Vehicle-mounted mobile platform multi-unmanned aerial vehicle cooperative remote sensing task planning method and system

Abstract

The invention provides a vehicle-mounted mobile platform multi-unmanned aerial vehicle cooperative remote sensing task planning method and system, and the method comprises the steps: determining a multi-unmanned aerial vehicle cooperative planning region according to a vehicle-mounted mobile platform and unmanned aerial vehicles; determining constraint conditions of unmanned aerial vehicle cooperative remote sensing task planning based on flight task constraints and aircraft performance limiting conditions; the multi-unmanned-aerial-vehicle collaborative planning area is constrained according to the constraint conditions, and a multi-unmanned-aerial-vehicle path planning mathematical model is constructed; the multi-unmanned aerial vehicle path planning mathematical model is utilized, an improved Hungary algorithm and a BM-IPSO-SA algorithm are adopted for multi-unmanned aerial vehicle cooperative control path planning, and an optimal planning path is obtained; and completing a multi-unmanned aerial vehicle cooperative remote sensing acquisition task of the vehicle-mounted mobile platform according to the optimal planning route. According to different task scenes, adaptive task loads, unmanned aerial vehicles and optimal take-off and landing points can be automatically analyzed, and multi-vehicle multi-sensor integrated application is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicles, and in particular to a method and system for planning a collaborative remote sensing mission for multiple unmanned aerial vehicles on a vehicle-mounted mobile platform. Background Art

[0002] With the rapid development of drone technology, multi-drone collaborative operations have become an important trend in remote sensing mapping, environmental monitoring, disaster assessment and other fields. A vehicle-mounted mobile platform equipped with multiple drones can achieve rapid deployment, flexible scheduling and efficient operation, but how to plan the collaborative tasks of multiple drones to ensure the accuracy and efficiency of task execution is an urgent problem to be solved. Therefore, this invention patent aims to propose a method and system for planning collaborative remote sensing tasks of multiple drones on a vehicle-mounted mobile platform.

[0003] Traditional UAV remote sensing operations mostly use a single-machine operation mode, which has problems such as limited operating range and low efficiency. However, the vehicle-mounted mobile platform carrying multiple UAVs for collaborative operations can significantly improve the operating range and efficiency. However, how to efficiently plan the task allocation, path planning and collaborative control of multiple UAVs is an urgent problem that needs to be solved. Summary of the invention

[0004] The present invention provides a method and system for planning a collaborative remote sensing task of multiple unmanned aerial vehicles (UAVs) on a vehicle-mounted mobile platform, which are used to solve the defects of UAVs in the collaborative operation in the prior art.

[0005] In a first aspect, the present invention provides a method for planning a multi-UAV collaborative remote sensing mission on a vehicle-mounted mobile platform, comprising: Determine the multi-UAV collaborative planning area based on the vehicle-mounted mobile platform and the UAV; Determine the constraints of UAV collaborative remote sensing mission planning based on flight mission constraints and aircraft performance limitations; The multi-UAV collaborative planning area is constrained by the constraint conditions to construct a multi-UAV path planning mathematical model; By using the multi-UAV path planning mathematical model, the improved Hungarian algorithm and BM-IPSO-SA algorithm are used to perform multi-UAV collaborative control path planning to obtain the optimal planned route; According to the optimal planned route, the multi-UAV collaborative remote sensing acquisition task of the vehicle-mounted mobile platform is completed.

[0006] According to a vehicle-mounted mobile platform multi-UAV collaborative remote sensing mission planning method provided by the present invention, a multi-UAV collaborative planning area is determined according to the vehicle-mounted mobile platform and the UAV, including: Obtaining geometric information of the operating area corresponding to the vehicle-mounted mobile platform and performance parameters of the UAV; The polygonal circularity of the operation area is calculated according to the perimeter and area in the geometric information, and the drone scanning mode is determined according to the polygonal circularity, wherein the drone scanning mode includes a lawn mowing scanning mode and a spiral scanning mode; Calculate the maximum scanning area of ​​each drone and the number of drones required based on the drone performance parameters; The operation area is divided into a plurality of sub-areas, and a drone and a drone task are assigned to each sub-area.

