Multi-uav path planning method, device, equipment, storage medium and product

Abstract

This application discloses a multi-aircraft path planning method, apparatus, device, storage medium, and product, relating to the field of path planning technology. The multi-aircraft path planning method includes: acquiring initial state information and target range information for each aircraft; establishing a path model for each aircraft based on the initial state information and the target range information; optimizing and solving the path models using a preset hierarchical optimization algorithm to obtain the optimal path planning solution; and obtaining the optimal path for each aircraft through parallel computation based on the optimal path planning solution. This application employs path modeling and a hierarchical optimization algorithm, achieving a significant improvement in the optimization efficiency of multi-aircraft path planning technology through the hierarchical optimization algorithm.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of path planning, in particular to a multi-aircraft path planning method, device, equipment, storage medium and product. BACKGROUND

[0002] With the rapid development of unmanned aircraft technology, multi-aircraft cooperative task execution has been widely applied in the fields of logistics distribution, agricultural plant protection, etc. Path planning, as one of the core technologies of aircraft autonomous navigation, directly affects the efficiency and safety of task execution.

[0003] The current multi-aircraft path planning technology generally has the following defects: the generated path is poor in smoothness and does not conform to the aircraft dynamics characteristics; the path curvature is discontinuous, leading to flight control difficulties; it is difficult to directly consider the minimum turning radius, maximum acceleration and other dynamic constraints; there is a lack of precise control over the path length, which cannot meet the strict requirements of flight time or fuel consumption for specific tasks, resulting in low optimization efficiency of multi-aircraft path planning technology, difficulty in guaranteeing global optimal solution, and poor real-time performance. SUMMARY

[0004] The main purpose of the present application is to provide a multi-aircraft path planning method, device, equipment, storage medium and product, aiming to solve the technical problem of low optimization efficiency of multi-aircraft path planning technology.

[0005] To achieve the above-mentioned purpose, the present application provides a multi-aircraft path planning method, which comprises:

[0006] obtaining the initial state information and target range information of each aircraft;

[0007] establishing a path model for each aircraft based on the initial state information and the target range information;

[0008] optimizing and solving the path model by a preset hierarchical optimization algorithm to obtain a path planning optimal solution, wherein the objective function of hierarchical optimization is in the form of weighted multi-objective, the objective function includes the weighted sum of the absolute deviation of path length and target path length, the constraint deviation of maximum curvature and the reciprocal of minimum turning radius, and other constraint terms, and the size of each weight coefficient in the objective function is determined according to the preset path planning requirements to balance the path length accuracy, curvature constraint satisfaction degree and individualized optimization target;

[0009] obtaining the optimal path of each aircraft through parallel computing based on the path planning optimal solution.

[0010] In an embodiment, the step of optimizing and solving the path model by a preset hierarchical optimization algorithm to obtain a path planning optimal solution comprises:

[0011] performing a first layer global optimization on the path model, performing a global search in a parameter space of the path model by a linear approximation constraint optimization algorithm to determine a feasible solution region;

[0012] performing a second layer local optimization based on the feasible solution region, and obtaining an accurate optimal solution satisfying all constraint conditions by using a local refinement algorithm;

[0013] verifying whether the accurate optimal solution satisfies all preset constraint conditions, and outputting a path planning optimal solution satisfying the constraint conditions.

[0014] In an embodiment, the step of obtaining optimal paths of the respective aircraft based on the path planning optimal solution by parallel computing comprises:

[0015] using a multi-thread parallel computing technology to simultaneously perform path optimization calculation for multiple aircrafts;

[0016] allocating path optimization tasks of the respective aircrafts to different computing threads through a parallel loop structure;

[0017] synchronously collecting calculation results of the respective threads to obtain optimal paths of all aircrafts.

[0018] In an embodiment, according to the complexity of the path planning problem, a cubic Bezier curve or a quintic Bezier curve is selected as the path model;

[0019] calculating path model trajectory points of the Bezier curve based on the control point parameters obtained by optimization;

[0020] calculating position information, velocity information and curvature information of each path point in the Bezier curve in the path model to verify path feasibility.

[0021] In an embodiment, the step of obtaining optimal paths of the respective aircraft based on the path planning optimal solution by parallel computing comprises:

[0022] outputting an optimal path point sequence of the optimal path of each aircraft;

[0023] According to the optimal path point sequence, path quality evaluation data including path length error and maximum curvature value can be provided;

[0024] supporting multiple formats of data output to realize visual display and analysis of path planning results.

