Expressway and bridge unmanned cluster route planning load visual field area planning method, system and equipment

Abstract

The invention provides an expressway and bridge unmanned cluster route planning load visual field area planning method. The method comprises the steps of determining visual field information of an unmanned cluster in a target area based on a task instruction; determining a view field influence factor of the unmanned cluster based on the environment condition of the target area, and determining an effective view field area of the unmanned cluster based on the view field influence factor; and determining a planned route on the target area based on the effective view field area and the path type of the unmanned cluster. The method can be applied to the field of expressway and bridge inspection, and in the unmanned cluster multi-task route planning process, transition parameters, characteristic factors and long endurance requirements are introduced in combination with unmanned cluster aircraft state and load information to carry out approximate processing on an unmanned cluster carrying load view area. The method can provide a view area calculation method for route planning, support unmanned cluster route planning, and improve the cluster view effect.

Description

Technical Field

[0001] This application relates to the technical field of unmanned cluster path planning, and particularly to a method, system, and device for planning the field of view area of an unmanned cluster flight path planning load on highways and bridges. Background Art

[0002] Unmanned clusters are applied in various search scenarios. In military scenarios such as battlefield reconnaissance and battlefield rescue, as well as civilian scenarios such as disaster relief, post-disaster search and rescue, and highway and bridge inspection, they often face problems such as harsh environments and inconvenient transportation. Ground searches are difficult to carry out and progress is difficult. However, unmanned clusters, based on their advantages such as being unmanned and having little impact from the environment, can effectively conduct aerial searches, detect effective information, and lay an important foundation for rescue. Path planning technology is one of the key technologies of unmanned clusters and is an important manifestation of upper-level decision-making in the execution of tasks.

[0003] Existing path planning algorithms consider less the characteristics of unmanned clusters themselves and the irregularity of the area, lack consideration of the impact of formation types and payload performance, lack consideration of changes in the task area and planning requirements during the execution process, and are less applicable to unmanned clusters. Some algorithms are highly time-dependent and cannot be generated online. They do not consider the impact of successive coverage monitoring on the planned path and the degree of path coverage, and it is difficult to meet the real-time requirements of search tasks and the requirements of long-duration all-weather coverage of the coverage area.

[0004] Therefore, in the field of highway and bridge inspection, there is an urgent need for a method, system, and device for planning the field of view area of an unmanned cluster flight path planning load. Summary of the Invention

[0005] The present invention proposes a method, system, and device for planning the field of view area of an unmanned cluster flight path planning load on highways and bridges, which consider the formation type of the unmanned cluster and the performance parameters of the carried payload, calculate the field of view area considering characteristic factors, design transition parameters for irregular areas, and can change in real time according to the task area and monitoring time requirements to achieve coverage search of the task area. The specific technical solutions are as follows:

[0006] A method for planning the field of view area of an unmanned cluster flight path planning load on highways and bridges, the method comprising:

[0007] Determine the field of view information of the unmanned cluster in the target area based on the task instruction, and determine the path type of the unmanned cluster in the target area based on the purpose of the task instruction;

[0008] Determine the field of view influence factor of the unmanned cluster based on the environmental conditions of the target area, and determine the effective field of view area of the unmanned cluster based on the field of view influence factor;

[0009] Determine the planned route on the target area based on the effective field of view area and path types of the unmanned cluster.

[0010] In another embodiment of the present invention, the determining the field of view information of the unmanned cluster in the target area based on the task instruction includes:

[0011] Obtain the demand parameters corresponding to the task instruction, where the demand parameters are the parameters or indicators that the unmanned cluster needs to achieve when executing the task instruction under the current environmental conditions;

[0012] Based on the demand parameters, determine the cluster information of the unmanned cluster, where the cluster information is the set data and execution data of the unmanned cluster when executing the task instruction;

[0013] Determine the field of view information of the unmanned cluster based on the demand parameters and the cluster information.

