Flight mission planning and management system and method for cooperation of multiple unmanned aerial vehicles

Abstract

The invention relates to the technical field of unmanned aerial vehicles, in particular to a flight mission planning and management system and method for cooperation of multiple unmanned aerial vehicles, and the method comprises the steps: decomposing a flight region of an unmanned aerial vehicle based on a flight mission of the unmanned aerial vehicle, and dividing the flight region; path planning is carried out in the flight area of the unmanned aerial vehicle, global flight path planning is carried out by adopting a genetic algorithm, and local flight path planning is carried out by adopting an A * algorithm after the global path planning; an obstacle avoidance strategy of the unmanned aerial vehicle in the flight process is constructed, and after the obstacle avoidance strategy is triggered, local flight path planning is carried out again; according to unmanned aerial vehicle flight task constraints, a priority judgment criterion of multiple unmanned aerial vehicle tasks is formulated, and unmanned aerial vehicle resources are allocated and dispatched. The intelligent level of unmanned aerial vehicle task planning can be remarkably improved, the adaptability to complex environments is enhanced, flight safety and efficient task execution are ensured, and a landing technical support is provided for actual application of the unmanned aerial vehicle.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) technology, specifically to a flight mission planning and management system and method for multi-UAV collaboration. Background Art

[0002] With the rapid development of drone technology and the continuous expansion of its application fields, utilizing multiple drones to collaboratively execute complex tasks has become a research hotspot. Multi-drone collaborative systems can significantly improve task execution efficiency and success rates through the division of labor and cooperation among multiple drones. However, how to rationally plan and schedule multiple drones for tasks, enabling them to work together efficiently, safely, and orderly during task execution, still faces many challenges.

[0003] Existing multi-UAV mission planning methods employ centralized control, with ground stations or central nodes centrally planning and scheduling UAV missions. While this approach can achieve globally optimal solutions, it suffers from single-point-of-failure risks, high computational complexity, and difficulty adapting to large-scale UAV swarms and dynamically changing environments. Other methods utilize a fully distributed architecture, where each UAV autonomously plans its mission and path based on local information. While this approach offers good robustness and scalability, it lacks global information and coordination mechanisms, making it prone to mission conflicts and resource waste.

[0004] Therefore, existing multi-UAV mission planning and management methods generally suffer from insufficient real-time performance, poor adaptability, and low reliability when dealing with large-scale, dynamic, and complex environments, making it difficult to meet the needs of practical applications. A more efficient, flexible, and robust mission planning and management method is needed, comprehensively considering the heterogeneity of UAV swarms, the dynamic nature of the environment, and the complexity of the mission, to achieve collaborative work among multiple UAVs under complex conditions and improve mission execution efficiency.

[0005] In view of this, the present invention proposes a flight mission planning and management system and method for multi-UAV collaboration. Summary of the Invention

[0006] To achieve the above objectives, this invention provides a flight mission planning and management system and method for multi-UAV collaboration, the specific technical solution of which is as follows: A flight mission planning and management method for multi-UAV collaboration, comprising: Based on the drone's flight mission, the drone's flight area is decomposed, and the flight area is divided based on the drone's flight parameters. Path planning is performed within the flight area of ​​the UAV. A genetic algorithm is used for global flight path planning, and after global path planning, the A* algorithm is used for local flight path planning. Construct an obstacle avoidance strategy for the drone during flight, and re-plan the local flight path when the obstacle avoidance strategy is triggered; Based on the constraints of UAV flight missions, priority judgment criteria for multi-UAV missions are formulated, and UAV resources are allocated and scheduled.

[0007] Preferably, based on the UAV's mission objectives and execution range, the geographical boundary of the entire flight mission area is determined and denoted as the mission area. ,in The coordinates of the points within the region. These are the boundary coordinates of the region; Collection mission area An environmental feature matrix is ​​constructed based on environmental information including topography, meteorology, and airspace control within the area. ,in Indicates location Environmental characteristic values; Obtain the drone's flight parameters, based on the tasks involved. The model and number of drones were determined, and their flight parameters, including maximum range, were extracted. Maximum load Maximum flight speed ; Estimate the completion of the entire task area Total flight time required and total load requirements ,in drones Flight time and payload requirements; Delineate flight zones, taking mission areas into account. Environmental feature matrix and the flight parameters of the drone ,Will Divided into A set of non-overlapping subregions is formed. Define the partitioning loss function. This is used to evaluate the merits of different partitioning schemes; Minimize the partitioning loss function ,in Using the weights as coefficients, we find the minimum value of the partitioning loss function to obtain the optimal partitioning scheme. ;in, It is the degree of difference in environmental characteristics within the sub-region. For the area uniformity of sub-regions, The task relevance between sub-regions; Match drones with sub-regions and calculate each sub-region. area and task complexity Among them, task complexity The results are weighted and summed using the indicators of the number, density, and priority of task points within the sub-region; Estimate the execution sub-region based on the sub-region area and task complexity. Number of drones required for the mission The formula is: ,in This indicates rounding down. Indicates a single drone The maximum task coverage area; From those who participated in the mission Selected from drones The frame, based on the drone's flight parameters Environmental characteristics of sub-regions The drones are assigned to various sub-regions to form an optimal matching relationship. ,in Indicates that drones Assigned to sub-region .

