Unmanned aerial vehicle highway bridge engineering measurement method and system based on path planning

Abstract

This invention relates to the field of unmanned aerial vehicle (UAV) measurement technology, specifically to a UAV-based method for highway bridge engineering measurement based on path planning, comprising the following steps: setting an effective measurement interval; setting a grid resolution and dividing the geometric space model into several grids using the grid resolution; determining whether the coordinate interval of each grid contains the coordinates of the structural surface in the geometric space model; if it does, defining the grid as an idle grid; otherwise, defining the grid as a grid to be measured; if the grid to be measured and an idle grid are adjacent, redefining the grid resolution and dividing the idle grid again; calculating the geometric center coordinates of each idle grid as initial candidate waypoints; calculating the structural surface coordinates within the effective measurement distance corresponding to each effective candidate waypoint; determining whether multiple measured structural surface coordinates can be stitched together to form a complete bridge structure measurement coverage area; constructing candidate paths; and constructing a cost function to generate the optimal path with the shortest total flight distance as the objective.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) measurement technology, and more specifically, to a UAV-based method and system for highway and bridge engineering measurement based on path planning. Background Technology

[0002] As my country's transportation infrastructure construction continues to advance, highway bridges are characterized by increasingly larger spans, more complex structures, and wider distribution areas. Throughout the entire process of planning, design, construction monitoring, and long-term operation and maintenance, the demands for the accuracy, efficiency, and safety of measurement data are constantly increasing. Traditional highway bridge engineering surveying methods are increasingly unable to meet the refined and efficient requirements of modern engineering. Traditional surveying relies heavily on equipment such as total stations and levels, requiring workers to conduct high-altitude operations using bridge inspection vehicles, suspended platforms, or even climbing. This is not only labor-intensive and time-consuming, but also often necessitates multiple station relocations and setups for long-span or high-pier bridges, significantly extending the measurement time. Furthermore, workers are constantly exposed to safety risks such as falls from heights and traffic disruptions. Manual surveying is also susceptible to subjective differences, making it difficult to accurately capture minute surface cracks and concrete spalling on the bridge. Moreover, the cost of purchasing and maintaining such specialized equipment is high. Meanwhile, drone technology, with its high mobility, flexible operation, and ability to carry multiple sensors such as high-definition cameras and lidar, is gradually becoming a core auxiliary technology for highway bridge engineering surveying. Existing methods typically involve using surveying equipment carried by drones, as well as ground-based auxiliary surveying equipment such as total stations and levels. Surveyors then remotely control and observe the drone's flight attitude and measurement data before conducting manual intervention in bridge engineering surveying. However, when surveying highway bridge engineering, the structures are characterized by large spans, high piers, narrow spaces at the bottom of the beams, and severe obstructions. Relying solely on real-time observation by surveyors presents several challenges. First, large-span bridges make it difficult for surveyors to accurately control the long-distance flight trajectory, leading to missed measurements in certain areas due to blind spots. Second, when working at heights above high piers, surveyors struggle to clearly observe the relative position of the drone to the piers, making it impossible to adjust the flight attitude in time to obtain stable data. Furthermore, the narrow spaces and obstructions at the bottom of the beams completely block the surveyors' view, making it impossible to determine the drone's flight status and measurement progress in that area. Additionally, the connection points between piers and beams, supports, and other hidden critical areas are difficult for surveyors to accurately identify and guide the drone for measurement due to obstructed views. Consequently, it is difficult to achieve comprehensive, blind-spot-free measurement of the entire bridge structure. Therefore, we propose a path planning-based drone-based highway bridge engineering surveying method and system. Summary of the Invention

[0003] The purpose of this invention is to solve the problems of unreasonable path planning, blind spots in measurement coverage, poor flight safety, and insufficient measurement efficiency and accuracy in traditional UAV highway bridge measurement.

[0004] To achieve the above objectives, this invention provides a method for UAV-based highway bridge engineering surveying based on path planning, comprising the following steps: S1: Obtain the geometric space model including basic data of highway bridge engineering, and the effective measurement distance corresponding to the sensors carried by the UAV; set a safe distance threshold, and set the difference between the effective measurement distance and the safe distance threshold as the effective measurement interval; S2: Obtain the simulated dimensions of the UAV in the geometric space model, the UAV flight parameters, and the UAV flight dynamics performance parameters; set the grid resolution based on the UAV flight parameters and simulated dimensions; dynamically divide the geometric space model into several grids using the grid resolution and flight dynamics performance parameters; S3: Retrieve the 3D coordinate range of each grid in the geometric space model, and determine whether the coordinate range of each grid contains the coordinates of the structural surface in the geometric space model. If it does, define the corresponding grid as an idle grid; otherwise, define the corresponding grid as a grid to be tested. If the grid to be measured and the free grid are adjacent, then the simulated size in step S2 is directly defined as the grid resolution, and the free grid is divided again; Calculate the geometric center coordinates of each free grid cell as the initial candidate waypoint, and determine whether the initial candidate waypoint is a valid candidate waypoint by using the valid measurement interval in step S1; S4: Calculate the structural surface coordinates within the effective measurement distance corresponding to each valid candidate waypoint, determine whether multiple measured structural surface coordinates can be stitched together to form a complete bridge structure measurement coverage area, combine multiple valid candidate waypoints that can achieve complete coverage in different orders to construct candidate paths, and construct a cost function to generate the optimal path with the shortest total flight distance as the objective.

[0005] As a further improvement to this technical solution, the simulated dimensions required when setting the grid resolution in step S2 include: the maximum longitudinal dimension of the UAV, the maximum lateral dimension of the UAV, and the maximum height dimension of the UAV. The UAV flight parameters are specifically the minimum maneuver space thresholds for the UAV, which include a planar maneuver threshold and an altitude maneuver threshold. Specifically, the planar maneuver threshold is the minimum safe redundancy space in the planar direction required for the UAV to complete in-plane position fine-tuning and turning actions; the altitude maneuver threshold is the minimum safe redundancy space in the altitude direction required for the UAV to complete vertical position fine-tuning.

[0006] As a further improvement to this technical solution, step S2 specifically sets the grid resolution by using the minimum maneuver space threshold and the size of the UAV: ​​based on the maximum simulated size of the UAV in the longitudinal, lateral, and height directions, the corresponding planar maneuver threshold and height maneuver threshold are superimposed to obtain the grid resolution.

[0007] As a further improvement to this technical solution, in step S2, when dividing the geometric space model into several grids by grid resolution, the extreme coordinates of the geometric space model on the X-axis, Y-axis and Z-axis are first extracted, and the spatial coverage is defined by the extreme coordinates. Then, using the UAV flight parameters as dynamic constraints, the mapping relationship between the three-dimensional size of the grid resolution and the X, Y, and Z axes of the spatial coordinate system is dynamically adjusted. Using the grid resolution corresponding to each axis after adjustment as the unit scale, the defined spatial coverage area is divided along the X, Y, and Z axes, and the continuous space is discretized into multiple cubic grids.

