A GIS-based urban road inspection path planning method

Abstract

The present application discloses a method for planning inspection paths of urban roads based on GIS, belonging to the technical field of path planning; it includes the following steps: obtaining information on each road in the city using a GIS database and performing rasterization processing; determining the standard height value of each road based on the coincidence between the road imaging edge and the actual road edge during UAV inspection; obtaining the inspection position error of each grid center point based on the evaluation results of the inspection accuracy when inspecting at the center point of each grid; obtaining the total cost of the UAV from the starting point to the grid center point based on the inspection position errors of all grid center points between the starting point and the grid center point; using the A-star algorithm to obtain the optimal inspection path during inspection based on the total cost. The present application takes the flight height and imaging quality as influencing factors for planning the path, making the planned inspection path more accurate, reducing the probability of missed inspections, and improving the accuracy of inspection results.

Description

Technical Field

[0001] This application relates to the technical field of path planning, and specifically relates to a method for planning an inspection path for urban roads based on GIS. Background Art

[0002] Currently, drones are usually used to inspect urban roads, and the inspection paths of drones are planned based on the road information in the GIS (Geographic Information System) database. At the same time, it is used to collect road information to assist urban road inspections, facilitating the observation and mastery of urban road information.

[0003] When using drones to inspect urban roads, the flight altitude and no-fly zones of drones need to be restricted according to relevant regulations. On this basis, the coordinate information from the starting point to the target point is set, and the path planning algorithm is used to obtain the inspection path for urban roads. Among them, the GIS database can provide a large amount of spatial information and coordinate information. When using the A* algorithm to determine the inspection path for urban roads, usually only the distance cost between the starting point and the target point is considered. However, since the drone needs to dynamically adjust its flight altitude according to the heights of surrounding buildings, obstacles and other objects during flight, the resolution of the road imaging during inspection changes, resulting in blurred inspection images and prone to mis-inspection problems during urban road inspections. Summary of the Invention

[0004] To solve the deficiencies in the prior art, this application provides a method for planning an inspection path for urban roads based on GIS. This method is calculated based on the A* algorithm. When calculating the actual cost between the starting point and the current grid, the position quality of the road images collected by the drone passing above the center point of each grid is introduced. The height of the drone flying above the center point of each grid is used as a factor for planning the inspection path, making the planned inspection path more accurate. It can make the road image information collected by the drone along the planned path clearer, thereby improving the inspection quality.

[0005] The purpose of this application is to provide a method for planning an inspection path for urban roads based on GIS, including the following steps:

[0006] Use the GIS database to obtain road information in the city and perform grid processing;

[0007] Based on the coincidence between the edge of the road imaging during drone inspection and the actual edge of the road, determine the standard height value of the drone flying above each road;

[0008] Based on the evaluation results of the inspection accuracy when inspecting the road at the center point of each grid on each road, obtain the inspection position error of the center point of each grid on each road;

[0009] Obtain the total cost of the UAV from the starting point to the grid center point based on the inspection position errors of all grid center points between the starting point and the center point of each grid;

[0010] Use the A* algorithm to obtain the optimal inspection path of the UAV during urban road inspection based on the total cost of each grid center point.

[0011] In one embodiment, the method for obtaining the standard height value is as follows:

[0012] The flight height of the UAV when the edge line in the road image coincides with the actual edge of the road during inspection on each road is the standard height value of each road.

[0013] In one embodiment, the method for obtaining the inspection position error of each grid on each road is as follows:

[0014] Determine the distance between each grid and the central axis of the road where the grid is located;

[0015] Calculate the absolute value of the difference between the actual height value of the UAV flying on each grid on each road and the standard height value of the UAV on each road;

[0016] Take the product of the absolute value and the distance as the inspection position error of the center point of each grid on each road.

[0017] In one embodiment, the method for determining the total cost of the UAV from the starting point to the grid center point is as follows:

[0018] Determine the error characteristic value of each grid center point based on the inspection position errors of all grid center points between the starting point and each grid; based on the error characteristic value, combine the inspection position error of each grid center point and the inspection position error of the starting point to determine the actual cost of each grid center point;

[0019] Determine the total cost from the starting point to each grid based on the estimated cost from the starting point to each grid and the actual cost of each grid center point.