[0008] Preferably, a genetic algorithm is used for global flight path planning, and the flight area of ​​the UAV is defined as a three-dimensional space. Any point in space is represented by coordinates. express; Define a set of path points ,in Indicates the The coordinates of the path points The total number of path points; define the population. ,in For population size, Indicates the A path, consisting of path points Composition; Definition of fitness function Used to evaluate paths The advantages and disadvantages are determined by comprehensively considering factors such as path length, smoothness, flight time, and energy consumption. The steps for global flight path planning using a genetic algorithm are as follows: Step 1: Initialize the population Randomly generated Path; Step 2: Calculate the fitness of each path ; Step 3: Perform the selection operation, based on the fitness level, with probability. Choose the best path as the parent generation; Step 4: Perform crossover operation: Perform pairwise crossovers on the parent paths, with probability... Generate new child paths; Step 5: Perform mutation operations with probability. Randomly mutate the child paths, introducing new path points or deleting existing path points; Step 6: Repeat steps 2 through 5 until the maximum number of iterations is reached. ; Output the global path with the highest fitness As a result of global flight path planning.

[0009] Preferably, the A* algorithm is used for local flight path planning, which will then be used to plan the global path. Discretize into a series of grid points to form a grid map. Define the set of grid points ,in Indicates the The coordinates of each grid point This represents the total number of grid points. Define open list and close list Store the grid points to be expanded and those already expanded separately, and define the heuristic function. , indicating from grid point To the target point Estimate the cost; define the cost function. , indicating from the starting point To grid point The actual cost; define the evaluation function Used to evaluate grid points Extension priority; The steps for local flight path planning using the A* algorithm are as follows: Step a: Set the starting point Join the open list ,calculate ; Step b: Retrieve from the open list Minimum grid point Add it to the close list ; Step c: Expand All adjacent grid points ,calculate and ; Step d: For each adjacent grid point If it is in the closed list, ignore it; if it is in the open list, ignore it. If smaller, then update. and If it is not in the open list, add it to the open list. Step e: Repeat steps b through d until the target point is reached. Added to the close list; Output from arrive Optimal local path , and global path The data is matched and pieced together to form a complete flight path.

[0010] Preferably, an obstacle avoidance strategy is constructed, and a set of obstacles is defined. ,in Indicates the An obstacle, The total number of obstacles; drones are considered obstacles to each other; define the safe distance between drones. , representing the minimum permissible distance between the drone and the obstacle; define the collision detection function. Used to determine drones Is it related to obstacles? A collision occurs when the distance between the two objects is less than [a certain value]. ; During the drone's flight, the distance between the drone and all obstacles is continuously monitored, and the collision detection function is calculated in real time. When detected True, meaning the drone is in contact with an obstacle. If a collision risk occurs, the obstacle avoidance strategy is triggered, the current flight path is immediately stopped, and a local path replanning mode is entered.

[0011] Preferably, the local path replanning after obstacle avoidance is defined as the current location of the UAV. The target location is ,by Centered on, with Construct a local path planning space with radius as the radius. ,in For local planning range parameters, Discretize into a series of grid points to form a local grid map. ; Define the set of local grid points ,in Indicates the The coordinates of a local grid point The total number of local grid points, for each obstacle. Check whether it is consistent with the local path planning space. If they intersect, then place them in... The projected area is marked as impassable; Define a local path point set ,in Indicates the The coordinates of a local path point Given the total number of local path points, use the A* algorithm in... Search from arrive Local optimal path During the search, avoid all impassable areas; Local optimal path Compared with the original global path By concatenating the results, we obtain the updated global path. The drone follows the updated global path. Continue flying until the target location is reached. Or the obstacle avoidance strategy may be triggered again.