[0014] In another embodiment of the present invention, the determining the field of view influence factor of the unmanned cluster based on the environmental conditions of the target area includes:

[0015] Obtain the environmental condition information of the target area, where the environmental condition information includes the air quality, rainfall, electromagnetic interference, and wind conditions of the target area;

[0016] Query the preset field of view influence factor based on the environmental condition information to determine the field of view influence factor of the unmanned cluster in the current situation.

[0017] In another embodiment of the present invention, the determining the effective field of view area of the unmanned cluster based on the field of view influence factor includes:

[0018] Determine the theoretical field of view area of the unmanned cluster based on the field of view information;

[0019] Determine the effective field of view area of the unmanned cluster based on the theoretical field of view area and the field of view influence factor.

[0020] In another embodiment of the present invention, the determining the path types of the unmanned cluster in the target area based on the purpose of the task instruction includes:

[0021] Determine the purpose of the task instruction based on the task instruction;

[0022] Query in the preset set of path types based on the purpose of the instruction to determine the corresponding path types.

[0023] In another embodiment of the present invention, the determining the planned route on the target area based on the effective field of view area and path types of the unmanned cluster includes:

[0024] Determine the path planning type of the unmanned cluster in the target area based on the path type, where the path planning type includes a tiling type and a grid type;

[0025] Based on the effective field of view area, continuously cover the target area according to the route of the path planning type to determine the planned air route.

[0026] In a second aspect, the present invention further provides a highway and bridge unmanned cluster air route planning payload field of view area planning system, and the system includes: a task instruction processing module, a field of view determination module, and a path planning module;

[0027] The task instruction processing module is configured to determine the field of view information of the unmanned cluster in the target area based on the task instruction, and determine the path type of the unmanned cluster in the target area based on the purpose of the task instruction;

[0028] The field of view determination module is configured to determine the field of view influence factor of the unmanned cluster based on the environmental conditions of the target area, and determine the effective field of view area of the unmanned cluster based on the field of view influence factor;

[0029] The path planning module is configured to determine the planned air route in the target area based on the effective field of view area and the path type of the unmanned cluster.

[0030] In another embodiment of the present invention, the task instruction processing module is further configured to:

[0031] Obtain the demand parameters corresponding to the task instruction, where the demand parameters are the parameters or indicators that the unmanned cluster needs to achieve when executing the task instruction under the current environmental conditions;

[0032] Determine the cluster information of the unmanned cluster based on the demand parameters, where the cluster information is the set data and execution data of the unmanned cluster when executing the task instruction;

[0033] Determine the field of view information of the unmanned cluster based on the demand parameters and the cluster information.

[0034] In another embodiment of the present invention, the field of view determination module is further configured to:

[0035] Obtain the environmental condition information of the target area, where the environmental condition information includes the air quality, rainfall, electromagnetic interference, and wind conditions of the target area;

[0036] Query the preset field of view influence factor based on the environmental condition information to determine the field of view influence factor of the unmanned cluster in the current situation;

[0037] Determine the theoretical field of view area of the unmanned cluster based on the field of view information;

[0038] Determine the effective field of view area of the unmanned cluster based on the theoretical field of view area and the field of view influence factor.

[0039] In another embodiment of the present invention, the path planning module is further configured to:

[0040] Determine the path planning type of the unmanned cluster on the target area based on the path type, where the path planning type includes a tiling type and a grid type;

[0041] Based on the effective field of view area, continuously cover the target area according to the route of the path planning type to determine the planned flight path.

[0042] In a third aspect, the present invention further provides an electronic device, which includes a processor and a memory electrically connected to the processor. The memory is used to store a computer program, and the processor is used to call the computer program to execute the method described in any embodiment of the first aspect.