[0020] In one embodiment, the determination method of the error characteristic value of each grid center point is as follows:

[0021] For each grid on any road, arrange the inspection position errors of all grid center points between the starting point and each grid in ascending order of the Euclidean distance from the starting point to obtain the characteristic sequence of each grid center point;

[0022] Use the method of data fitting to obtain the mean value of the fitting slopes of all elements in the characteristic sequence as the error characteristic value of each grid center point.

[0023] In one embodiment, the method for determining the actual cost of each grid center point is as follows:

[0024] Calculate the difference between the inspection position error of each grid center point and the inspection position error of the starting point; take the product of the mapping result of the difference in the exponential function and the mapping result of the error eigenvalue of each grid center point in the exponential function as the actual cost of each grid center point.

[0025] In one embodiment, the method for determining the total cost from the starting point to each grid is as follows:

[0026] Calculate the product of the Euclidean distance between the starting point and each grid center point and the actual cost of each grid center point, and take the sum of the product and the estimated cost of each grid center point as the total cost from the starting point to each grid.

[0027] In one embodiment, the method for obtaining the actual height value of the drone flying on each grid on each road is as follows:

[0028] If the altitude of the road is not considered, the actual height value when the drone flies above each grid is based on the height value obtained by the GPS module on the drone;

[0029] If the altitude of the road is considered, then take the difference between the height value obtained by the GPS module and the altitude of the road as the actual height value of the drone.

[0030] In one embodiment, the method for obtaining the optimal inspection path is as follows:

[0031] Determine the starting point of the path to be planned;

[0032] Define two queues, openlist and closelist. Openlist stores the grids to be explored, and closelist stores the grids that have been explored;

[0033] Use the A* algorithm to cyclically update the grids in the two queues openlist and closelist based on the calculation method of the total cost V until the end point exists in openlist or openlist is empty, and then stop the search;

[0034] Based on the parent nodes determined during the search process, start from the end point and backtrack reversely according to the parent node pointers until backtracking to the starting point, and then the optimal inspection path can be obtained.

[0035] In one embodiment, the inspection position error of the starting point of the path to be planned is initialized to 0.

[0036] The beneficial effects of the present application are as follows: A GIS-based urban road inspection path planning method provided by the present application first performs grid processing on urban inspection roads to ensure that the accuracy of each flight position in the subsequent planned UAV flight path is high enough to avoid positioning deviation. Secondly, based on the analysis of the road imaging error when the UAV conducts inspections on each road, the standard flight height of each road is determined, and the inspection error caused by the change in flight height is used as an influencing factor for calculating the total cost in the subsequent process, thereby improving the accuracy rate when selecting grids in sequence. Then, based on the change characteristics of the inspection error when the UAV passes through the remaining grids between the starting point and the center point of each grid, the overall level of the inspection error during the process from the starting point to the center point of the j-th grid on the i-th road is evaluated to obtain the actual cost of each grid center point. By optimizing the calculation method of the total cost in the A* algorithm and introducing the position quality of the road image collected when the UAV passes above the center point of each grid and the flight height as influencing factors for planning the inspection path, the planned inspection path is more accurate, and the road image information collected by the UAV along the planned path can be clearer, thus improving the inspection quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0038] Figure 1 It is a schematic flowchart of the implementation process of a GIS-based urban road inspection path planning method of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0039] The following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, rather than all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present application.

[0040] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which this application belongs.

[0041] A GIS-based urban road inspection path planning method provided by the present application, see Figure 1 as shown, includes the following steps:

[0042] S001. Obtain the information of each road in the city using the GIS database and perform rasterization processing.

[0043] The purpose of this application is to obtain the inspection path during the inspection of urban roads. Therefore, it is first necessary to obtain the relevant data of each road in the city. In this application, the information of each road in the city is obtained according to the urban road GIS database, and the road is rasterized to obtain evenly distributed grids on the urban road.

[0044] Specifically, the GIS database mainly includes the geographical data of each geographical object within the urban spatial range. The geographical objects include, but are not limited to, buildings, roads, subway stations, and squares within the urban area. Therefore, in the embodiment of this application, by obtaining the GIS database of a certain urban road, the information of each road in the city is obtained.