[0012] Preferably, a set of drone missions is defined. ,in Indicates the One task, The total number of tasks; for each task Extract its task attribute vector ,in Indicates task The Each attribute value The total number of attributes; define the task priority evaluation function. ,in For the The weight coefficient of each attribute; Calculate the priority evaluation value for each task. , and according to The tasks are sorted in descending order of their size to obtain the task priority queue. ,in Indicates priority number High-level tasks; Define drone resource pool ,in Indicates the A drone, The total number of drones; for each drone Extract its flight parameter vector ,in Indicates drone The One flight parameter value, This represents the total number of flight parameters. Construct a function to evaluate the match between the task and the drone: ,in For the Weighting coefficients for each flight parameter The indicator function represents the unmanned aerial vehicle (UAV). Does the task meet the requirements? The constraint condition is 1 if satisfied, and 0 otherwise; Initialize task and drone allocation matrix ,in Indicate whether to assign the task Assigned to drones ; according to task priority queue Process each task in sequence; for each task... Computational tasks Matching degree with each idle drone Select the drone with the highest matching degree. Perform the task; Output the task and drone allocation matrix when all tasks are assigned or all drones are occupied. ,according to The task execution sequence of each drone is obtained, forming a multi-drone task scheduling scheme.

[0013] A flight mission planning and management system for multi-UAV collaboration, which is used to implement the flight mission planning and management method for multi-UAV collaboration, includes: a flight area division module, a flight path planning module, a flight obstacle avoidance module, and a flight scheduling module; The flight area division module decomposes the flight area of ​​the UAV based on the UAV's flight mission and divides the flight area based on the UAV's flight parameters. The flight path planning module performs path planning within the UAV's flight area, employing a genetic algorithm for global flight path planning and then using the A* algorithm for local flight path planning after planning the global path. The flight obstacle avoidance module is used to construct the obstacle avoidance strategy of the UAV during flight. When the obstacle avoidance strategy is triggered, the local flight path is re-planned. The flight scheduling module formulates priority judgment criteria for multiple UAV missions based on UAV flight mission constraints, and allocates and schedules UAV resources.

[0014] An electronic device includes a processor and a memory, wherein the memory stores a computer program that can be called by the processor; the processor executes the flight mission planning and management method for multi-UAV collaboration by calling the computer program stored in the memory.

[0015] A computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the aforementioned method for flight mission planning and management for multi-UAV collaboration.

[0016] The beneficial effects of this invention are: by decomposing regions and dividing parameters, this invention reduces task complexity, optimizes resource matching, and improves the precision and feasibility of task planning.

[0017] This invention combines genetic algorithms with A* algorithms to search for optimal flight paths in complex environments, achieving efficient, smooth, energy-saving, and safe path planning.

[0018] This invention constructs a real-time obstacle avoidance monitoring and response system to avoid obstacles and ensure safety; after obstacle avoidance is triggered, rapid replanning is performed to minimize the impact on flight plans.

[0019] This invention optimizes matching and scheduling based on task priority and flight parameters, thereby improving the quality and efficiency of multi-UAV collaborative task planning.

[0020] This invention covers the key aspects of UAV mission planning, from area decomposition, path planning, obstacle avoidance strategies to mission scheduling, forming a complete multi-UAV collaborative mission planning system. Through optimized design and algorithm selection in each aspect, the intelligence level of UAV mission planning can be significantly improved, the adaptability to complex environments can be enhanced, flight safety and efficient mission execution can be ensured, and practical technical support can be provided for the actual application of UAVs. Attached Figure Description

[0021] Figure 1 A flowchart of the flight mission planning and management method for multi-UAV collaboration provided by the present invention; Figure 2 This is a structural diagram of the flight mission planning and management system for multi-UAV collaboration provided by the present invention. Detailed Implementation

[0022] To better understand the invention, various aspects of the invention will be described in more detail with reference to the accompanying drawings. It should be understood that these detailed descriptions are merely illustrative of exemplary embodiments of the invention and are not intended to limit the scope of the invention in any way. Throughout the specification, the same reference numerals refer to the same elements. The expression "and / or" includes any and all combinations of one or more of the associated listed items.

[0023] In the accompanying drawings, the size, dimensions, and shapes of the elements have been slightly adjusted for ease of illustration. The drawings are for illustrative purposes only and are not strictly to scale. As used herein, the terms “approximately,” “about,” and similar terms are used to indicate approximation rather than degree, and are intended to illustrate inherent deviations in measured or calculated values ​​that will be recognized by one of ordinary skill in the art. Furthermore, the order in which the steps are described in this invention does not necessarily indicate the order in which these steps occur in actual operation, unless otherwise expressly defined or deduced from the context.

[0024] It should also be understood that expressions such as "comprising," "including," "having," "containing," and / or "comprising" are open-ended rather than closed-ended expressions in this specification, indicating the presence of the stated features, elements, and / or components, but not excluding the presence of one or more other features, elements, components, and / or combinations thereof. Furthermore, when expressions such as "at least one of..." appear after a list of listed features, they modify the entire list of features, not just individual elements in the list. Additionally, when describing embodiments of the invention, the word "may" is used to mean "one or more embodiments of the invention." And the term "exemplary" is intended to refer to examples or illustrations.