[0025] In addition, to achieve the above-mentioned purposes, the present application further provides a multi-aircraft path planning device, which comprises: an acquisition module configured to acquire starting state information and target range information of each aircraft;

[0026] a modeling module configured to establish a path model of each of the aircraft based on the initial state information and the target range information;

[0027] an optimization module configured to solve the path model by a preset hierarchical optimization algorithm to obtain a path planning optimal solution, wherein a target function of the hierarchical optimization is in a weighted multi-objective form, the target function includes a weighted sum of an absolute deviation of a path length from a target path length, a constraint deviation of a maximum curvature from a reciprocal of a minimum turning radius, and other constraint terms, and each weight coefficient in the target function is determined according to a preset path planning requirement to balance a path length accuracy, a curvature constraint satisfaction degree, and a personalized optimization target;

[0028] a calculation module configured to obtain an optimal path of each of the aircraft by parallel calculation based on the path planning optimal solution.

[0029] In addition, to achieve the above object, the present application further provides a multi-aircraft path planning device, which comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, and the computer program is configured to implement the steps of the multi-aircraft path planning method.

[0030] In addition, to achieve the above object, the present application further provides a storage medium, which is a computer readable storage medium, and the storage medium stores a computer program, and the computer program is executed by a processor to implement the steps of the multi-aircraft path planning method.

[0031] In addition, to achieve the above object, the present application further provides a computer program product, which comprises a computer program, and the computer program is executed by a processor to implement the steps of the multi-aircraft path planning method.

[0032] The one or more technical solutions provided by the present application have at least the following technical effects:

[0033] Compared to related technologies, which lack precise control over path length and cannot meet the stringent requirements of specific missions regarding flight time or fuel consumption, resulting in low optimization efficiency, difficulty in guaranteeing a globally optimal solution, and poor real-time performance, this application addresses these issues by acquiring the initial state information and target range information of each aircraft; establishing path models for each aircraft based on these information; and optimizing the path models using a pre-defined hierarchical optimization algorithm to obtain the optimal path planning solution. The objective function of the hierarchical optimization is a weighted multi-objective form, comprising the absolute deviation between the path length and the target path length, the constraint deviation between the maximum curvature and the reciprocal of the minimum turning radius, and a weighted sum of other constraint terms. The weight coefficients in the objective function are determined according to pre-defined path planning requirements to balance path length accuracy, curvature constraint satisfaction, and personalized optimization objectives. Based on the optimal path planning solution, the optimal path for each aircraft is obtained through parallel computation. It is understood that this application employs path modeling and a hierarchical optimization algorithm, which significantly improves the optimization efficiency of multi-aircraft path planning technology. Attached Figure Description

[0034] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0035] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0036] Figure 1 This is a flowchart illustrating an embodiment of the multi-vehicle path planning method of this application.

[0037] Figure 2 This is a flowchart illustrating Embodiment 2 of the multi-vehicle path planning method of this application.

[0038] Figure 3 A simplified flowchart illustrating the multi-vehicle path planning method provided in Embodiment 2 of this application;

[0039] Figure 4 This is a schematic diagram of the module structure of the multi-aircraft path planning device according to an embodiment of this application;

[0040] Figure 5 This is a schematic diagram of the equipment structure of the hardware operating environment involved in the multi-aircraft path planning method in the embodiments of this application.

[0041] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0042] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0043] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0044] The main solution in this application's embodiments is:

[0045] Acquire the initial status information and target range information of each aircraft;

[0046] Based on the initial state information and the target range information, a path model for each of the aircraft is established;

[0047] The path model is optimized and solved by a preset hierarchical optimization algorithm to obtain the optimal solution for path planning. The objective function of the hierarchical optimization is a weighted multi-objective form. The objective function includes the absolute deviation between the path length and the target path length, the constraint deviation between the maximum curvature and the reciprocal of the minimum turning radius, and the weighted sum of other constraint terms. The magnitude of each weight coefficient in the objective function is determined according to the preset path planning requirements to balance the accuracy of path length, the degree of curvature constraint satisfaction, and personalized optimization objectives.

[0048] Based on the optimal solution of the path planning, the optimal path for each of the aircraft is obtained through parallel computation.

[0049] In this embodiment, the application uses a multi-vehicle path planning device as the execution subject. For ease of description, it will be referred to as "device" in the following detailed description.

