: Perform mission feasibility analysis using the maximum speed of the aircraft;

[0015] Step A3: Perform minimum feasible capacity analysis when the task is not feasible at maximum speed;

[0016] Step A4: Initialize particle swarm;

[0017] Step A5: Use the velocity sequence obtained by the particle swarm algorithm to perform forward evolution through the level set algorithm;

[0018] Step A6: According to the level set forward evolution results, the fitness value of each particle is calculated to evaluate the performance of its velocity sequence in terms of energy consumption, time cost and task constraints;

[0019] Step A7: Update the individual optimal and group optimal values ​​of the particle swarm;

[0020] Step A8: Update the velocity and position of the particle swarm and check the boundaries;

[0021] Step A9: Particle swarm algorithm iteration termination determination;

[0022] Step A10: Perform level set backward evolution on the velocity sequence corresponding to the particle swarm optimal result and output the final optimal trajectory.

[0023] As an improvement of the above method, step A2 includes:

[0024] Maximum speed of the aircraft Conduct task feasibility analysis: First, construct the level set signed distance function , let the initial At this moment, the level set function takes the starting point Initialize the space G with a circle with a center and a grid discrete spacing r as the radius as the boundary; in the wind field velocity vector Under the influence of At the maximum speed of the aircraft , forward evolution along the gradient direction;

[0025] The feasibility of the task is analyzed by forward evolution of the level set: In the arrival task, when the level set function evolves to the end point Time Satisfaction and , it indicates that the arrival mission of the aircraft within the specified time is feasible; in the stay mission, if When , the level set function satisfies , it means that the aircraft can stay in The resident mission is feasible in the area with as the center and R as the radius.

[0026] As an improvement of the above method, step A3 comprises:

[0027] At the current maximum speed Perform level set forward evolution, If the task cannot be completed within the time limit, set The optimal speed at which the aircraft can complete the mission in this wind field is obtained from the final result.

[0028] As an improvement of the above method, step A4 includes:

[0029] Initialize the activity range and movement speed of the particle swarm;

[0030] And the particle swarm position is calculated within the particle swarm activity range. and speed Initialize.

[0031] As an improvement of the above method, step A5 includes:

[0032] Particle position The velocity sequence represented by the level set is forward evolved; first, the level set signed distance function is constructed , let the initial At this moment, the level set function takes the starting point Initialize the space G with a circle with a center and a grid discrete spacing r as the radius as the boundary; in the wind field velocity vector Under the influence of At the speed of the aircraft , forward evolution along the gradient direction;

[0033] The forward evolution result of the level set is used as the judgment indicator for the calculation of the particle swarm fitness function, which is divided into the following two cases:

[0034] Case 1: For arrival mission: If Arrived at the end of the mission The level set function at >0 becomes , which means that the level set function is the velocity sequence represented by the position of particle i in the maximum time Can evolve to , that is, the aircraft can reach the destination within the mission time, and the termination time is ;like , that is, the level set function has evolved to the maximum flight time of the aircraft in the arrival mission When The value of the level set function at As the judgment index of the terminal constraint term represented by the particle swarm fitness function in the next step, the termination time ;

[0035] Case 2: For resident tasks: For any , both have , it means that in the selected wind field, the velocity sequence of the aircraft represented by the position of particle i during the mission time Can reside in is the residence area with the residence center R as the radius; otherwise, there is ,make If the aircraft cannot stay in the parking area within the mission time, It is used as a judgment indicator for the residence constraint item represented by the particle swarm fitness function in the next step.

[0036] As an improvement of the above method, step A6 includes:

[0037] According to the velocity sequence of the particle group Based on the result of the forward evolution of the level set, the particle swarm fitness function is established according to the task objectives and task constraints:

[0038] Build an aircraft Energy consumption cost function over time , the expression is:

[0039]

[0040] in, Indicates that the aircraft is Energy consumption during the time period; Indicates Flight speed in time; Indicates the step length of the task duration segment;

[0041] Establishing nighttime energy constraint penalty :The nighttime energy constraint is expressed by the external point penalty function as follows:

[0042]

[0043] in, Indicates the night time period of the flight time; Indicates the maximum energy consumption of the aircraft at night;

[0044] Building the arrival time cost function , the expression is:

[0045]

[0046] Create terminal constraints :If in When , the level set function at the end of the task is , it means that the level set function failed to evolve to , using the external point penalty function Indicates the penalty for not reaching the end point within the maximum time:

[0047]

[0048] Establish the cost function of the trajectory endpoint distance to the residence center : The expression is:

[0049]

[0050] Establishing the exterior point penalty function , the expression is:

[0051]

[0052] Particle swarm fitness function arriving at the task , and the particle swarm fitness function of the resident task , the expressions are:

[0053]

[0054]

[0055] in, , , and Respectively The weight of each fitness term in the fitness function; , , and Respectively The weight of each fitness term in the fitness function.

[0056] As an improvement of the above method, step A7 includes:

[0057] Each particle updates its individual optimal fitness by comparing its own historical optimal fitness ; At the same time, the optimal fitness of the group is updated by comparing the fitness between particles ; If the individual optimal fitness and the group optimal fitness are updated, the individual optimal position is updated accordingly and the optimal position of the group .

[0058] As an improvement of the above method, step A8 includes:

[0059] The particle group velocity and position update expressions are as follows:

[0060]

[0061]

[0062]

[0063] in, is the optimal fitness of the i-th particle in the k-th iteration The corresponding position when At the kth iteration, the optimal fitness of all particles in the particle swarm is The corresponding position; Represents the inertia weight of the particle at the kth iteration of the particle swarm; and are the initial inertia weight and the maximum number of iterations respectively The inertia weight at time and represent individual learning factor and group learning factor respectively; and is a random function; is the position of the i-th particle in the k-th iteration; is the velocity of the i-th particle in the k-th iteration;

[0064] When a particle crosses the boundary, the particle group speed and location Update the expression to:

[0065]

[0066]

[0067] in, and are the first Dimensional velocity and position; , and , The particle swarm The maximum speed and maximum position of the dimension.

[0068] As an improvement of the above method, step A9 includes:

[0069] If the number of iterations k does not reach the maximum number, return to step A5. If it meets the requirements, end the loop and get the optimal position of the group. .

[0070] As an improvement of the above method, step A10 includes:

[0071] Optimal position of particle swarm , and also to satisfy the optimal speed sequence for the aircraft's day-and-night arrival mission or day-and-night stay mission;

[0072] In the mission of the aircraft arriving across day and night, the optimal speed sequence From the starting point To the end The aircraft movement speed that satisfies the night energy constraint and minimizes energy consumption and arrival time. The minimum arrival time under this speed sequence can also be obtained through the forward evolution of the level set function. , then from the end point The level set function Along the gradient direction and wind field vector The velocity sequence can be obtained by backtracking in the opposite direction The corresponding optimal trajectory point;

[0073] In the day-night stay mission of the aircraft, the optimal speed sequence From the starting point To the central point , at time The aircraft movement speed that satisfies the night energy constraint and the residence area constraint and minimizes the energy consumption and the final distance from the residence center; then, on the boundary of the final reachable set obtained by forward evolution of the level set, take the distance from the residence center point The nearest point As the end point of the trajectory, The level set function Along the gradient direction and wind field vector The optimal speed sequence can be obtained by backtracking in the opposite direction The corresponding optimal trajectory point.