[0025] Unless otherwise specified, all terms used herein (including engineering and technical terms) shall have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. It should also be understood that, unless expressly stated herein, terms defined in common dictionaries shall be interpreted as having the meaning consistent with their meaning in the context of the relevant art, and not in an idealized or overly formalized sense.

[0026] It should be noted that, unless otherwise specified, the embodiments and features described in this invention can be combined with each other. The invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0027] Example 1 Reference Figure 1 This is the first embodiment of the present invention, which provides a method for flight mission planning and management for multi-UAV collaboration.

[0028] S1: Based on the UAV's flight mission, the flight area of ​​the UAV is decomposed and the flight area is divided based on the UAV's flight parameters.

[0029] Based on the UAV's mission objectives and operational range, the geographical boundary of the entire flight mission area is determined and denoted as the mission area. ,in The coordinates of the points within the region. These are the boundary coordinates of the region.

[0030] Collection mission area An environmental feature matrix is ​​constructed based on environmental information including topography, meteorology, and airspace control within the area. ,in Indicates location Environmental characteristic values.

[0031] Obtain the drone's flight parameters, based on the tasks involved. The model and number of drones were determined, and their flight parameters, including maximum range, were extracted. Maximum load Maximum flight speed .

[0032] Estimate the completion of the entire task area Total flight time required and total load requirements ,in drones The flight time and payload requirements.

[0033] Delineate flight zones, taking mission areas into account. Environmental feature matrix and the flight parameters of the drone ,Will Divided into A set of non-overlapping subregions is formed. Define the partitioning loss function. To evaluate the merits of a partitioning scheme, the loss function should comprehensively consider the following factors: the degree of difference in environmental characteristics within the sub-regions. ,in sub-region The average environmental characteristic value.

[0034] Uniformity of sub-region area ,in sub-region area, , The average area is denoted as .

[0035] Task relevance between sub-regions ,in sub-region and Task relevance weights between them.

[0036] Minimize the partitioning loss function ,in The weighting coefficients are adjusted according to actual needs. The minimum value of the partitioning loss function is found using heuristic search or simulated annealing optimization algorithms to obtain the optimal partitioning scheme. .

[0037] Match drones with sub-regions and calculate each sub-region. area and task complexity Among them, task complexity The results are weighted and summed using indicators of the number, density, and priority of task points within the sub-region.

[0038] Estimate the execution sub-region based on the sub-region area and task complexity. Number of drones required for the mission The formula is: ,in This indicates rounding down. Indicates a single drone The maximum task coverage area.

[0039] From those who participated in the mission Selected from drones The frame, based on the drone's flight parameters Environmental characteristics of sub-regions By employing methods such as the Hungarian algorithm and bipartite graph matching, drones are assigned to various sub-regions to form optimal matching relationships. ,in Indicates that drones Assigned to sub-region .

[0040] Step S1, by decomposing and dividing the UAV flight area and combining environmental information and UAV parameters, can effectively reduce mission complexity, improve the precision and feasibility of mission planning, optimize the matching between the UAV and the mission area, maximize the utilization of UAV resources, and improve mission execution efficiency.

[0041] S2: Path planning is performed within the UAV's flight area. A genetic algorithm is used for global flight path planning, and after planning the global path, the A* algorithm is used for local flight path planning.

[0042] A genetic algorithm is used for global flight path planning, defining the UAV's flight area as a three-dimensional space. Any point in space can be represented by coordinates. express.

[0043] Define a set of path points ,in Indicates the The coordinates of the path points This represents the total number of path points.

[0044] Define population ,in For population size, Indicates the A path, consisting of path points composition.

[0045] Define fitness function Used to evaluate paths The advantages and disadvantages are determined by comprehensively considering factors such as path length, smoothness, flight time, and energy consumption.

[0046] The main steps of a genetic algorithm are as follows: Step 1: Initialize the population Randomly generated Path; Step 2: Calculate the fitness of each path ; Step 3: Perform the selection operation, based on the fitness level, with probability. Choose the best path as the parent generation; Step 4: Perform crossover operation: Perform pairwise crossovers on the parent paths, with probability... Generate new child paths; Step 5: Perform mutation operations with probability. Randomly mutate the child paths, introducing new path points or deleting existing path points; Step 6: Repeat steps 2 through 5 until the termination condition is met (e.g., the maximum number of iterations is reached). or continuous (No significant improvement in fitness in subsequent iterations).

[0047] Output the global path with the highest fitness As a result of global flight path planning.

[0048] The A* algorithm is used for local flight path planning, which will then be used to plan the global path. Discretize into a series of grid points to form a grid map. Define the set of grid points ,in Indicates the The coordinates of each grid point This represents the total number of grid points.

