CN114578848A - Unmanned aerial vehicle routing inspection path planning method based on discrete point density and global planning - Google Patents
Unmanned aerial vehicle routing inspection path planning method based on discrete point density and global planning Download PDFInfo
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Abstract
The invention discloses an unmanned aerial vehicle routing inspection path planning method based on discrete point density and global planning, which comprises the following steps: (1) and (3) dividing the inspection range: firstly, clustering is carried out by using a DBSCAN clustering algorithm according to the spatial position and density relation of discrete points to be patrolled, the points to be patrolled in the space are divided into m areas to be patrolled, and m unmanned aerial vehicles are dispatched to be respectively responsible for patrolling the points to be patrolled in each area; (2) routing inspection path construction: aiming at each area to be inspected, constructing a Delaunay triangulation network for points to be inspected in the area, and networking discrete points, wherein each edge of the triangulation network is used as an optional path for unmanned aerial vehicle inspection; (3) routing inspection path planning: the planning of the routing inspection path of the unmanned aerial vehicle in each area to be inspected can be abstracted to a tracking SalesmanProblem problem, namely, the unmanned aerial vehicle starts from a certain point to be inspected, takes each edge in the triangulation network as an optional path, traverses all points to be inspected in the area to be inspected, does not repeatedly pass through the same point to be inspected, and simultaneously makes the total path shortest.
Description
Technical Field
The invention belongs to the technical field of path optimization, and particularly relates to an unmanned aerial vehicle routing inspection path planning method based on discrete point density and global planning.
Background
With the increasing development speed of the economic society of China, the city construction is steadily developed. The coordinated development of urban economy depends on the planning of urban roads, and therefore, road construction becomes a crucial aspect in urban construction. The performance and the state of the road facilities are directly related to urban traffic safety and trip experience, and in the management and maintenance work of urban roads, the routing inspection of the roads is a key link for improving the urban road facilities. The road inspection information can be applied to the fields of road disease information screening, abnormal vehicle detention, intersection environment monitoring and the like. Road monitoring is not a one-time process, but rather is a periodic tracking of road maintenance and facility operation. Therefore, it is very important to select an efficient and timely inspection mode.
The traditional road inspection mainly adopts a manual inspection mode, has large workload, low efficiency and high cost, and is difficult to meet the requirement of large-scale road facility management. Compare in traditional mode of patrolling and examining, the novel road of adopting unmanned aerial vehicle patrols and examines the mode cost lower relatively, mobility can be good, the measurement is comparatively meticulous, can replace artifical completion road to patrol and examine work. With the development of the 5G technology, the "unmanned aerial vehicle + 5G" technology is applied to daily life. The unmanned aerial vehicle is adopted for road inspection, the data transmission rate is high, the network delay is low, high-definition images can be obtained in real time, and convenience is brought to heavy road management and maintenance work.
However, when the unmanned aerial vehicle is adopted for road inspection, the characteristics of the unmanned aerial vehicle need to be considered. At present, the small unmanned aerial vehicle mostly adopts battery powered, and the energy of carrying is more limited, and the time of carrying out the task of patrolling and examining is also limited. Compared with manual inspection, the unmanned aerial vehicle which uses the number as small as possible to complete the same workload as manual inspection in the inspection process is an urgent need of the urban road inspection work. Therefore, how to efficiently finish the routing inspection task within the maximum range of the endurance mileage of the unmanned aerial vehicle is a problem which needs to be solved urgently. Using unmanned aerial vehicles for road inspection presents several major challenges: 1) wait to patrol and examine the interval between the point far away, the point that needs to patrol and examine is more, and an unmanned aerial vehicle is difficult to accomplish and patrols and examines the task. Too far distance between the points to be inspected can cause the unmanned aerial vehicle to only fly and not sample, thus causing waste of carried energy; 2) the points to be inspected are discrete data points, the points to be inspected are independent, and a passage exists between any two points, so that the multiple points are difficult to comprehensively consider, namely scientific and reasonable relation is established from the whole situation; 3) because the limit of the endurance mileage of the unmanned aerial vehicle, the unmanned aerial vehicle does not repeatedly traverse each point to be patrolled and examined in the area to be patrolled and examined, the area to be patrolled and examined does not produce the overlap, and the optimization of the path is realized on the basis.
Disclosure of Invention
In order to comprehensively consider the characteristic of using the unmanned aerial vehicle for inspection and obtain a more optimized unmanned aerial vehicle inspection path, the invention aims to provide an unmanned aerial vehicle inspection path planning method based on discrete point density and global planning. The method divides the whole area to be inspected into m small areas to be inspected through a DBSCAN clustering algorithm based on the spatial position and density relation of discretely distributed points to be inspected. Aiming at each area to be inspected, by a method of constructing a Delaunay triangulation network, discrete points are networked and an optional path is constructed, and an unmanned aerial vehicle inspection path is optimized on the basis.