[0074] The present application also provides a system for planning the trajectory of a stratospheric aircraft across day and night missions in a dynamic wind environment, which is implemented based on the above method, and the system includes:

[0075] Obtaining task information module, used to obtain task objectives and task constraints;

[0076] Initialization space and wind field module, used to initialize the aircraft motion space and wind field environment data;

[0077] Mission feasibility analysis module, used to perform mission feasibility analysis using the maximum speed of the aircraft;

[0078] Minimum feasible capacity analysis module, used to perform minimum feasible capacity analysis when the task is not feasible at maximum speed;

[0079] Initialize particle swarm module, used to initialize particle swarm;

[0080] The level set forward evolution module is used to perform forward evolution of the velocity sequence obtained by the particle swarm algorithm through the level set algorithm;

[0081] The particle swarm fitness function calculation module is used to calculate the fitness value of each particle according to the forward evolution results of the level set, and evaluate the performance of its speed sequence in terms of energy consumption, time cost and task constraints;

[0082] Individual optimization and global optimization modules are used to update the individual optimal and group optimal values ​​of the particle swarm;

[0083] Update particle swarm and boundary check module, used to update the speed and position of particle swarm and boundary check;

[0084] Termination judgment module, used for particle swarm algorithm iteration termination judgment;

[0085] The level set backward evolution module is used to perform level set backward evolution on the velocity sequence corresponding to the optimal result of the particle swarm and output the final optimal trajectory.

[0086] Compared with the prior art, the advantages of this application are:

[0087] 1. The present invention innovatively combines the level set algorithm with the particle swarm algorithm to successfully solve the complex problem of aircraft trajectory planning under dynamic wind fields. The impact of wind field changes on trajectories is often nonlinear and dynamic. The level set algorithm evolves the specified initial velocity sequence during the solution process, which can accurately simulate the diffusion range and trajectory changes of the aircraft under complex wind conditions, and the solution results are more in line with the actual environment. At the same time, the particle swarm algorithm optimizes the velocity sequence in the level set algorithm to search for the optimal solution, and considers multiple constraints (such as speed constraints, energy consumption constraints, mission feasibility constraints, etc.) through the fitness function design, so that the final trajectory not only meets the constraints, but also can achieve the mission objectives.

[0088] 2. The trajectory planning results of the present invention show a good ability to balance multiple constraints and task progress in day-and-night missions. Due to the energy constraints at night, the planned feasible trajectory is actively advanced during the day, and actively seeks areas with less wind at night, reducing the speed required for nighttime advancement to reduce nighttime energy consumption; by reducing the goal of total energy consumption, it is natural to take advantage of favorable tailwinds and avoid unfavorable headwinds, and plan the best trajectory for day-and-night missions.

[0089] 3. The combination of the level set algorithm and the particle swarm algorithm effectively complements the advantages and disadvantages of the two algorithms. The combination of the two algorithms improves the efficiency and accuracy of solving in complex environments. The evolution process of the level set algorithm depends on the selection of the initial velocity sequence, and the particle swarm algorithm searches for the velocity sequence, breaking through the limitation of "strong dependence on initial conditions" in the traditional level set algorithm. In addition, the particle swarm algorithm in this method only needs to search in the velocity space, which reduces the dimension of the search space compared to other heuristic trajectory planning methods (such as searching directly in the position space), and significantly reduces the probability of the algorithm falling into a local optimal solution.

[0090] 4. Mission feasibility analysis and optimization capabilities. The present invention can quickly evaluate the feasibility of the mission under different wind conditions by forward evolving the maximum speed of the aircraft, providing valuable reference for subsequent trajectory planning. For example, for some tasks, under specific wind conditions, forward evolution using the maximum speed may not be able to complete the target task, especially in headwind, strong wind conditions or unstable wind environments. At this time, the method can search the speed sequence through the particle swarm algorithm to find a minimum speed that can meet the mission objectives, and perform further trajectory planning. This process not only helps to ensure the feasibility of the mission, but also provides support for the system design of the aircraft. In practical applications, the maximum speed of the aircraft is often limited by multiple factors, including the power system, climatic conditions, etc., and the particle swarm algorithm can intelligently optimize under these limitations to ensure the realization of the mission objectives. In addition, through the optimization of the particle swarm algorithm, the system designer can accurately calculate the minimum speed required to complete the mission, thereby optimizing the power system configuration, energy consumption management and navigation strategy of the aircraft.

[0091] 5. The present invention has strong versatility and expansibility, and can adapt to a variety of complex application scenarios. The proposed method can not only solve the complex trajectory planning problem of aircraft in a dynamic environment, but also has strong flexibility. By adjusting the fitness function of the particle swarm algorithm and the evolution process of the level set algorithm, it can be easily extended to other types of stratospheric aircraft trajectory planning tasks, such as different types of flight missions, considering the collection of daytime solar energy in the trajectory, considering the temperature difference between day and night, etc. In addition, this method can also be extended to trajectory planning problems in complex flow field environments under multiple constraints in other fields, such as constrained trajectory planning of ships and underwater navigation robots in ocean current scenarios, effectively processing multi-factor constraints such as speed, energy consumption, time, etc., and providing the optimal solution.

[0092] 6. The present invention focuses on trajectory planning for day-and-night missions. Although the maximum mission time is 24 hours, long-duration missions of aircraft can also be planned through rolling planning. BRIEF DESCRIPTION OF THE DRAWINGS

[0093] Figure 1 Shown is a schematic diagram of the forward evolution of the level set;

[0094] Figure 2 Shown is a schematic diagram of the backward evolution of the level set;

[0095] Figure 3 The figure shows a block diagram of the trajectory planning method for a stratospheric aircraft mission across day and night in a dynamic wind environment;

[0096] Figure 4 The figure shows a flow chart of a trajectory planning method for a stratospheric aircraft mission across day and night in a dynamic wind environment;

[0097] Figure 5 The figure shows a schematic diagram of the mission of planning the flight path for the aircraft to arrive across day and night;

[0098] Figure 6 The figure shows a schematic diagram of the mission of trajectory planning for aircraft staying across day and night;

[0099] Figure 7 Shown is a schematic diagram of the forward evolution of the aircraft level set in the wind field;

[0100] Figure 8 Shown is a schematic diagram of the backward evolution of the aircraft level set in the wind field;

[0101] Fig. 9 The figure shows the convergence result of the global optimal fitness of the particle swarm for resident mission trajectory planning with the number of iterations under the historical reanalysis wind field in a certain area;

[0102] Fig.10The figure shows the trajectory planning results of the residence mission under the historical reanalysis wind field in a certain area. The red curve in the figure represents the daytime trajectory, and the green curve represents the nighttime trajectory.