[0049] Define open list and close list Store the grid points to be expanded and those already expanded separately, and define the heuristic function. , indicating from grid point To the target point The estimated cost can be measured using Euclidean distance, Manhattan distance, etc.

[0050] Define the cost function , indicating from the starting point To grid point The actual cost can be calculated based on flight distance, number of turns, and energy consumption factors.

[0051] Define the evaluation function Used to evaluate grid points The extension priority.

[0052] The main steps of the A* algorithm are as follows: Step a: Set the starting point Join the open list ,calculate ; Step b: Retrieve from the open list Minimum grid point Add it to the close list ; Step c: Expand All adjacent grid points ,calculate and ; Step d: For each If it is in the closed list, ignore it; if it is in the open list, ignore it. If smaller, then update. and If it is not in the open list, then add it to the open list; Step e: Repeat steps b through d until the target point is reached. It has been added to the closed list, or the open list is empty.

[0053] Output from arrive Optimal local path , and global path The data is matched and pieced together to form a complete flight path.

[0054] The genetic algorithm in step S2 is used for global path planning. It can search for the optimal flight path in complex environments and comprehensively consider multiple factors to achieve efficient, smooth, and energy-saving paths. The A* algorithm is used for local path planning. It can dynamically avoid obstacles, generate safe and feasible local paths, and effectively connect with the global path to ensure the optimization of the overall flight path.

[0055] S3: Construct obstacle avoidance strategies for the drone during flight. When the obstacle avoidance strategy is triggered, re-plan the local flight path.

[0056] Construct an obstacle avoidance strategy and define the set of obstacles. ,in Indicates the An obstacle, The total number of obstacles is [number], and drones are considered obstacles to each other; for each obstacle... Define its shape, size, and position; it can be approximated by simple geometric shapes such as spheres, cubes, and cylinders, or precisely described by polyhedra; define the safe distance of the drone. , representing the minimum permissible distance between the drone and the obstacle; define the collision detection function. Used to determine drones Is it related to obstacles? In the event of a collision or excessive proximity, the determining factor is whether the distance between the two objects is less than [a certain value]. .

[0057] During the drone's flight, the distance between the drone and all obstacles is continuously monitored, and the collision detection function is calculated in real time. When detected True, meaning the drone is in contact with an obstacle. If a collision risk occurs, the obstacle avoidance strategy is triggered, the current flight path is immediately stopped, and a local path replanning mode is entered.

[0058] Local path replanning after obstacle avoidance is triggered defines the current location of the drone. The target location is ,by Centered on, with Construct a local path planning space with radius as the radius. ,in For local planning range parameters, Discretize into a series of grid points to form a local grid map. .

[0059] Define the set of local grid points ,in Indicates the The coordinates of a local grid point The total number of local grid points, for each obstacle. Check whether it is consistent with the local path planning space. If they intersect, then place them in... The projected area is marked as impassable.

[0060] Define a local path point set ,in Indicates the The coordinates of a local path point Given the total number of local path points, use the A* algorithm in... Search from arrive Local optimal path During the search, avoid all impassable areas.

[0061] Local optimal path Compared with the original global path By concatenating the results, we obtain the updated global path. The drone follows the updated global path. Continue flying until the target location is reached. Or the obstacle avoidance strategy may be triggered again.

[0062] The S3 step constructs a real-time obstacle avoidance strategy during UAV flight. Through continuous monitoring and rapid response, it can avoid obstacles to the greatest extent possible, ensuring flight safety. Once obstacle avoidance is triggered, local path replanning can quickly generate an obstacle avoidance path online, minimizing the impact on the original flight plan and improving the ability to respond to emergencies.

[0063] S4: Based on the constraints of UAV flight missions, formulate priority judgment criteria for multiple UAV missions, and allocate and schedule UAV resources.

[0064] Define the set of drone missions ,in Indicates the One task, The total number of tasks; for each task Extract its task attribute vector ,in Indicates task The Each attribute value The total number of attributes; task attributes may include the importance of the task, time urgency, execution difficulty, etc.; define the task priority evaluation function. ,in For the The weight coefficient of each attribute reflects the degree of influence of that attribute on task priority.

[0065] Calculate the priority evaluation value for each task. , and according to The tasks are sorted in descending order of their size to obtain the task priority queue. ,in Indicates priority number A high-level task.

[0066] Define drone resource pool ,in Indicates the A drone, The total number of drones; for each drone Extract its flight parameter vector ,in Indicates drone The One flight parameter value, This represents the total number of flight parameters; flight parameters may include the UAV's maximum range, maximum payload, and flight speed, among others.

