Multi-unmanned aerial vehicle cooperative area coverage flight path planning method

Abstract

The invention provides a multi-unmanned aerial vehicle cooperative area coverage flight path planning method. The method is applied to the technical field of unmanned aerial vehicle flight path planning and comprises the steps of obtaining a coverage coefficient according to a task region area and an area threshold value; determining the number of unmanned aerial vehicles according to the coverage coefficient; obtaining a corresponding performance coefficient according to the performance parameter of each unmanned aerial vehicle; determining a distribution task of the corresponding unmanned aerial vehicle according to the performance coefficient; obtaining a corresponding path optimization coefficient according to the flight path length of each unmanned aerial vehicle and a preset path length; judging whether the corresponding path planning is reasonable or not according to the path optimization coefficient; obtaining a time difference coefficient according to the completion duration of each unmanned aerial vehicle task and a preset duration; judging whether the task can be completed on time according to the time difference coefficient; obtaining an electric quantity risk level according to the electric quantity of each unmanned aerial vehicle; and according to the risk level, determining whether to continue executing the task. In this way, the efficiency of path planning can be improved.

Description

Technical Field

[0001] The present disclosure relates to the technical field of unmanned aerial vehicle (UAV) trajectory planning, and particularly to a multi-UAV collaborative area coverage trajectory planning method. Background Art

[0002] Multi-UAV collaborative area coverage trajectory planning refers to that within a given area, multiple UAVs complete the full coverage of the area through collaborative cooperation in the shortest possible time. The flight trajectory of each UAV not only considers its own path planning but also needs to coordinate with other UAVs to ensure that all target areas can be effectively covered, avoiding duplicate coverage and void coverage. Through the collaborative cooperation of multiple UAVs, tasks can be assigned to different UAVs, improving the execution speed and efficiency of the tasks; through the collaborative operation of multiple UAVs, information can be exchanged between multiple UAVs to ensure that each part of the area can be effectively covered, avoiding omission or over-coverage, thereby improving the accuracy and reliability of the tasks; multi-UAV collaborative area coverage trajectory planning can adapt to more complex environments, respond to environmental changes in real time, quickly adjust the trajectory according to real-time task requirements, flexibly respond to changes, and improve the response speed and flexibility of task completion; multi-UAV collaborative operation can optimize the flight routes of UAVs by reasonably allocating tasks, reduce duplicate paths, save energy, reduce the burden on a single UAV by sharing tasks, and also extend its endurance time. Therefore, multi-UAV collaborative area coverage trajectory planning is of great significance.

[0003] Currently, in the process of multi-UAV collaborative area coverage trajectory planning, all UAVs are usually regarded as having exactly the same performance parameters or the influence of environmental factors is not considered, resulting in low efficiency of multi-UAV collaborative area coverage trajectory planning. Summary of the Invention

[0004] The present disclosure provides a multi-UAV collaborative area coverage trajectory planning method.

[0005] According to a first aspect of the present disclosure, there is provided a multi-UAV collaborative area coverage trajectory planning method. The method includes:

[0006] Obtaining a coverage coefficient according to the area of the task region and an area threshold; determining the number of UAVs according to the coverage coefficient;

[0007] Obtaining corresponding performance coefficients according to the performance parameters of each UAV, where the performance parameters include flight speed, load capacity, and endurance time; determining the assigned tasks of the corresponding UAVs according to the performance coefficients;

[0008] After determining the tasks of each UAV, according to the optimization of flight time and flight path, based on the flight path length of each UAV and the preset path length, the corresponding path optimization coefficient is obtained; according to the path optimization coefficient, it is determined whether the corresponding path planning is reasonable;

[0009] If it is reasonable, according to the completion duration of each UAV task and the preset duration, the time difference coefficient is obtained; according to the time difference coefficient, it is determined whether the task can be completed on time;

[0010] If it can be completed on time and during the multi-UAV collaborative task, according to the battery power of each UAV, the battery power risk level is obtained; according to the risk level, it is determined whether to continue the task;

[0011] After all tasks are completed, according to the task completion degree, the completion degree coefficient is obtained; according to the completion degree coefficient, it is determined whether the task is successfully completed.

[0012] Furthermore, if the coverage coefficient < 1, it is determined that the task area is small, and the first number of UAVs is used for coverage; if the coverage coefficient ≥ 1, it is determined that the task area is large, and the second number of UAVs is used for coverage; the first number < the second number.

[0013] Furthermore, the calculation formula of the coverage coefficient Fgxs is:

[0014]

[0015] where S is the task area area, S 0 is the area threshold, k 1 is the environmental adjustment coefficient.