[0103] Fig.11 The figure shows the convergence result of the global optimal fitness of the particle swarm for arriving mission trajectory planning with the number of iterations under the historical reanalysis wind field in a certain area;

[0104] Fig.12 The figure shows the trajectory planning results of the arrival mission under the historical reanalysis wind field in a certain area. The red curve in the figure represents the daytime trajectory, and the green curve represents the nighttime trajectory. DETAILED DESCRIPTION

[0105] The technical solution of the present application is described in detail below with reference to the accompanying drawings.

[0106] The present invention discloses a method and system for planning the trajectory of a stratospheric aircraft across day and night missions in a dynamic wind field environment. The method solves the complex multi-constraint trajectory planning problem in a dynamic wind field by combining the level set algorithm with the particle swarm algorithm. The method can provide the optimal trajectory that meets the mission constraints and nighttime energy constraints for the arrival and stay missions of the stratospheric aircraft in the dynamic wind field, as well as the minimum speed requirements and energy requirements that can be achieved in the mission in a strong wind environment. The method includes the following steps: obtaining the mission start time, the mission start position, the mission end position or the mission stay center point position and the stay radius; obtaining the external wind field information; constructing the motion model and cost function of the stratospheric aircraft in the dynamic wind field; using the level set method combined with the particle swarm optimization to output the minimum feasibility requirements under the dynamic wind field conditions and the optimal trajectory when completing the mission.

[0107] The trajectory planning method for stratospheric aircraft across day and night missions in a dynamic wind environment is mainly divided into two parts: the particle swarm algorithm can handle the aircraft speed constraints, night energy consumption constraints and mission feasibility constraints to find the speed sequence of the optimal fitness function; the level set algorithm can be combined with the particle motion model of the aircraft in the wind field to obtain the farthest flight range of an aircraft at a certain speed in a dynamic wind field, and the final trajectory can be obtained by performing a reverse operation of the level set from the final node. In the arrival and residence missions, different particle swarm fitness functions and the diffusion end points of the level set are designed to obtain trajectories that meet the mission objectives. The algorithm block diagram is shown below. Figure 3 The following is a detailed introduction to this method.

[0108] Step 1. First, describe the problem: This invention focuses on the trajectory planning of a stratospheric aircraft across day and night. For the sake of convenience, the following description uses an airship as a representative of a stratospheric aircraft. In addition to airships, other stratospheric aircraft are also suitable for this method. Considering that the airship moves in a dynamic time-varying wind field, the mission time The position of the airship at time t is , the speed is S(t), and the wind speed vector is Then the kinematic point mass model formula of the stratospheric airship in the wind field can be expressed as follows:

[0109] (1)

[0110] in is a unit vector, indicating the airship is This model does not consider the specific dynamics of the airship. In application scenarios where the airship's motion space is much larger than the airship's size, this specification is completely reasonable.

[0111] The thrust power function model of the airship can be expressed as:

[0112] (2)

[0113] (3)

[0114] in, is the thrust power. is the propeller efficiency, is the motor efficiency, both of which are fixed values. From the combined formulas (2) and (3), it can be seen that the thrust power of the airship is the cube of the airspeed S. When the airship is cruising, thrust consumption is the main energy consumption. Therefore, the airship power at time t can be It is expressed as:

[0115]

[0116] (4)

[0117] in, is the drag coefficient of the airship, is the windward area corresponding to the volume of the airship, is the air density at the airship flight altitude. Since the present invention only considers the fixed altitude flight of the airship, is the constant coefficient of the energy consumption model.

[0118] The present invention focuses on the trajectory planning of cross-day and night missions of stratospheric airships that meet nighttime energy constraints. The maximum mission duration is 24 hours, and long-duration missions exceeding 24 hours can be achieved through rolling planning. According to the energy cycle strategy of the stratospheric airship, available energy at night = daytime energy storage = daytime converted solar energy - daytime energy consumption (ignoring energy loss). Therefore, in the cross-day and night trajectory planning, on the one hand, it is necessary to ensure that the nighttime energy consumption meets the constraint of available energy at night, that is, the maximum energy storage during the day; on the other hand, it is necessary to minimize the energy consumption of the entire trajectory to reduce daytime energy consumption, retain more energy for nighttime use, and minimize nighttime energy consumption. Then the cross-day and night arrival mission and the cross-day and night residence mission can be described as the following optimization problem:

[0119] 1). Day and night arrival mission: The goal is to plan a route from the starting point To the end The flight trajectory of the stratospheric airship is designed to minimize the energy consumption and arrival time of the entire trajectory (5). Following the kinematic model (6) and energy consumption model (7), satisfying the arrival time constraint (9), the maximum speed constraint of the airship (10) and the night energy constraint (11), it can be defined as the following optimization problem:

[0120] , (5)

[0121] (6)

[0122] (7)

[0123] (8)

[0124] (9)

[0125] (10)

[0126] (11)

[0127] 2). Day and night stay mission: The goal is to plan a route from the starting point The starting trajectory makes the trajectory Stay in the residence center for a period of time In the circular residence area with centered at and R as radius, the energy consumption of the entire trajectory and the distance between the end point of the trajectory and the residence center are minimized (12). Following the kinematic model (13) and energy consumption model (14), satisfying the residence area constraint (16), time constraint (17), maximum speed constraint of the airship (18) and night energy constraint (19), the optimization problem can be defined as follows:

[0128] , (12)

[0129] (13)

[0130] (14)

[0131] (15)

[0132] (16)

[0133] (17)

[0134] (18)

[0135] (19)

[0136] Step 2. Trajectory planning based on level set-particle swarm algorithm:

[0137] The present invention first uses the particle swarm algorithm to find the optimal navigation speed sequence, the level set forward evolution determines the feasibility of the speed sequence, and the fitness function calculates the energy / time consumption and constrains the night energy; through multiple rounds of iteration, the optimal particle / speed sequence is obtained. Finally, the optimal trajectory is obtained through the level set backward evolution as the optimal navigation path. In this method, the motion space G of the airship is discretized into dimensional grid space, discretizing the task duration into segment, the time step is , the total task time . Indicates time period The speed sequence of the inner airship, Indicates The airship speed in time. The 24h wind field environment data is interpolated in In dimensional space, time The wind vector at the location is The following are the detailed steps.