[0008] As a further improvement to this technical solution, when dynamically adjusting the grid resolution based on the flight dynamics performance parameters of the UAV in step S2, the grid resolution is adjusted relative to the inter-axis dimensions of the geometric space model according to the real-time flight direction of the UAV, and the X-axis, Y-axis, and Z-axis of the geometric space model are linked in real time. The flight direction of the UAV is randomly adjusted through the flight dynamics performance parameters. When the flight direction of the UAV changes, the corresponding grid resolution is adjusted according to the converted flight direction.

[0009] As a further improvement to this technical solution, step S3 retrieves the three-dimensional coordinate range corresponding to each grid in the geometric space model, determines whether the coordinate range of each grid contains the coordinates of the structural surface, assigns the test attribute to the corresponding grid if it contains the coordinates, and assigns the idle attribute if it does not contain the coordinates.

[0010] As a further improvement to this technical solution, step S3 calculates the geometric center coordinates of each free grid cell as initial candidate waypoints; then, it calculates the spatial distance from each initial candidate waypoint to the coordinates of the structural surface in the geometric space model; and compares each spatial distance with the effective measurement interval in step S1. If the spatial distance is within the effective measurement range, it means that the initial candidate waypoint meets the safe distance requirements between the UAV and the structure and the sensor can achieve the preset measurement accuracy. If the spatial distance is not within the effective measurement range, it means that the initial candidate waypoint has a collision risk or cannot guarantee measurement accuracy. Therefore, the initial candidate waypoint corresponding to the spatial distance being within the effective measurement range is defined as the effective candidate waypoint.

[0011] As a further improvement to this technical solution, when redefining the grid resolution in step S3, multiple free coordinates corresponding to the grid to be tested are identified, and the free coordinates are connected with the free coordinates in the adjacent free grids to form a continuous free area; it is determined whether the spatial scale of the formed continuous free area is greater than the grid resolution. If the continuous free area is greater than the grid resolution, it means that the continuous free area has the spatial conditions for UAV maneuvering and adjustment operations; at this time, starting from the coordinates of the structural surface in the grid to be tested, multiple free grids are generated again through step S2.

[0012] As a further improvement to this technical solution, the specific steps for generating the optimal path in step S4 are as follows: Step 1: Filter the coordinates of the starting and ending points of the path, and calculate the structural surface coordinates corresponding to each valid candidate waypoint. Step 2: Generate the structural surface coordinate intervals corresponding to the initial candidate waypoints using the structural surface coordinates, and merge the structural surface coordinate intervals corresponding to multiple initial candidate waypoints to obtain the integrated set of measurement structures; Step 3: Retrieve the surface coordinates of all bridge structures in the geometric space model and construct a set of coordinates to be matched on the structural surfaces; Step 4: Verify whether the integrated set of measurement structures completely includes the set of coordinates to be matched and whether there are no gaps between the measurement structures. This will help determine whether the coordinates of multiple structural surfaces can be spliced ​​together to form a complete bridge structure measurement coverage. Step 5: After the judgment is completed, multiple valid candidate waypoints that can achieve complete coverage are combined in different orders to construct candidate paths; finally, a cost function is constructed with the goal of minimizing the total flight distance to generate the optimal path.

[0013] A path planning-based UAV-based highway bridge engineering surveying system includes: The measurement interval setting module acquires a geometric spatial model including basic data of highway bridge engineering, as well as the effective measurement distance corresponding to the sensors carried by the UAV; sets a safety distance threshold, and sets the difference between the effective measurement distance and the safety distance threshold as the effective measurement interval; The grid space division module acquires the simulated dimensions of the UAV within the geometric space model, the UAV's flight parameters, and the UAV's flight dynamics performance parameters; sets the grid resolution based on the UAV's flight parameters and simulated dimensions; and dynamically divides the geometric space model into several grids using the grid resolution and flight dynamics performance parameters. The candidate waypoint processing module retrieves the 3D coordinate range corresponding to each grid in the geometric space model, determines whether the coordinate range of each grid contains the coordinates of the structural surface in the geometric space model, and defines the corresponding grid as an idle grid if it does not, otherwise defines the corresponding grid as a grid to be tested. If the grid to be measured and the free grid are adjacent, then the simulated size in step S2 is directly defined as the grid resolution, and the free grid is divided again; Calculate the geometric center coordinates of each free grid cell as the initial candidate waypoint, and determine whether the initial candidate waypoint is a valid candidate waypoint by using the valid measurement interval in step S1; The optimal path generation module calculates the structural surface coordinates corresponding to valid candidate waypoints, determines whether multiple measured structural surface coordinates can be pieced together to form a complete bridge structure measurement coverage area, constructs candidate paths, and generates the optimal path by constructing a cost function with the shortest total flight distance as the objective.