[0007] According to a method for planning a multi-UAV cooperative remote sensing mission on a vehicle-mounted mobile platform provided by the present invention, based on flight mission constraints and aircraft performance constraints, constraints for planning the UAV cooperative remote sensing mission are determined, including: Determine the UAV's own performance constraints including minimum track segment length, maximum track length, minimum and maximum flight altitude, and maximum pitch angle; Determine external influencing factors based on weather influencing factors, terrain influencing factors, third-party obstacle influencing factors, and threat location factors; The task constraints are determined by the total time required for the UAV to complete the planned task and the maximum path length value; Determine the priority allocation constraints based on the UAV number, mission importance, target importance and planned path length.

[0008] According to a method for planning a multi-UAV collaborative remote sensing mission on a vehicle-mounted mobile platform provided by the present invention, the multi-UAV collaborative planning area is constrained by the constraint conditions, and a multi-UAV path planning mathematical model is constructed, including: Determine a binary function of mapping execution status between multiple UAVs and multiple targets; Determine a matrix constraint condition that each drone in the drone group only executes one task point, and form a task allocation matrix by the matrix constraint condition and the binary function of the mapping execution status; A plurality of task paths are calculated according to the BM-IPSO-SA algorithm, and a path cost matrix is ​​constructed by the plurality of task paths; A multi-UAV path planning mathematical model is constructed based on the matrix elements in the task allocation matrix and the path cost matrix.

[0009] According to a method for planning a multi-UAV cooperative remote sensing mission on a vehicle-mounted mobile platform provided by the present invention, the multi-UAV path planning mathematical model is used, and the improved Hungarian algorithm and BM-IPSO-SA algorithm are used to perform multi-UAV cooperative control path planning to obtain the optimal planned route, including: Step 1: Calculate the path cost from any UAV to any mission point and obtain the path cost matrix; Step 2, subtract the minimum value of the row from the row element in the path cost matrix; Step 3, subtract the minimum value of the column from the column element in the path cost matrix; Step 4, determine the distribution of 0 elements in the new path cost matrix. If the 0 elements are distributed in different rows and columns, end the operation, otherwise jump to step 5; Step 5: Draw horizontal lines on the rows of the new path cost matrix and vertical lines on the columns of the new path cost matrix, using as few horizontal lines or vertical lines as possible to cover all zero elements in the matrix; Step 6, determine the minimum value from the uncovered elements, subtract the minimum value from the uncovered elements, add the minimum value to the elements at the intersection of the straight lines, and go to step 4 to determine the optimal planned route.

[0010] According to a vehicle-mounted mobile platform multi-UAV collaborative remote sensing task planning method provided by the present invention, the vehicle-mounted mobile platform multi-UAV collaborative remote sensing acquisition task is completed according to the optimal planning route, including: Obtain the flight status and mission execution status of the drone in real time; Dynamically adjust the flight parameters of the drone according to the current mission requirements and drone performance parameters; If it is determined that an abnormal situation occurs when the drone is performing a mission, the emergency response mechanism will be activated, the task allocation will be adjusted, the drone will be recalled, and a new drone will be replaced; Information sharing and collaborative operations among multiple UAVs are achieved through the collaborative control module.

[0011] In a second aspect, the present invention further provides a vehicle-mounted mobile platform multi-UAV collaborative remote sensing mission planning system, comprising: The vehicle-mounted mobile platform is used to provide storage, take-off and landing of drones; Multi-UAV system, consisting of several UAVs, used to perform remote sensing tasks; The mission planning module is used to generate multi-UAV mission allocation and path planning solutions based on operational requirements; Cooperative control module, responsible for the cooperative control of multiple drones; The human-computer interaction interface provides an interactive interface between users and the system for inputting job requirements and monitoring task execution.

[0012] In a third aspect, the present invention also provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method for planning collaborative remote sensing tasks of multiple UAVs on a vehicle-mounted mobile platform as described in any one of the above-mentioned methods is implemented.

[0013] In a fourth aspect, the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a method for planning a multi-UAV collaborative remote sensing mission on a vehicle-mounted mobile platform as described in any one of the above-mentioned methods.