[0043] The beneficial effects of the present invention are as follows:

[0044] The present invention provides a method for planning the field of view area of the load of the unmanned cluster for highway and bridge route planning, including: determining the field of view information of the unmanned cluster in the target area based on the task instruction, and determining the path type of the unmanned cluster in the target area based on the purpose of the task instruction; determining the field of view influence factor of the unmanned cluster based on the environmental conditions of the target area, and determining the effective field of view area of the unmanned cluster based on the field of view influence factor; determining the planned flight path on the target area based on the effective field of view area and the path type of the unmanned cluster. This method first takes into account the formation type of the unmanned cluster and the influence of the load performance on the detection field of view. In addition, the method of the present invention realizes online generation, reduces the correlation between the algorithm and time, and meets the real-time requirements of the search task and the all-weather post-detection requirements of the coverage area. Description of the Drawings

[0045] Figure 1 Is a flowchart of a method for planning the field of view area of the load of the unmanned cluster for highway and bridge route planning;

[0046] Figure 2 Is an algorithm flowchart of a method for planning the field of view area of the load of the unmanned cluster for highway and bridge route planning;

[0047] Figure 3 Is a path planning example of a method for planning the field of view area of the load of the unmanned cluster for highway and bridge route planning;

[0048] Figure 4 This is an application example of path planning for the path planning application of the vision area planning method for unmanned cluster flight path planning on highways and bridges. Specific implementation manners

[0049] To make the objectives, technical solutions, and advantages of this specification clearer, the technical solutions of this application will be clearly and completely described below in conjunction with the specific embodiments of this application and their corresponding drawings. Obviously, the described embodiments are only a part rather than all of the embodiments of this specification. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in this application without creative efforts shall fall within the scope of protection of this document.

[0050] The following will be described in detail Figures 1-4 the technical solutions provided in each embodiment of this specification. Specific embodiment 1:

[0052] A method for planning the vision area of the load for unmanned cluster flight path planning on highways and bridges, the steps of which are as follows:

[0053] S1: Obtain task information

[0054] The external control device sends a task instruction to the unmanned cluster control system. After receiving the task instruction, the unmanned cluster control system determines the corresponding required parameters according to the content of the instruction. The required parameters include but are not limited to: the target location to be explored by the unmanned cluster, coordinate position information, altitude information, predetermined target, resolution, execution time (start time, end time, action duration). In addition to the above basic requirements, it also includes parameters corresponding to special scenario requirements, and requirements are triggered under certain conditions. For example, detecting a certain sound, or conditions such as rain.

[0055] According to the task information, a series of points are selected in sequence (either clockwise or counterclockwise) on the map to form a task area, and a serial number id is set for each area point in the selected order area = 0, 1, 2, …, N area - 1, and the task area is composed of N area points. The position information loc(id area ) of the area point id area = [lon(id area ), lat(id area ), alt(id area )], where lon(id area ), lat(id area ), alt(id area ) are the longitude, latitude, and altitude of the area point id area respectively.

[0056] S2: Obtain the field of view information according to the mission requirements

[0057] After the unmanned cluster control system determines the demand parameters, the unmanned cluster information is determined according to the unmanned cluster resources that the system can call. The unmanned cluster information includes: the number of unmanned clusters N, the initial position p of the unmanned cluster org , formation shape parameters, detection payload parameters, etc.

[0058] Determine the planning requirements according to the demand parameters, including the expected altitude h of the flight path, the expected search angle theta, the reconnaissance field of view overlap rate k view , whether to return to the starting point isBack, etc. The expected search angle theta is 0 with respect to the north, and positive in the clockwise direction.

[0059] The detection payload determines the reconnaissance field of view that a single detection payload can provide according to the planning requirements, and determines the reconnaissance field of view of all detection payloads according to the cluster information, that is, the field of view information. The specific parameters of the field of view information are as follows:

[0060] The size of the field of view area is related to parameters such as the field of view angle alpha, magnification k, payload angle beta, flight altitude h, etc. The single-aircraft reconnaissance field of view can be approximated as a rectangle, [d view_x ,d view_y = a rectangular area composed of f(alpha,k,beta,h), where d view_x ,d view_y are the length and width of the rectangle. The formation shape parameters are generally the formation spacing d, and the formation spacing d = f(d view_x ,d view_y ,k view ). The payload field of view angle is determined by the lens parameters and magnification k, and its horizontal field of view angle and vertical field of view angle can be expressed as:

