CN117876624A - Complex environment track planning method based on unmanned aerial vehicle remote sensing image - Google Patents
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Abstract
The invention discloses a complex environment track planning method based on unmanned aerial vehicle remote sensing images, which comprises the following steps: data acquisition, data preprocessing, feature extraction, planning model establishment and model planning analysis; the invention relates to the technical field of flight path planning, which comprises the steps of acquiring remote sensing image data of a complex environment, extracting characteristic information, establishing a three-dimensional space coordinate system, generating a flight path planning model, planning a flight path of an unmanned aerial vehicle more accurately, avoiding flight risks caused by complex environment, effectively improving the efficiency and accuracy of flight path planning, dynamically adjusting the flight path according to the average value of the total flight time by recording the flight distance and the flight time of the unmanned aerial vehicle in real time, improving the flexibility and the adaptability of inspection, pre-arranging the inspection paths of each inspection unmanned aerial vehicle, setting a reference inspection height, calculating the total flight time of each inspection path, and realizing the collaborative inspection of multiple unmanned aerial vehicles and improving the inspection efficiency.
Description
Technical Field
The invention relates to the technical field of track planning, in particular to a complex environment track planning method based on unmanned aerial vehicle remote sensing images.
Background
Along with the development of science and technology, unmanned aerial vehicles are increasingly widely applied in various fields. Particularly in the fields of geographic information systems, environmental monitoring, disaster assessment and the like, the use of unmanned aerial vehicles greatly improves the working efficiency and accuracy. However, there are problems with the prior art for track planning in complex environments.
First, the acquisition of geographical information of a complex environment is difficult. Traditional geographic information acquisition methods mainly depend on ground measurement or satellite remote sensing, and the two methods have limitations. The ground measurement workload is large, the efficiency is low, and the ground measurement cannot be performed in certain severe environments; satellite remote sensing can cover a large area, but has limited resolution and cannot acquire detailed geographic information. Second, the planning of the path of the complex environment requires consideration of various factors, such as terrain, buildings, vegetation, etc. These factors all have an impact on the flight of the unmanned aerial vehicle and need to be considered in the flight path planning. However, the existing track planning method only considers partial factors, and cannot fully reflect the actual situation of the complex environment. Again, the trajectory planning of the complex environment needs to take into account the safety of the drone. In complex environments, various obstructions may be present, such as tall buildings, trees, electrical wiring, and the like. The unmanned aerial vehicle needs to avoid the obstacles in the flight process, otherwise collision can happen. However, existing track planning methods often do not take this into account.
In order to solve the problems, the invention provides a complex environment track planning method based on unmanned aerial vehicle remote sensing images, which can comprehensively reflect the actual conditions of the complex environment and improve the accuracy and safety of track planning.
Disclosure of Invention
The invention aims to provide a complex environment track planning method based on unmanned aerial vehicle remote sensing images, which solves the technical problems in the background technology.
The aim of the invention can be achieved by the following technical scheme:
a complex environment track planning method based on unmanned aerial vehicle remote sensing images comprises the following steps:
step one, data acquisition
Acquiring remote sensing image data of a target area, wherein the remote sensing image data comprises a remote sensing image and position coordinates corresponding to an image frame in the remote sensing image;
step two, data preprocessing
Removing noise in the remote sensing image data, smoothing the image and enhancing the image contrast of the remote sensing image data:
step three, feature extraction
Extracting characteristic information from the remote sensing image, wherein the characteristic information comprises a topography elevation and an individual contour, and the individual contour comprises a topography contour, a building contour and a vegetation contour;
step four, planning model establishment
Establishing a three-dimensional space coordinate system according to position coordinates corresponding to image frames in the remote sensing images, generating three-dimensional coordinate nodes according to position coordinates corresponding to the image frames in the remote sensing images and characteristic information, and drawing to generate a track planning model according to the three-dimensional coordinate nodes in the three-dimensional space coordinate system;
step five, model planning analysis
Dividing equal-quantity equidistant inspection routes in a flight path planning model by acquiring the number of the inspection unmanned aerial vehicles, setting a reference inspection height, calculating a barrier avoidance height according to the reference inspection height and a terrain height, and calculating the average value of the total flight time of each corresponding inspection route and the total flight time according to the transverse flight speed of the inspection unmanned aerial vehicle, the longitudinal flight speed of the inspection unmanned aerial vehicle, the distance of the inspection route, the distance of all obstacle routes on the inspection route and the corresponding barrier avoidance height according to the obtained distance, and the average value of the total flight time of each inspection route, and planning a flight path on each inspection route according to the average value of the total flight time of each inspection route, wherein the reference inspection height is expressed as the normal flight height of the inspection unmanned aerial vehicle without any obstacle during inspection;
step six, route planning execution
And acquiring corresponding flying spots from the first track route, the second track route and the … … track route, adjusting the patrol unmanned aerial vehicle to the flying spot of the corresponding track route, and carrying out patrol execution on the patrol unmanned aerial vehicle according to the corresponding track route.