[0014] The method and system for planning multi-UAV collaborative remote sensing tasks on a vehicle-mounted mobile platform provided by the present invention significantly improve the execution efficiency of remote sensing tasks through the collaborative operation of multiple UAVs; adopt advanced intelligent planning algorithms, which can automatically optimize UAV task allocation and flight paths according to task requirements, ensure efficient use of resources, and improve the accuracy and efficiency of data collection; the multi-UAV collaborative control mechanism can adjust strategies in real time to cope with complex and changeable operating environments; reduce manual operations and decisions, and reduce the risk of human errors, and the automated flight of UAVs also reduces the safety risks of on-site personnel; the integrated design of the vehicle-mounted platform simplifies the transportation and deployment process, further improving the overall economic benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0016] Figure 1 It is a flow chart of a method for collaborative remote sensing mission planning of multiple UAVs on a vehicle-mounted mobile platform provided by the present invention; Figure 2 It is a structural schematic diagram of a multi-UAV collaborative remote sensing mission planning system for a vehicle-mounted mobile platform provided by the present invention; Figure 3 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings in the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] In view of the many deficiencies of UAVs in collaborative operations in the prior art, the present invention proposes a multi-machine collaborative task adaptive planning technology for application scenarios such as remote sensing of agriculture in complex field environments, disaster detection and emergency response, etc., which can automatically plan, adjust, allocate and execute remote sensing patrol tasks by evaluating and analyzing the performance of airborne sensors, the endurance of UAVs, and the accuracy requirements of results, so that the UAVs can better cooperate with each other. When a single UAV may be unable to continue to perform the task due to various factors (such as insufficient power), another UAV (or sufficient power) will automatically take over the task, meeting the needs of large-scale multi-source remote sensing data collection and forming a new mode of rapid remote sensing data collection combined with vehicles and machines. A set of vehicle-mounted UAV automatic patrol platform and multi-machine collaborative operation system are developed, which can automatically analyze and adapt the task load, UAV and the best take-off and landing point according to different task scenarios, and realize the integrated application of multiple machines and multiple sensors.

[0019] Figure 1 is a flow chart of a method for collaborative remote sensing mission planning of multiple UAVs on a vehicle-mounted mobile platform provided by an embodiment of the present invention, such as Figure 1 As shown, including: Step 100: Determine a multi-UAV collaborative planning area based on the vehicle-mounted mobile platform and the UAV; Step 200: Determine the constraints of the UAV collaborative remote sensing mission planning based on the flight mission constraints and the aircraft performance constraints; Step 300: constraining the multi-UAV collaborative planning area by the constraint conditions, and constructing a multi-UAV path planning mathematical model; Step 400: using the multi-UAV path planning mathematical model, adopting the improved Hungarian algorithm and BM-IPSO-SA algorithm to perform multi-UAV collaborative control path planning, and obtaining the optimal planned route; Step 500: According to the optimal planned route, the vehicle-mounted mobile platform and multiple UAVs collaborative remote sensing acquisition task is completed.

[0020] Based on the task allocation algorithm, the embodiment of the present invention focuses on task priority, UAV performance and environmental conditions, designs and develops functions such as multi-machine multi-task planning, automatic task allocation and adjustment, and remote control of UAVs, so as to realize rapid acquisition of multi-source remote sensing data such as visible light, thermal infrared, multi-spectral, hyperspectral, and lidar, and meet the requirements of various remote sensing data acquisition such as routine inspections and emergency response, etc., for different application scenarios. Specifically, it includes: 1. UAV collaborative planning area The planning space of path planning is determined according to the starting point, mission area, environmental terrain conditions and air threats. First, the operation area is decomposed into several sub-areas according to the geometric characteristics of the operation area and the performance parameters of the UAV. Then, according to the number and performance of the UAVs, a multi-UAV collaborative area coverage path planning algorithm is used to assign UAVs and their tasks to each sub-area.

[0021] Determine the geometric information of the operating area (such as boundaries, shape, area, etc.) and the performance parameters of the drone (such as flight speed, endurance time, scanning width, etc.).

[0022] According to the geometric characteristics of the working area, its perimeter, area, roundness and other parameters are calculated.

[0023] When a drone performs coverage scanning of an area, there are two main scanning modes, namely, mowing scanning mode and spiral scanning mode. Different scanning modes are selected according to the closeness of the coverage area to a circle. Circularity is usually used to describe the degree to which a polygon is close to a circle. This method is used to calculate the circularity of a polygon. The calculation formula is as follows: (1) Where S is the area of ​​the polygon, L is the perimeter of the polygon, and when the polygon is closer to a circle, C is closer to 1, and vice versa, C is closer to 0. If the circularity of the working area is greater than 0.86, the spiral scanning mode is used; if it is less than 0.86, the mowing scanning mode is used.