[0016] Furthermore, if the performance coefficient < the performance threshold, it is determined that the coverage ability of the corresponding UAV is insufficient, and the task is reallocated; if the performance coefficient ≥ the performance threshold, it is determined that the corresponding UAV can execute the task, and the assigned task is maintained.

[0017] Furthermore, if the time difference coefficient ≤ 0, it is determined that the collaborative effect between UAVs is good and the task can be completed on time; if the time difference coefficient > 0, it is determined that the collaborative effect between UAVs is poor and path and task rescheduling are required.

[0018] Furthermore, if the battery power of the UAV < 20% of the rated battery power, it is determined that the corresponding battery power risk level is high, and the continuation of the task is interrupted;

[0019] If the battery power of the UAV ≥ 20% of the rated battery power, it is determined that the corresponding battery power risk level is low, and the task continues to be executed.

[0020] According to a second aspect of the present disclosure, an electronic device is provided. The electronic device includes: a memory and a processor, where a computer program is stored on the memory, and when the processor executes the program, the method is implemented.

[0021] According to a third aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored, and when the program is executed by a processor, the method is implemented.

[0022] By calculating the coverage coefficient based on the task area and the area threshold, the present disclosure can effectively evaluate the relationship between the task scope and the required number of drones, thereby avoiding resource waste or insufficiency and ensuring the efficient completion of the task; determining the number of drones according to the coverage coefficient helps to rationally allocate resources under different task complexities and avoid inefficiencies or task failures caused by too many or too few drones in the task; calculating the performance coefficient based on performance parameters such as the flight speed, load capacity, and endurance time of the drones can more reasonably allocate tasks and ensure that each drone performs tasks within its capabilities, maximizing the utilization of the drones' performance; through flight time and path optimization, by comparing the actual flight path of the drone with the preset path, the path optimization coefficient is obtained, thereby judging the rationality of the path planning, reducing the flight time, improving the operation efficiency, and reducing the task delay, and further improving the efficiency of the path planning; by calculating the time difference coefficient to determine whether the task can be completed on time, real-time monitoring of the task progress is provided, which effectively helps decision-makers determine whether they need to adjust the task execution strategy or allocate additional resources to ensure the task is completed on time; in a multi-drone collaborative task, considering the battery risk level of the drones can timely detect drones with insufficient battery power, make adjustments or withdrawals in advance, and avoid task failures caused by battery problems, improving the reliability of task execution; by using the task completion degree coefficient to determine whether the task is successfully completed, a quantitative evaluation of the task success rate can be provided, helping relevant personnel understand the task execution situation and facilitating subsequent improvement and optimization of the task execution process.

[0023] It should be understood that the content described in the summary of the invention section is not intended to limit the key or important features of the embodiments of the present disclosure, nor is it used to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In combination with the drawings and with reference to the following detailed description, the above and other features, advantages, and aspects of the embodiments of the present disclosure will become more apparent. The drawings are used to better understand the solution and do not constitute a limitation to the present disclosure. In the drawings, the same or similar reference numerals represent the same or similar elements, where:

[0025] Figure 1Shows a flowchart of a multi - UAV collaborative area coverage trajectory planning method according to an embodiment of the present disclosure;

[0026] Figure 2 Shows a block diagram of an exemplary electronic device capable of implementing the embodiments of the present disclosure. Detailed implementation manners

[0027] To make the objectives, technical solutions, and advantages of the embodiments of the present disclosure clearer, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present disclosure. Obviously, the described embodiments are some, but not all, of the embodiments of the present disclosure. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present disclosure without creative efforts shall fall within the protection scope of the present disclosure.

[0028] In addition, the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B may represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this article generally represents an "or" relationship between the associated objects before and after.

[0029] Figure 1 Shows a flowchart of a multi - UAV collaborative area coverage trajectory planning method according to an embodiment of the present disclosure. The method includes:

[0030] S1. Obtain a coverage coefficient according to the task area and an area threshold; determine the number of UAVs according to the coverage coefficient;

[0031] S2. Obtain corresponding performance coefficients according to the performance parameters of each UAV, where the performance parameters include flight speed, load - carrying capacity, and endurance time; determine the assigned tasks of the corresponding UAVs according to the performance coefficients;

[0032] S3. After determining the tasks of each UAV, optimize according to the flight time and flight path, and obtain corresponding path - optimization coefficients according to the flight path lengths of each UAV and a preset path length; determine whether the corresponding path planning is reasonable according to the path - optimization coefficients;

[0033] S4. If it is reasonable, obtain a time - difference coefficient according to the completion time of each UAV task and a preset time; determine whether the task can be completed on time according to the time - difference coefficient;

[0034] S5. If it can be completed on time and during the multi - UAV collaborative task, obtain a power - risk level according to the power of each UAV; determine whether to continue the task according to the risk level;

[0035] S6. After completing all tasks, obtain a completion coefficient according to the task completion degree; determine whether the task is successfully completed according to the completion coefficient.