[0050] Because existing technologies lack precise control over path length, they cannot meet the stringent requirements of specific missions regarding flight time or fuel consumption, resulting in low optimization efficiency for multi-vehicle path planning technology.

[0051] This application provides a solution that employs path modeling and hierarchical optimization algorithms. Through the hierarchical optimization algorithm, feasible solution regions are first searched over a large area, and then the optimal solution is found through local optimization, thereby significantly improving the optimization efficiency of multi-aircraft path planning technology.

[0052] Based on this, embodiments of this application provide a multi-vehicle path planning method, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the multi-vehicle path planning method of this application.

[0053] In this embodiment, the multi-vehicle path planning method includes steps S10 to S40:

[0054] Step S10: Obtain the initial state information and target range information of each aircraft;

[0055] It should be noted that the initial state information includes, but is not limited to, the aircraft's current position coordinates, initial heading angle, and flight speed; the target range information refers to a circular area centered on the target point and constrained by a preset radius, including the target point coordinates and the constraint radius parameter.

[0056] Understandably, by accurately obtaining the initial state of the aircraft and the target mission area, the necessary input conditions are provided for subsequent path planning, ensuring that the planned path meets the requirements of the actual flight scenario.

[0057] Step S20: Based on the initial state information and the target range information, establish a path model for each of the aircraft;

[0058] It should be noted that the path model refers to the mathematical model of the flight trajectory represented by Bézier curves, in which the control points are determined based on the initial state and target range information, including cubic Bézier curve models and quintic Bézier curve models.

[0059] It is understandable that by utilizing the inherent continuity and smoothness of Bézier curves to construct a path model, the curvature continuity of the generated path can be guaranteed from a mathematical perspective, thus satisfying the dynamic constraints of the aircraft.

[0060] Step S30: The path model is optimized and solved using a preset hierarchical optimization algorithm to obtain the optimal solution for path planning. The objective function of the hierarchical optimization is a weighted multi-objective form. The objective function includes the absolute deviation between the path length and the target path length, the constraint deviation between the maximum curvature and the reciprocal of the minimum turning radius, and the weighted sum of other constraint terms. The magnitude of each weight coefficient in the objective function is determined according to the preset path planning requirements to balance the accuracy of path length, the degree of curvature constraint satisfaction, and the personalized optimization objective.

[0061] It should be noted that the hierarchical optimization algorithm includes a first-level global optimization and a second-level local optimization. The global optimization uses a linear approximation constraint optimization algorithm to search the feasible region, while the local optimization is a fine-grained solution based on the global optimization results. The optimal solution for path planning refers to the comprehensive optimal set of control point parameters that satisfies curvature constraints, path length constraints, and endpoint constraints.

[0062] It is understandable that by using a hierarchical optimization strategy, first determining the range of feasible solutions and then performing local refinement, the optimization efficiency can be significantly improved while ensuring the quality of the solutions, effectively handling complex path planning problems under multiple constraints.

[0063] It should be noted that the weighted multi-objective form refers to the method of combining multiple optimization objectives into a single objective function through weighted summation; absolute deviation refers to the absolute value of the difference between the actual path length and the expected path length; constraint deviation refers to the difference between the actual path curvature and the maximum curvature allowed by the minimum turning radius of the aircraft; other constraint terms include, but are not limited to, additional optimization objectives such as endpoint position deviation and flight time constraints; weighting coefficients are proportional parameters used to adjust the importance of each optimization objective in the overall objective function.

[0064] Understandably, by constructing the objective function using a weighted multi-objective approach, multiple optimization objectives in path planning can be unified within a single mathematical framework. Adjusting the weighting coefficients according to specific task requirements allows the generated path to achieve a balance among different optimization objectives, satisfying basic flight constraints while adapting to specific requirements in different scenarios. This objective function design method enhances the adaptability and practicality of the path planning algorithm, enabling the generation of flight paths that meet various optimization objectives for different task needs.

[0065] For example, a hierarchical optimization strategy;

[0066] To improve optimization efficiency, a hierarchical optimization strategy is adopted.

[0067] First-level optimization: global search to determine the approximate parameter range.

[0068] Algorithm: COBYLA (linear approximation constraint optimization);

[0069] Number of assessments: High (e.g., 2000 times);

[0070] Objective: To find a region of feasible solutions.

[0071] Second-level optimization: local refinement, which is a precise optimization based on the results of the first level.