As a further scheme of the invention: the processing mode in the second step is as follows:
removing noise in remote sensing image data by adopting a median filtering and/or wavelet transform denoising technology;
StepA2, smoothing the image by adopting a convolution smoothing algorithm;
StepA3, which adopts histogram equalization and/or adaptive enhancement technology to enhance the image contrast of the remote sensing image data.
As a further scheme of the invention: in step three, the terrain elevation is acquired by unmanned aerial vehicle-mounted radar or optical sensor, and the individual contours are extracted by image segmentation and edge detection algorithms.
As a further scheme of the invention: the specific mode in the fourth step is as follows:
a three-dimensional space coordinate system is established by taking the position coordinate of a flying spot corresponding to the unmanned aerial vehicle carrying the remote sensing equipment as an origin;
calculating corresponding coordinate differences according to the position coordinates corresponding to the image frames in the remote sensing image and the position coordinates of the flying spots corresponding to the unmanned aerial vehicle, wherein the coordinate differences are represented as coordinate differences between the X-axis coordinates, the Y-axis coordinates and the Z-axis coordinates corresponding to the image frames in the remote sensing image and the X-axis coordinates, the Y-axis coordinates and the Z-axis coordinates of the flying spots corresponding to the unmanned aerial vehicle respectively;
step pB3, establishing a coordinate sub-node at a corresponding coordinate position of the three-dimensional space coordinate system according to the corresponding coordinate difference, wherein the coordinate sub-node is expressed as coordinate values of an X axis and a Y axis in the three-dimensional space coordinate system;
step pB4, adding Z-axis coordinate values to the corresponding coordinate child nodes according to the extracted characteristic information, and generating three-dimensional coordinate nodes;
and generating a track planning model by connecting all three-dimensional coordinate nodes in the three-dimensional space coordinate system.
As a further scheme of the invention: the specific mode in the fifth step is as follows:
step pC1, obtaining the number of the inspection unmanned aerial vehicles in a target area, wherein the specification and the model of each inspection unmanned aerial vehicle are consistent;
StepC2, pre-arranging the inspection routes of all the inspection robots according to a plane coordinate system formed by the corresponding number of the inspection robots and an X axis and a Y axis in a three-dimensional space coordinate system;
StepC3, setting a reference inspection height;
step pC4, obtaining Z-axis coordinate values corresponding to three-dimensional coordinate nodes on each inspection route;
meanwhile, comparing each Z-axis coordinate value with a reference inspection height:
when the Z-axis coordinate value is larger than the reference inspection height, the three-dimensional coordinate node is indicated to have an obstacle, otherwise, the three-dimensional coordinate node is indicated to have no corresponding obstacle;
step pC5, on the inspection route, acquiring Z-axis coordinate values which are continuously adjacent and larger than the reference inspection height, and marking each continuously adjacent three-dimensional coordinate node as a corresponding obstacle route;
meanwhile, calculating the obstacle avoidance height corresponding to the obstacle route according to the Z-axis coordinate value and the reference inspection height;
step pC6, obtaining the transverse flight speed and the longitudinal flight speed of the inspection unmanned aerial vehicle, obtaining the distance of the inspection route, the distance of all obstacle routes on the inspection route and the corresponding obstacle avoidance heights, and then carrying out calculation processing on each inspection route to obtain the total flight time;
step pC7, obtaining the average value of all the total flight time through the sum of the total flight time corresponding to all the routing inspection routes and the average value of all the routing inspection routes;
StepC8, sequencing all the inspection routes according to the end-to-end connection sequence, and simultaneously comparing the total flight time of the first inspection route with the average value of the total flight time:
if the average value of the total flight time is smaller than or equal to the total flight time of the inspection route arranged at the first position, acquiring the real-time flight time corresponding to the inspection route, then acquiring the corresponding equal real-time flight time according to the average value of the total flight time, acquiring the corresponding real-time flight distance according to the acquired real-time flight distance, and marking the position from the departure point on the inspection route to the node corresponding to the acquired real-time flight distance as a track route I;
and then taking the ending node of the track route I as a departure point of the pre-divided track route II, sequentially acquiring corresponding real-time flight time and corresponding real-time flight distance thereof on all the inspection routes sequenced according to the head-tail connection sequence according to the average value of the total flight time, and sequentially marking the positions from the corresponding departure point to the corresponding node of the corresponding real-time flight distance as the track route II, the track route III and the track route … … until all the inspection routes are completely re-divided.