[0014] Compared with the prior art, the beneficial effects of the present invention are as follows: In this path planning-based UAV highway bridge engineering measurement method and system, steps S1 and S2 define the effective measurement range and grid resolution. In step S2, setting the grid resolution is constrained by UAV flight parameters and the simulated dimensions of the UAV within the geometric space model. The geometric space model is then divided into several grids based on the grid resolution. Specifically, during the grid resolution setting process, the flight attitude of the UAV during flight is simulated using the UAV's flight dynamics performance parameters. This allows the grid resolution to vary randomly within the geometric space model, avoiding issues such as poor adaptability of a fixed resolution, excessive grid redundancy in wide areas leading to sparse waypoints and missed measurement details, and excessively large grid sizes in narrow areas that cannot accommodate the UAV. The problem of human-machine flight is addressed by ensuring that the shape and size of the grid cells always fit the real-time flight status of the drone and the complex spatial characteristics of the bridge. For example, for the main beam area of ​​the bridge with a long span, the flight attitude of the drone cruising at high speed along the X-axis is simulated, and the grid resolution is set to an adaptive size of 5m on the major axis, 2m on the minor axis, and 1m in height. This reduces the number of grid cells and the complexity of path planning, while ensuring that the waypoint distribution meets the needs of long-distance rapid measurement. For the narrow gap area connecting the pier and the main beam, the flight attitude of the drone hovering and turning is simulated, and the grid resolution is adjusted to a fine size of 0.5m × 0.5m × 0.5m. This ensures that the grid cells can be accurately embedded in the narrow space and generate sufficient waypoints to cover the hidden measurement parts, providing an accurate and efficient spatial carrier for subsequent waypoint generation and path planning. Furthermore, by simulating different attitudes during the flight of the drone, after the optimal path is generated in the subsequent step S4, the flight attitude of the drone in each trajectory segment can be further matched with the grid space height. For example, when the drone of the optimal path planning passes through the gap of the guardrail along the Y-axis, since the grid resolution under this attitude has been simulated in the early stage, the drone can directly adjust the flight parameters according to the adapted grid size without additional attitude correction. This not only improves the flight safety of the drone in complex spaces, but also ensures that the sensor is always in the effective measurement range to obtain high-precision bridge structure measurement data. At the same time, it can also reduce the number of attitude adjustments during flight, reduce the energy consumption of the drone, and extend the working endurance time. Further step S3 assigns attribute labels of "to be measured" and "idle" to each grid cell, and verifies the validity of the initial candidate waypoints corresponding to the geometric center coordinates of the idle grid cells again using the effective measurement range set in step S1. Invalid waypoints exceeding the measurement accuracy and safety boundaries are filtered out. At the same time, the grid cell to be measured and its adjacent idle grid cells are retrieved again, and the simulated size of the UAV is redefined as the grid resolution. A refined grid cell adapted to narrow spaces and the corresponding initial candidate waypoints are generated a second time. This can accurately fill the waypoint gaps in the local hidden areas of the bridge, and solve the problem of missing measurements of key parts such as supports and pier connections caused by the excessive resolution of the initial grid division. This not only ensures that all valid candidate waypoints are within the effective measurement range, ensuring that the sensor can stably acquire bridge structural data that meets the accuracy requirements at the waypoints, but also allows the measurement areas corresponding to the waypoints to form a continuous network. The continuous and uninterrupted coverage avoids measurement blind spots, significantly improving the overall coverage capability of bridge engineering surveying. After obtaining numerous valid candidate waypoints generated in steps S2 and S3, step S4 verifies whether the measurement range of the waypoint combination completely covers the entire bridge area in the geometric space model based on the structural surface coordinates within the effective measurement distance corresponding to each waypoint. Then, a cost function is constructed with the goal of minimizing the total flight distance, and the optimal flight path is generated through a path optimization algorithm. This not only ensures that the bridge structure is measured without blind spots, completely solving the problems of missed measurements and repeated measurements in traditional manually operated UAV surveying, but also reduces the energy consumption of the UAV through path optimization with the shortest flight distance, extends the endurance of a single operation, and shortens the overall measurement operation time. At the same time, it takes into account both measurement integrity and operational efficiency, ultimately achieving accurate, safe, and efficient UAV highway bridge engineering surveying.

[0015] In addition to the objectives, features, and advantages described above, the present invention has other objectives, features, and advantages. The invention will now be described in further detail with reference to the figures. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the overall module of the present invention; Figure 2This is a flowchart illustrating the working principle of the present invention.

[0017] The meanings of the labels in the diagram are as follows: 100. Measurement interval setting module; 200. Grid space division module; 300. Candidate waypoint processing module; 400. Optimal path generation module. Detailed Implementation

[0018] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] refer to Figure 1 As shown, the UAV-based highway bridge engineering surveying method based on path planning includes the following steps: S1: Obtain the geometric space model including basic data of highway bridge engineering, and the effective measurement distance corresponding to the sensors carried by the UAV; set a safe distance threshold, and set the difference between the effective measurement distance and the safe distance threshold as the effective measurement interval; S2: Obtain the simulated dimensions of the UAV in the geometric space model, the UAV flight parameters, and the UAV flight dynamics performance parameters; set the grid resolution based on the UAV flight parameters and simulated dimensions; dynamically divide the geometric space model into several grids using the grid resolution and flight dynamics performance parameters; S3: Retrieve the 3D coordinate range of each grid in the geometric space model, and determine whether the coordinate range of each grid contains the coordinates of the structural surface in the geometric space model. If it does, define the corresponding grid as an idle grid; otherwise, define the corresponding grid as a grid to be tested. If the grid to be measured and the free grid are adjacent, then the simulated size in step S2 is directly defined as the grid resolution, and the free grid is divided again; Calculate the geometric center coordinates of each free grid cell as the initial candidate waypoint, and determine whether the initial candidate waypoint is a valid candidate waypoint by using the valid measurement interval in step S1; S4: Calculate the structural surface coordinates within the effective measurement distance corresponding to each valid candidate waypoint, determine whether multiple measured structural surface coordinates can be stitched together to form a complete bridge structure measurement coverage area, combine multiple valid candidate waypoints that can achieve complete coverage in different orders to construct candidate paths, and construct a cost function to generate the optimal path with the shortest total flight distance as the objective.

[0020] In the implementation of the above embodiments, the effective measurement range set in step S1 is used to clarify the operational boundary of the UAV, avoiding risks such as measurement data distortion and UAV collisions caused by exceeding measurement accuracy or safe distance, thus ensuring the basic safety and data validity of the measurement task. Step S2 sets the grid resolution and divides the geometric space model into several grids based on the grid resolution, dividing the complex and continuous geometric space model into multiple standardized grids. The grid resolution is dynamically set based on the UAV flight parameters and simulation size, ensuring that the grid resolution is highly compatible with the UAV's performance and simulation size, avoiding space waste or the inability to cover narrow areas due to a fixed grid resolution, and providing a clear spatial carrier for waypoint generation in step S4. After assigning attributes to each grid in step S3, the effective measurement range in step S1 is used to determine whether the initial candidate waypoints are valid. Candidate waypoints are generated by re-selecting the grid to be measured and the adjacent idle grids, redefining the grid resolution, and generating a second grid and corresponding initial candidate waypoints. This allows for the precise selection of effective waypoints that meet the needs of bridge measurement, filling the gaps in waypoints in narrow local areas. This ensures that the initial candidate waypoints are within the effective measurement range to guarantee data accuracy, and also solves the problem of missing waypoints caused by unreasonable initial grid resolution through secondary grid division and waypoint generation, thereby improving the comprehensiveness of the measurement coverage. After obtaining multiple initial candidate waypoints, step S4 can use the initial candidate waypoints to complete the optimal path planning under the corresponding full coverage of the geometric space model. This not only ensures that the bridge structure is measured without blind spots, but also reduces UAV energy consumption and shortens operation time through path optimization with the shortest flight distance, while taking into account both measurement integrity and operational efficiency, ultimately achieving accurate, safe, and efficient UAV highway bridge engineering measurement.