[0072] Algorithm: Different local optimization algorithms can be selected;

[0073] Number of assessments: relatively small (e.g., 50 times);

[0074] Objective: To obtain the exact optimal solution.

[0075] Step S40: Based on the optimal solution of the path planning, the optimal path for each of the aircraft is obtained through parallel computation.

[0076] It should be noted that parallel computing refers to processing the path generation tasks of multiple aircraft simultaneously; the optimal path is a discrete path point sequence calculated based on the control point parameters in the optimal solution of the path planning, including the position, velocity and curvature information of each path point.

[0077] Understandably, generating multi-aircraft paths synchronously through parallel computing can significantly improve the overall planning efficiency of the system and meet the requirements for real-time performance and synchronization in multi-aircraft collaborative tasks.

[0078] This embodiment provides a multi-aircraft path planning method, which adopts path modeling and hierarchical optimization algorithm. Through the hierarchical optimization algorithm, the optimization efficiency of multi-aircraft path planning technology is significantly improved.

[0079] In one feasible implementation, the step of optimizing the path model using a preset hierarchical optimization algorithm to obtain the optimal solution for path planning includes:

[0080] The first-level global optimization is performed on the path model by using a linear approximation constraint optimization algorithm to perform a global search in the parameter space of the path model to determine the feasible solution region.

[0081] Based on the feasible solution region, a second layer of local optimization is performed, and a local refinement algorithm is used to obtain the exact optimal solution that satisfies all constraints.

[0082] Verify whether the exact optimal solution satisfies all preset constraints, and output the solution that satisfies the constraints as the optimal solution for path planning.

[0083] It should be noted that linear approximation constraint optimization algorithms include, but are not limited to, the COBYLA algorithm, which handles constraints in the parameter space through linear approximation. The parameter space refers to a multi-dimensional space composed of optimization variables such as control point distance parameters and endpoint angle parameters. The feasible solution region refers to the range of parameter values ​​that satisfy basic requirements such as curvature constraints and length constraints. Local refinement algorithms can employ various local optimization methods, including sequential quadratic programming algorithms. Constraints include, but are not limited to, maximum curvature constraints, path length error constraints, and endpoint position constraints.

[0084] Understandably, by first performing a global search to determine the feasible solution region and then performing local refinement, this hierarchical optimization strategy can effectively avoid getting trapped in local optima, improving optimization efficiency while ensuring solution quality. The verification step ensures that the final optimal solution for path planning strictly satisfies all preset physical constraints and task requirements, thereby guaranteeing the actual executability of the generated path.

[0085] In one feasible implementation, the step of obtaining the optimal path for each of the aircraft through parallel computation based on the optimal solution of the path planning includes:

[0086] Multi-threaded parallel computing technology is used to simultaneously perform path optimization calculations for multiple aircraft.

[0087] The path optimization tasks for each aircraft are distributed to different computation threads using a parallel loop structure;

[0088] The calculation results of each thread are collected synchronously to obtain the optimal path for all aircraft.

[0089] It should be noted that path optimization calculation refers to the calculation process of generating a specific flight path based on the control point parameters in the optimal solution of the path planning; parallel loop structure is a parallel programming mode that automatically allocates iterative tasks to multiple threads; the computing thread is the basic unit for task scheduling in the operating system; synchronous collection refers to the data processing method of waiting for all parallel computing tasks to complete and then uniformly obtaining the results.

[0090] Understandably, the parallel loop structure enables automatic allocation and load balancing of computational tasks, ensuring efficient utilization of system resources. The synchronous collection mechanism guarantees the consistency and integrity of all aircraft path data, meeting the time consistency requirements of multi-aircraft collaborative operations. This parallel processing approach allows the algorithm to adapt to application scenarios with high real-time requirements.

[0091] For example, parallel computing optimization, such as for multi-vehicle path planning, employs OpenMP parallel computing technology, for instance:

[0092] ```cpp

[0093] #pragma omp parallel for

[0094] for (int i = 0; i <num_vehicles; i++) {

[0095] / / Parallel computation of the optimal path for each aircraft

[0096] optimizeVehiclePath(vehicles[i]);

[0097] }

[0098] ```

[0099] For example, the conversion between geographic coordinate systems and local coordinate systems is supported.

[0100] Geographic coordinates to ENU coordinate conversion:

[0101]

[0102] in: For rotational moment; The latitude and longitude of the reference point.