As a further scheme of the invention: in StepC2, equally equidistant inspection routes are divided by inspection administrator custom settings.
As a further scheme of the invention: in StepC5, the calculation formula of the obstacle avoidance height is: obstacle avoidance height = Z-axis coordinate value-reference inspection height + compensation parameter, where the compensation parameter is a preset value.
As a further scheme of the invention: the specific manner in StepC6 is as follows:
the calculation processing mode is as follows:
selecting a routing inspection route, firstly acquiring the transverse flight speed and the longitudinal flight speed of the routing inspection unmanned aerial vehicle, and respectively marking the transverse flight speed and the longitudinal flight speed as HS and ZS;
the method comprises the steps of simultaneously obtaining the distance of a routing inspection route and the distance and the corresponding obstacle avoidance height of all obstacle routes on the routing inspection route, marking the distance of the routing inspection route as XL, simultaneously marking the distance and the corresponding obstacle avoidance height of each obstacle route as BLi and BGi respectively, wherein i=1, 2 and … … m, m represent the number of the obstacle routes, and the distance of the obstacle route is determined according to the first three-dimensional coordinate node and the second three-dimensional coordinate node in the corresponding continuous adjacent three-dimensional coordinate nodes, namely the distance selected on the routing inspection route by the first three-dimensional coordinate node and the second three-dimensional coordinate node;
then by the formula:
obtaining the total flight time T of the unmanned aerial vehicle on the inspection route, wherein beta 1 and beta 2 are preset compensation scale factors, and in the embodiment, beta 1 = 1.02135 and beta 2 = 1.12214;
and so on, obtaining the total flight time of each inspection route;
as a further scheme of the invention: in StepC 8:
if the average value of the total flight time is larger than the total flight time of the inspection route arranged at the first position, the method indicates that the corresponding real-time flight time cannot be obtained on the inspection route according to the average value of the total flight time, then the absolute value of the difference value between the average value of the total flight time and the corresponding total flight time of the inspection route is calculated, meanwhile, the real-time flight time corresponding to the inspection route arranged at the second position is obtained, the corresponding equal real-time flight time is obtained according to the corresponding absolute value of the difference value, then the corresponding real-time flight distance is obtained according to the obtained real-time flight distance, and the position from the departure point arranged on the inspection route at the first position to the node corresponding to the real-time flight distance obtained on the inspection route at the second position is marked as a track route I.
As a further scheme of the invention: the inspection unmanned aerial vehicle is further used for recording real-time flight distance and corresponding real-time flight time on the corresponding inspection route, wherein the real-time flight distance is expressed as the distance from the corresponding departure point to the approach point of the inspection unmanned aerial vehicle on the inspection route, and the real-time flight time is the time from the corresponding departure point to the approach point of the inspection unmanned aerial vehicle on the inspection route.
The invention has the beneficial effects that:
according to the invention, by acquiring the remote sensing image data of the complex environment, extracting the characteristic information, establishing the three-dimensional space coordinate system, generating the flight path planning model, the flight path of the unmanned aerial vehicle can be planned more accurately, the flight risk caused by the complex environment is avoided, and the efficiency and accuracy of the flight path planning are effectively improved;
according to the invention, the flight distance and the flight time of the unmanned aerial vehicle are recorded in real time, and the track route is dynamically adjusted according to the average value of the total flight time, so that the flexibility and the adaptability of the inspection are improved;
according to the invention, the inspection routes of the unmanned aerial vehicles are pre-arranged, the reference inspection height is set, and the total flight time of each inspection route is calculated, so that the cooperative inspection of a plurality of unmanned aerial vehicles can be realized, and the inspection efficiency is improved;
according to the invention, by identifying the obstacle on the inspection route and calculating the obstacle avoidance height, the automatic obstacle avoidance of the unmanned aerial vehicle can be realized, the flight safety is improved, and the unmanned aerial vehicle has good obstacle avoidance capability;
according to the invention, the remote sensing image data is effectively preprocessed by the methods of median filtering and wavelet transformation denoising technology, convolution smoothing algorithm, histogram equalization, self-adaptive enhancement technology and the like, so that the quality of the image and the accuracy of subsequent processing are improved.