[0024] Based on the drone performance parameters, calculate the maximum scanning area of ​​each drone and the number of drones required.

[0025] The operation area is divided into several sub-areas, and drones and their tasks are assigned to each sub-area.

[0026] 2. Set Constraints According to the mission constraints and the performance limitations of the aircraft, the constraints of UAV collaborative remote sensing mission planning are determined.

[0027] UAV’s own performance constraints: The UAV’s own performance constraints include minimum track segment length, maximum track length, minimum and maximum flight altitude, maximum pitch angle and other constraints.

[0028] External factors: UAVs may encounter many threats during missions, such as bad weather, buildings, birds, terrain obstacles, etc. The location of the threat must also be considered, and the threat information must be obtained in a timely manner to ensure the safe flight of UAVs in complex environments.

[0029] Mission constraints: Time constraints refer to the total time required for the UAV to complete the planned mission. The important constraint in the path constraint is the maximum path length value.

[0030] Priority allocation constraints: In UAV mission planning, in order to facilitate task allocation and avoid collisions between UAVs during flight, different planning systems and platforms provide priority evaluation indicators such as aircraft number, mission importance, target importance, and length of planned path.

[0031] 3. Mission Planning Determine the path planning mathematical model based on mission constraints and aircraft performance limitations.

[0032] When a fleet of drones arrives at a specific inspection area at the same time, each drone is equipped with different equipment and needs to reach different mission points to perform its respective tasks.

[0033] In this embodiment, the path planned by the Boyer Moore-Improved Particle Swarm Optimization-Simulated Annealing (BM-IPSO-SA) algorithm can achieve good results for a single machine.

[0034] First, define a binary function to express the relationship between multiple drones and multiple target points: (2) in, C ij Indicates i A drone against j The surveying and mapping execution status of target points, U i Indicates i A drone, T i Indicates j target points, i Indicates drone, j represents the target point, N Represents the total number of drones and target points.

[0035] The binary function value forms the multi-UAV mapping task allocation matrix C. Because each UAV in the UAV swarm can only go to one of the task points, the allocation matrix needs to meet the following two constraints: (3) (4) in, n Represents the upper limit of the summation, that is, the summation variable i or j Starting from 1, up to n .

[0036] After constructing the task allocation matrix, it is also necessary to construct the path cost matrix D for each UAV to complete the mapping task. Each value of this matrix Dij All can be calculated by the previous BM-IPSO-SA algorithm: (5) After constructing the task allocation matrix and path cost matrix, the mathematical model of the total cost of multi-UAV mapping paths can be constructed using their matrix elements: (6) Furthermore, a multi-machine collaborative path planning algorithm based on the Hungarian algorithm and the BM-IPSO-SA algorithm is used to find the optimal feasible path that meets the constraints in the planning space.

[0037] The core idea of ​​the Hungarian algorithm is that if we look at the path cost matrix Dij Add (or subtract) a constant t to each row element or column element of to obtain a new path cost matrix . Then The new allocation problem represented by has the same optimal solution as the original allocation problem, but its optimal value is reduced by t.

[0038] The path cost matrix Dij Subtract the minimum value of the row or column in turn to get the zero element of the matrix. When there are N zero elements in different rows and columns (hereinafter referred to as independent zero elements), the matrix is ​​used as the new path cost matrix. The path cost matrix Dij The independent zero elements in correspond to the task allocation matrix Cij The value of the element in the task allocation matrix is ​​1, and the rest of the elements in the task allocation matrix are 0. After taking the values ​​in this way, it can be seen that the total cost of the multi-UAV mapping path in the above text is 0, that is, the minimum value is obtained. After adding the constant value subtracted from each row and column of the path cost matrix, the optimal solution of the total cost of the multi-UAV mapping path can be obtained. The adaptive values ​​from the drone group to the task point group are obtained in turn through formula (5), which is used as the path cost matrix. The task allocation matrix is ​​configured by the path cost matrix to obtain the multi-machine collaborative path planning algorithm based on the Hungarian algorithm and the BM-IPSO-SA algorithm. The specific algorithm flow of this scheme is as follows: ① Use the above formula to calculate the path cost from the i-th UAV to the j-th mission point and obtain the path cost matrix. ② Subtract the minimum value of the row from all elements in the row. ③Subtract the minimum value of this column from all elements in this column. ④ Check the distribution of 0 in the matrix. If 0 is distributed in different rows and columns, end the operation. If not, go to ⑤. ⑤ Draw horizontal lines on the rows of the matrix and vertical lines on the columns of the matrix, using as few horizontal or vertical lines as possible to cover all the zeros in the matrix. ⑥Find the minimum value from the elements that are not covered in the previous step, then subtract this minimum value from all these elements, add this minimum value to the elements at the intersection of the straight lines, and go to ④.