[0103] In one feasible implementation, the step of establishing the path model for each of the aircraft includes:

[0104] Based on the complexity of the path planning problem, either a cubic or quintic Bézier curve is selected as the path model.

[0105] Based on the optimized control point parameters, the path model trajectory points of the Bézier curve are calculated;

[0106] The position, velocity, and curvature information of each path point in the Bézier curve in the path model are calculated to verify the feasibility of the path.

[0107] It should be noted that the complexity of path planning problems includes, but is not limited to, factors such as obstacle density, path length requirements, accuracy requirements, and the number of aircraft; a cubic Bézier curve is a parametric curve defined by four control points, and a quintic Bézier curve is a parametric curve defined by six control points; control point parameters are the key coordinate points that determine the shape of the Bézier curve; path model trajectory points are the discrete points of the curve calculated through parametric equations; position information includes the coordinate values ​​of the path points in two-dimensional or three-dimensional space; velocity information includes the magnitude and direction of the tangential velocity at the path point; curvature information characterizes the degree of curvature of the path at that point.

[0108] For example, four control points are used for cubic Bézier curves. , , , Define path:

[0109]

[0110] in: The starting point is determined by the aircraft's current position; The endpoint is located on the target constraint circle; , The control points need to be determined through optimization.

[0111] Formula for calculating control points:

[0112]

[0113]

[0114] in: , To control the distance parameter; This is the initial heading angle; The endpoint angle.

[0115] For more complex path requirements, a quintic Bézier curve with six control points is used:

[0116]

[0117] Quintic Bézier curves offer greater degrees of freedom and can handle more complex geometric constraints.

[0118] Cubic Bézier curve optimization variables:

[0119] : Distance parameter of the first control point, range ;

[0120] : Distance parameter of the second control point, range ;

[0121] Endpoint angle parameter, range .

[0122] Fifth-order Bézier curve optimization variables:

[0123] , : Control point distance parameter;

[0124] , Interpolation parameters, range ;

[0125] , : Normal offset parameter;

[0126] Endpoint angle parameter.

[0127] Understandably, choosing an appropriate Bézier curve order based on problem complexity can optimize computational efficiency while ensuring path quality. By calculating complete pathpoint information, the actual feasibility of the generated path can be comprehensively evaluated, ensuring that the path meets the dynamic constraints of the aircraft and mission requirements. This modeling approach provides an accurate mathematical model foundation for subsequent optimization solutions and also provides necessary data support for path feasibility verification.

[0128] In one feasible implementation, the step of obtaining the optimal path for each of the aircraft through parallel computation based on the optimal solution of the path planning includes:

[0129] Output the optimal path point sequence for each aircraft.

[0130] Based on the optimal path point sequence, path quality assessment data can be provided, including path length error and maximum curvature value;

[0131] It supports data output in multiple formats, enabling visualization and analysis of path planning results.

[0132] It should be noted that the optimal path point sequence refers to the set of coordinate data composed of discrete points along the path in flight order; path quality assessment data is a set of indicators used to quantify path performance, including but not limited to path length error and maximum curvature value; path length error refers to the difference between the actual generated path length and the preset target length; maximum curvature value represents the extreme curvature value in the entire path; multiple data output formats include but are not limited to text format, graphic format, and data interface format that can be used for simulation; visualization display refers to the intuitive display of path planning results in the form of graphs or curves.

[0133] Understandably, by outputting structured path point sequences, directly usable input data is provided for the aircraft's trajectory tracking control. Providing path quality assessment data allows users to objectively judge the applicability of the generated path, ensuring it meets mission requirements. Support for multiple output formats enhances the system's compatibility and usability, facilitating data interaction with other systems. Visualization capabilities help users quickly understand and analyze path planning results, improving the overall usability and engineering application value of the planning system.

[0134] For example, the minimum turning radius constraint of an aircraft is achieved by controlling the maximum curvature of a Bézier curve. The formula for calculating the curvature at any point on the curve is:

[0135]

[0136] in: and These are the first and second derivatives of the curve, respectively.

[0137] The constraints are expressed as follows:

[0138]

[0139] Where: $ This is the minimum turning radius of the aircraft.

[0140] Path length is calculated by integration:

[0141]

[0142] The length constraint is expressed as:

[0143]

[0144] in: The target path length; This is the allowable error.