[0067] Construct a function to evaluate the match between the task and the drone: ,in For the Weighting coefficients for each flight parameter, The indicator function represents the unmanned aerial vehicle (UAV). Does the task meet the requirements? The constraint condition is 1 if satisfied, otherwise 0.

[0068] Initialize task and drone allocation matrix ,in Indicate whether to assign the task Assigned to drones ; according to task priority queue Process each task in sequence; for each task... Calculate its matching degree with each idle drone. Select the drone with the highest matching degree. Execute the task.

[0069] Output the task and drone allocation matrix when all tasks are assigned or all drones are occupied. ,according to The task execution sequence of each drone is obtained, forming a multi-drone task scheduling scheme.

[0070] Step S4, based on task priorities and UAV flight parameters, through task attribute extraction, priority evaluation, and task-UAV matching optimization, can scientifically formulate priority judgment criteria for multi-UAV tasks, achieve optimal matching and scheduling of tasks and UAVs, and improve the quality and efficiency of multi-UAV collaborative task planning.

[0071] Example 2 Reference Figure 2 This is the second embodiment of the present invention, which provides a flight mission planning and management system for multi-UAV collaboration.

[0072] The system includes: a flight area division module, a flight path planning module, a flight obstacle avoidance module, and a flight scheduling module.

[0073] The flight area division module decomposes the flight area of ​​the UAV based on the UAV's flight mission and divides the flight area based on the UAV's flight parameters.

[0074] The flight path planning module performs path planning within the UAV's flight area, employing a genetic algorithm for global flight path planning and then using the A* algorithm for local flight path planning after planning the global path.

[0075] The flight obstacle avoidance module is used to construct an obstacle avoidance strategy for the UAV during flight. When the obstacle avoidance strategy is triggered, the local flight path is replanned.

[0076] The flight scheduling module formulates priority judgment criteria for multiple UAV missions based on UAV flight mission constraints, and allocates and schedules UAV resources.

[0077] Example 3 The present invention also provides an electronic device. This electronic device may include one or more processors and one or more memories. The memories store computer-readable code, which, when executed by the one or more processors, can perform the flight mission planning and management method for multi-UAV collaboration as described above.

[0078] The methods or systems according to embodiments of the present invention can also be implemented using the architecture of the electronic devices of the present invention.

[0079] Electronic devices may include buses, one or more CPUs, read-only memory (ROM), random access memory (RAM), communication ports connected to a network, input / output components, hard disks, etc.

[0080] Storage devices in electronic devices, such as ROM or hard disks, can store the flight mission planning and management method for multi-UAV collaboration provided by this invention.

[0081] A method for flight mission planning and management for multi-UAV collaboration includes: decomposing the flight area of ​​UAVs based on their flight missions and dividing the flight area according to their flight parameters; performing path planning within the flight area of ​​the UAVs, using a genetic algorithm for global flight path planning and an A* algorithm for local flight path planning after global path planning; constructing an obstacle avoidance strategy for UAVs during flight, and re-planning the local flight path when the obstacle avoidance strategy is triggered; and formulating priority judgment criteria for multi-UAV missions based on UAV flight mission constraints, and allocating and scheduling UAV resources.

[0082] Furthermore, the electronic device may also include a user interface. Of course, the architecture of this invention is merely exemplary; in implementing different devices, one or more components of the electronic device disclosed in this invention may be omitted according to actual needs.

[0083] Example 4 The present invention also discloses a computer-readable storage medium.

[0084] Computer-readable instructions are stored on a computer-readable storage medium.

[0085] When computer-readable instructions are executed by a processor, the flight mission planning and management method for multi-UAV cooperation according to an embodiment of the present invention, as described with reference to the above figures, can be performed.

[0086] Storage media include, but are not limited to, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and cache memory. Non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. Furthermore, according to embodiments of the present invention, the processes described in the above-mentioned flowcharts can be implemented as computer software programs.

[0087] For example, this invention provides a non-transitory machine-readable storage medium storing machine-readable instructions that can be executed by a processor to perform instructions corresponding to the method steps provided by this invention. For example: based on the UAV's flight mission, the flight area of ​​the UAV is decomposed and divided based on the UAV's flight parameters; path planning is performed within the UAV's flight area, using a genetic algorithm for global flight path planning, and then using the A* algorithm for local flight path planning after global path planning; an obstacle avoidance strategy for the UAV during flight is constructed, and local flight path planning is re-performed when the obstacle avoidance strategy is triggered; based on the UAV flight mission constraints, priority judgment criteria for multiple UAV missions are formulated, and UAV resources are allocated and scheduled.

[0088] When the computer program is executed by the central processing unit (CPU), it performs the functions defined in the method of the present invention. The method, apparatus, and device of the present invention may be implemented in many ways. For example, the method, apparatus, and device of the present invention may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware.