[0138] 2.1 Particle Swarm Initialization

[0139] The particle swarm is used to search and optimize the velocity sequence S of the airship when it is evolving forward in the wind field using the level set algorithm. Moving in dimensional space, represents the position of the ith particle in space, represents the movement speed of the i-th particle. According to the problem description in step 1, the activity range and movement speed of the particle swarm are specified as follows:

[0140] (20)

[0141] (twenty one)

[0142] in is also the maximum speed of the airship. Then N positions are randomly generated within the activity range of the particle swarm as The particle moves at a speed of , which is expressed as follows:

[0143] (twenty two)

[0144] (twenty three)

[0145] 2.2 Level Set Forward Evolution

[0146] This step uses the velocity sequence obtained by the particle swarm algorithm Perform forward evolution of the level set and use the reachable set and arrival time obtained by forward evolution Calculate the fitness function of the particle swarm.

[0147] Assume that the reachable set of the airship in the dynamic wind field at time t is Ω, and the boundary curve function of the reachable set is . Construct the level set signed distance function , the value is the boundary of the reachable set from the point in space G The level set function of the point with the shortest distance on the airship reachable set , the level set function unit outside the airship reachable set , the value of the level set function on the boundary of the reachable set So the reachable set boundary curve function Also called level set function The zero level set curve at the time. Then let the initial At this moment, the level set function is based on the starting point is the center of the circle, the grid spacing of space G On a circle with radius , the initial level set function is:

[0148] (twenty four)

[0149] Then the level set function At speed is the evolution speed, the level set curve The gradient direction Indicates the direction in which the zero level set function evolves fastest, and the level set function in the wind field is obtained The level set evolution equation in is:

[0150] (25)

[0151] The evolution of the zero level set boundary in the space G accurately represents the velocity of the aircraft. Wind farm The maximum flight range under the influence. When or , indicating that the level set function has evolved to the end point of the task Or the maximum flight time of the airship for the arrival\stay mission has been reached, and the end time is or . Then the zero level set curve The evolution process of The airship is in the speed sequence The maximum flight range, and when reaching the end point time The fastest arrival time.

[0152] The schematic diagram of the forward evolution of the level set is as follows Figure 1 and Figure 7 As shown, For the speed sequence The elements in .

[0153] 2.3 Particle swarm fitness calculation

[0154] This step constructs the particle swarm fitness function based on the result of the previous step, so that each particle in the particle swarm can update and iterate for different optimization goals of the arrival task and the residence task. Speed ​​sequence in time The maximum reach of the airship in the wind farm and the airship's arrival at the destination The fastest arrival time ; At the same time, for the residence task, the center point of residence at any time can be obtained through the definition of the level set function Distance to airship reachable set boundary Distance And the energy consumption formula (4) of the airship can be used to calculate the energy consumption of the airship. The energy consumption of flying within the time. Then the following fitness term can be designed using the result of level set forward evolution.

[0155] (1) Energy consumption cost function :Assume the airship is The energy consumption cost function in time is: The expression is as follows:

[0156] (26)

[0157] (2) Nighttime energy constraint penalty :The nighttime energy constraint is expressed as an exterior point penalty function: , expressed as follows:

[0158] (27)

[0159] in Indicates the night time period of the flight time. Indicates maximum energy expenditure at night.

[0160] (3) Arrival time cost function term : In the arrival task, the arrival time needs to be minimized, so the arrival time cost function is set to , the formula is:

[0161] (28)

[0162] (4) Terminal constraints :If in When , the level set function at the end of the task is , it means that the level set function failed to evolve to , using the external point penalty function Indicates the penalty for not reaching the end point within the maximum time:

[0163] (29)

[0164] (5) Cost function term of the distance between the trajectory end point and the residence center :In the residence task, it is necessary to minimize the distance between the trajectory end point and the residence center. The trajectory end point can be the final reachable set curve in the level set evolution The minimum distance between the end point of the trajectory and the residence center can be expressed by the level set distance function as So the distance cost function from the trajectory end point to the residence center is It can be expressed as:

[0165] (30)

[0166] (6) Residency Constraint Penalty :In addition, the airship trajectory in the residence mission also needs to remain in the residence area within the mission time, which can be expressed as Moment, stay center The fastest reachable curve to the airship The minimum distance on must be smaller than the radius R of the resident area, then the external point penalty function is used It can be expressed as:

[0167] (31)

[0168] Assume that the fitness function of the particle swarm arriving at the task is , the particle swarm fitness function of the resident task is , respectively expressed by (32) (33):

[0169] (32)

[0170] (33)

[0171] in , , , Respectively The weight of each fitness term in the fitness function; , , Respectively The weight of each fitness term in the fitness function.

[0172] 2.4 Particle Swarm Iteration

[0173] Based on the fitness of the particle calculated in the previous step, each particle updates its individual optimal fitness by comparing its own historical optimal fitness. ; At the same time, the optimal fitness of the group is updated by comparing the fitness between particles ; If the individual optimal fitness and the group optimal fitness are updated, the individual optimal position is updated accordingly and the optimal position of the group .

[0174] 2.5 Updating particle swarm and boundary checking

[0175] The particle group velocity and position update expressions are as follows:

[0176] (34)

[0177] (35)

[0178] (36)

[0179] In the formula is the optimal fitness of the i-th individual in the k-th iteration The particle position corresponding to time ; At the kth iteration, the optimal fitness of all particles in the particle swarm is The corresponding position; Represents the inertia weight of the particle at the kth iteration of the particle swarm , and are the initial inertia weight and the maximum number of iterations respectively The inertia weight at time , represent the individual learning factor and the group learning factor respectively, and is a random function.

[0180] When a particle crosses the boundary, the particle group velocity and position update expressions are:

[0181] (37)

[0182] (38)

[0183] In the formula and are the first Dimensional velocity and position; , and , The particle swarm The maximum speed and maximum position of the dimension.

[0184] 2.6 Termination Decision

[0185] If the number of iterations k does not reach the maximum number, return to step 2.2. If the requirements are met, end the loop and get the optimal position of the group. .

[0186] 2.7 The airship trajectory is obtained by backward evolution of the level set:

[0187] After the above steps, the optimal position of the group is obtained , which is also the optimal speed sequence for the airship's cross-day and night arrival mission or cross-day and night stay mission.

[0188] In the airship's day-night arrival mission, the optimal speed sequence From the starting point To the end The airship movement speed that satisfies the nighttime energy constraint and minimizes energy consumption and arrival time. The minimum arrival time under this speed sequence can also be obtained through the forward evolution of the level set function. , then from the end point The level set function Along the gradient direction and wind field vector The velocity sequence can be obtained by backtracking in the opposite direction The corresponding optimal trajectory point, the backward evolution formula is as follows:

[0189] (39)

[0190] in, That is the differential of the airship's position at time t.

[0191] In the airship's day-night stay mission, the optimal speed sequence From the starting point To the central point , at time The airship movement speed that satisfies the nighttime energy constraint and the residence area constraint and minimizes the energy consumption and the final distance from the residence center. Then the final reachable set boundary obtained from the forward evolution of the level set is Upper distance to the center point The nearest point First, let’s use the level set function Along the gradient direction and wind field vector The velocity sequence can be obtained by backtracking in the opposite direction through equation (39). The corresponding optimal trajectory point.