Drawings
The invention is further described below with reference to the accompanying drawings.
Fig. 1 is a flow chart of a complex environment track planning method based on unmanned aerial vehicle remote sensing images.
Fig. 2 is a schematic flow chart of data preprocessing in the complex environment track planning method based on unmanned aerial vehicle remote sensing images.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
Referring to fig. 1, the invention discloses a complex environment track planning method based on unmanned aerial vehicle remote sensing images, which comprises the following steps:
first step, data acquisition
Acquiring remote sensing image data of a complex environment;
the method comprises the steps of obtaining remote sensing image data of a target area through remote sensing equipment carried by an unmanned aerial vehicle, wherein the remote sensing image data comprises a remote sensing image and position coordinates corresponding to an image frame in the remote sensing image;
second step, feature extraction
Extracting feature information from the preprocessed remote sensing image, wherein the feature information comprises a terrain elevation and an individual contour, and in the embodiment, the individual contour comprises but is not limited to a terrain contour, a building contour and a vegetation contour;
the terrain elevation is obtained by carrying a radar or an optical sensor on the unmanned aerial vehicle;
the individual contours are extracted through image segmentation and edge detection algorithms, and the technology is the prior art, so that details are not repeated;
third step, planning model establishment
Establishing a three-dimensional space coordinate system according to position coordinates corresponding to image frames in the remote sensing images, establishing corresponding coordinate sub-nodes according to the position coordinates corresponding to the image frames in the remote sensing images and the position coordinates of flying spots corresponding to the unmanned aerial vehicle respectively, generating three-dimensional coordinate nodes by combining the coordinate sub-nodes with characteristic information, and drawing to generate a track planning model according to each three-dimensional coordinate node in the three-dimensional space coordinate system;
the method comprises the following steps:
a three-dimensional space coordinate system is established by taking the position coordinate of a flying spot corresponding to the unmanned aerial vehicle carrying the remote sensing equipment as an origin;
calculating corresponding coordinate differences according to the position coordinates corresponding to the image frames in the remote sensing image and the position coordinates of the flying spots corresponding to the unmanned aerial vehicle, wherein the coordinate differences are represented as coordinate differences between the X-axis coordinates, the Y-axis coordinates and the Z-axis coordinates corresponding to the image frames in the remote sensing image and the X-axis coordinates, the Y-axis coordinates and the Z-axis coordinates of the flying spots corresponding to the unmanned aerial vehicle respectively;
step pB3, establishing a coordinate sub-node at a corresponding coordinate position of the three-dimensional space coordinate system according to the corresponding coordinate difference, wherein the coordinate sub-node is expressed as coordinate values of an X axis and a Y axis in the three-dimensional space coordinate system;
step pB4, adding Z-axis coordinate values to the corresponding coordinate child nodes according to the extracted characteristic information, and generating three-dimensional coordinate nodes;
StepB5, through connecting each three-dimensional coordinate node in the three-dimensional space coordinate system, produce the planning model of the route;
fourth step, model planning analysis
StepC1, obtaining the number of inspection unmanned aerial vehicles in a target area; in the embodiment, the specification and model numbers of all the inspection unmanned aerial vehicles are consistent;
StepC2, pre-arranging the inspection routes of all the inspection robots according to a plane coordinate system formed by the corresponding number of the inspection robots and an X axis and a Y axis in a three-dimensional space coordinate system;
the number of the pre-arranged inspection routes is consistent with that of the unmanned aerial vehicles, and the route distances of the pre-arranged inspection routes are consistent;
in this embodiment, the pre-programmed inspection route is custom set by the inspection manager;
StepC3, setting a reference inspection height, wherein the reference inspection height is expressed as a normal flight height of the unmanned aerial vehicle without obstacles during inspection;
step pC4, obtaining Z-axis coordinate values corresponding to three-dimensional coordinate nodes on each inspection route;
meanwhile, comparing each Z-axis coordinate value with a reference inspection height:
when the Z-axis coordinate value is larger than the reference inspection height, the three-dimensional coordinate node is indicated to have an obstacle, otherwise, the three-dimensional coordinate node is indicated to have no corresponding obstacle;
step pC5, on the inspection route, acquiring Z-axis coordinate values which are continuously adjacent and larger than the reference inspection height, and marking each continuously adjacent three-dimensional coordinate node as a corresponding obstacle route;