[0089] The above-described order of steps for the method is for illustrative purposes only, and the steps of the method of the present invention are not limited to the order specifically described above, unless otherwise specifically stated.

[0090] Furthermore, in some embodiments, the invention may also be implemented as a program recorded on a recording medium, the program including machine-readable instructions for implementing the method according to the invention. Therefore, the invention also covers recording media storing programs for performing the method according to the invention.

[0091] In addition, the parts of the technical solutions provided in the embodiments of the present invention that are consistent with the implementation principles of the corresponding technical solutions in the prior art have not been described in detail, so as to avoid excessive elaboration.

[0092] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the invention. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A flight mission planning and management method for multi-UAV collaboration, characterized in that: include: Based on the flight mission of the UAV, the flight area of ​​the UAV is decomposed, and the flight area is divided based on the flight parameters of the UAV; Perform path planning within the UAV's flight area, use genetic algorithm for global flight path planning, and use A* algorithm for local flight path planning after planning the global path; Build an obstacle avoidance strategy for the drone during flight. When the obstacle avoidance strategy is triggered, re-plan the local flight path. According to the UAV flight mission constraints, formulate the priority judgment criteria for multiple UAV tasks, and allocate and schedule UAV resources.

2. The flight mission planning and management method for multi-UAV collaboration according to claim 1 is characterized in that: Based on the mission objectives and execution scope of the UAV, the geographical boundaries of the entire flight mission area are determined, which is recorded as the mission area. ,in are the coordinate points in the region, are the boundary coordinates of the region; Collection Mission Area The environmental information of terrain, weather and airspace control within the area is used to construct an environmental feature matrix ,in Indicates location Environmental characteristic values; Get the flight parameters of the drone, according to the participating missions The model and number of drones, extracting the flight parameters of the drones, including the maximum range , Maximum load , Maximum flight speed ; Estimate the completion of the entire task area Total flight time required , and the total load demand ,in UAV Flight time and payload requirements; Divide the flight area and consider the mission area Environmental characteristics matrix , and the flight parameters of the drone ,Will Divide into non-overlapping sub-regions, forming a sub-region set ; Define the partition loss function , used to evaluate the pros and cons of the partitioning scheme; Minimize the partition loss function ,in is the weight coefficient, solve the minimum value of the partition loss function, and get the optimal partitioning solution ;in, is the difference of the internal environmental characteristics of the sub-region, is the sub-region area balance, is the task relevance between sub-regions; Match drones to sub-areas and calculate each sub-area Area and task complexity , where the task complexity The weighted sum is calculated using the number, density, and priority of task points in the sub-region; Estimate the execution sub-area based on the sub-area area and task complexity Number of drones required for the mission , the formula is: ,in Indicates rounding down. Represents a single drone Maximum mission coverage area; From participating in the task Select from drones According to the flight parameters of the drone and environmental characteristics of the sub-regions , assign drones to each sub-area to form the best matching relationship ,in Indicates that drones Assign to sub-area .

3. The flight mission planning and management method for multi-UAV collaboration according to claim 2 is characterized in that: Genetic algorithm is used for global flight path planning, and the flight area of ​​the drone is defined as three-dimensional space. , any point in space is represented by its coordinates express; Define a set of waypoints ,in Indicates The coordinates of the path points, is the total number of path points; define the population ,in is the population size, Indicates Paths, consisting of waypoints Composition; define fitness function , used to evaluate the path The advantages and disadvantages of the two technologies are comprehensively considered, including path length, smoothness, flight time and energy consumption. The steps for global flight path planning using genetic algorithm are as follows: Step 1: Initialize the population , randomly generated Paths; Step 2: Calculate the fitness of each path ; Step 3: Perform selection operation, according to the fitness size, with probability Select the good path as the parent; Step 4: Perform crossover operation: perform pairwise crossover on the parent paths, with probability Generate new child paths; Step 5: Perform mutation operation with probability Randomly mutate the offspring path, introduce new path points or delete the original path points; Step 6: Repeat steps 2 to 5 until the maximum number of iterations is reached. ; Output the global path with the highest fitness As a result of global flight path planning.

4. The flight mission planning and management method for multi-UAV collaboration according to claim 3 is characterized in that: The A* algorithm is used to plan the local flight path and the global path Discretize into a series of grid points to form a grid map , define the grid point set ,in Indicates The coordinates of the grid points, is the total number of grid points; Defining an open list and close list , respectively store the grid points to be expanded and those that have been expanded, and define the heuristic function , which means from the grid point To the destination Estimated cost of; define the cost function , indicating that from the starting point To grid point The actual cost of; define the evaluation function , used to evaluate the grid points The expansion priority of The steps for local flight path planning using the A* algorithm are as follows: Step a: Set the starting point Join the open list ,calculate ; Step b: Take from the open list Minimum grid point , add it to the closed list ; Step c: Extension All adjacent grid points ,calculate and ; Step d: For each adjacent grid point , if in the closed list, it is ignored; if in the open list and Smaller, newer and ; If it is not in the open list, add it to the open list; Step e: Repeat steps b to d until the target point Added to the closed list; Output from arrive The optimal local path , with the global path Match and splice to form a complete flight path.