[0039] The route planning is carried out according to the final cost value and allocation results obtained from the task allocation.

[0040] 4. Collaborative Control Method Based on the theory of multi-UAV collaboration and the characteristics of remote sensing operations, the flight parameters and task allocation of UAVs are dynamically adjusted by real-time monitoring of the flight status and task execution of UAVs. The specific collaborative control process includes: Obtain the flight status and mission execution of the drone in real time. Dynamically adjust the flight parameters of the drone (such as speed, altitude, heading, etc.) according to the current mission requirements and drone performance parameters. If an abnormal situation is found when the drone is performing a mission (such as insufficient power, communication interruption, etc.), immediately activate the emergency response mechanism, adjust the task allocation, recall the drone, and send a new drone to replace it. Through the collaborative control module, information sharing and collaborative operation between multiple drones are realized to ensure efficient execution of tasks.

[0041] 5. Vehicle-mounted UAV collaborative remote sensing mission planning system Based on the above embodiments, the present invention proposes a vehicle-mounted mobile platform multi-UAV collaborative remote sensing mission planning system, which can automatically analyze and adapt mission loads, UAVs and optimal take-off and landing points according to different mission scenarios, and realize multi-machine and multi-sensor integrated applications. Figure 2 As shown in the figure, the system mainly includes key components such as vehicle-mounted mobile platform, multi-UAV cluster, mission planning module, collaborative control module and human-computer interaction interface.

[0042] Vehicle-mounted mobile platform: As a carrier and mobile platform for drones, it provides storage, take-off and landing functions for drones.

[0043] Multi-UAV system: consists of several UAVs and is used to perform remote sensing tasks.

[0044] Mission planning module: Generates multi-UAV mission allocation and path planning solutions based on operational requirements.

[0045] Collaborative control module: responsible for the collaborative control between multiple drones to ensure the smooth execution of the mission.

[0046] Human-computer interaction interface: provides an interactive interface between users and the system for inputting job requirements, monitoring task execution, etc.

[0047] (1) Design of vehicle-mounted mobile platform As the core carrier of the entire system, the design of the vehicle-mounted mobile platform must fully consider the requirements of drone storage, rapid deployment, safe landing, and stable driving. The platform is usually equipped with: Drone takeoff and landing: The modular design can store multiple drones safely and efficiently, and has the ability to take off and land quickly, ensuring that the drones can take off and perform tasks in a short time and land accurately to complete the operation.

[0048] Communication and control system: The advanced communication module is integrated to ensure real-time communication and control between the vehicle-mounted platform and the UAV, and between UAVs. At the same time, the platform is also equipped with a powerful central processor to handle complex mission planning and collaborative control algorithms.

[0049] (2) Multi-UAV Cluster Design As the main force for remote sensing missions, the design of multi-UAV swarms needs to consider factors such as the type, performance, quantity and coordination ability of UAVs. Specifically, they include: Drone selection: Choose the appropriate drone type based on the specific needs of the remote sensing mission. At the same time, ensure that the drone has sufficient endurance, load capacity and stability.

[0050] Performance optimization: Optimize the performance of the drone, including increasing flight speed, increasing scanning width, and improving data transmission rate, to meet the needs of efficient execution of remote sensing tasks.

[0051] Collaborative capability: By integrating advanced collaborative control algorithms, information sharing, task allocation and collaborative operations among multiple drones can be achieved, ensuring that drones can cooperate with each other during the execution of tasks and jointly complete complex remote sensing tasks.

[0052] (3) Implementation of the task planning module The mission planning module is the core of the entire system, responsible for generating multi-UAV mission allocation and path planning solutions based on the specific requirements of the remote sensing mission. The implementation of this module mainly includes the following steps: Mission requirement analysis: First, analyze the specific requirements of the remote sensing mission, including the scope, shape, and topographic features of the operating area, as well as the resolution and accuracy of the remote sensing data.