The specific technical scheme for realizing the purpose of the invention is as follows:
an unmanned aerial vehicle routing inspection path planning method based on discrete point density and global planning comprises the following steps:
step 1: and (3) dividing the inspection range: acquiring the longitude and latitude of each discrete point to be patrolled, and setting a search radius r and the minimum number n of points to be patrolled of a single unmanned aerial vehicle according to the spatial position and density relation of the points to be patrolled; clustering the points to be inspected, dividing all the points to be inspected into m categories as m areas to be inspected, and respectively inspecting the m areas to be inspected by using m unmanned aerial vehicles;
step 2: routing inspection path construction: in order to obtain the spatial relationship between points, a triangular net is respectively constructed for the points to be inspected in each area to be inspected, each edge in the triangular net has a side length attribute, namely the distance between two points to be inspected connected with the edge, and each edge is used as an optional path for planning the inspection path of the unmanned aerial vehicle;
and step 3: routing inspection path planning: respectively planning routing inspection paths for each to-be-inspected area clustered in the step 1; when the unmanned aerial vehicle is sent from a certain point to be patrolled, abstracting the selectable path obtained in the step 2 into a tracking Salesman Problem Problem, and planning a shortest unmanned aerial vehicle patrolling path.
Preferably, the search radius r in step 1 is greater than the distance between two nearest points to be inspected and is less than the maximum range of the unmanned aerial vehicle.
Preferably, the points to be inspected are clustered in step 1, and a DBSCAN clustering algorithm is selected; two parameters in the DBSCAN clustering algorithm: taking r and n as the scanning radius and the minimum contained point number respectively; after clustering is completed, the number k of points to be patrolled in each area to be patrolled is larger than or equal to n, and the number k of points to be patrolled in different areas to be patrolled is different.
Preferably, the triangulation network constructed in the step 2 is a Delaunay triangulation network; the Delaunay triangulation network is a collection of connected, but non-overlapping, Delaunay triangles, and the circumscribed circle of each of the Delaunay triangulation networks does not include any other point within a plane.
Preferably, in step 3, the tracking Salesman Problem is that a drone accesses k points to be inspected, and the restriction of the path is: each point to be inspected needs to be accessed, and each point to be inspected can be accessed only once; the selection targets of the paths are: the determined path distance is the minimum value of all paths.
According to the method, the density relation of the points to be inspected and the characteristic that the energy carried by the unmanned aerial vehicle is limited are comprehensively considered, the energy loss caused by the fact that the unmanned aerial vehicle moves between the points to be inspected which are too far away is reduced by dividing the whole area to be inspected, and meanwhile, the flight path of the unmanned aerial vehicle is optimized, so that the road inspection is lower in implementation cost, higher in efficiency and finer in measurement.
Drawings
FIG. 1 is a flow chart of the present invention;
FIGS. 2, 3 and 4 are pseudo code diagrams of the present invention;
FIG. 5 is a diagram illustrating a path planning method according to an embodiment of the present invention.
Detailed Description
The invention is described in further detail below with reference to the figures and examples.
Referring to fig. 1, the present invention includes the following specific steps:
step 1: and (3) dividing the inspection range: acquiring the longitude and latitude of each discrete point to be patrolled, and setting a search radius r and the minimum number n of points to be patrolled of a single unmanned aerial vehicle according to the spatial position and density relation of the points to be patrolled; clustering the points to be inspected, dividing all the points to be inspected into m categories as m areas to be inspected, and inspecting the m areas to be inspected by using m unmanned aerial vehicles;
step 2: routing inspection path construction: in order to obtain the spatial relationship between points, a triangular net is respectively constructed for the points to be inspected in each area to be inspected, each edge in the triangular net has a side length attribute, namely the distance between two points to be inspected connected with the edge, and each edge is used as an optional path for planning the inspection path of the unmanned aerial vehicle;
and step 3: routing inspection path planning: respectively planning routing inspection paths for each to-be-inspected area clustered in the step 1; when the unmanned aerial vehicle is sent from a certain point to be patrolled, abstracting the selectable path obtained in the step 2 into a tracking Salesman Problem Problem, and planning a shortest unmanned aerial vehicle patrolling path.
And in the step 1, the search radius r is larger than the distance between two nearest points to be patrolled and examined and is smaller than the maximum range of the unmanned aerial vehicle.
Clustering the points to be inspected in the step 1, and selecting a DBSCAN clustering algorithm; two parameters in the DBSCAN clustering algorithm: taking r and n as the scanning radius and the minimum contained point number respectively; after clustering is completed, the number k of points to be patrolled in each area to be patrolled is larger than or equal to n, and the number k of points to be patrolled in different areas to be patrolled is different.
Wherein, the triangulation network constructed in the step 2 is a Delaunay triangulation network; the Delaunay triangulation network is a collection of connected, but non-overlapping, Delaunay triangles, and the circumscribed circle of each of the Delaunay triangulation networks does not include any other point within a plane.
Wherein, in step 3, the tracking Salesman Problem Problem is that an unmanned aerial vehicle visits k points to be patrolled and examined, and the restriction of the path is: each point to be inspected needs to be accessed, and each point to be inspected can be accessed only once; the selection targets of the paths are: the determined path distance is the minimum value of all paths.