[0061] θ view_hor = θ hor / k

[0062] θ view_vtc = θ vtc / k

[0063] S3: Calculate the field of view area considering the characteristic factors

[0064] When the camera altitude h, the camera vertical deflection angle θ cam_vtc and the vertical field of view angle θ view_vtc are known, according to the geometric relationship, the distance d from the camera to the lower boundary of the field of view area in the horizontal direction can be calculated lb , the distance d from the camera to the upper boundary of the field of view area in the horizontal direction ub , and the length d of the field of view area corresponding to the vertical field of view angle view_vtc, as shown below.

[0065]

[0066] d view_vtc = d ub -d lb

[0067] The length of the field of view area corresponding to the horizontal field of view angle of the camera is

[0068]

[0069] In the reconnaissance mission, due to environmental factors such as weather interference, the influence factor on the effective field of view area is η. Regarding the influence on the field of view area, the effective field of view area is η(d view_vtc *d view_hor ).

[0070] S4: Calculate the planned route based on the transition parameter

[0071] Considering the long-endurance planning requirements, the monitoring intermittent time requirement is t wait , the length of the planned route is L wp , the average flight speed is v wp , and the flight time is t fly = δ1 * t ud + δ2 * (L wp / v wp ), where δ1 and δ2 are time fluctuation factors, and t ud is the takeoff and landing conversion time.

[0072] Considering the avoidance influence of the irregular area on the path planning process, design the transition parameter for avoiding the irregular area. The transition parameter at the trajectory point p is λ p = ω p * e η ∫β p + d pmin / v p * e η t share , where ω p is the allowable angular velocity of the UAV at point p, β p is the potential energy influence of the surrounding obstacles and the area boundary on point p, d pmin is the minimum distance allowance to ensure the safe flight of the UAV at p, v p is the flight speed of the UAV at point p, and t share is the maximum allowable time at the current point p considering the monitoring intermittent time requirement of t wait , and e η is the influence parameter of the influence factor of the effective field of view area on the path planning at the current point p.

[0073] From N area task area points, the N area area boundaries can be calculated pairwise. The flight path spacing D is determined by the formation spacing, the number of clusters N, and the formation shape shape, D = f(d, N). Starting from the departure position of the unmanned cluster, a series of straight lines are formed with the expected search angle theta and the flight path spacing D. The intersection points of these lines with the task area are the required waypoints wp(i)=[lon(i), lat(i), alt(i)] = f(loc(id area ), D, theta, λ p , t fly ), where i = 0, 1, 2,..., M - 1, id area = 0, 1, 2,..., N area - 1, M is the number of waypoints, lon(i), lat(i), alt(i) are the longitude, latitude, and altitude of the waypoints, and the order of the waypoints is related to theta.

[0074] S5: Check if the requirements are met and output the planned path

[0075] Check if the requirements are met. If not, return to S3 to adjust the characteristic factors and transition parameters; if so, output the obtained sequence of waypoints and transmit it to the unmanned cluster formation for area search. Specific Embodiment 2:

[0077] Specific Embodiment 1 describes and explains the concept of the present invention as a whole. As a specific implementation, this embodiment describes the area scan in detail. The specific technical solution is as follows:

[0078] A method for planning the field of view area of an unmanned cluster flight path for highways and bridges, the steps of which are as follows:

[0079] S1: Obtain task information

[0080] The external control device sends a task instruction to the unmanned cluster control system. The task instruction includes the target area detected by the unmanned cluster, the purpose corresponding to the task instruction, the path type, and the path planning type. Among them, there is a corresponding relationship between the purpose of the task instruction and the path type, and there is a corresponding relationship between the path type and the path planning type. For example, in this embodiment, if the task is "area scan", the path type is tiled scan and the path planning type is "S-shaped scan". This corresponding relationship is stored in the database, and the corresponding path type and path planning type are retrieved according to the specific task instruction.