[0053] Area decomposition and allocation: Based on the geometric characteristics of the operation area and the performance parameters of the drone, the operation area is decomposed into several sub-areas, and drones and their tasks are allocated to each sub-area. In this process, factors such as the number, performance and operation efficiency of drones need to be considered.

[0054] Path planning: After determining the mission assignment of the drones, a multi-drone collaborative path planning algorithm is used to generate a flight path for each drone. Path planning needs to take into account factors such as the drone’s flight speed, altitude, heading, and obstacle avoidance to ensure that the drone can perform its mission safely and efficiently.

[0055] Optimization and adjustment: After the initial generation of the mission planning plan, the plan is adjusted using optimization methods such as local search algorithms. By adjusting the flight path, take-off / landing points and other parameters of the drone, the mission execution time can be further reduced and the operation efficiency can be improved.

[0056] (4) Collaborative control module implementation The collaborative control module is responsible for realizing the collaborative operation between multiple drones. This module dynamically adjusts the flight parameters and task allocation of drones by monitoring the flight status and task execution of drones in real time. Specifically, it includes: Flight status monitoring: The flight status information of the drone, such as position, speed, altitude, power, etc., can be obtained in real time through the sensors carried by the drone and the communication module of the vehicle platform.

[0057] Mission execution monitoring: Real-time monitoring of the UAV's mission execution, including remote sensing data collection, transmission and processing, etc. At the same time, timely response and processing of abnormal situations that occur during mission execution.

[0058] Collaborative control strategy: Based on the collaborative theory of multiple UAVs and the characteristics of remote sensing operations, a collaborative control strategy is formulated. By adjusting the flight parameters of the UAVs (such as speed, altitude, heading, etc.) and the task allocation plan, information sharing and collaborative operations between multiple UAVs can be achieved.

[0059] (5) Human-computer interaction interface design The human-computer interaction interface is the interactive interface between the user and the system. The interface design should be concise and easy to operate so that users can easily input work requirements, monitor task execution and obtain remote sensing data. Specifically, it includes: Job requirement input: Provides a user-friendly job requirement input interface, allowing users to input the scope, shape, geomorphic features of the job area, and the resolution and accuracy of remote sensing data.

[0060] Mission execution monitoring: Real-time display of the UAV’s flight status and mission execution, including the UAV’s location, speed, altitude, and the progress of remote sensing data collection and processing.

[0061] Data display and export: Provides remote sensing data display and export functions, allowing users to view and export remote sensing data collected during task execution. At the same time, it supports subsequent data processing and analysis.

[0062] The vehicle-mounted mobile platform multi-UAV collaborative remote sensing mission planning method and system provided by the present invention realizes rapid regional coverage and accurate data collection. Compared with other technologies, the multi-UAV remote sensing planning method provided by the present invention has the following advantages: Efficient collaborative work: The collaborative work of multiple drones significantly improves the efficiency of remote sensing missions. Drones can work in parallel, quickly cover large areas, shorten mission cycles, and meet emergency or large-scale remote sensing needs.

[0063] Intelligent planning and optimization: Using advanced intelligent planning algorithms, it can automatically optimize drone task allocation and flight paths according to mission requirements, ensure efficient use of resources, and improve the accuracy and efficiency of data collection.

[0064] High flexibility and adaptability: The vehicle-mounted mobile platform provides a flexible deployment and recovery environment for drones, adapting to different terrain and climate conditions. The multi-drone collaborative control mechanism can adjust strategies in real time to cope with complex and changing operating environments.

[0065] Reduce manual intervention and risks: The system is highly automated and intelligent, which reduces manual operations and decision-making, and reduces the risk of human error. Automated drone flight also reduces safety risks for on-site personnel.

[0066] Significant cost-effectiveness: Through the collaborative operation of multiple drones, the equipment utilization rate is improved and the unit operation cost is reduced. The integrated design of the vehicle-mounted platform simplifies the transportation and deployment process, further improving the overall economic benefits.