[0021] The working principles of each step in the above-mentioned path planning-based UAV highway bridge engineering surveying method are as follows: Step S1: Obtain the geometric space model of the basic database, which includes basic data of highway and bridge engineering (including bridge BIM design model, as-built drawings, historical measurement reports and surrounding terrain and obstacle distribution data). Through UAV sensor parameter calibration and field prediction tests, accurately obtain the effective measurement distance of the UAV-mounted sensors (such as high-pixel cameras and LiDAR) (i.e., the operating distance range that the UAV can meet the preset measurement accuracy when measuring by carrying measurement sensors). Step S1 sets a safe distance threshold according to flight safety regulations, and sets the difference between the effective measurement distance and the safe distance threshold as the effective measurement range (i.e., the operating distance range that can meet the preset measurement accuracy when the UAV measures by carrying measurement sensors). When setting safe distance thresholds through flight safety regulations, the specific basis should be the relevant national and industry regulations on drone flight safety (such as the "Regulations on the Management of Drone Flight Safety," the "Regulations on the Management of Civil Drone Pilots," and the "Technical Specifications for Drone Bridge Monitoring"). First, identify the general safe distance requirements for different scenarios such as close-range operations, high-altitude flights, and complex environment operations, as well as the minimum safe distance standards for specific operation types such as bridge surveying. Then, combine this with the classification of obstacle risk levels in the regulations (e.g., classifying power lines and buildings around bridges as high-risk obstacles, and guardrails and piers as regular obstacles) to extract the baseline safe distance value for the corresponding risk level. Finally, verify the relevant regulations regarding drones... The parameters such as drone model (e.g., small, light drones), flight speed, and operating altitude should be matched with the flight parameters of the drones currently used for bridge surveying. If the standard has specific safety distance requirements for a particular drone model, the specific standard should be adopted first. At the same time, the requirements for safety redundancy in the standard should be strictly followed, and a reasonable amount of redundancy should be reserved on the basis of the benchmark safety distance (the standard usually requires the redundancy to be no less than 20%-30% of the benchmark value) to cope with uncertainties such as small attitude deviations and wind speed interference during drone flight. The final determined safety distance threshold not only fully complies with the mandatory requirements of the standard, but also ensures flight safety through the redundancy design required by the standard, ensuring that the drone maintains a sufficient safe distance from surrounding obstacles during bridge surveying operations and avoids the risk of collision.

[0022] S2: Set as the raster resolution. Divide the geometric spatial model in the basic data of highway bridge engineering into several raster grids based on the raster resolution. Considering that the UAV needs to perform maneuvers such as in-plane position fine-tuning (e.g., ±0.5m translation), turning (e.g., 180° turn), and vertical ascent and descent (e.g., ±0.3m adjustment) during flight measurement, in order to avoid the UAV fuselage or rotors touching the grid boundaries during these actions, and to ensure the attitude space when adjusting the shooting angle, the UAV flight parameters are obtained. Specifically, the UAV flight parameters are the minimum maneuver space thresholds for the UAV, which include the planar maneuver threshold. High mobility threshold Specifically: Planar maneuver threshold This refers to the minimum planar safety redundancy space required for the UAV to perform in-plane position fine-tuning (such as ±0.5m translation) and turning maneuvers (such as 180° turns). It is used to ensure that the UAV's maneuvers within the divided grid do not touch the boundaries or altitude maneuver thresholds. The minimum safety margin in the height direction is required for the drone to complete fine-tuning of its vertical position (such as ±0.3m elevation change) to accommodate attitude changes during shooting angle adjustments; and the planar maneuver threshold. High mobility threshold The specific settings are based on the drone's hardware performance indicators (such as maximum horizontal turning angle, maximum climb / glide angle, and minimum turning radius), for example, setting a planar maneuver threshold. Set the altitude maneuver threshold for the maximum allowed horizontal turning angle of the drone (typically 30°-60°). The maximum allowable climb angle for the drone (typically 15°-30°).

[0023] The simulated dimensions of the UAV equipped with measurement sensors for highway bridge engineering surveying are obtained in the geometric space model. The simulated dimensions include: the maximum longitudinal dimension of the UAV (e.g., the straight-line distance between the rotor tips in the direction of nose to tail), the maximum lateral dimension of the UAV (e.g., the straight-line distance between the rotor tips in the direction of left and right fuselage), and the maximum height dimension of the UAV (e.g., the vertical distance from the bottom of the fuselage to the top of the back, including the gimbal and battery). Step S2 specifically sets the grid resolution by using the minimum maneuvering space threshold and the simulated size of the UAV. The specific working principle is as follows: based on the maximum simulated size in the longitudinal, lateral, and vertical directions, the corresponding planar maneuvering threshold is superimposed. High mobility threshold This ensures that the grids subsequently divided by the grid resolution can fully accommodate the entire UAV while providing sufficient buffer space for various maneuvers, ultimately yielding the grid resolution. Specifically, the grid resolution is the first resolution in the X-axis direction of the geometric space model. The second resolution in the Y-axis direction The third resolution in the z-axis direction The specific expression is as follows: ; in: The first candidate resolution value is calculated based on the longitudinal dimensions of the UAV; This is a candidate value for the second resolution calculated based on the lateral dimensions of the UAV; Maximum longitudinal dimension of the drone; This is a function to find the maximum value. , , These represent the maximum dimensions of the UAV along the X-axis, Y-axis, and z-axis in the geometric space model, respectively. This represents the planar maneuver threshold for the UAV in the planar direction within the geometric space model. This represents the altitude maneuver threshold for the UAV in the altitude direction within the geometric space model. The third resolution specifically in the z-axis direction It is the maximum z-axis dimension of the drone. Add 2 times the high mobility threshold The calculated values; and the first resolution candidate values ​​in the x and y axis directions. Second resolution candidate value The two values ​​are based on the maximum longitudinal and lateral dimensions of the drone, respectively, with twice the planar maneuver threshold added to each. Received; Candidate values ​​at first resolution Second resolution candidate value For example, using the planar maneuver threshold calculate Second resolution candidate value This buffer zone is used to reserve a planar maneuvering space threshold on both sides of the UAV's longitudinal / lateral dimensions. This ensures that the UAV fuselage can be completely contained within the grid, while providing sufficient safety redundancy for in-plane maneuvers such as position adjustments and turns, preventing the fuselage or rotor from touching the grid boundaries during operation. The final planar resolution is taken from the aforementioned first resolution candidate value. Second resolution candidate value The corresponding maximum value.