[0045] Furthermore, the widths of each road in the current city in the world coordinate system are obtained using the existing GIS database. It should be noted that if the width levels of each road are available in the GIS database, the middle value of the corresponding width range is taken as the width of the current urban road according to the width range corresponding to each width level. If the lengths and areas of each urban road segment are available in the GIS database, since the widths of each urban road segment in the same city are basically the same, the width corresponding to each urban road can be obtained by dividing the area by the length.

[0046] Secondly, rasterize the road surface to obtain evenly distributed grids on the road surface. It should be noted that rasterizing the road can determine the distance from each point on the road to the central axis of the road, and it is also convenient for subsequent determination of the specific position of the drone above the grid center point.

[0047] S002. Respectively obtain the distance between the center point of each grid and the central axis of the road and the actual height value when the drone flies above each grid, and determine the standard height value when the drone flies above each road.

[0048] During the process of using drones for road inspections on urban roads, if the width of the road increases while the height remains unchanged during the drone inspection, it will cause the drone to be unable to obtain complete information about the road, resulting in missed inspections during urban road inspections. On the other hand, if the path planning of the drone during the inspection is too biased towards the side of the road, it may also cause missed inspections during urban road inspections because the road information on the other side of the road cannot be captured. For relatively narrow urban roads, if the height remains unchanged during the drone inspection, the information of the relatively narrow route obtained by the drone will be relatively blurred, and false inspections will also occur. Therefore, the flight height is usually reduced to ensure that the road is not missed during inspection while ensuring sufficient clarity of the road imaging. Based on the above analysis, the height of the drone flying above the center point of each grid is used as a factor for planning the inspection path, making the planned inspection path more accurate, enabling the road image information collected by the drone along the planned path to be clearer, and thus improving the accuracy of the inspection results.

[0049] First, determine the distance from the center point of each grid to the road central axis after rasterizing each road according to the road information in the GIS database.

[0050] Preferably, as an embodiment of the present application, the method for obtaining the distance from the center point of each grid to the road central axis is: obtain the position information of the center point of each grid on the corresponding road according to the GIS database, and calculate the Euclidean distance between the center point of each grid in the world coordinate system and the road surface central axis where it is located.

[0051] In another embodiment, it can also be obtained according to the width of each road in the world coordinate system in the current city. Count the number of grids n between each grid and the central axis, and use the ratio of the width of each road to the total number of grids as the width L of each grid. Then the distance from the center point of each grid to the road surface central axis is . For example, if the width of a certain road is 9 meters and there are 9 grids along the road width direction, it means that the size of each grid is 1 meter. The distance from the center point of the grid on the outermost side of the road to the road central axis is meters.

[0052] Secondly, when the drone conducts inspections on urban roads, adjust the inspection flight altitude of the drone to the standard altitude value for each road according to whether the edge lines in the road image coincide with the actual edges of the road. Among them, when the edge lines on both sides of the road shown in the road image coincide with the actual edges, it is considered that the flight altitude of the drone on the road reaches the standard altitude value; otherwise, it is considered that the altitude of the drone during road inspection does not reach the standard altitude value. The purpose of doing this is to dynamically adjust the flight altitude of the drone to the standard altitude value for each road according to the actual width of each road during the inspection process, and reduce the probability of missed inspections and false inspections during the inspection process.

[0053] It should be noted that for the actual altitude value when the drone flies above each grid, if the altitude of the road is not considered, the actual altitude value is based on the altitude value obtained by the GPS module on the drone. If the altitude of the road is considered, the altitude value obtained by the GPS module needs to be subtracted by the altitude of the road, and the difference is used as the true flight altitude of the drone.

[0054] S003. Analyze the evaluation results of the inspection accuracy when conducting road inspections at the center points of each grid on each road, and obtain the inspection position error of each grid on each road.

[0055] When planning the path for the drone to conduct road inspections, adjusting the flight altitude of the drone to the standard altitude value for each road can avoid the influence of missed inspections or unclear road imaging on the inspection results. However, when the drone switches from the current road to the next road, or is affected by the positions of buildings, road signs, roadblocks, etc. on both sides of the road that are not fixed, the altitude of the drone during road inspection flight is not always maintained at the standard altitude value for each road. This will cause a certain difference between the road imaging results on the drone and the imaging results at the standard altitude value. When using the A* algorithm to plan the training path of the drone, the difference between the road imaging results and the imaging results at the standard altitude value should be made as small as possible to ensure the accuracy of the road inspection results.