[0067] Figure 3 An example of a physical structure diagram of an electronic device is shown in FIG. Figure 3As shown, the electronic device may include: a processor 310, a communication interface 320, a memory 330 and a communication bus 340, wherein the processor 310, the communication interface 320 and the memory 330 communicate with each other through the communication bus 340. The processor 310 may call the logic instructions in the memory 330 to execute the vehicle-mounted mobile platform multi-UAV collaborative remote sensing task planning method, the method comprising: determining the multi-UAV collaborative planning area according to the vehicle-mounted mobile platform and the UAV; determining the constraint conditions of the UAV collaborative remote sensing task planning based on the flight mission constraint and the aircraft performance constraint conditions; constraining the multi-UAV collaborative planning area by the constraint conditions, and constructing a multi-UAV path planning mathematical model; using the multi-UAV path planning mathematical model, adopting the improved Hungarian algorithm and the BM-IPSO-SA algorithm to perform multi-UAV collaborative control path planning, and obtaining the optimal planning route; completing the vehicle-mounted mobile platform multi-UAV collaborative remote sensing acquisition task according to the optimal planning route.

[0068] In addition, the logic instructions in the above-mentioned memory 330 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0069] On the other hand, the present invention also provides a computer program product, which includes a computer program, and the computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the vehicle-mounted mobile platform multi-UAV collaborative remote sensing task planning method provided by the above-mentioned methods, the method including: determining a multi-UAV collaborative planning area based on the vehicle-mounted mobile platform and the UAV; determining the constraint conditions for the UAV collaborative remote sensing task planning based on the flight mission constraints and the aircraft performance constraints; constraining the multi-UAV collaborative planning area by the constraint conditions, and constructing a multi-UAV path planning mathematical model; using the multi-UAV path planning mathematical model, adopting the improved Hungarian algorithm and the BM-IPSO-SA algorithm to perform multi-machine collaborative control path planning, and obtain the optimal planned route; according to the optimal planned route, completing the vehicle-mounted mobile platform multi-UAV collaborative remote sensing acquisition task.

[0070] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to execute the vehicle-mounted mobile platform multi-UAV collaborative remote sensing task planning method provided by the above-mentioned methods, the method comprising: determining a multi-UAV collaborative planning area based on the vehicle-mounted mobile platform and the UAV; determining constraint conditions for UAV collaborative remote sensing task planning based on flight mission constraints and aircraft performance limitations; constraining the multi-UAV collaborative planning area by the constraint conditions, and constructing a multi-UAV path planning mathematical model; utilizing the multi-UAV path planning mathematical model, adopting an improved Hungarian algorithm and a BM-IPSO-SA algorithm to perform multi-machine collaborative control path planning, and obtaining an optimal planned route; and completing the vehicle-mounted mobile platform multi-UAV collaborative remote sensing acquisition task according to the optimal planned route.

[0071] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0072] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0073] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for planning a multi-UAV collaborative remote sensing mission on a vehicle-mounted mobile platform, characterized in that: include: Determine the multi-UAV collaborative planning area based on the vehicle-mounted mobile platform and UAVs; Determine the constraints of UAV collaborative remote sensing mission planning based on flight mission constraints and aircraft performance limitations; The multi-UAV collaborative planning area is constrained by the constraint conditions to construct a multi-UAV path planning mathematical model; Utilizing the multi-UAV path planning mathematical model, the improved Hungarian algorithm and string search-improved particle swarm optimization-simulated annealing BM-IPSO-SA algorithm are used to perform multi-UAV collaborative control path planning to obtain the optimal planned route; According to the optimal planned route, the multi-UAV collaborative remote sensing acquisition task of the vehicle-mounted mobile platform is completed.

2. The method for collaborative remote sensing mission planning of multiple UAVs on a vehicle-mounted mobile platform according to claim 1 is characterized in that: According to the vehicle-mounted mobile platform and drones, determine the multi-drone collaborative planning area, including: Obtaining geometric information of the operating area corresponding to the vehicle-mounted mobile platform and performance parameters of the UAV; The polygonal circularity of the operation area is calculated according to the perimeter and area in the geometric information, and the drone scanning mode is determined according to the polygonal circularity, wherein the drone scanning mode includes a lawn mowing scanning mode and a spiral scanning mode; Calculate the maximum scanning area of ​​each drone and the number of drones required based on the drone performance parameters; The operation area is divided into a plurality of sub-areas, and a drone and a drone task are assigned to each sub-area.