[0024] The specific working principle of dividing the geometric space model into several grids by grid resolution in step S2 is as follows: Extract the extreme coordinates (i.e., the minimum and maximum values ​​in each axis) of the geometric spatial model along the X-axis (bridge length direction), Y-axis (bridge width direction), and Z-axis (height direction) to define the spatial coverage area. ,in , , , , , , , represent the extreme coordinates of the geometric space model on the X-axis, Y-axis, and Z-axis, respectively; , , represent the minimum and maximum values ​​in the X-axis, Y-axis, and Z-axis directions, respectively; Then, using the UAV's flight dynamics performance parameters (flight direction, attitude, etc.) as dynamic constraints, the mapping relationship between the three-dimensional size of the grid resolution and the X, Y, and Z axes of the spatial coordinate system is dynamically adjusted to ensure that the grid division adapts to the UAV's real-time flight state. Finally, using the adjusted grid resolution corresponding to each axis as the unit scale, the defined spatial coverage area is divided along the X, Y, and Z axes, discretizing the continuous space into multiple cubic grids. The number of grids divided along each axis is as follows: ,in For a single grid cell in the i-th row, j-th column, and k-th layer, , , These represent the number of grid cells divided along the X, Y, and Z axes, respectively. This is the floor function; Furthermore, during the dynamic adjustment of the grid resolution based on flight dynamics performance parameters, the inter-axis dimensions of the grid resolution relative to the geometric space model are adjusted according to the real-time flight direction of the UAV. Key parameters such as the UAV's flight direction and attitude are used as trigger conditions for the dynamic adjustment of the three-axis dimensions of the grid resolution. The X-axis (bridge length direction), Y-axis (bridge width direction), and Z-axis (altitude direction) of the geometric space model are linked in real time, and the UAV's flight direction is randomly adjusted based on the flight dynamics performance parameters. When the UAV's flight direction changes, the corresponding grid resolution is adjusted based on the converted flight direction. For example, if the UAV's flight direction in the geometric space model is the X-axis (bridge length direction), then the major axis dimension of the grid resolution in the geometric space model corresponds to the X-axis, the minor axis dimension corresponds to the Y-axis, and the altitude dimension corresponds to the Z-axis. If the flight direction switches to the Y-axis (bridge width direction), the grid resolution is simultaneously adjusted accordingly. The major axis dimension of the grid is switched to match the Y-axis, the minor axis dimension remains matched to the X-axis, and the height dimension still corresponds to the Z-axis. This achieves a precise adaptation between the dimensions of the grid resolution axes and the UAV's flight state, ensuring that the grid division always fits the actual operational needs of the UAV. Furthermore, by dynamically adjusting the grid resolution, if the geometric center coordinates of the grid corresponding to the grid resolution in subsequent step S4 are valid candidate waypoints, then the flight dynamics performance parameters at this time can be measured. This allows the UAV's flight state at the waypoint to be highly adapted to the grid shape and measurement requirements. This ensures that the UAV has sufficient attitude adjustment space at the waypoint to avoid collision risks, and that the matched grid resolution ensures that the sensor is within the effective measurement range, acquiring high-precision bridge structure measurement data. At the same time, it also allows the measurement area corresponding to the waypoint to form continuous coverage with the surrounding grid, avoiding measurement blind spots and improving the efficiency and completeness of the overall measurement task.

[0025] S3: Retrieve the set of 3D coordinates corresponding to each grid cell in the geometric space model. Determine whether the coordinate range of each grid cell contains the coordinates of the structural surface in the geometric space model. If it does, define the corresponding grid cell as an idle grid cell; otherwise, define the corresponding grid cell as the grid cell to be tested. Specifically: The set of three-dimensional coordinates of the k-th grid is received as follows ,in The coordinate interval of the k-th grid cell in the X-axis direction (minimum value) The maximum value is ), The coordinate interval of the k-th grid cell in the Y-axis direction (minimum value) The maximum value is ), The coordinate interval of the k-th grid cell in the Z-axis direction (minimum value) The maximum value is ); Receive the coordinates of the structural surfaces in the geometric space model. ,like (i.e., structural surface coordinates) ∈ Three-dimensional coordinate set Then define the coordinates of the structural surface. The grid cell to be tested is defined as an empty grid cell; otherwise, it is defined as an empty grid cell.

[0026] Calculate the geometric center coordinates of each free grid cell as initial candidate waypoints, where the initial candidate waypoint for the k-th grid cell is... Then, the initial candidate waypoints are calculated. To the coordinates of the structural surface in the geometric space model spatial distance Compare the spatial distance with the effective measurement range in step S1: If the spatial distance is within the effective measurement range, it means that the initial candidate waypoint meets the safe distance requirements between the UAV and the structure and the sensor can achieve the preset measurement accuracy. If the spatial distance is not within the effective measurement range, it means that the initial candidate waypoint has a collision risk or cannot guarantee measurement accuracy. Therefore, the initial candidate waypoint corresponding to the spatial distance being within the effective measurement range is defined as a valid candidate waypoint. Step S3 further considers the space utilization and waypoint optimization requirements of the grid under test, identifies multiple free coordinates within the grid under test. If there are free grids adjacent to the grid under test, it means that the free space of the grid under test and the adjacent free grids can be integrated into a larger continuous free area. The spatial scale of the continuous free area can cover the maneuvering and adjustment space required for UAV operation. Therefore, multiple free coordinates are connected with multiple free coordinates in the adjacent free grids to form a continuous free area that can meet the UAV's operational space requirements. At this time, starting with the structural surface coordinates in the grid under test and constrained by the effective measurement interval in step S1, multiple free grids are generated again through step S2. At this time, the initial candidate waypoints corresponding to each free grid are all within the effective measurement interval. Furthermore, the continuous free area is greater than the area that can be constructed by the grid resolution in step S2. Therefore, when generating multiple free grids through step S2, the simulated size of the UAV in the geometric space model in step S2 is directly defined as the grid resolution, thereby reducing the granularity of grid division and improving the accuracy of waypoint planning and the completeness of measurement coverage. The specific reason is that, although step S2 can dynamically adjust the mapping relationship between the three-dimensional size of the grid resolution and the spatial coordinate system X, Y, and Z axes by using the flight dynamic performance parameters of the UAV (flight direction, attitude, etc.) as dynamic constraints to generate corresponding grids for different flight states when generating multiple grids according to the corresponding grid resolution, step S2 fully considers the basic operational safety of the UAV when setting the grid resolution. It needs to cover the UAV's own size and the minimum maneuvering space threshold (to ensure that the UAV has sufficient adjustment margin within the grid). Thus, although step S2 can theoretically guarantee the flight safety and maneuverability of the UAV within the grid, it does not fully take into account the actual spatial characteristics of highway bridge engineering. Specifically, the structure of a highway bridge is not a regular continuous space. Its surface has beam protrusions, supports, guardrails, and other ancillary facilities. Therefore, the superstructure will occupy a large amount of actual workable space, resulting in a significant compression of the effective work space in local areas. This leads to insufficient grid generation in step S2 when generating grids using grid resolution. Specifically, some areas in the geometric space model corresponding to the highway bridge project cannot be divided into the theoretically required number of grids due to insufficient actual space. For instance, some local areas of the highway bridge project (such as the gap between the beam bottom and the support, the inner side of the guardrail, etc.) have space for UAVs to fly in and conduct measurements, but the spatial scale only meets the needs of the aircraft's passage and measurement operations, and cannot provide the redundant space required for attitude adjustment. When these local areas are divided using the grid resolution in step S2, they cannot be divided into effective grids due to the dual space requirements for accommodation and adjustment. In reality, such areas are measured by pre-adjusting the attitude of adjacent empty areas and then cutting in for measurement at a short distance. Therefore, the grid division rules in step S2 do not consider this special operational scenario, resulting in insufficient grid generation and some measurable structures failing to generate corresponding grids, with the actual number of divisible grids being less than the theoretical number. This leads to the formation of such ungenerated grid areas when judging the integrity of the coordinate splicing of multiple structural surfaces in step S1. In step S3, the idle coordinates within the grid to be tested are identified and connected with the idle coordinates of adjacent idle grids to construct a continuous idle region. Simultaneously, starting with the structural surface coordinates of the grid to be tested and constrained by the effective measurement range of step S1, an idle grid is regenerated. Furthermore, the simulated dimensions of the UAV in the geometric space model are directly defined as the new grid resolution to reduce the granularity of the division. This allows the adjusted grid resolution to better adapt to the narrow operational space of the bridge. Even if the space in the local area can only accommodate the UAV fuselage, attitude adjustment can be completed using the redundant space of adjacent continuous idle regions, thus specifically addressing the challenges of step S3. The problem of insufficient grid generation in local areas due to excessively large grid resolution in step 2 is addressed by using continuous empty areas to meet the spatial requirements of the UAV's flight dynamics performance parameters, and by reducing the grid granularity to allow the newly generated grids to adapt to the narrow but workable space of the bridge. This ensures that the initial candidate waypoints corresponding to the newly generated grids are all within the effective measurement range, effectively avoiding the situation in step S1 where gaps occur during the measurement structure splicing and the bridge structure cannot be completely covered. At the same time, it also improves the accuracy of waypoint planning, ensures the integrity of the bridge structure measurement coverage, and allows subsequent candidate paths that meet the complete measurement requirements to be successfully generated.