Examples
The specific process of the embodiment comprises three steps:
step 1: referring to fig. 5, there are 19 points to be inspected in the whole area to be inspected, and the longitude and latitude of each point to be inspected are obtained respectively. In order to meet the cost of single flight of the unmanned aerial vehicle, n is set to be 3; the shortest distance between the points to be inspected and the maximum range of the unmanned aerial vehicle are comprehensively considered, the spatial position and the density relation of the inspection points are observed, 3 or more points to be inspected are more in line with the density distribution of the points to be inspected within the range of 2 kilometers in radius, and therefore the search radius r is set to be 2 kilometers. Referring to fig. 2, clustering the points to be inspected by using the DBSCAN algorithm, wherein the algorithm randomly accesses a point, finds all points which are less than or equal to 2 kilometers away from the point, and if the number of the points in the neighborhood is greater than or equal to 3, takes the point as a core point, clusters the points in the neighborhood into a cluster, and if other core points exist in the neighborhood of the core point, combines the clusters to obtain the maximum set of the points with connected density; if the selected point is not the core point, the next point is searched, and the process is repeated until each point is gathered into a certain cluster. And finally clustering to obtain 4 regions to be patrolled and examined, and dispatching 4 unmanned aerial vehicles to patrol and examine the 4 regions to be patrolled and examined respectively.
Step 2: referring to the algorithm shown in fig. 3, Delaunay triangulation networks are respectively constructed for the points to be inspected in the 4 areas to be inspected, the point surrounded by the dotted line and the solid line in fig. 3 is the Delaunay triangulation network of the embodiment, each edge in the triangulation network has a side length attribute, that is, the distance between two points to be inspected connected with the edge, and each edge is used as an optional path for routing the inspection path of the unmanned aerial vehicle.
And step 3: referring to the algorithm shown in fig. 4, routing inspection path planning is respectively performed on each to-be-inspected area clustered in the step 1; abstracting the selectable path to be a tracking Salesman Problem Problem based on the selectable path obtained in the step 2; the algorithm randomly selects a point as a starting point, searches for a point which is closest to the current point in the points which are not visited each time, and takes the point as a point to be visited next time until all the points are traversed, so that a patrol route which is not repeatedly visited, traverses all the points and has the shortest total route is obtained.
Finally, aiming at each area to be patrolled, an unmanned aerial vehicle patrolling path is obtained, as shown in fig. 5.
The above embodiments are not intended to limit the present invention, and all modifications and improvements made without departing from the spirit of the present invention are within the scope of the present invention.
Claims (5)
1. An unmanned aerial vehicle routing inspection path planning method based on discrete point density and global planning is characterized in that: the method comprises the following specific steps:
step 1: and (3) dividing the inspection range: acquiring the longitude and latitude of each discrete point to be patrolled, and setting a search radius r and the minimum number n of points to be patrolled of a single unmanned aerial vehicle according to the spatial position and density relation of the points to be patrolled; clustering the points to be inspected, dividing all the points to be inspected into m categories as m areas to be inspected, and respectively inspecting the m areas to be inspected by using m unmanned aerial vehicles;
step 2: routing inspection path construction: in order to obtain the spatial relationship between points, a triangular net is respectively constructed for the points to be inspected in each area to be inspected, each edge in the triangular net has a side length attribute, namely the distance between two points to be inspected connected with the edge, and each edge is used as an optional path for planning the inspection path of the unmanned aerial vehicle;
and step 3: routing inspection path planning: respectively planning routing inspection paths for each to-be-inspected area clustered in the step 1; when the unmanned aerial vehicle is sent from a certain point to be patrolled, abstracting the selectable path obtained in the step 2 into a tracking Salesman Problem Problem, and planning a shortest unmanned aerial vehicle patrolling path.
2. The unmanned aerial vehicle inspection path planning method based on discrete point density and global planning of claim 1, wherein: in the step 1, the search radius r is larger than the distance between two nearest points to be patrolled and examined and is smaller than the maximum range of the unmanned aerial vehicle.
3. The unmanned aerial vehicle inspection path planning method based on discrete point density and global planning of claim 1, wherein: clustering the points to be inspected in the step 1, and selecting a DBSCAN clustering algorithm; two parameters in the DBSCAN clustering algorithm: taking r and n as the scanning radius and the minimum contained point number respectively; after clustering is completed, the number k of points to be patrolled in each area to be patrolled is larger than or equal to n, and the number k of points to be patrolled in different areas to be patrolled is different.
4. The unmanned aerial vehicle inspection path planning method based on discrete point density and global planning of claim 1, wherein: the triangulation network is constructed in the step 2, and is a Delaunay triangulation network; the Delaunay triangulation network is a collection of connected, but non-overlapping, Delaunay triangles, and the circumscribed circle of each of the Delaunay triangulation networks does not include any other point within a plane.
5. The unmanned aerial vehicle inspection path planning method based on discrete point density and global planning of claim 1, wherein: in step 3, the tracking Salesman Problem Problem is that an unmanned aerial vehicle accesses k points to be patrolled, and the restriction of the path is as follows: each point to be inspected needs to be accessed, and each point to be inspected can be accessed only once; the selection targets of the paths are: the determined path distance is the minimum value of all paths.
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