[0081] S2: Obtain field of view information according to task requirements

[0082] This step is the same as the first embodiment.

[0083] S3: Calculation of the field of view area considering characteristic factors

[0084] This step is the same as that in the first embodiment.

[0085] S4: Calculate the planned route based on the transition parameters

[0086] According to the path planning type determined in S1, an S-shaped scan is performed on the target area, and the approximate planned path of the unmanned cluster is determined to be S-shaped. In actual planning, it is required that the effective field of view area covers more than 95% of the target area.

[0087] S5: Check whether the requirements are met and output the planned route

[0088] If the effective field of view area covers more than 95% of the target area, the obtained waypoint sequence is output and transmitted to the unmanned cluster formation for area search. If the coverage rate cannot be achieved, return to S3 to adjust the characteristic factors and transition parameters. Specific embodiment 3:

[0090] The difference from specific embodiment 2 is that the task instruction in this embodiment is to search for a target. Taking the search for wild boars as an example, the appearance of wild boars is detected at a certain observation point in a wheat field. The observation point sends the signal of the appearance of wild boars to the unmanned cluster control system. The system queries that the path planning type corresponding to this search target is "circular scan with the center". This circular scan is centered on the position of the wild boar observed last time, and the unmanned cluster is dispatched to perform a circular space scan with a gradually increasing radius. After the wild boars are found, some units of the unmanned cluster change to the tracking state for the wild boar target, and the remaining units perform an S-shaped scan on the remaining un-scanned areas. Specific embodiment 4:

[0092] The present invention provides a method, system, and device for planning the field of view area of the load of the unmanned cluster for highway and bridge route planning. Considering the types of unmanned cluster formations and the performance parameters of the carried loads in highway and bridge inspection applications, and calculating the field of view area considering characteristic factors, designing transition parameters for irregular areas, which can change in real time according to the task area and monitoring time requirements, and realize the coverage search of the task area. The specific technical solutions are as follows:

[0093] A method for planning the field of view area of the load of the unmanned cluster for highway and bridge route planning, the method comprising:

[0094] Determine the field of view information of the unmanned cluster in the target area based on the task instruction, and determine the path type of the unmanned cluster in the target area based on the purpose of the task instruction;

[0095] Determine the field of view influence factor of the unmanned cluster based on the environmental conditions of the target area, and determine the effective field of view area of the unmanned cluster based on the field of view influence factor;

[0096] Determine the planned route on the target area based on the effective field of view area and route types of the unmanned cluster.

[0097] In another embodiment of the present invention, the determining the visual field information of the unmanned cluster in the target area based on the task instruction includes:

[0098] Obtain the demand parameters corresponding to the task instruction, where the demand parameters are the parameters or indicators that the unmanned cluster needs to achieve when executing the task instruction under the current environmental conditions;

[0099] Determine the cluster information of the unmanned cluster based on the demand parameters, where the cluster information is the set data and execution data of the unmanned cluster when executing the task instruction;

[0100] Determine the visual field information of the unmanned cluster based on the demand parameters and the cluster information.

[0101] In another embodiment of the present invention, the determining the field of view influence factor of the unmanned cluster based on the environmental conditions of the target area includes:

[0102] Obtain the environmental condition information of the target area, where the environmental condition information includes the air quality, rainfall, electromagnetic interference, and wind conditions of the target area;

[0103] Query the preset field of view influence factor based on the environmental condition information to determine the field of view influence factor of the unmanned cluster in the current situation.

[0104] In another embodiment of the present invention, the determining the effective field of view area of the unmanned cluster based on the field of view influence factor includes:

[0105] Determine the theoretical field of view area of the unmanned cluster based on the visual field information;

[0106] Determine the effective field of view area of the unmanned cluster based on the theoretical field of view area and the field of view influence factor.