[0027] S4: First, filter the starting and ending coordinates of the path, prioritizing safe and open areas at both ends of the bridge as the starting and ending points; then calculate the structural surface coordinates corresponding to each valid candidate waypoint to form the initial candidate waypoint structural surface coordinate interval, and merge the structural surface coordinate intervals corresponding to multiple initial candidate waypoints to obtain the integrated measurement structure set; next, retrieve the surface coordinates corresponding to all bridge structures in the geometric space model to construct the set of coordinates to be matched for the structural surfaces, and verify whether the integrated measurement structure set completely contains the set of coordinates to be matched and whether there are no gaps between the measurement structures, thereby determining whether multiple structural surface coordinates can be pieced together to form a complete bridge structure measurement coverage area. The specific work steps are as follows: Step 1: Calculate the i-th valid candidate waypoint Corresponding effective measurement distance The coordinates of the internal structural surface, taking a wide-angle fixed-focus camera mounted on a drone as an example: Horizontal measurement range (horizontal coverage width of a single photograph) to determine effective candidate waypoints Effective measurement distance to structural surface The adjacent side of a right triangle, the horizontal field of view of a wide-angle fixed-focus camera. Half of the angle is acute. The opposite side is calculated using the tangent function, and then multiplied by 2 to obtain the complete horizontal coverage. The specific expression is: ; Vertical measurement range (vertical coverage height of a single photograph) for effective candidate waypoints. Effective measurement distance to structural surface The adjacent side of a right triangle, the vertical field of view of a wide-angle fixed-focus camera. Half of the angle is acute. The opposite side is calculated using the tangent function, and then multiplied by 2 to obtain the complete horizontal coverage. The specific expression is: Step 2: Select valid candidate waypoints Centered on the i-th valid candidate waypoint, the measurement range is expanded horizontally and vertically to obtain the structural surface coordinate interval. ,in , , These are the i-th valid candidate waypoints. Coordinates on the X-axis (bridge length / horizontal direction), Y-axis (bridge width / horizontal direction), and Z-axis (height direction); Valid candidate waypoints The coverage area in the X-axis direction (with the waypoint X coordinate as the center, extending half of the horizontal range to both sides); Valid candidate waypoints The coverage area along the Y-axis (with the waypoint Y coordinate as the center, extending half of the vertical range to both sides); Valid candidate waypoints The coverage area in the Z-axis direction (with the waypoint Z coordinate as the center, extending half of the vertical range to both sides); Step 3: Merge the coordinate intervals of the structural surfaces corresponding to multiple initial candidate waypoints to obtain the integrated set of measurement structures. ,in , These represent the minimum coordinate values ​​along the X-axis and the minimum coordinate values ​​along the surface of the structure corresponding to all initial candidate waypoints. , These represent the maximum and minimum coordinate values ​​along the Y-axis within the coordinate interval of the structural surface corresponding to all initial candidate waypoints. , These are the maximum and minimum coordinate values ​​in the Z-axis direction within the coordinate interval of the structural surface corresponding to all initial candidate waypoints; The set of coordinates to be matched is: ; The coordinate range of the entire bridge structure surface in the X-axis direction (from the minimum to the maximum value of the bridge structure surface itself on the X-axis). The coordinate range of all structural surfaces of the bridge in the Y-axis direction. The coordinate range of all structural surfaces of the bridge in the Z-axis direction; Step 4: Verify that all the coordinates to be matched on the bridge structure surface are included in the integrated measurement structure set, thereby ensuring that the measurement structure spatially covers the entire bridge structure. The specific expression is as follows: Furthermore, there is a non-empty overlap between any two adjacent measurement structures to avoid gaps between them. The specific expression is: For any two adjacent measurement structures... and ,have If the coordinates of multiple structural surfaces can be stitched together to form a complete bridge structure measurement coverage area, then it can be determined that the coordinates of these multiple structural surfaces can be stitched together to form a complete bridge structure measurement coverage area.

[0028] Step 5: After the judgment is completed, multiple valid candidate waypoints that can achieve complete coverage are combined in different orders to construct candidate paths; finally, a cost function is constructed with the goal of minimizing the total flight distance. The cost function is used to evaluate and search each candidate path to obtain the optimal path from the starting point to the destination. Specifically: Given a candidate path containing n valid candidate waypoints, the corresponding path trajectory order is as follows: The valid candidate waypoints for the origin are The valid candidate waypoints for the destination are Then the cost function for the total flight distance along the path is: ; in Valid candidate waypoints and valid candidate waypoints spatial distance For the nth valid candidate waypoint valid candidate waypoints to the destination ; Indicates starting from the first valid candidate waypoint To the nth valid candidate waypoint The sum of spatial distances between all adjacent waypoints; valid candidate waypoints for the origin Up to 1 valid candidate waypoint Spatial distance; Finally, the optimal path is obtained by searching for the waypoint sequence that minimizes the cost function J.