[0036] In some embodiments, if the coverage coefficient < 1, it is determined that the task area is small, and the first number of drones is used for coverage; if the coverage coefficient ≥ 1, it is determined that the task area is large, and the second number of drones is used for coverage; the first number < the second number. According to the embodiments of the present invention, by setting the coverage coefficient to dynamically determine the required number of drones, the optimal allocation of resources is ensured. When the task area is small, only a small number of drones need to be deployed, avoiding unnecessary resource waste; while when the task area is large, the number of drones is increased to ensure effective coverage of the task. This flexible configuration can efficiently utilize drone resources in tasks of different scales, reduce energy consumption and improve work efficiency.

[0037] In some embodiments, the calculation formula of the coverage coefficient Fgxs can be:

[0038]

[0039] where S is the area of the task area, S 0 is the area threshold, and k 1 is the environmental adjustment coefficient.

[0040] In some embodiments, if the average wind speed on the day is 0 - 12 km / h, the value range of the environmental adjustment coefficient k 1 is 0.85 - 0.95; if the average wind speed on the day is 12 - 20 km / h, the value range of the environmental adjustment coefficient k 1 is 1.15 - 1.43; if the average wind speed on the day is 20 - 50 km / h, the value range of the environmental adjustment coefficient k 1 is 1.51 - 1.86. According to the embodiments of the present invention, by dynamically adjusting the environmental adjustment coefficient k1 according to the average wind speed on the day, the impact of different weather conditions on the task can be dealt with in real time. When the wind speed is low (0 - 12 km / h), the k1 coefficient is low (0.85 - 0.95), indicating that the environmental conditions are good and the task execution efficiency is high; while when the wind speed is high (12 - 50 km / h), the k1 coefficient increases accordingly (1.15 - 1.86), which reflects the possible impacts of higher wind speeds, such as reduced flight stability or increased energy consumption. By adjusting the coefficient to make up for these changes, the smooth execution of the task is ensured. This flexible adjustment mechanism enables the system to better adapt to different environmental conditions and improves the reliability and efficiency of the overall task execution.

[0041] In some embodiments, if the performance coefficient < performance threshold, it is determined that the coverage ability of the corresponding drone is insufficient, and the task is reallocated; if the performance coefficient ≥ performance threshold, it is determined that the corresponding drone is capable of executing the task, and the assigned task is maintained. According to the embodiments of the present invention, by judging based on the performance coefficient and performance threshold of the drone, the actual task execution ability of each drone can be intelligently evaluated. When the performance coefficient of a certain drone is lower than the threshold, the system automatically determines that its coverage ability is insufficient and promptly reallocates the task to other suitable drones to ensure the smooth execution of the task; when the performance coefficient reaches or exceeds the performance threshold, the system confirms that the task execution ability of the drone is sufficient, thereby maintaining the task assignment. It can be dynamically adjusted according to the real-time performance of the drone, improving the reliability and efficiency of task execution, and avoiding task failure or delay caused by the insufficient ability of a single drone.

[0042] In some embodiments, if the path optimization coefficient < 1, it is determined that the currently planned path is short and there is room for optimization, and the path planning needs to be further optimized; if the path optimization coefficient ≥ 1, it is determined that the path planning is reasonable and the current path can execute the task. According to the embodiments of the present invention, by introducing a judgment mechanism for the path optimization coefficient, the rationality of the path planning can be dynamically evaluated according to the coefficient value. When the path optimization coefficient is less than 1, it indicates that there is room for optimizing the current path, and this information can be used to further optimize the path to reduce unnecessary time and resource consumption and improve task execution efficiency; when the path optimization coefficient is greater than or equal to 1, it is determined that the path is already reasonable enough and the task can be executed as planned, enabling the adaptive optimization of the path planning, maximizing resource utilization and task efficiency while ensuring the smooth execution of the task.

[0043] In some embodiments, if the time difference coefficient ≤ 0, it is determined that the cooperation effect between drones is good and the task can be completed on time; if the time difference coefficient > 0, it is determined that the cooperation effect between drones is poor and the path and task need to be rescheduled. According to the embodiments of the present invention, by introducing the time difference coefficient to evaluate the cooperation effect between drones, the system can monitor the progress of the task in real time. When the time difference coefficient is less than or equal to 0, it indicates that the cooperation effect between drones is good and the task can be completed on time; when the time difference coefficient is greater than 0, it indicates that the cooperation effect of the drones is poor, which may lead to task delay or failure to execute as planned. Based on this information, dynamic adjustment is made to reschedule the path and task to ensure that the cooperation of multiple drones can optimize the efficiency and accuracy of task execution, enhancing the flexibility and reliability of task execution.