[0056] Furthermore, according to the distance between the center point of each grid and the mid-axis of the road surface, as well as the standard altitude value when the drone flies above each road and the actual altitude value when the drone flies above each grid, obtain the position quality of the road images collected by the drone above each grid.

[0057] Specifically, by comparing the actual flight height of each grid in each road during the inspection of the UAV with the standard height value during the inspection of the UAV on each road, the inspection error caused by the height difference during the inspection of the UAV on each grid is evaluated. Then, combined with the distance between the grid center and the central axis of the road, the overall error of the UAV during the flight inspection of each grid is comprehensively evaluated, and further the cost of the UAV taking each grid as the inspection path is judged.

[0058] Here, the inspection position error of each grid on each road is calculated to reflect the error when the UAV performs road imaging inspection above the center point of each grid. The calculation formula for the inspection position error of the j-th grid on the i-th road is:

[0059]

[0060] In the formula, represents the inspection position error of the j-th grid in the i-th road; represents the standard height value when the UAV inspects above the i-th road; represents the actual height value when the UAV flies above the j-th grid in the i-th road; represents the distance between the center point of the j-th grid in the i-th road and the central axis of the road surface of the i-th road.

[0061] It should be noted that the distance between the center point of the j-th grid and the central axis of the i-th road The larger it is, the more the UAV's position tends to one side of the urban road when the j-th grid is used as a flight position on the planned inspection path, and the more likely it is to miss inspections. At the same time, its The larger the value is, the closer the UAV is to the boundary of the i-th road. When switching from the i-th road to the (i + 1)-th road, the UAV needs to make a large adjustment in the flight angle to reduce the influence of buildings and obstacles on both sides of the road and ensure that there is no obvious deviation in the inspection position on the (i + 1)-th road. The higher the planning cost of the A* algorithm when the j-th grid is used as a flight position on the planned inspection path; when and The larger the difference is, it indicates that the difference between the current flight height of the UAV and the standard height on the i-th road is greater, the resolution of the road imaging quality is worse, and the probability of missing inspections during the inspection is higher; that is The larger the value is, the greater the error of the UAV during the road inspection through the j-th grid in the i-th road.

[0062] S004. Obtain the total cost of the UAV from the starting point to the grid center point based on the inspection position errors of all grid center points between the starting point and each grid center point.

[0063] When using the A* algorithm to plan a path, it is necessary to first determine the position information of the starting point and the ending point. In this application, according to the coordinate information of the starting point and the ending point of the urban road inspection, the grids where the starting point and the ending point are located are found in the gridded road respectively.

[0064] It should be noted that at present, the A* algorithm only uses the Manhattan distance or the Euclidean distance as the heuristic function to estimate the expected cost from the grid to the ending point. This kind of calculation method ignores the dynamic adjustment of the flight height on different roads and the influence of different standard height values on the total valuation when planning the path of the unmanned aerial vehicle (UAV) for urban road inspection, which will lead to a certain inspection error in the planned inspection path.

[0065] Based on this, this application calculates the actual cost G of the starting point moving along the planned path to the center point of the grid where it is located according to the distance between the starting point and the center point of the grid where it is located. Secondly, based on the inspection position error of the UAV at each grid on each road and the distance between the center point of the grid where the starting point belongs and the ending point, the expected cost H is determined, and the sum of the actual cost G and the expected cost H is used as the total valuation of the starting point. In this way, by comparing the actual flight height with the standard height value and optimizing the calculation method of the total valuation according to the distance of each grid from the central axis of the road, not only the inspection path distance of the UAV is considered, but also the road imaging quality of the UAV on each road is taken into account, reducing the probability of missed inspection.