[0192] The schematic diagram of the level set backward evolution is as follows Figure 2 and Figure 8 As shown, For the speed sequence elements.

[0193] Example 1

[0194] like Figure 3 and Figure 4 As shown, still taking the airship as the embodiment target, the trajectory planning method for the stratosphere aircraft across day and night mission in a dynamic wind field environment includes:

[0195] Step S1: Initialization

[0196] Discretize the airship's motion space G into Dimensional grid space, set the maximum time of the task , discretize the task duration into segment, the time step is ( Not longer than the time step of the wind field data), the total mission time . Set the mission target: Arrive Mission: Set the airship starting position , arrive at the end of the mission ; Resident mission: set the airship starting position , stay at the center And the radius of the residence area R. Setting the night energy constraint . Set the daytime and night time .

[0197] Step S2: Load wind farm environment data

[0198] The wind field environment data (which can be reanalysis wind field data or wind field forecast data) is interpolated in In dimensional space, time The wind vector at the location is .

[0199] Step S3: Perform mission feasibility analysis using maximum speed

[0200] The maximum speed of an airship using the level set method ( The level set forward evolution is performed with α being the nominal flight speed of the airship to analyze the feasibility of the mission.

[0201] Assume that the reachable set of the airship in the dynamic wind field at time t is Ω, and the boundary curve function of the reachable set is . Construct the level set signed distance function , the value is the boundary of the reachable set from the point in space G The level set function of the point with the shortest distance on the airship reachable set , the level set function unit outside the airship reachable set , the value of the level set function on the boundary of the reachable set So the reachable set boundary curve function Also known as the level set function The zero level set curve at . Assume that the initial At this moment, the level set function is based on the starting point is the center of the circle, the grid spacing of space G On a circle with radius , the initial level set function is:

[0202] (40)

[0203] Then the level set function At speed is the evolution speed, the level set curve The gradient direction Indicates the direction in which the zero level set function evolves fastest, and the level set function in the wind field is obtained The level set evolution equation in is:

[0204] (41)

[0205] In the arrival task, when Time , it means that the airship can reach the destination of the mission at the maximum speed within the specified time in the selected wind field. In the resident mission, hour, , it means that the airship can stay in the selected wind field at the maximum speed within the specified time. It is within the residence area with a radius of R at the residence center.

[0206] Step S4: Perform minimum feasible capacity analysis when the maximum speed of the task is not feasible:

[0207] If the above conditions are not met, it means that the current maximum speed If the task cannot be completed, you can increase The minimum achievable speed sequence that can complete the task can be obtained by performing the following steps:

[0208] Step S5: Particle swarm initialization:

[0209] The particle swarm is used to search and optimize the velocity sequence S of the airship when it is evolving forward in the wind field using the level set algorithm. Moving in dimensional space, represents the position of the ith particle in space, represents the movement speed of the i-th particle. According to the problem description in step 1, the activity range and movement speed of the particle swarm are specified as follows:

[0210] (42)

[0211] (43)

[0212] in is also the maximum speed of the airship. Then N positions are randomly generated within the activity range of the particle swarm as The particle moves at a speed of , which is expressed as follows:

[0213] (44)

[0214] (45)

[0215] Step S6: Level set forward evolution:

[0216] The position of the i-th particle in the previous step of the particle swarm algorithm is used as the speed sequence of the airship Perform level set forward evolution.

[0217] Assume that the reachable set of the airship in the dynamic wind field at time t is Ω, and the boundary curve function of the reachable set is . Construct the level set signed distance function , the value is the boundary of the reachable set from the point in space G The level set function of the point with the shortest distance on the airship reachable set , the level set function unit outside the airship reachable set , the value of the level set function on the boundary of the reachable set So the reachable set boundary curve function Also the level set function value The zero level set curve at the time. Then let the initial At this moment, the level set function is based on the starting point is the center of the circle, the grid spacing of space G On a circle with radius , the initial level set function is:

[0218] (46)

[0219] Then the level set function At speed is the evolution speed, the level set curve The gradient direction Indicates the direction in which the zero level set function evolves fastest, and the level set function in the wind field is obtained The level set evolution equation in is:

[0220] (47)

[0221] The forward evolution results of the level set are used as the judgment index for the calculation of the particle swarm fitness function, which can be divided into the following cases:

[0222] (1) For arrival missions: If Arrived at the end of the mission The level set function , which means that the level set function is the velocity sequence represented by the position of particle i in the maximum time Can evolve to , that is, the airship can reach the destination within the mission time, and the termination time is ;like , that is, the level set function has evolved to the maximum flight time of the airship in the arrival mission When The value of the level set function at As the terminal constraint fitness term in the next step Judgment indicators, termination time .

[0223] (2) For resident tasks: For any , both have , which means that in the selected wind field, the velocity sequence of the airship represented by the position of particle i during the mission time is Can reside in is the residence area with the residence center R as the radius. On the contrary, there is ,make If the airship cannot stay in the station area within the mission time, As the mainstream constraint fitness term in the next step judgment indicators.

[0224] Step S7: Calculate the particle swarm fitness function according to the level set forward evolution result:

[0225] (1) Calculate the energy consumption cost function :Assume the airship is The energy consumption cost function in time is: The expression is as follows:

[0226] (48)

[0227] (2) Calculate the nighttime energy constraint penalty :The nighttime energy constraint is expressed as an exterior point penalty function: , expressed as follows:

[0228] (49)

[0229] in Indicates the night time period of the flight time. Indicates maximum energy expenditure at night.

[0230] (3) Arrival time cost function term : In the arrival task, the arrival time needs to be minimized, so the arrival time cost function is set to , the formula is:

[0231] (50)

[0232] (4) Terminal constraints :If in When , the level set function at the end of the task is , it means that the level set function failed to evolve to , using the external point penalty function Indicates the penalty for not reaching the end point within the maximum time:

[0233] (51)

[0234] (5) Distance to residence center cost function :In the residence task, it is necessary to minimize the distance between the trajectory end point and the residence center. The trajectory end point can be the final reachable set curve in the level set evolution The minimum distance between the end point of the trajectory and the residence center can be expressed by the level set distance function as So the distance cost function from the trajectory end point to the residence center is It can be expressed as:

[0235] (52)

[0236] (6) Residency Constraint Penalty :In addition, the airship trajectory in the residence mission also needs to remain in the residence area within the mission time, which can be expressed as Moment, stay center The fastest reachable curve to the airship The minimum distance on must be smaller than the radius R of the resident area, then the external point penalty function is used It can be expressed as:

[0237] (53)

[0238] Assume that the fitness function of the particle swarm arriving at the task is , the particle swarm fitness function of the resident task is , respectively expressed by (54) (55):

[0239] (54)

[0240] (55)

[0241] in , , , Respectively The weight of each fitness term in the fitness function; , , , Respectively The weight of each fitness term in the fitness function.