[0044] In some embodiments, if the battery level of the UAV < 20% of the rated battery level, the corresponding battery risk level is determined to be high, and the task execution is interrupted and risk response measures are taken in a timely manner (such as dispatching backup UAVs, adjusting the flight path, etc.); if the battery level of the UAV ≥ 20% of the rated battery level, the corresponding battery risk level is determined to be low, and the task continues to be executed. According to the embodiments of the present invention, by monitoring the battery level of the UAV in real time and setting the battery risk level, when the battery level is lower than 20% of the rated battery level, the system will automatically determine that the battery risk level is high and take timely response measures, such as dispatching backup UAVs or adjusting the flight path, to ensure that the task can be completed safely and prevent the UAV from malfunctioning due to insufficient battery power. When the battery level is higher than 20%, the system determines that the battery risk is low and the task can continue to be executed, effectively avoiding the impact of insufficient battery power of the UAV on task execution, improving the safety and reliability of the task, and ensuring the smooth completion of the task as planned.

[0045] In some embodiments, if the completion coefficient ≥ 1, it is determined that the task is successfully completed, and the task data is recorded and analyzed; if the completion coefficient < 1, it is determined that the task does not meet the expected effect, and the problem is feedback and improvement is carried out.

[0046] According to the embodiments of the present disclosure, by calculating the coverage coefficient based on the task area and the area threshold, the relationship between the task scope and the required number of UAVs can be effectively evaluated, thereby avoiding resource waste or shortage and ensuring the efficient completion of the task; determining the number of UAVs according to the coverage coefficient helps to reasonably allocate resources under different task complexities and avoid inefficiencies or task failures caused by too many or too few UAVs in the task; calculating the performance coefficient based on the performance parameters such as the flight speed, load capacity, and endurance time of the UAV can more reasonably allocate tasks and ensure that each UAV executes tasks within its capabilities, maximizing the utilization of the UAV's performance; through flight time and path optimization, by comparing the actual flight path of the UAV with the preset path, the path optimization coefficient is obtained, thereby judging the rationality of the path planning, reducing the flight time, improving the operation efficiency, and reducing the task delay, and further improving the efficiency of the path planning; by calculating the time difference coefficient to determine whether the task can be completed on time, providing real-time monitoring of the task progress, effectively helping decision-makers judge whether they need to adjust the task execution strategy or allocate additional resources to ensure the timely completion of the task; in a multi-UAV collaborative task, considering the battery risk level of the UAV can timely detect UAVs with insufficient battery power, make adjustments or withdrawals in advance, and avoid task failures caused by battery problems, improving the reliability of task execution; by using the task completion coefficient to determine whether the task is successfully completed, a quantitative evaluation of the task success rate can be provided, helping relevant personnel understand the task execution situation and facilitating subsequent improvement and optimization of the task execution process.

[0047] It should be noted that for the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should understand that the present disclosure is not limited by the described action sequence, because according to the present disclosure, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also understand that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to the present disclosure.

[0048] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device and a readable storage medium.

[0049] Figure 2 A schematic block diagram of an electronic device that can be used to implement the embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as, for example, laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as, for example, personal digital processors, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0050] The electronic device includes a computing unit 201, which can execute various appropriate actions and processes according to the computer program stored in the ROM 202 or the computer program loaded from the storage unit 208 into the RAM 203. In the RAM 203, various programs and data required for the operation of the electronic device can also be stored. The computing unit 201, the ROM 202, and the RAM 203 are connected to each other through a bus 204. The I / O interface 205 is also connected to the bus 204.

[0051] A plurality of components in the electronic device are connected to the I / O interface 205, including: an input unit 206, such as a keyboard, a mouse, etc.; an output unit 207, such as various types of displays, speakers, etc.; a storage unit 208, such as a magnetic disk, an optical disk, etc.; and a communication unit 209, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 209 allows the electronic device to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0052] The computing unit 201 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 201 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 201 executes the various methods and processes described above, such as the multi-UAV collaborative area coverage trajectory planning method. For example, in some embodiments, the multi-UAV collaborative area coverage trajectory planning method can be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as the storage unit 208. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device via the ROM 202 and / or the communication unit 209. When the computer program is loaded into the RAM 203 and executed by the computing unit 201, one or more steps of the multi-UAV collaborative area coverage trajectory planning method described above can be executed. Alternatively, in other embodiments, the computing unit 201 can be configured to execute the multi-UAV collaborative area coverage trajectory planning method by any other suitable means (e.g., by means of firmware).