[0066] Furthermore, the inspection position errors of the starting point and the center points of each grid on each road are calculated respectively. For any center point of a grid, taking the j-th center point of the i-th road as an example, the inspection position errors of all center points between the starting point and the j-th center point of the i-th road are arranged in ascending order of the Euclidean distance from the starting point as the feature sequence of the j-th center point of the i-th road. The mean value of the slopes of the elements in the feature sequence is obtained by using the data fitting algorithm as the error feature value of the j-th center point of the i-th road to characterize the overall level of the inspection error in the process from the starting point to the j-th center point of the i-th road.

[0067] Among them, data fitting is a commonly used technology in the field of sequence analysis. Commonly used data fitting algorithms include but are not limited to linear fitting, polynomial fitting, and least squares fitting. Preferably, as an embodiment of this application, the least squares fitting method is used to determine the slope of the elements in the feature sequence. The least squares fitting is a well-known technology, and the specific process will not be elaborated here.

[0068] Specifically, in this application, the calculation method of the total cost V of the UAV from the starting point to the center point of each grid on each road is as follows:

[0069]

[0070]

[0071] In the formula, is the actual cost of training from the starting point to the center point of the j-th grid of the i-th road, is the error eigenvalue of the center point of the j-th grid of the i-th road, is an exponential function with the natural constant as the base, and are the inspection position errors of the j-th grid and the starting point in the i-th road respectively;

[0072] is the total cost of the UAV from the starting point to the center point of the j-th grid of the i-th road, is the Euclidean distance between the starting point and the center point of the j-th grid in the i-th road, is the predicted cost of the center point of the j-th grid on the i-th road, The size is equal to the Euclidean distance between the center point of the j-th grid in the i-th road and the end point.

[0073] Among them, reflects the change amount between the inspection position errors during the process of the UAV from the starting point to the center point of the j-th grid of the i-th road. Then, using the error eigenvalue of the center point of the j-th grid of the i-th road to perform weighted processing on the change amount to represent the actual cost of the UAV from the starting point to the center point of the j-th grid of the i-th road ; reflects that the farther the distance to the center point of the j-th grid of the i-th road during movement, the greater the inspection path loss cost from the starting point to the center point of the j-th grid of the i-th road; represents the distance when the current grid moves towards the end point. The farther the distance, the greater the path loss, the larger the value of ; that is, the larger the value of , it means that the total cost of the UAV from the starting point to the center point of the j-th grid of the i-th road is greater during inspection. The credibility of the inspection result of the current grid compared with the inspection result at the starting point will become worse, and this grid should not be selected more during path planning.

[0074] S005. Use the A* algorithm to obtain the optimal inspection path of the UAV during urban road inspection based on the total cost of each grid center point.

[0075] First of all, based on the A* algorithm, during the road inspection process, select the grid center point with the minimum total cost as the flight position of the UAV at each step until reaching the end point of the road inspection. The determined flight path is the optimal inspection path of the UAV during urban road inspection.

[0076] Specifically, in the embodiments of the present application, based on the A* algorithm, first determine the starting point of the path to be planned. Take any grid center point at the starting end of the path to be planned as the starting point. Define two queues, openlist and closelist. The openlist stores the grids to be explored, and the closelist stores the grids that have been explored. Initialize the inspection position error of the starting point to 0, and use the A* algorithm to cyclically update the grids in the two queues openlist and closelist based on the calculation method of the total cost V until there is an end point in the openlist or the openlist is empty, then stop the search. Based on the parent nodes determined during the search process, start from the end point and trace back reversely according to the parent node pointer until reaching the starting point, and the optimal inspection path can be obtained. This ensures that when selecting the optimal inspection path for inspection, the inspection quality of the current drone for urban roads can also be guaranteed. Among them, the A* algorithm is a commonly used technology in the field of path planning, and the specific iterative process will not be elaborated here.

[0077] In summary, the present application provides a GIS-based urban road inspection path planning method. When calculating the actual cost between the starting point and the current grid by the A* algorithm, the quality evaluation of the road images collected by the drone passing above the center point of each grid is introduced, and the adjustment of the flight height of the drone above the center point of each grid is used as a factor for planning the inspection path, making the planned inspection path more accurate, enabling the road image information collected by the drone along the planned path to be clearer, and thus improving the inspection quality.