meanwhile, the obstacle avoidance height corresponding to the obstacle route is obtained through the obstacle avoidance height = Z-axis coordinate value-reference inspection height + compensation parameter, the compensation parameter is a preset value, and the contact between the inspection unmanned aerial vehicle and the obstacle can be avoided through setting the compensation parameter;
StepC6, calculating the total flight time of each inspection route, wherein the processing mode is as follows:
taking a patrol route as an example,
firstly, acquiring the transverse flight speed and the longitudinal flight speed of the inspection unmanned aerial vehicle, and respectively marking the transverse flight speed and the longitudinal flight speed as HS and ZS;
the method comprises the steps of simultaneously obtaining the distance of a routing inspection route and the distance and the corresponding obstacle avoidance height of all obstacle routes on the routing inspection route, marking the distance of the routing inspection route as XL, simultaneously marking the distance and the corresponding obstacle avoidance height of each obstacle route as BLi and BGi respectively, wherein i=1, 2 and … … m, m represent the number of the obstacle routes, and the distance of the obstacle route is determined according to the first three-dimensional coordinate node and the second three-dimensional coordinate node in the corresponding continuous adjacent three-dimensional coordinate nodes, namely the distance selected on the routing inspection route by the first three-dimensional coordinate node and the second three-dimensional coordinate node;
then by the formula:
obtaining the total flight time T of the unmanned aerial vehicle on the inspection route, wherein beta 1 and beta 2 are preset compensation scale factors, and in the embodiment, beta 1 = 1.02135 and beta 2 = 1.12214;
and so on, obtaining the total flight time of each inspection route;
the inspection unmanned aerial vehicle is further used for recording real-time flight distance and corresponding real-time flight time on the corresponding inspection route, wherein the real-time flight distance is expressed as the distance from the corresponding departure point to the approach point of the inspection unmanned aerial vehicle on the inspection route, and the real-time flight time is the time from the corresponding departure point to the approach point of the inspection unmanned aerial vehicle on the inspection route;
step pC7, obtaining the average value of all the total flight time through the sum of the total flight time corresponding to all the routing inspection routes and the average value of all the routing inspection routes;
StepC8, sequencing all the inspection routes according to the end-to-end connection sequence, and simultaneously comparing the total flight time of the first inspection route with the average value of the total flight time:
if the average value of the total flight time is smaller than or equal to the total flight time of the inspection route arranged at the first position, acquiring the real-time flight time corresponding to the inspection route, then acquiring the corresponding equal real-time flight time according to the average value of the total flight time, acquiring the corresponding real-time flight distance according to the acquired real-time flight distance, and marking the position from the departure point on the inspection route to the node corresponding to the acquired real-time flight distance as a track route I;
if the average value of the total flight time is larger than the total flight time of the inspection route arranged at the first position, the method indicates that the corresponding real-time flight time cannot be obtained on the inspection route according to the average value of the total flight time, then the absolute value of the difference value between the average value of the total flight time and the corresponding total flight time of the inspection route is calculated, meanwhile, the real-time flight time corresponding to the inspection route arranged at the second position is obtained, the corresponding equal real-time flight time is obtained according to the corresponding absolute value of the difference value, then the corresponding real-time flight distance is obtained according to the obtained real-time flight distance, and the position from the departure point arranged on the inspection route at the first position to the node corresponding to the real-time flight distance obtained on the inspection route at the second position is marked as a track route I;
then taking the ending node of the track route I as a departure point of the pre-divided track route II, sequentially acquiring corresponding real-time flight time and corresponding real-time flight distance thereof on all the inspection routes sequenced according to the head-tail connection sequence according to the average value of the total flight time, and sequentially marking the positions from the corresponding departure point to the corresponding node of the corresponding real-time flight distance as the track route II, the track route III and the track route … … until all the inspection routes are completely re-divided;
fifth step, route planning execution
And (3) acquiring corresponding flying spots from the first track route, the second track route and the … … track route obtained in the fourth step, adjusting the inspection unmanned aerial vehicle to the flying spot of the corresponding track route, and then carrying out inspection execution on the inspection unmanned aerial vehicle according to the corresponding track route.