[0053] Various embodiments of the systems and techniques described above in this document can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special-purpose or general-purpose programmable processor, receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting the data and instructions to the storage system, the at least one input device, and the at least one output device.

[0054] The program code for implementing the methods of the present disclosure can be written in any combination of one or more programming languages. These program codes can be provided to the processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing devices, such that when the program codes are executed by the processor or controller, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The program codes can be executed entirely on the machine, partially on the machine, as an independent software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0055] In the context of the present disclosure, a readable storage medium may be a tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device. The readable storage medium may be a machine-readable signal medium or a machine-readable storage medium. The readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of the readable storage medium would include an electrical connection based on one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0056] For providing interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer. Other kinds of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0057] The systems and techniques described herein can be implemented in a computing system including backend components (e.g., as a data server), or a computing system including middleware components (e.g., an application server), or a computing system including frontend components (e.g., a user computer having a graphical user interface or a web browser through which the user can interact with an implementation of the systems and techniques described herein), or a computing system including any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected to each other by any form or medium of digital data communication (e.g., a communication network). Examples of the communication network include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0058] A computer system may include a client and a server. The client and the server are generally remote from each other and typically interact through a communication network. The relationship of the client and the server is generated by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, or a server of a distributed system, or a server incorporating a blockchain.

[0059] It should be understood that the various forms of the processes described above can be used, with steps reordered, added, or deleted. For example, the steps recited in the present disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired results of the technical solution of the present disclosure can be achieved, and no limitation is imposed herein.

[0060] The above specific embodiments do not constitute a limitation on the protection scope of the present disclosure. Those skilled in the art should understand that various modifications, combinations, sub - combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present disclosure shall be included within the protection scope of the present disclosure.

Claims

1. A method for multi-UAV collaborative regional coverage trajectory planning, characterized in that: include: According to the task area and area threshold, the coverage coefficient is obtained; Determining the number of drones according to the coverage factor; According to the performance parameters of each UAV, the performance parameters include flight speed, load capacity and endurance time, a corresponding performance coefficient is obtained; according to the performance coefficient, an assignment task of the corresponding UAV is determined; After determining the mission of each drone, according to the flight time and flight path optimization, the corresponding path optimization coefficient is obtained according to the flight path length of each drone and the preset path length; according to the path optimization coefficient, whether the corresponding path planning is reasonable is determined; If it is reasonable, the time difference coefficient is obtained according to the completion time of each drone task and the preset time; and according to the time difference coefficient, whether the task can be completed on time is determined; If it can be completed on time and during the multi-UAV collaborative mission, the power risk level is obtained based on the power of each UAV; Determine whether to continue to perform the task according to the risk level; After all tasks are completed, a completion coefficient is obtained based on the task completion degree; and based on the completion coefficient, it is determined whether the task is successfully completed.

2. The method for planning the trajectory of multi-UAV coordinated regional coverage according to claim 1, characterized in that: If the coverage coefficient is less than 1, the mission area is determined to be small, and the first number of drones is used to cover it; if the coverage coefficient is ≥ 1, the mission area is determined to be large, and the second number of drones is used to cover it; the first number is less than the second number.

3. The method for planning the trajectory of multi-UAV coordinated regional coverage according to claim 2, characterized in that: The calculation formula of the coverage factor Fgxs is: Among them, S is the area of ​​the task area, S0 is the area threshold, and k1 is the environmental adjustment coefficient.

4. The method for planning the trajectory of multi-UAV coordinated regional coverage according to claim 3 is characterized in that: If the performance coefficient is less than the performance threshold, the coverage capability of the corresponding drone is judged to be insufficient and the task is reallocated; If the performance coefficient ≥ the performance threshold, the corresponding UAV is judged to be able to perform the task and maintain the assigned task.

5. The method for planning the trajectory of multi-UAV coordinated regional coverage according to claim 4, characterized in that: If the time difference coefficient is ≤0, it is judged that the coordination effect between the UAVs is good and the task can be completed on time; if the time difference coefficient is greater than 0, it is judged that the coordination effect between the UAVs is poor and the path and task need to be rescheduled.

6. The method for planning the trajectory of multi-UAV coordinated regional coverage according to claim 5, characterized in that: If the UAV's power is less than the rated power × 20%, the corresponding power risk level is determined to be high, and the mission is interrupted; If the UAV's power level is ≥ rated power × 20%, the corresponding power risk level is judged to be low, and the mission continues.

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