[0242] Step S8: Particle swarm individual optimization and global optimization:

[0243] Based on the fitness of the particle calculated in the previous step, each particle updates its individual optimal fitness by comparing its own historical optimal fitness. ; At the same time, the optimal fitness of the group is updated by comparing the fitness between particles ; If the individual optimal fitness and the group optimal fitness are updated, the individual optimal position is updated accordingly and the optimal position of the group .

[0244] Step S9: Update particle swarm and boundary check:

[0245] The particle group velocity and position update expressions are as follows:

[0246] (56)

[0247] (57)

[0248] (58)

[0249] In the formula is the optimal fitness of the i-th individual in the k-th iteration The particle position corresponding to time ; At the kth iteration, the optimal fitness of all particles in the particle swarm is The corresponding position; Represents the inertia weight of the particle at the kth iteration of the particle swarm; and are the initial inertia weight and the maximum number of iterations respectively The inertia weight at time , represent the individual learning factor and the group learning factor respectively, and is a random function.

[0250] When a particle crosses the boundary, the particle group velocity and position update expressions are:

[0251] (59)

[0252] (60)

[0253] In the formula and are the first Dimensional velocity and position; , and , The particle swarm The maximum speed and maximum position of the dimension.

[0254] Step S10: Termination determination:

[0255] If the number of iterations k does not reach the maximum number, return to step S6. If the requirements are met, end the loop and obtain the optimal position of the group. .

[0256] Step S11: Level set backward evolution outputs the final trajectory:

[0257] After the above steps, the optimal position of the group is obtained , which is also the optimal speed sequence for the airship's cross-day and night arrival mission or cross-day and night stay mission.

[0258] In the airship's day-night arrival mission, the optimal speed sequence From the starting point To the end The airship movement speed that satisfies the nighttime energy constraint and minimizes energy consumption and arrival time. The minimum arrival time under this speed sequence can also be obtained through the forward evolution of the level set function. , then from the end point The level set function Along the gradient direction and wind field vector The velocity sequence can be obtained by backtracking in the opposite direction The corresponding optimal trajectory point. The backward evolution formula is as follows:

[0259] (61)

[0260] in, That is the differential of the airship's position at time t.

[0261] The schematic diagram of the optimal trajectory of the airship's day-night arrival mission is as follows Figure 5 shown.

[0262] In the airship's day-night stay mission, the optimal speed sequence From the starting point To the central point , at time The airship movement speed that satisfies the nighttime energy constraint and the residence area constraint and minimizes the energy consumption and the final distance from the residence center. Then the final reachable set boundary obtained from the forward evolution of the level set is Upper distance to the center point The nearest point First, let’s use the level set function Along the gradient direction and wind field vector The velocity sequence can be obtained by backtracking through equation (61) in the opposite direction. The optimal trajectory point corresponding to the airship's day and night stay mission is shown in the figure below. Figure 6 shown.

[0263] The airship trajectory planning method proposed in the present invention, which combines the particle swarm algorithm and the level set algorithm, solves the multiple constraint problems in airship trajectory planning under dynamic wind fields, especially the trajectory planning difficulties when performing long-term, day-and-night missions in complex environments. The combination of optimizing the speed sequence by the particle swarm algorithm and simulating the wind field influence by the level set algorithm significantly improves the accuracy and efficiency of trajectory planning, avoids local optimal solutions, and reduces the dimension of the search space. The method can analyze the feasibility of the task and provide the optimal speed plan and trajectory to ensure the executability of the task under different wind conditions and environments. It is particularly suitable for trajectory planning under complex meteorological conditions and multiple constraint tasks.

[0264] Fig. 9 and Fig.10 The results of trajectory planning for the resident mission under the historical reanalysis wind field in a certain area are shown. Fig.10 The red curve in the middle represents the daytime trajectory, and the green curve represents the nighttime trajectory. Subject to the energy minimization goal and the nighttime energy constraint, the optimized trajectory advances against the wind during the day. Although it increases the energy consumption during the day, it accumulates favorable positions and provides better wind resistance for weak energy retention at night. This strategy reflects the intelligence of the present invention in balancing short-term energy consumption with long-term mission goals.

[0265] Fig.11 and Fig.12 The results of the arrival mission trajectory planning under the historical reanalysis wind field in a certain area are shown. Fig.12 The red curve in the middle represents the daytime trajectory, and the green curve represents the nighttime trajectory. Subject to the energy minimization objective and the nighttime energy constraint, the optimized trajectory avoids strong winds by propulsion during the daytime and places the nighttime segment flight in a weak wind area, significantly reducing energy consumption.

[0266] Fig.12 The figure shows that the optimized trajectory effectively avoids high wind speed areas (yellow areas), especially during the day (red trajectory segments), indicating that the proposed method can sensitively perceive the wind field distribution and give priority to advancing in areas with lower wind speeds. The rationality of this path selection is particularly important in complex wind fields because it reduces the energy consumption of daytime propulsion and provides favorable conditions for the execution of nighttime missions.

[0267] During the trajectory planning process, it is obvious that the path distribution is different during the day (red segment) and at night (green segment). The night paths are mainly concentrated in the low wind speed area (blue area), indicating that the application method can flexibly adjust the strategy according to the mission period in a dynamic wind field. The specific performance is:

[0268] During the day, the airship's propulsion capabilities are used to propel it in areas with relatively high wind speeds.

[0269] At night, the vehicle will prioritize staying in or moving slowly in areas with lower wind speeds to reduce energy consumption, which fully demonstrates the effectiveness of this method in day-night optimization.

[0270] From the technical effect point of view, the present invention can realize efficient and accurate trajectory planning in dynamic wind fields. By rationally utilizing wind field resources and dynamically adjusting the propulsion and residence strategies, the algorithm can effectively reduce the energy consumption of tasks while meeting complex constraints. Through the rolling planning strategy, it can also be extended to trajectory planning for longer tasks, improving the flexibility of trajectory planning and the robustness of task execution.

[0271] From the perspective of economic effect, this method can provide support for the system design and energy consumption management of airships, provide more accurate path planning for day-night missions, reduce energy consumption and improve mission execution efficiency, thereby reducing operating costs. The widespread application of this technology can provide an efficient solution for trajectory planning of stratospheric airships and promote the intelligent and automated development of related industries.

[0272] From the perspective of social effects, the present invention can efficiently complete tasks in complex meteorological and dynamic environments, and has broad application prospects, especially in the fields of rescue, environmental monitoring, and cross-regional logistics. By providing a more accurate flight trajectory, it can ensure the safety and reliability of mission execution and provide the society with a more efficient and environmentally friendly aircraft operation solution. In scenarios that require long-term, multi-constraint optimization, it has significant technical advantages and application value.