The embodiment effectively improves the efficiency and accuracy of track planning. The method has the advantages that the remote sensing image data of the complex environment are acquired, the characteristic information is extracted, the three-dimensional space coordinate system is established, the flight path planning model is generated, the flight path of the unmanned aerial vehicle can be planned more accurately, the flight risk caused by the complex environment is avoided, the dynamic adjustment of the flight path is realized, the flight distance and the flight time of the unmanned aerial vehicle are recorded in real time, the dynamic adjustment of the flight path is carried out according to the average value of the total flight time, and the flexibility and the adaptability of the inspection are improved;
and meanwhile, the collaborative inspection of multiple unmanned aerial vehicles is realized. By pre-arranging the inspection routes of all the inspection unmanned aerial vehicles, setting a reference inspection height, calculating the total flight time of each inspection route, the collaborative inspection of a plurality of unmanned aerial vehicles can be realized, and the inspection efficiency is improved;
but also has good obstacle avoidance capability. By identifying the obstacle on the inspection route and calculating the obstacle avoidance height, the unmanned aerial vehicle can automatically avoid the obstacle, and the flight safety is improved.
Example two
Referring to fig. 2, as a second embodiment of the present invention, in comparison with the first embodiment, the difference between the technical solution of the present embodiment and the first embodiment is that the present embodiment is further used for preprocessing the remote sensing image data after the first step of the first embodiment:
the pretreatment mode is as follows:
removing noise in remote sensing image data by adopting a median filtering and/or wavelet transform denoising technology;
wherein median filtering is a nonlinear filtering technique that removes noise by replacing pixel values with the median of the pixel values in its neighborhood;
the wavelet transformation denoising is a method for denoising by decomposing an image into wavelet coefficients of different frequencies, then carrying out threshold processing on the coefficients and finally reconstructing the image; the method can effectively remove noise and retain detailed information of the image
StepA2, smoothing the image by adopting a convolution smoothing algorithm;
wherein the convolution smoothing algorithm performs convolution operation on the image and a predefined convolution kernel to achieve the purpose of smoothing the image, and in the embodiment, the convolution kernel comprises a mean kernel and a Gaussian kernel;
StepA3, enhancing the image contrast of the remote sensing image data by adopting a histogram equalization and/or adaptive enhancement technology;
wherein, the histogram equalization is a method for enhancing the contrast of the image by adjusting the histogram distribution of the image, and the adaptive enhancement is a method for adjusting the contrast according to the local characteristics of the image;
in this embodiment, the median filtering and wavelet transform denoising technique, convolution smoothing algorithm, histogram equalization and adaptive enhancement technique are the prior art, so that no description is given;
according to the embodiment, the remote sensing image data is effectively preprocessed, and the quality of the image and the accuracy of subsequent processing are improved through the methods of median filtering, wavelet transformation denoising technology, convolution smoothing algorithm, histogram equalization, self-adaptive enhancement technology and the like.
Example III
As an embodiment three of the present invention, in the present application, the technical solution of the present embodiment is to combine the solutions of the above embodiment one and embodiment two, compared with the embodiment one and embodiment two.
The above formulas are all formulas with dimensionality removed and numerical calculation, the formulas are formulas with the latest real situation obtained by software simulation through collecting a large amount of data, and preset parameters and threshold selection in the formulas are set by those skilled in the art according to the actual situation.