3. The method for planning a multi-UAV coordinated remote sensing mission on a vehicle-mounted mobile platform according to claim 1, characterized in that: Based on the flight mission constraints and aircraft performance limitations, determine the constraints for UAV collaborative remote sensing mission planning, including: Determine the UAV's own performance constraints including minimum track segment length, maximum track length, minimum and maximum flight altitude, and maximum pitch angle; Determine external influencing factors based on weather influencing factors, terrain influencing factors, third-party obstacle influencing factors, and threat location factors; The task constraints are determined by the total time required for the UAV to complete the planned task and the maximum path length value; Determine the priority allocation constraints based on the UAV number, mission importance, target importance and planned path length.

4. The method for collaborative remote sensing mission planning of multiple UAVs on a vehicle-mounted mobile platform according to claim 1 is characterized in that: The multi-UAV collaborative planning area is constrained by the constraint conditions, and a multi-UAV path planning mathematical model is constructed, including: Determine a binary function of mapping execution status between multiple UAVs and multiple targets; Determine a matrix constraint condition that each drone in the drone group only executes one task point, and form a task allocation matrix by the matrix constraint condition and the binary function of the mapping execution status; A plurality of task paths are calculated according to the BM-IPSO-SA algorithm, and a path cost matrix is ​​constructed by the plurality of task paths; A multi-UAV path planning mathematical model is constructed based on the matrix elements in the task allocation matrix and the path cost matrix.

5. The method for planning a multi-UAV collaborative remote sensing mission on a vehicle-mounted mobile platform according to claim 4 is characterized in that: By using the multi-UAV path planning mathematical model, the improved Hungarian algorithm and BM-IPSO-SA algorithm are used to perform multi-UAV collaborative control path planning, and the optimal planning route is obtained, including: Step 1: Calculate the path cost from any UAV to any mission point and obtain the path cost matrix; Step 2, subtract the minimum value of the row from the row element in the path cost matrix; Step 3, subtract the minimum value of the column from the column element in the path cost matrix; Step 4, determine the distribution of 0 elements in the new path cost matrix. If the 0 elements are distributed in different rows and columns, end the operation, otherwise jump to step 5; Step 5: Draw horizontal lines on the rows of the new path cost matrix and vertical lines on the columns of the new path cost matrix, using as few horizontal lines or vertical lines as possible to cover all zero elements in the matrix; Step 6, determine the minimum value from the uncovered elements, subtract the minimum value from the uncovered elements, add the minimum value to the elements at the intersection of the straight lines, and go to step 4 to determine the optimal planned route.

6. The method for planning a multi-UAV coordinated remote sensing mission on a vehicle-mounted mobile platform according to claim 1, characterized in that: According to the optimal planning route, the multi-UAV collaborative remote sensing acquisition task of the vehicle-mounted mobile platform is completed, including: Obtain the flight status and mission execution status of the drone in real time; Dynamically adjust the flight parameters of the drone according to the current mission requirements and drone performance parameters; If it is determined that an abnormal situation occurs when the drone is performing a mission, the emergency response mechanism will be activated, the task allocation will be adjusted, the drone will be recalled, and a new drone will be replaced; Information sharing and collaborative operations among multiple UAVs are achieved through the collaborative control module.

7. A vehicle-mounted mobile platform multi-UAV collaborative remote sensing mission planning system, based on the vehicle-mounted mobile platform multi-UAV collaborative remote sensing mission planning method described in any one of claims 1 to 6, characterized in that: It includes a vehicle-mounted mobile platform, a multi-UAV system, a mission planning module, a collaborative control module and a human-computer interaction interface, among which: The vehicle-mounted mobile platform is used to provide storage, take-off and landing of drones; Multi-UAV system, consisting of several UAVs, used to perform remote sensing tasks; The mission planning module is used to generate multi-UAV mission allocation and path planning solutions based on operational requirements; Cooperative control module, responsible for the cooperative control of multiple drones; The human-computer interaction interface provides an interactive interface between users and the system for inputting job requirements and monitoring task execution.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the method for planning multi-UAV collaborative remote sensing tasks on a vehicle-mounted mobile platform as described in any one of claims 1 to 6 is implemented.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, the method for planning a multi-UAV collaborative remote sensing mission for a vehicle-mounted mobile platform as described in any one of claims 1 to 7 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by the processor, the method for planning a multi-UAV collaborative remote sensing mission for a vehicle-mounted mobile platform as described in any one of claims 1 to 6 is implemented.

Images