[0145] The endpoint must be located within a circle centered on the target point with a radius of [missing information]. On the circle:

[0146]

[0147] in: The angle parameter for the endpoint on the circle needs to be determined through optimization.

[0148] For example, to help understand the implementation process of the multi-vehicle path planning method obtained by combining this embodiment with the above embodiment one, please refer to... Figure 2 and Figure 3 , specifically:

[0149] First, the system initializes by reading the initial states of all aircraft, including position, heading angle, and velocity, and sets their respective target points and endpoint constraint circle radii. This stage corresponds to... Figure 2 The parameter configuration stage is shown in the diagram. Following this, the modeling stage begins. Based on the complexity of the mission scenario, the system adaptively selects a cubic or quintic Bézier curve as the path model for each aircraft and sets the boundary conditions for the optimization variables.

[0150] The critical optimization solution stage employs a layered strategy. For example... Figure 2 As shown in the optimization module, the first-layer global optimization uses algorithms such as COBYLA to perform a broad search in the parameter space to quickly locate feasible solution regions. Based on this, the second-layer local optimization performs a fine-grained solution, ultimately obtaining the optimal path planning solution that satisfies all curvature, length, and endpoint constraints. The entire process is achieved through... Figure 3 The path diagram is shown, and the smooth Bézier curve trajectory in the diagram intuitively demonstrates the good characteristics of the optimization result.

[0151] During the path generation phase, the system calculates the discrete points of the Bézier curves for each aircraft in parallel based on the optimized control point parameters, generating a specific path point sequence. For example... Figure 3 As shown in the actual trajectory following results, the generated path is not only smooth and continuous, but also fully conforms to the dynamic constraints of the aircraft. Finally, the system outputs the optimal path point sequence for all aircraft and provides quality assessment data including path length error and maximum curvature value. It supports data output in multiple formats and visualization, facilitating subsequent simulation analysis and practical applications.

[0152] This complete process fully demonstrates the effectiveness and practicality of the proposed method in solving multi-vehicle path planning problems, achieving an organic balance between path smoothness, constraint satisfaction, and computational efficiency.

[0153] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the multi-vehicle path planning method of this application. Any simple modifications based on this technical concept are within the protection scope of this application.

[0154] This application also provides a multi-aircraft path planning device; please refer to... Figure 4 The multi-vehicle path planning device includes:

[0155] The acquisition module is used to acquire the initial state information and target range information of each aircraft.

[0156] The modeling module is used to establish path models for each of the aircraft based on the initial state information and the target range information;

[0157] The optimization module is used to optimize and solve the path model using a preset hierarchical optimization algorithm to obtain the optimal solution for path planning. The objective function of the hierarchical optimization is in the form of a weighted multi-objective. The objective function includes the absolute deviation between the path length and the target path length, the constraint deviation between the maximum curvature and the reciprocal of the minimum turning radius, and the weighted sum of other constraint terms. The magnitude of each weight coefficient in the objective function is determined according to the preset path planning requirements to balance the accuracy of path length, the degree of curvature constraint satisfaction, and personalized optimization objectives.

[0158] The calculation module is used to obtain the optimal path for each of the aircraft through parallel calculation based on the optimal solution of the path planning.

[0159] And / or, the multi-vehicle path planning device includes:

[0160] The first optimization module is used to perform a first-level global optimization on the path model by performing a global search in the parameter space of the path model through a linear approximation constraint optimization algorithm to determine the feasible solution region.

[0161] The second optimization module is used to perform a second-level local optimization based on the feasible solution region, and to obtain the exact optimal solution that satisfies all constraints by using a local refinement algorithm.

[0162] The first verification module is used to verify whether the exact optimal solution satisfies all preset constraints, and outputs the path planning optimal solution as the one that satisfies the constraints.

[0163] And / or, the multi-vehicle path planning device includes:

[0164] The first computing module is used to perform path optimization calculations for multiple aircraft simultaneously using multi-threaded parallel computing technology.

[0165] The first allocation module is used to allocate the path optimization tasks of each aircraft to different computing threads through a parallel loop structure;

[0166] The first collection module is used to synchronously collect the calculation results of each thread and obtain the optimal path for all aircraft.

[0167] And / or, the multi-vehicle path planning device includes:

[0168] The first selection module is used to determine whether to use a cubic or quintic Bézier curve as the path model based on the complexity of the path planning problem.

[0169] The second calculation module is used to calculate the path model trajectory points of the Bézier curve based on the optimized control point parameters.