The foregoing is merely specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily think about changes or substitutions within the technical scope of the present application, and the changes and substitutions are intended to be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (9)
1. The complex environment track planning method based on the unmanned aerial vehicle remote sensing image is characterized by comprising the following steps of:
firstly, acquiring remote sensing image data of a target area, wherein the remote sensing image data comprises a remote sensing image and position coordinates corresponding to image frames in the remote sensing image, and simultaneously extracting characteristic information from the remote sensing image, wherein the characteristic information comprises a terrain elevation and an individual contour, and the individual contour comprises a terrain contour, a building contour and a vegetation contour;
secondly, establishing a three-dimensional space coordinate system according to position coordinates corresponding to image frames in the remote sensing images, generating three-dimensional coordinate nodes according to position coordinates corresponding to the image frames in the remote sensing images and characteristic information, and drawing to generate a track planning model according to the three-dimensional coordinate nodes in the three-dimensional space coordinate system;
thirdly, carrying out track route planning analysis according to a track planning model, wherein the specific mode is as follows:
step pC1, obtaining the number of the inspection unmanned aerial vehicles in a target area, wherein the specification and the model of each inspection unmanned aerial vehicle are consistent;
StepC2, pre-arranging the inspection routes of all the inspection robots according to a plane coordinate system formed by the corresponding number of the inspection robots and an X axis and a Y axis in a three-dimensional space coordinate system;
StepC3, setting a reference inspection height, wherein the reference inspection height is expressed as a normal flight height of the unmanned aerial vehicle without an obstacle during inspection;
step pC4, obtaining Z-axis coordinate values corresponding to three-dimensional coordinate nodes on each inspection route;
meanwhile, comparing each Z-axis coordinate value with a reference inspection height:
when the Z-axis coordinate value is larger than the reference inspection height, the three-dimensional coordinate node is indicated to have an obstacle, otherwise, the three-dimensional coordinate node is indicated to have no corresponding obstacle;
step pC5, on the inspection route, acquiring Z-axis coordinate values which are continuously adjacent and larger than the reference inspection height, and marking each continuously adjacent three-dimensional coordinate node as a corresponding obstacle route;
meanwhile, calculating the obstacle avoidance height corresponding to the obstacle route according to the Z-axis coordinate value and the reference inspection height;
step pC6, obtaining the transverse flight speed and the longitudinal flight speed of the inspection unmanned aerial vehicle, obtaining the distance of the inspection route, the distance of all obstacle routes on the inspection route and the corresponding obstacle avoidance heights, and then carrying out calculation processing on each inspection route to obtain the total flight time;
step pC7, obtaining the average value of all the total flight time through the sum of the total flight time corresponding to all the routing inspection routes and the average value of all the routing inspection routes;
StepC8, sequencing all the inspection routes according to the end-to-end connection sequence, and simultaneously comparing the total flight time of the first inspection route with the average value of the total flight time:
if the average value of the total flight time is smaller than or equal to the total flight time of the inspection route arranged at the first position, acquiring the real-time flight time corresponding to the inspection route, then acquiring the corresponding equal real-time flight time according to the average value of the total flight time, acquiring the corresponding real-time flight distance according to the acquired real-time flight distance, and marking the position from the departure point on the inspection route to the node corresponding to the acquired real-time flight distance as a track route I;
and then taking the ending node of the track route I as a departure point of the pre-divided track route II, sequentially acquiring corresponding real-time flight time and corresponding real-time flight distance thereof on all the inspection routes sequenced according to the head-tail connection sequence according to the average value of the total flight time, and sequentially marking the positions from the corresponding departure point to the corresponding node of the corresponding real-time flight distance as the track route II, the track route III and the track route … … until all the inspection routes are completely re-divided.
2. The method for planning a complex environment track based on an unmanned aerial vehicle remote sensing image according to claim 1, wherein in the first step, the method further comprises the steps of removing noise in the remote sensing image data, smoothing an image, and enhancing the image contrast of the remote sensing image data;
the specific mode of the step is as follows:
removing noise in remote sensing image data by adopting a median filtering and/or wavelet transform denoising technology;
StepA2, smoothing the image by adopting a convolution smoothing algorithm;
StepA3, which adopts histogram equalization and/or adaptive enhancement technology to enhance the image contrast of the remote sensing image data.
3. The method for planning a complex environment track based on unmanned aerial vehicle remote sensing images according to claim 1, wherein in the first step, the terrain elevation is obtained by unmanned aerial vehicle-mounted radar or optical sensor, and the individual contour is extracted by image segmentation and edge detection algorithm.