5. The flight mission planning and management method for multi-UAV collaboration according to claim 4 is characterized in that: Build an obstacle avoidance strategy and define the obstacle set ,in Indicates obstacles, is the total number of obstacles. UAVs are considered obstacles to each other. Defines the safe distance between UAVs , represents the minimum allowed distance between the drone and the obstacle; defines the collision detection function , used to judge the drone Is there an obstacle? A collision occurs, and the judgment condition is whether the distance between the two is less than ; During the flight of the drone, the distance between the drone and all obstacles is continuously monitored, and the collision detection function is calculated in real time ; When detected True, that is, the drone is in contact with an obstacle If a collision risk occurs, the obstacle avoidance strategy is triggered, the current flight path is stopped immediately, and the local path replanning mode is entered.

6. The flight mission planning and management method for multi-UAV collaboration according to claim 5 is characterized in that: The local path replanning after obstacle avoidance is triggered defines the current position of the drone as , the target location is ,by Centered on As the radius, construct a local path planning space ,in is the local planning range parameter, Discretize into a series of grid points to form a local grid map ; Define a local grid point set ,in Indicates The coordinates of the local grid points, is the total number of local grid points, for each obstacle , check whether it is consistent with the local path planning space intersect, if so, place it in The projected area in is marked as impassable; Define a local path point set ,in Indicates The coordinates of the local path points, is the total number of local path points, using the A* algorithm Search from arrive The local optimal path , avoiding all inaccessible areas during the search; The local optimal path With the original global path Perform splicing to get the updated global path ; The drone follows the updated global path Continue flying until you reach the target location Or trigger the obstacle avoidance strategy again.

7. The flight mission planning and management method for multi-UAV collaboration according to claim 6 is characterized in that: Define a set of drone missions ,in Indicates tasks, is the total number of tasks; for each task , extract its task attribute vector ,in Representation Task No. attribute values, is the total number of attributes; define the task priority evaluation function ,in For the The weight coefficient of each attribute; Calculate the priority evaluation value for each task , and according to Arrange the tasks in descending order according to their size to obtain the task priority queue ,in Indicates the priority High tasks; Define the drone resource pool ,in Indicates drone, is the total number of drones; for each drone , extract its flight parameter vector ,in Indicates drone No. Flight parameter values, is the total number of flight parameters; Construct a task and drone matching evaluation function: ,in For the The weight coefficients of the flight parameters, is the indicator function, indicating that the drone Is the task met? If the constraint condition is met, it is 1, otherwise it is 0; Initialize the task and drone allocation matrix ,in Indicates whether the task Assign to drone ; According to the task priority queue order, processing each task one by one; for tasks , computing tasks Matching degree with each idle drone , select the drone with the highest matching degree Execute tasks; When all tasks are assigned or all drones are occupied, output the task and drone allocation matrix ,according to The task execution sequence of each UAV is obtained to form a task scheduling plan for multiple UAVs.

8. A flight mission planning and management system for multi-UAV collaboration, which is used to implement the flight mission planning and management method for multi-UAV collaboration as described in any one of claims 1 to 7, characterized in that: include: Flight area division module, flight path planning module, flight obstacle avoidance module and flight scheduling module; The flight area division module decomposes the flight area of ​​the UAV based on the flight mission of the UAV, and divides the flight area based on the flight parameters of the UAV; The flight path planning module performs path planning within the flight area of ​​the UAV, uses a genetic algorithm to perform global flight path planning, and uses an A* algorithm to perform local flight path planning after planning the global path; The flight obstacle avoidance module is used to construct an obstacle avoidance strategy for the UAV during flight. When the obstacle avoidance strategy is triggered, the local flight path planning is re-performed; The flight scheduling module formulates priority judgment criteria for multiple UAV tasks according to the UAV flight mission constraints, and allocates and schedules UAV resources.

9. An electronic device, characterized in that: include: A processor and a memory, wherein the memory stores a computer program that can be called by the processor; the processor executes the flight mission planning and management method for multi-UAV collaboration as described in any one of claims 1 to 7 by calling the computer program stored in the memory.

10. A computer-readable storage medium, characterized in that: Instructions are stored, and when the instructions are executed on a computer, the computer executes the flight mission planning and management method for multi-UAV collaboration as described in any one of claims 1 to 7.

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