[0170] The third calculation module is used to calculate the position, velocity, and curvature information of each path point on the Bézier curve in the path model in order to verify the feasibility of the path.

[0171] And / or, the multi-vehicle path planning device includes:

[0172] The first output module is used to output the optimal path point sequence for each aircraft.

[0173] The first evaluation module is used to provide path quality evaluation data, including path length error and maximum curvature value, based on the optimal path point sequence.

[0174] The first visualization module supports data output in multiple formats, enabling the visualization and analysis of path planning results.

[0175] The multi-aircraft path planning device provided in this application, employing the multi-aircraft path planning method described in the above embodiments, can solve the technical problem of low optimization efficiency in multi-aircraft path planning technology. Compared with the prior art, the beneficial effects of the multi-aircraft path planning device provided in this application are the same as those of the multi-aircraft path planning method provided in the above embodiments, and other technical features in the multi-aircraft path planning device are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0176] This application provides a multi-aircraft path planning device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, which are executed by the at least one processor to enable the at least one processor to perform the multi-aircraft path planning method in Embodiment 1 above.

[0177] The following is for reference. Figure 5The diagram illustrates a structural schematic of a multi-aircraft path planning device suitable for implementing embodiments of this application. The multi-aircraft path planning device in the embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, tablets, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PMPs (Portable Media Players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital televisions and desktop computers. Figure 5 The multi-aircraft path planning device shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0178] like Figure 5 As shown, the multi-aircraft path planning device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.) that can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1003 into a random access memory (RAM) 1004. The RAM 1004 also stores various programs and data required for the operation of the multi-aircraft path planning device. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touchscreen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 1003 including, for example, magnetic tape, hard disk, etc.; and communication devices 1009. Communication device 1009 allows the multi-aircraft path planning device to communicate wirelessly or wiredly with other devices to exchange data. Although the figure shows a multi-aircraft path planning device with various systems, it should be understood that implementation or possession of all the systems shown is not required. More or fewer systems may be implemented alternatively.

[0179] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0180] The multi-aircraft path planning device provided in this application, employing the multi-aircraft path planning method described in the above embodiments, can solve the technical problem of low optimization efficiency in multi-aircraft path planning technology. Compared with the prior art, the beneficial effects of the multi-aircraft path planning device provided in this application are the same as those of the multi-aircraft path planning method provided in the above embodiments, and other technical features of this multi-aircraft path planning device are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0181] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0182] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0183] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the multi-aircraft path planning method in the above embodiments.

[0184] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0185] The aforementioned computer-readable storage medium may be included in the multi-aircraft path planning device; or it may exist independently and not be assembled into the multi-aircraft path planning device.

[0186] The aforementioned computer-readable storage medium carries one or more programs that, when executed by the multi-aircraft path planning device, cause the multi-aircraft path planning device to:

[0187] Acquire the initial status information and target range information of each aircraft;

[0188] Based on the initial state information and the target range information, a path model for each of the aircraft is established;

[0189] The path model is optimized and solved by a preset hierarchical optimization algorithm to obtain the optimal solution for path planning. The objective function of the hierarchical optimization is a weighted multi-objective form. The objective function includes the absolute deviation between the path length and the target path length, the constraint deviation between the maximum curvature and the reciprocal of the minimum turning radius, and the weighted sum of other constraint terms. The magnitude of each weight coefficient in the objective function is determined according to the preset path planning requirements to balance the accuracy of path length, the degree of curvature constraint satisfaction, and personalized optimization objectives.

[0190] Based on the optimal solution of the path planning, the optimal path for each of the aircraft is obtained through parallel computation.

[0191] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0192] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0193] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0194] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described multi-aircraft path planning method, thereby solving the technical problem of low optimization efficiency in multi-aircraft path planning technology. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the multi-aircraft path planning method provided in the above embodiments, and will not be repeated here.

[0195] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the multi-aircraft path planning method described above.

[0196] The computer program product provided in this application can solve the technical problem of low optimization efficiency in multi-aircraft path planning technology. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the multi-aircraft path planning method provided in the above embodiments, and will not be repeated here.

[0197] The above description is only a part of the embodiments of this application and does not limit the scope of protection of this application. All equivalent structural transformations made under the technical concept of this application and using the content of this application specification and drawings, or direct / indirect applications in other related technical fields, are included in the scope of protection of this application.