4. The complex environment track planning method based on unmanned aerial vehicle remote sensing images according to claim 1, wherein the specific mode in the second step is as follows:
a three-dimensional space coordinate system is established by taking the position coordinate of a flying spot corresponding to the unmanned aerial vehicle carrying the remote sensing equipment as an origin;
calculating corresponding coordinate differences according to the position coordinates corresponding to the image frames in the remote sensing image and the position coordinates of the flying spots corresponding to the unmanned aerial vehicle, wherein the coordinate differences are represented as coordinate differences between the X-axis coordinates, the Y-axis coordinates and the Z-axis coordinates corresponding to the image frames in the remote sensing image and the X-axis coordinates, the Y-axis coordinates and the Z-axis coordinates of the flying spots corresponding to the unmanned aerial vehicle respectively;
step pB3, establishing a coordinate sub-node at a corresponding coordinate position of the three-dimensional space coordinate system according to the corresponding coordinate difference, wherein the coordinate sub-node is expressed as coordinate values of an X axis and a Y axis in the three-dimensional space coordinate system;
step pB4, adding Z-axis coordinate values to the corresponding coordinate child nodes according to the extracted characteristic information, and generating three-dimensional coordinate nodes;
and generating a track planning model by connecting all three-dimensional coordinate nodes in the three-dimensional space coordinate system.
5. The method for planning a complex environment track based on unmanned aerial vehicle remote sensing images according to claim 1, wherein in StepC2, equal-amount equidistant routing routes are divided by custom settings of routing administrators.
6. The complex environment track planning method based on unmanned aerial vehicle remote sensing images according to claim 1, wherein in StepC5, the calculation formula of the obstacle avoidance height is: obstacle avoidance height = Z-axis coordinate value-reference inspection height + compensation parameter, where the compensation parameter is a preset value.
7. The complex environment track planning method based on unmanned aerial vehicle remote sensing images according to claim 1, wherein the specific mode in StepC6 is as follows:
the calculation processing mode is as follows:
selecting a routing inspection route, firstly acquiring the transverse flight speed and the longitudinal flight speed of the routing inspection unmanned aerial vehicle, and respectively marking the transverse flight speed and the longitudinal flight speed as HS and ZS;
the method comprises the steps of simultaneously obtaining the distance of a routing inspection route and the distance and the corresponding obstacle avoidance height of all obstacle routes on the routing inspection route, marking the distance of the routing inspection route as XL, simultaneously marking the distance and the corresponding obstacle avoidance height of each obstacle route as BLi and BGi respectively, wherein i=1, 2 and … … m, m represent the number of the obstacle routes, and the distance of the obstacle route is determined according to the first three-dimensional coordinate node and the second three-dimensional coordinate node in the corresponding continuous adjacent three-dimensional coordinate nodes, namely the distance selected on the routing inspection route by the first three-dimensional coordinate node and the second three-dimensional coordinate node;
then by the formula:
obtaining total flight time T of the unmanned aerial vehicle on the inspection route, wherein beta 1 and beta 2 are preset compensation scale factors, and beta 1= 1.02135 and beta 2= 1.12214;
and so on, obtaining the total flight time of each inspection route.
8. The method for planning a complex environment track based on unmanned aerial vehicle remote sensing images according to claim 1, wherein in StepC 8:
if the average value of the total flight time is larger than the total flight time of the inspection route arranged at the first position, the method indicates that the corresponding real-time flight time cannot be obtained on the inspection route according to the average value of the total flight time, then the absolute value of the difference value between the average value of the total flight time and the corresponding total flight time of the inspection route is calculated, meanwhile, the real-time flight time corresponding to the inspection route arranged at the second position is obtained, the corresponding equal real-time flight time is obtained according to the corresponding absolute value of the difference value, then the corresponding real-time flight distance is obtained according to the obtained real-time flight distance, and the position from the departure point arranged on the inspection route at the first position to the node corresponding to the real-time flight distance obtained on the inspection route at the second position is marked as a track route I.
9. The complex environment track planning method based on the remote sensing image of the unmanned aerial vehicle according to claim 1, wherein the inspection unmanned aerial vehicle is further used for recording real-time flight distances and corresponding real-time flight times of the inspection unmanned aerial vehicle on corresponding inspection routes, the real-time flight distances are represented as distances from corresponding departure points to route points of the inspection unmanned aerial vehicle on the inspection routes, and the real-time flight times are times from the corresponding departure points to the route points of the inspection unmanned aerial vehicle on the inspection routes.
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Denomination of invention: A complex environment trajectory planning method based on unmanned aerial vehicle remote sensing images Granted publication date: 20240507 Pledgee: Bank of Nanjing Co.,Ltd. Nanjing Chengnan sub branch Pledgor: Zonggu (Jiangsu) Intelligent Technology Co.,Ltd. Registration number: Y2024980040656 |