[0273] Example 2

[0274] The present application also provides a system for planning the trajectory of a stratospheric aircraft across day and night missions in a dynamic wind environment, which is implemented based on the above method, and the system includes:

[0275] Obtaining task information module, used to obtain task objectives and task constraints;

[0276] Initialization space and wind field module, used to initialize the aircraft motion space and wind field environment data;

[0277] Mission feasibility analysis module, used to perform mission feasibility analysis using the maximum speed of the aircraft;

[0278] Minimum feasible capacity analysis module, used to perform minimum feasible capacity analysis when the task is not feasible at maximum speed;

[0279] Initialize particle swarm module, used to initialize particle swarm;

[0280] The level set forward evolution module is used to perform forward evolution of the velocity sequence obtained by the particle swarm algorithm through the level set algorithm;

[0281] The particle swarm fitness function calculation module is used to calculate the fitness value of each particle according to the forward evolution results of the level set, and evaluate the performance of its speed sequence in terms of energy consumption, time cost and task constraints;

[0282] Individual optimization and global optimization modules are used to update the individual optimal and group optimal values ​​of the particle swarm;

[0283] Update particle swarm and boundary check module, used to update the speed and position of particle swarm and boundary check;

[0284] Termination judgment module, used for particle swarm algorithm iteration termination judgment;

[0285] The level set backward evolution module is used to perform level set backward evolution on the velocity sequence corresponding to the optimal result of the particle swarm and output the final optimal trajectory.

[0286] The present application may also provide a computer device, comprising: at least one processor, a memory, at least one network interface and a user interface. The various components in the device are coupled together through a bus system. It is understood that the bus system is used to achieve connection and communication between these components. In addition to the data bus, the bus system also includes a power bus, a control bus and a status signal bus.

[0287] The user interface may include a display, a keyboard or a pointing device, such as a mouse, a trackball, a touch pad or a touch screen.

[0288] It is understood that the memory in the disclosed embodiments of the present application may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct memory bus random access memory (DRRAM). The memories described herein are intended to include, but are not limited to, these and any other suitable types of memories.

[0289] In some embodiments, the memory stores the following elements, executable modules or data structures, or a subset thereof, or an extended set thereof: an operating system and applications.

[0290] The operating system includes various system programs, such as a framework layer, a core library layer, a driver layer, etc., which are used to implement various basic services and process hardware-based tasks. The application includes various application programs, such as a media player (Media Player), a browser (Browser), etc., which are used to implement various application services. The program for implementing the method of the embodiment of the present disclosure can be included in the application.

[0291] In the above embodiment, the processor may also call a program or instruction stored in the memory, specifically, a program or instruction stored in an application program, and is used to:

[0292] Execute the steps of the above method.

[0293] The above method can be applied to a processor or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in the processor or an instruction in the form of software. The above processor can be a general processor, a digital signal processor (Digital Signal Processor, DSP), an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), a field programmable gate array (Field Programmable Gate Array, FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The above-disclosed methods, steps and logic block diagrams can be implemented or executed. The general processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the above-disclosed method can be directly embodied as a hardware decoding processor to execute, or the hardware and software modules in the decoding processor can be combined to execute. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

[0294] It is understood that the embodiments described in the present application can be implemented by hardware, software, firmware, middleware, microcode or a combination thereof. For hardware implementation, the processing unit can be implemented in one or more application specific integrated circuits (ASIC), digital signal processors (DSP), digital signal processing devices (DSPD), programmable logic devices (PLD), field programmable gate arrays (FPGA), general-purpose processors, controllers, microcontrollers, microprocessors, other electronic units for performing the functions described in the present application or a combination thereof.

[0295] For software implementation, the technology of the present application can be implemented by executing the functional modules (such as procedures, functions, etc.) of the present application. The software code can be stored in a memory and executed by a processor. The memory can be implemented in the processor or outside the processor.

[0296] The present application may also provide a non-volatile storage medium for storing a computer program. When the computer program is executed by a processor, each step in the above method embodiment can be implemented.

[0297] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present application and are not intended to limit it. Although the present application is described in detail with reference to the embodiments, a person skilled in the art should understand that any modification or equivalent replacement of the technical solution of the present application does not depart from the spirit and scope of the technical solution of the present application and should be included in the scope of the claims of the present application.

Claims

1. A method for planning a trajectory of a stratospheric aircraft across day and night missions in a dynamic wind environment, comprising: Obtaining the mission constraints of the stratosphere aircraft's day-night arrival mission or stay mission; Obtain external wind field information; Construct the motion model and cost function of stratospheric aircraft in dynamic wind fields; The level set method is combined with particle swarm optimization to output the optimal trajectory of the day-night mission under dynamic wind conditions. The method specifically comprises: Step A0: Obtain task objectives and task constraints; Step A1: Initialize the aircraft motion space and wind field environment data; Step A2: Perform mission feasibility analysis using the maximum speed of the aircraft; Step A3: Perform minimum feasible capacity analysis when the task is not feasible at maximum speed; Step A4: Initialize particle swarm; Step A5: Use the velocity sequence obtained by the particle swarm algorithm to perform forward evolution through the level set algorithm; Step A6: According to the level set forward evolution results, the fitness value of each particle is calculated to evaluate the performance of its velocity sequence in terms of energy consumption, time cost and task constraints; Step A7: Update the individual optimal and group optimal values ​​of the particle swarm; Step A8: Update the velocity and position of the particle swarm and check the boundaries; Step A9: Particle swarm algorithm iteration termination determination; Step A10: Perform level set backward evolution on the velocity sequence corresponding to the particle swarm optimal result and output the final optimal trajectory.

2. The method for planning the trajectory of a stratospheric aircraft mission across day and night in a dynamic wind environment according to claim 1 is characterized in that: The step A2 comprises: Maximum speed of the aircraft Conduct task feasibility analysis: First, construct the level set signed distance function , let the initial At this moment, the level set function takes the starting point is the center of the circle and the grid discrete spacing r The circle with radius is the boundary of the space G Initialize; in the wind field velocity vector Under the influence of At the maximum speed of the aircraft , forward evolution along the gradient direction; The feasibility of the task is analyzed by forward evolution of the level set: In the arrival task, when the level set function evolves to the end point Time Satisfaction and , it indicates that the arrival mission of the aircraft within the specified time is feasible; in the stay mission, if When , the level set function satisfies , it means that the aircraft can stay in The resident mission is feasible in the area with as the center and R as the radius.

3. The method for planning the trajectory of a stratospheric aircraft mission across day and night in a dynamic wind environment according to claim 2 is characterized in that: The step A3 comprises: At the current maximum speed Perform level set forward evolution, If the task cannot be completed within the time limit, set The optimal speed at which the aircraft can complete the mission in this wind field is obtained from the final result.

4. The method for planning the trajectory of a stratospheric aircraft across day and night missions in a dynamic wind environment according to claim 3 is characterized in that: The step A4 comprises: Initialize the activity range and movement speed of the particle swarm; And the particle swarm position is adjusted within the particle swarm activity range. and speed Initialize.