[0078] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features. These modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

[0079] Each embodiment in this specification is described in a progressive manner. The same or similar parts among the embodiments can be referred to each other, and the key points of each embodiment are the differences from other embodiments.

Claims

1. A GIS-based urban road inspection path planning method, characterized in that: The following steps are involved: Use GIS database to obtain information about each road in the city and perform grid processing; The standard altitude value for the drone to fly above each road is determined based on the overlap between the edge of the road imaged during the drone inspection and the actual edge of the road; Based on the evaluation results of the inspection accuracy when the road inspection is performed at each grid center point on each road, the inspection position error of each grid center point on each road is obtained; The total cost of the drone from the starting point to the center point of the grid is obtained based on the inspection position error of all grid center points between the starting point and each grid center point; The A-star algorithm is used to obtain the optimal inspection path for UAVs to inspect urban roads based on the total cost of each grid center point. The method for determining the total cost of the drone from the starting point to the center point of the grid is: Determine the error characteristic value of each grid center point based on the inspection position error of all grid center points between the starting point and each grid; Based on the error characteristic value, combined with the inspection position error of each grid center point and the inspection position error of the starting point, the actual cost of each grid center point is determined; The total cost from the starting point to each grid is determined based on the estimated cost from the starting point to each grid and the actual cost of the center point of each grid.

2. The GIS-based urban road inspection route planning method according to claim 1 is characterized in that: The method for obtaining the standard height value is: The flight height of the drone when the edge line in the road image coincides with the actual edge of the road when the drone inspects each road is taken as the standard height value of each road.

3. The GIS-based urban road inspection route planning method according to claim 1 is characterized in that: The method for obtaining the inspection position error of each grid on each road is as follows: Determine the distance between each grid and the central axis of the road where each grid is located; Calculate the absolute value of the difference between the actual altitude value of the drone flying on each grid on each road and the standard altitude value of the drone on each road; The product of the absolute value and the distance is used as the inspection position error of each grid center point on each road.

4. The GIS-based urban road inspection route planning method according to claim 1 is characterized in that: The error characteristic value of each grid center point is determined as follows: For each grid on any road, the inspection position errors of all grid center points between the starting point and each grid are arranged in the order of the Euclidean distance from the starting point from small to large to obtain the characteristic sequence of each grid center point; The mean value of the fitting slopes of all elements in the characteristic sequence is obtained by data fitting as the error characteristic value of each grid center point.

5. The GIS-based urban road inspection route planning method according to claim 1 is characterized in that: The actual cost of each grid center point is determined as follows: Calculate the difference between the inspection position error of each grid center point and the inspection position error of the starting point; and take the product of the mapping result of the difference in the exponential function and the mapping result of the error characteristic value of each grid center point in the exponential function as the actual cost of each grid center point.

6. The GIS-based urban road inspection route planning method according to claim 1 is characterized in that: The total cost from the starting point to each grid is determined as follows: The product of the Euclidean distance between the starting point and each grid center point and the actual cost of each grid center point is calculated, and the sum of the product and the estimated cost of each grid center point is taken as the total cost from the starting point to each grid.

7. The method for urban road inspection route planning based on GIS according to claim 3 is characterized in that: The method for obtaining the actual altitude value of the UAV flying on each grid on each road is as follows: If the elevation of the road is not taken into account, the actual altitude value of the drone when flying above each grid is based on the altitude value obtained by the GPS module on the drone; If the altitude of the road is taken into account, the difference between the altitude value obtained by the GPS module and the altitude of the road is taken as the actual altitude value of the drone.

8. The GIS-based urban road inspection route planning method according to claim 1 is characterized in that: The method for obtaining the optimal inspection path is: Determine the starting point of the path to be planned; Define two queues, openlist and closelist. Openlist stores grids to be explored, and closelist stores grids that have been explored. The A-star algorithm is used to cyclically update the grids in the two queues openlist and closelist based on the calculation method of the total cost V until there is an end point in the openlist or the openlist is empty, then the search stops; Based on the parent node determined during the search process, the optimal inspection path can be obtained by tracing back from the end point along the parent node pointer until it reaches the starting point.

9. The GIS-based urban road inspection route planning method according to claim 8 is characterized in that: The inspection position error of the starting point of the path to be planned is initialized to 0.

Images