[0107] In another embodiment of the present invention, the determining the route types of the unmanned cluster in the target area based on the purpose of the task instruction includes:

[0108] Determine the purpose of the task instruction based on the task instruction;

[0109] Query in the preset set of route types based on the purpose of the instruction to determine the corresponding route type.

[0110] In another embodiment of the present invention, determining the planned flight path on the target area based on the effective field of view area and path types of the unmanned cluster includes:

[0111] Determining the path planning type of the unmanned cluster on the target area based on the path types, where the path planning type includes a tiling type and a grid type;

[0112] Based on the effective field of view area, continuously covering the target area according to the path of the path planning type to determine the planned flight path.

[0113] In a second aspect, the present invention also provides a highway and bridge unmanned cluster flight path planning payload field of view area planning system, and the system includes: a task instruction processing module, a field of view determination module, and a path planning module;

[0114] The task instruction processing module is used to determine the field of view information of the unmanned cluster in the target area based on the task instruction, and determine the path types of the unmanned cluster in the target area based on the purpose of the task instruction;

[0115] The field of view determination module is used to determine the field of view influence factor of the unmanned cluster based on the environmental conditions of the target area, and determine the effective field of view area of the unmanned cluster based on the field of view influence factor;

[0116] The path planning module is used to determine the planned flight path on the target area based on the effective field of view area and path types of the unmanned cluster.

[0117] In another embodiment of the present invention, the task instruction processing module is further used for:

[0118] Obtaining the demand parameters corresponding to the task instruction, where the demand parameters are the parameters or indicators that the unmanned cluster needs to achieve when executing the task instruction under the current environmental conditions;

[0119] Determining the cluster information of the unmanned cluster based on the demand parameters, where the cluster information is the set data and execution data of the unmanned cluster when executing the task instruction;

[0120] Determining the field of view information of the unmanned cluster based on the demand parameters and the cluster information.

[0121] In another embodiment of the present invention, the field of view determination module is further used for:

[0122] Obtaining the environmental condition information of the target area, where the environmental condition information includes the air quality, rainfall, electromagnetic interference, and wind force of the target area;

[0123] Query a preset field of view impact factor based on the environmental condition information, and determine the field of view impact factor of the unmanned cluster in the current situation;

[0124] Determine the theoretical field of view area of the unmanned cluster based on the field of view information;

[0125] Determine the effective field of view area of the unmanned cluster based on the theoretical field of view area and the field of view impact factor.

[0126] In another embodiment of the present invention, the path planning module is further configured to:

[0127] Determine the path planning type of the unmanned cluster on the target area based on the path type, and the path planning type includes a tiling type and a grid type;

[0128] Based on the effective field of view area, continuously cover the target area according to the route of the path planning type to determine the planned air route.

[0129] In a third aspect, the present invention further provides an electronic device, the device includes a processor and a memory electrically connected to the processor, the memory is used to store a computer program, and the processor is used to call the computer program to execute the method described in any embodiment of the first aspect.

[0130] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these changes and modifications.

Claims

1. A method for planning the field of view area of an unmanned cluster flight path for highways and bridges, characterized in that, The method includes: Determining the field of view information of the unmanned cluster in the target area based on the task instruction, and determining the path type of the unmanned cluster in the target area based on the purpose of the task instruction; Determining the field of view influence factor of the unmanned cluster based on the environmental conditions of the target area, and determining the effective field of view area of the unmanned cluster based on the field of view influence factor; Determining the planned flight path in the target area based on the effective field of view area and path type of the unmanned cluster.

2. The method for planning the field of view area of the load for the unmanned cluster route planning of highways and bridges according to claim 1, wherein The determining the field of view information of the unmanned cluster in the target area based on the task instruction includes: Obtaining the demand parameters corresponding to the task instruction, where the demand parameters are the parameters or indicators that the unmanned cluster needs to achieve when executing the task instruction under the current environmental conditions; Determining the cluster information of the unmanned cluster based on the demand parameters, where the cluster information is the set data and execution data of the unmanned cluster when executing the task instruction; Determining the field of view information of the unmanned cluster based on the demand parameters and the cluster information.