[0029] In the process of generating the optimal path in step S4 above, based on the multiple initial candidate waypoints in step S3, the waypoints that meet the requirements of full bridge coverage are combined into candidate paths in different orders according to the measurement structure integrity constraints corresponding to the multiple initial candidate waypoints. Specifically, the initial candidate waypoints generated in step S3 have adapted to the narrow space of the bridge and are within the effective measurement range. Based on this, the combined path can take into account both coverage integrity and operational feasibility. Thus, the waypoints refined in step S3 can fill the measurement gaps in the narrow areas of the bridge, and the candidate paths combined in multiple orders can ensure the flexibility of path planning. After generating the optimal path, the UAV can use the path trajectory corresponding to the optimal path and the dynamic constraints (flight direction, attitude, etc.) corresponding to each path trajectory in step S2 to determine the attitude adjustment method and flight parameters of the UAV after flying to different path trajectories. This combines the path trajectory and dynamic constraints, allowing the UAV to match the flight state of each trajectory segment in advance during flight. This avoids the risk of collision due to untimely attitude adjustment and improves the measurement quality of the measurement equipment under the adapted flight parameters.

[0030] In summary: Step S1 uses both effective measurement distance and safe distance thresholds as constraints to obtain the effective measurement range for UAVs measuring highway bridge engineering via measurement sensors. This range forms the basic parameters and safety boundaries for UAV bridge measurement, thus mitigating the risk of UAVs operating beyond their measurement accuracy range or colliding with obstacles, ensuring the validity of measurement data and the basic safety of flight operations. Step S2 uses the UAV's flight dynamics performance parameters (flight direction, attitude, etc.) as dynamic constraints, combined with a planar maneuver threshold. High mobility threshold By adjusting the mapping relationship between the grid resolution and the coordinate system, and then dividing and defining the geometric space model along the X-axis, Y-axis, and Z-axis in the geometric space model to obtain multiple grids, it is possible to adapt to different flight states during the UAV measurement process during grid division. This avoids the problem of space waste or narrow areas not being covered due to fixed grid resolution, and provides a clear spatial carrier for waypoint planning. Step S3 optimizes the initial candidate waypoints and measurement structure. First, attributes are assigned to multiple grids from step S2, and initial candidate waypoints are calculated. Then, a continuous free area is constructed by integrating the grid to be measured with adjacent free grids, and the UAV size is used as the new grid resolution to generate initial candidate waypoints adapted to the narrow space. This avoids the problem of missing initial candidate waypoints caused by excessively large grid resolution in step S2. Furthermore, when generating initial candidate waypoints, the effective measurement interval from step S1 is used as a constraint again, ensuring that all generated initial candidate waypoints are within the effective measurement interval. Step S4 filters the bridge... Using the safety zone as the starting and ending point, the system verifies whether the integrated measurement structure completely covers the coordinates of the bridge structure surface. Waypoints that meet the coverage requirements are combined into candidate paths, and the optimal path is searched with the shortest flight distance as the objective. At the same time, dynamic constraints are combined to clarify the flight parameters and attitude adjustment methods of each trajectory segment, ensuring full-area measurement without blind spots during the measurement process. Meanwhile, the optimal path reduces flight energy consumption and operation time, further improving measurement efficiency. Furthermore, the combination of path and dynamic constraints further ensures the flight safety of UAVs in complex bridge environments, ultimately achieving accurate, efficient, and safe bridge engineering measurement.

[0031] refer to Figure 2 As shown, the UAV-based highway bridge engineering surveying system includes: The measurement interval setting module 100 acquires a geometric space model including basic data of highway bridge engineering, as well as the effective measurement distance corresponding to the sensor carried by the UAV; sets a safety distance threshold, and sets the difference between the effective measurement distance and the safety distance threshold as the effective measurement interval; The grid space division module 200 acquires the simulated dimensions of the UAV in the geometric space model, the UAV flight parameters, and the UAV flight dynamics performance parameters; sets the grid resolution based on the UAV flight parameters and simulated dimensions; and dynamically divides the geometric space model into several grids using the grid resolution and flight dynamics performance parameters. The candidate waypoint processing module 300 retrieves the three-dimensional coordinate range corresponding to each grid in the geometric space model, determines whether the coordinate range of each grid contains the coordinates of the structural surface in the geometric space model, and defines the corresponding grid as an idle grid if it does not contain the coordinates of the structural surface in the geometric space model; otherwise, it defines the corresponding grid as a grid to be tested. If the grid to be measured and the free grid are adjacent, then the simulated size in step S2 is directly defined as the grid resolution, and the free grid is divided again; Calculate the geometric center coordinates of each free grid cell as the initial candidate waypoint, and determine whether the initial candidate waypoint is a valid candidate waypoint by using the valid measurement interval in step S1; The optimal path generation module 400 calculates the structural surface coordinates corresponding to valid candidate waypoints, determines whether multiple measured structural surface coordinates can be pieced together to form a complete bridge structure measurement coverage area, constructs candidate paths, and generates the optimal path by constructing a cost function with the shortest total flight distance as the objective.

[0032] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely preferred examples and are not intended to limit the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for highway bridge engineering surveying of a UAV based on path planning, characterized in that, Includes the following steps: S1: Obtain the geometric space model including basic data of highway bridge engineering, and the effective measurement distance corresponding to the sensors carried by the UAV; set a safe distance threshold, and set the difference between the effective measurement distance and the safe distance threshold as the effective measurement interval; S2: Obtain the simulated dimensions of the UAV in the geometric space model, the UAV flight parameters, and the UAV flight dynamics performance parameters; set the grid resolution based on the UAV flight parameters and simulated dimensions; dynamically divide the geometric space model into several grids using the grid resolution and flight dynamics performance parameters; S3: Retrieve the 3D coordinate range of each grid in the geometric space model, and determine whether the coordinate range of each grid contains the coordinates of the structural surface in the geometric space model. If it does, define the corresponding grid as an idle grid; otherwise, define the corresponding grid as a grid to be tested. If the grid to be tested and the free grid are adjacent, then the simulated size in step S2 is directly defined as the grid resolution, and the free grid is divided again; Calculate the geometric center coordinates of each free grid cell as the initial candidate waypoint, and determine whether the initial candidate waypoint is a valid candidate waypoint by using the valid measurement interval in step S1; S4: Calculate the structural surface coordinates within the effective measurement distance corresponding to each valid candidate waypoint, determine whether multiple measured structural surface coordinates can be stitched together to form a complete bridge structure measurement coverage area, combine multiple valid candidate waypoints that can achieve complete coverage in different orders to construct candidate paths, and construct a cost function to generate the optimal path with the shortest total flight distance as the objective. 2.The path planning based UAV highway bridge engineering surveying method of claim 1, wherein: The simulated dimensions required when setting the grid resolution in step S2 include: the maximum longitudinal dimension of the UAV, the maximum lateral dimension of the UAV, and the maximum height dimension of the UAV. The UAV flight parameters are specifically the minimum maneuver space thresholds for the UAV, which include a planar maneuver threshold and an altitude maneuver threshold. Specifically, the planar maneuver threshold is the minimum safe redundancy space in the planar direction required for the UAV to complete in-plane position fine-tuning and turning actions; the altitude maneuver threshold is the minimum safe redundancy space in the altitude direction required for the UAV to complete vertical position fine-tuning. 3.The path planning based UAV highway bridge engineering surveying method of claim 2, wherein: Step S2 specifically sets the grid resolution by using the minimum maneuver space threshold and the UAV size: the grid resolution is obtained by superimposing the corresponding planar maneuver threshold and altitude maneuver threshold based on the maximum simulated size of the UAV in the longitudinal, lateral, and altitude directions.