Claims

1. A multi-vehicle path planning method, characterized in that, The method includes: Acquire the initial status information and target range information of each aircraft; Based on the initial state information and the target range information, a path model for each of the aircraft is established; The path model is optimized and solved using a pre-defined hierarchical optimization algorithm to obtain the optimal solution for path planning. The objective function of the hierarchical optimization is a weighted multi-objective form. The objective function includes the absolute deviation between the path length and the target path length, the constraint deviation between the maximum curvature and the reciprocal of the minimum turning radius, and the weighted sum of other constraint terms. The other constraint terms include the endpoint position deviation and flight time constraint. The magnitude of each weight coefficient in the objective function is determined according to the pre-defined path planning requirements to balance the accuracy of path length, the degree of curvature constraint satisfaction, and personalized optimization objectives. The step of optimizing the path model using a preset hierarchical optimization algorithm to obtain the optimal solution for path planning includes: The first-level global optimization is performed on the path model by using a linear approximation constraint optimization algorithm to perform a global search in the parameter space of the path model to determine the feasible solution region. Based on the feasible solution region, a second layer of local optimization is performed, and a local refinement algorithm is used to obtain the exact optimal solution that satisfies all constraints. Verify whether the exact optimal solution satisfies all preset constraints, and output the optimal solution for path planning that satisfies the constraints; Based on the optimal solution of the path planning, the optimal path for each of the aircraft is obtained through parallel computation.

2. The method as described in claim 1, characterized in that, The step of obtaining the optimal path for each of the aircraft through parallel computation based on the optimal solution of the path planning includes: Multi-threaded parallel computing technology is used to simultaneously perform path optimization calculations for multiple aircraft. The path optimization tasks for each aircraft are distributed to different computation threads using a parallel loop structure; The calculation results of each thread are collected synchronously to obtain the optimal path for all aircraft.

3. The method as described in claim 1, characterized in that, The steps for establishing the path models for each of the aforementioned aircraft include: Based on the complexity of the path planning problem, either a cubic or quintic Bézier curve is selected as the path model. Based on the optimized control point parameters, the path model trajectory points of the Bézier curve are calculated; The position, velocity, and curvature information of each path point in the Bézier curve in the path model are calculated to verify the feasibility of the path.

4. The method as described in claim 1, characterized in that, The step of obtaining the optimal path for each of the aircraft through parallel computation based on the optimal solution of the path planning includes: Output the optimal path point sequence for each aircraft. Based on the optimal path point sequence, path quality assessment data is provided, including path length error and maximum curvature value; It supports data output in multiple formats, enabling visualization and analysis of path planning results.

5. A multi-aircraft path planning device, characterized in that, The device includes: The acquisition module is used to acquire the initial state information and target range information of each aircraft. The modeling module is used to establish path models for each of the aircraft based on the initial state information and the target range information; the optimization module is used to optimize and solve the path models using a preset hierarchical optimization algorithm to obtain the optimal solution for path planning. The objective function of the hierarchical optimization is in the form of a weighted multi-objective. The objective function includes the absolute deviation between the path length and the target path length, the constraint deviation between the maximum curvature and the reciprocal of the minimum turning radius, and the weighted sum of other constraint terms. The other constraint terms include the endpoint position deviation and flight time constraint. The magnitude of each weight coefficient in the objective function is determined according to the preset path planning requirements to balance the accuracy of path length, the degree of curvature constraint satisfaction, and the personalized optimization objective. The step of optimizing the path model using a preset hierarchical optimization algorithm to obtain the optimal solution for path planning includes: The first-level global optimization is performed on the path model by using a linear approximation constraint optimization algorithm to perform a global search in the parameter space of the path model to determine the feasible solution region. Based on the feasible solution region, a second layer of local optimization is performed, and a local refinement algorithm is used to obtain the exact optimal solution that satisfies all constraints. Verify whether the exact optimal solution satisfies all preset constraints, and output the optimal solution for path planning that satisfies the constraints; The calculation module is used to obtain the optimal path for each of the aircraft through parallel calculation based on the optimal solution of the path planning.

6. A multi-aircraft path planning device, characterized in that, The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the multi-vehicle path planning method as described in any one of claims 1 to 4.

7. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, it implements the steps of the multi-vehicle path planning method as described in any one of claims 1 to 4.

8. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the steps of the multi-vehicle path planning method as described in any one of claims 1 to 4.

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