3. A method for planning the field of view area of an unmanned cluster flight path for highways and bridges according to claim 1, characterized in that, The determining the field of view influence factor of the unmanned cluster based on the environmental conditions of the target area includes: Obtaining the environmental condition information of the target area, where the environmental condition information includes the air quality, rainfall, electromagnetic interference, and wind conditions of the target area; Querying the preset field of view influence factor based on the environmental condition information to determine the field of view influence factor of the unmanned cluster in the current situation.

4. The method for planning the load vision area of the unmanned cluster flight path on expressways and bridges according to claim 1, wherein, The determining the effective field of view area of the unmanned cluster based on the field of view influence factor includes: Determining the theoretical field of view area of the unmanned cluster based on the field of view information; Determining the effective field of view area of the unmanned cluster based on the theoretical field of view area and the field of view influence factor.

5. A method for planning the field of view area of an unmanned cluster flight path for highways and bridges as described in claim 1, characterized in that, The determining the path type of the unmanned cluster in the target area based on the purpose of the task instruction includes: Determining the purpose of the task instruction based on the task instruction; Querying in the preset path type set based on the purpose of the instruction to determine the corresponding path type.

6. A method for planning the field of view area of an unmanned cluster flight path for highways and bridges as described in claim 1, characterized in that The determining the planned flight path in the target area based on the effective field of view area and path type of the unmanned cluster includes: Determining the path planning type of the unmanned cluster in the target area based on the path type, where the path planning type includes a tiling type and a grid type; Continuously covering the target area according to the line of the path planning type based on the effective field of view area to determine the planned flight path.

7. An unmanned cluster air route planning load visual field area planning system for highways and bridges, characterized in that, The system includes: a task instruction processing module, a field of view determination module, and a path planning module; The task instruction processing module is used to determine the field of view information of the unmanned cluster in the target area based on the task instruction, and determine the path type of the unmanned cluster in the target area based on the purpose of the task instruction; The field of view determination module is used to determine the field of view influence factor of the unmanned cluster based on the environmental conditions of the target area, and determine the effective field of view area of the unmanned cluster based on the field of view influence factor; The path planning module is used to determine the planned flight path in the target area based on the effective field of view area and path type of the unmanned cluster.

8. The unmanned cluster air route planning and payload field of view area planning system for highways and bridges according to claim 7, characterized in that The task instruction processing module is further used for: Obtain the requirement parameters corresponding to the task instruction, where the requirement parameters are the parameters or metrics that the unmanned cluster needs to achieve when executing the task instruction under the current environmental conditions; Based on the requirement parameters, determine the cluster information of the unmanned cluster, where the cluster information is the set data and execution data of the unmanned cluster when executing the task instruction; Determine the field of view information of the unmanned cluster based on the requirement parameters and the cluster information.

9. A highway and bridge unmanned cluster route planning payload field of view area planning system according to claim 8, characterized in that, The field of view determination module is further configured to: Obtain the environmental condition information of the target area, where the environmental condition information includes the air quality, rainfall, electromagnetic interference, and wind conditions of the target area; Query the preset field of view influence factors based on the environmental condition information to determine the field of view influence factors of the unmanned cluster in the current situation; Determine the theoretical field of view area of the unmanned cluster based on the field of view information; Determine the effective field of view area of the unmanned cluster based on the theoretical field of view area and the field of view influence factors.

10. The unmanned cluster air route planning and payload vision area planning system for expressways and bridges according to claim 7, characterized in that, The path planning module is further configured to: Determine the path planning type of the unmanned cluster on the target area based on the path type, where the path planning type includes a tiling type and a grid type; Based on the effective field of view area, continuously cover the target area according to the route of the path planning type to determine the planned flight path.

11. An electronic device, characterized in that, The device includes a processor and a memory electrically connected to the processor. The memory is used to store a computer program, and the processor is used to call the computer program to execute the method according to any one of claims 1-6.

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