4. The UAV-based highway bridge engineering surveying method according to claim 3, characterized in that: In step S2, when dividing the geometric space model into several grids by grid resolution, the extreme coordinates of the geometric space model on the X-axis, Y-axis, and Z-axis are first extracted, and the spatial coverage is defined by the extreme coordinates. Then, using the UAV flight parameters as dynamic constraints, the mapping relationship between the three-dimensional size of the grid resolution and the X, Y, and Z axes of the spatial coordinate system is dynamically adjusted. Using the grid resolution corresponding to each axis after adjustment as the unit scale, the defined spatial coverage area is divided along the X, Y, and Z axes, and the continuous space is discretized into multiple cubic grids.

5. The UAV-based highway bridge engineering surveying method according to claim 4, characterized in that: In step S2, when dynamically adjusting the grid resolution based on the UAV's flight dynamics performance parameters, the grid resolution is adjusted relative to the interaxial dimensions of the geometric space model according to the UAV's real-time flight direction. The X-axis, Y-axis, and Z-axis of the geometric space model are linked in real time, and the UAV's flight direction is randomly adjusted through the flight dynamics performance parameters. When the UAV's flight direction changes, the corresponding grid resolution is adjusted according to the converted flight direction.

6. The UAV-based highway bridge engineering surveying method according to claim 4, characterized in that: Step S3 retrieves the three-dimensional coordinate range corresponding to each grid in the geometric space model, and determines whether the coordinate range of each grid contains the coordinates of the structural surface. If it does, the grid is assigned the attribute to be measured; otherwise, it is assigned the idle attribute.

7. The UAV-based highway bridge engineering surveying method according to claim 6, characterized in that: Step S3 calculates the geometric center coordinates of each free grid cell as the initial candidate waypoint; Then, the spatial distance from each initial candidate waypoint to the coordinates of the structural surface in the geometric space model is calculated; each spatial distance is then compared with the effective measurement interval in step S1. If the spatial distance is within the effective measurement range, it means that the initial candidate waypoint meets the safe distance requirements between the UAV and the structure and the sensor can achieve the preset measurement accuracy. If the spatial distance is not within the effective measurement range, it means that the initial candidate waypoint has a collision risk or cannot guarantee measurement accuracy. Therefore, the initial candidate waypoint corresponding to the spatial distance being within the effective measurement range is defined as the effective candidate waypoint.

8. The UAV-based highway bridge engineering surveying method according to claim 7, characterized in that: When redefining the grid resolution in step S3, multiple free coordinates corresponding to the grid to be tested are identified, and these free coordinates are connected with the free coordinates in adjacent free grids to form a continuous free area. It is determined whether the spatial scale of the formed continuous free area is greater than the grid resolution. If the continuous free area is greater than the grid resolution, it means that the continuous free area has the spatial conditions for UAV maneuvering and adjustment operations. At this time, starting from the coordinates of the structural surface in the grid to be tested, multiple free grids are generated again through step S2.

9. The UAV-based highway bridge engineering surveying method according to claim 8, characterized in that: The specific steps for generating the optimal path in step S4 are as follows: Step 1: Filter the coordinates of the starting and ending points of the path, and calculate the structural surface coordinates corresponding to each valid candidate waypoint. Step 2: Generate the structural surface coordinate intervals corresponding to the initial candidate waypoints using the structural surface coordinates, and merge the structural surface coordinate intervals corresponding to multiple initial candidate waypoints to obtain the integrated set of measurement structures; Step 3: Retrieve the surface coordinates of all bridge structures in the geometric space model and construct a set of coordinates to be matched on the structural surfaces; Step 4: Verify whether the integrated set of measurement structures completely includes the set of coordinates to be matched and whether there are no gaps between the measurement structures. This will help determine whether the coordinates of multiple structural surfaces can be spliced ​​together to form a complete bridge structure measurement coverage. Step 5: After the judgment is completed, multiple valid candidate waypoints that can achieve complete coverage are combined in different orders to construct candidate paths; finally, a cost function is constructed with the goal of minimizing the total flight distance to generate the optimal path.

10. A path planning-based UAV highway bridge engineering surveying system, applied to any one of the path planning-based UAV highway bridge engineering surveying methods according to claims 1-9, characterized in that, include: The measurement interval setting module (100) acquires a geometric space model including basic data of highway bridge engineering and the effective measurement distance corresponding to the sensor carried by the UAV; sets a safety distance threshold, and sets the difference between the effective measurement distance and the safety distance threshold as the effective measurement interval; The grid space division module (200) acquires the simulated size of the UAV in the geometric space model, the UAV flight parameters, and the UAV flight dynamics performance parameters; sets the grid resolution based on the UAV flight parameters and simulated size; and dynamically divides the geometric space model into several grids using the grid resolution and flight dynamics performance parameters. The candidate waypoint processing module (300) retrieves the three-dimensional coordinate range corresponding to each grid in the geometric space model, determines whether the coordinate range of each grid contains the coordinates of the structural surface in the geometric space model, and defines the corresponding grid as an idle grid if it contains the coordinates of the structural surface in the geometric space model, otherwise defines the corresponding grid as a grid to be tested. If the grid to be tested and the free grid are adjacent, then the simulated size in step S2 is directly defined as the grid resolution, and the free grid is divided again; Calculate the geometric center coordinates of each free grid cell as the initial candidate waypoint, and determine whether the initial candidate waypoint is a valid candidate waypoint by using the valid measurement interval in step S1; The optimal path generation module (400) calculates the structural surface coordinates corresponding to the valid candidate waypoints, determines whether multiple measured structural surface coordinates can be spliced ​​into a complete bridge structure measurement coverage area, constructs candidate paths, and generates the optimal path by constructing a cost function with the shortest total flight distance as the objective.

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