5. The method for planning the trajectory of a stratospheric aircraft mission across day and night in a dynamic wind environment according to claim 4 is characterized in that: The step A5 comprises: Particle position The velocity sequence represented by the level set is forward evolved; first, the level set signed distance function is constructed , let the initial At this moment, the level set function takes the starting point is the center of the circle and the grid discrete spacing r The circle with radius is the boundary of the space G Initialize; in the wind field velocity vector Under the influence of At the speed of the aircraft , forward evolution along the gradient direction; The forward evolution result of the level set is used as the judgment indicator for the calculation of the particle swarm fitness function, which is divided into the following two cases: Case 1: For arrival mission: If Arrived at the end of the mission The level set function at >0 becomes , it means that the level set function takes particles as i The position represents the velocity sequence Can evolve to , that is, the aircraft can reach the destination within the mission time, and the termination time is ;like , that is, the level set function has evolved to the maximum flight time of the aircraft in the arrival mission When The value of the level set function at As the judgment index of the terminal constraint term represented by the particle swarm fitness function in the next step, the termination time ; Case 2: For resident tasks: For any , both have , it means that in the selected wind field, the aircraft can generate particles within the mission time. i The position represents the velocity sequence Can reside in For the resident center R is the residence area with radius; otherwise, there is ,make If the aircraft cannot stay in the parking area within the mission time, It is used as a judgment indicator for the residence constraint item represented by the particle swarm fitness function in the next step.

6. The method for planning the trajectory of a stratospheric aircraft mission across day and night in a dynamic wind environment according to claim 5, characterized in that: The step A6 comprises: According to the velocity sequence of the particle group Based on the result of the forward evolution of the level set, the particle swarm fitness function is established according to the task objectives and task constraints: Build an aircraft Energy consumption cost function over time , the expression is: in, Indicates that the aircraft is Energy consumption during the time period; Indicates Flight speed in time; Indicates the step length of the task duration segment; Establishing nighttime energy constraint penalty :The nighttime energy constraint is expressed by the external point penalty function as follows: in, Indicates the night time period of the flight time; Indicates the maximum energy consumption of the aircraft at night; Building the arrival time cost function , the expression is: Create terminal constraints :If in When , the level set function at the end of the task is , it means that the level set function failed to evolve to , using the external point penalty function Indicates the penalty for not reaching the end point within the maximum time: Establish the cost function of the trajectory endpoint distance to the residence center : The expression is: Establishing the exterior point penalty function , the expression is: Particle swarm fitness function arriving at the task , and the particle swarm fitness function of the resident task , the expressions are: in, ， ， and Respectively The weight of each fitness term in the fitness function; ， ， and Respectively The weight of each fitness term in the fitness function.

7. The method for planning the trajectory of a stratospheric aircraft mission across day and night in a dynamic wind environment according to claim 6, characterized in that: The step A7 comprises: Each particle updates its individual optimal fitness by comparing its own historical optimal fitness ; At the same time, the optimal fitness of the group is updated by comparing the fitness between particles ; If the individual optimal fitness and the group optimal fitness are updated, the individual optimal position is updated accordingly and the optimal position of the group .

8. The method for planning the trajectory of a stratospheric aircraft mission across day and night in a dynamic wind environment according to claim 7, characterized in that: The step A8 comprises: The particle group velocity and position update expressions are as follows: in, For the k In the iteration, i The particle has the best fitness The corresponding position when For the k At the iteration, the optimal fitness of all particles in the particle swarm The corresponding position; Represents the particle swarm k The inertia weight of the particle at the iteration; and are the initial inertia weight and the maximum number of iterations respectively Inertia weight at time and represent individual learning factor and group learning factor respectively; and is a random function; For the k In the iteration i The position of a particle; For the k In the iteration i The speed of a particle; When a particle crosses the boundary, the particle group speed and location Update the expression to: in, and Respectively k+1 In the iteration i The particle Dimensional velocity and position; , and They are particle swarm The maximum speed and maximum position of the dimension.

9. The method for planning the trajectory of a stratospheric aircraft mission across day and night in a dynamic wind environment according to claim 8, characterized in that: The step A9 comprises: If the number of iterations k If the maximum number of times is not reached, return to step A5. If the requirements are met, end the loop and obtain the optimal position of the group. .

10. The method for planning the trajectory of a stratospheric aircraft mission across day and night in a dynamic wind environment according to claim 9, characterized in that: The step A10 comprises: Optimal position of particle swarm , and also to satisfy the optimal speed sequence for the aircraft's day-and-night arrival mission or day-and-night stay mission; In the mission of the aircraft arriving across day and night, the optimal speed sequence From the starting point To the end The aircraft movement speed that satisfies the night energy constraint and minimizes energy consumption and arrival time. The minimum arrival time under this speed sequence can also be obtained through the forward evolution of the level set function. , then from the end point The level set function Along the gradient direction and wind field vector The velocity sequence can be obtained by tracing back in the opposite direction The corresponding optimal trajectory point; In the day-night stay mission of the aircraft, the optimal speed sequence From the starting point To the central point , at time The aircraft movement speed that satisfies the night energy constraint and the residence area constraint and minimizes the energy consumption and the final distance from the residence center; then, on the boundary of the final reachable set obtained by forward evolution of the level set, take the distance from the residence center point The nearest point As the end point of the trajectory, The level set function Along the gradient direction and wind field vector The optimal speed sequence can be obtained by backtracking in the opposite direction The corresponding optimal trajectory point.

11. A system for planning the trajectory of a stratospheric aircraft mission across day and night in a dynamic wind environment, implemented based on the method described in any one of claims 1 to 10, characterized in that: The system comprises: Obtaining task information module, used to obtain task objectives and task constraints; Initialization space and wind field module, used to initialize the aircraft motion space and wind field environment data; Mission feasibility analysis module, used to perform mission feasibility analysis using the maximum speed of the aircraft; Minimum feasible capacity analysis module, used to perform minimum feasible capacity analysis when the task is not feasible at maximum speed; Initialize particle swarm module, used to initialize particle swarm; The level set forward evolution module is used to use the velocity sequence obtained by the particle swarm algorithm to perform forward evolution through the level set algorithm; The particle swarm fitness function calculation module is used to calculate the fitness value of each particle according to the forward evolution results of the level set, and evaluate the performance of its speed sequence in terms of energy consumption, time cost and task constraints; Individual optimization and global optimization modules are used to update the individual optimal and group optimal values ​​of the particle swarm; Update particle swarm and boundary check module, used to update the speed and position of particle swarm and boundary check; A termination determination module is used for determining the termination of particle swarm algorithm iterations; and The level set backward evolution module is used to perform level set backward evolution on the velocity sequence corresponding to the optimal result of the particle swarm and output the final optimal trajectory.

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