CN115344057A - Method and system for planning routes in vegetation-dense area, storage medium and electronic equipment - Google Patents
Method and system for planning routes in vegetation-dense area, storage medium and electronic equipment Download PDFInfo
- Publication number
- CN115344057A CN115344057A CN202210765900.XA CN202210765900A CN115344057A CN 115344057 A CN115344057 A CN 115344057A CN 202210765900 A CN202210765900 A CN 202210765900A CN 115344057 A CN115344057 A CN 115344057A
- Authority
- CN
- China
- Prior art keywords
- vegetation
- route
- remote sensing
- nodes
- coverage
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/10—Simultaneous control of position or course in three dimensions
- G05D1/101—Simultaneous control of position or course in three dimensions specially adapted for aircraft
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
- G06V10/764—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/10—Terrestrial scenes
- G06V20/17—Terrestrial scenes taken from planes or by drones
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/10—Terrestrial scenes
- G06V20/188—Vegetation
Abstract
The invention discloses a method and a system for planning a course of a vegetation dense area, a storage medium and electronic equipment. And then dividing the remote sensing image into a plurality of position nodes with the same size and shape according to the size and shape of the remote sensing image, and calculating the vegetation coverage of each position node according to the vegetation pixel and the non-vegetation pixel of the position node coverage area. Then, the vegetation coverage is used as an influence coefficient and introduced into the transfer probability calculation of the ant colony algorithm, and the state transfer rule of the unmanned aerial vehicle waypoint is optimized, so that the unmanned aerial vehicle route node of the vegetation dense area is searched. And finally, planning the flight route of the unmanned aerial vehicle in the vegetation dense area based on the searched route nodes. Compared with the conventional route planning, the obtained route can autonomously avoid the ground environment with high coverage degree planted in the survey area, and the probability of obtaining ground points is improved.
Description
Technical Field
The invention relates to the technical field of instruments for executing navigation calculation, in particular to a method and a system for planning a course of an vegetation dense area, a storage medium and electronic equipment.
Background
The unmanned aerial vehicle three-dimensional laser scanning technology is an important means for obtaining accurate terrain information in ground disaster prevention and control. However, the current unmanned aerial vehicle track planning only considers factors such as operation time, plane and elevation precision, and is difficult to solve the adverse effect of the sparse ground laser foot points in the vegetation coverage area on the later fine terrain extraction. Therefore, a technology capable of increasing the number of laser foot points acquired by the three-dimensional laser scanning system of the unmanned aerial vehicle in the vegetation dense area is needed.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a method and a system for planning a course of a planted compact area, a storage medium and electronic equipment. The route capable of improving the probability of the unmanned aerial vehicle three-dimensional laser scanning system for obtaining ground points can be planned, so that the number of laser foot points acquired by the unmanned aerial vehicle three-dimensional laser scanning system on the ground in a vegetation dense area is increased. The specific technical scheme is as follows:
in a first aspect, a vegetation dense area route planning method is provided, and includes:
obtaining a remote sensing image of a vegetation dense area;
classifying the remote sensing image, and determining vegetation pixels and non-vegetation pixels in the remote sensing image;
dividing the remote sensing image into a plurality of position nodes with the same size, and calculating the vegetation coverage of each position node according to the vegetation pixel and the non-vegetation pixel of the position node coverage area;
searching a route node from all position nodes by adopting an ant colony algorithm based on the vegetation coverage;
and planning the unmanned aerial vehicle air route of the vegetation dense area according to the searched air route nodes.
With reference to the first aspect, in a first implementation manner of the first aspect, a random forest algorithm is used to classify the remote sensing images.
With reference to the first aspect, in a second implementable manner of the first aspect, when searching for the route node, a transition probability between two location nodes is calculated according to the vegetation coverage.
With reference to the first aspect, in a third implementation manner of the first aspect, a least square curve fitting method is used to perform curve fitting on all route nodes to obtain an unmanned aerial vehicle route of a vegetation-dense area.
In a second aspect, there is provided an unmanned aerial vehicle route planning system for a vegetation dense area, comprising:
the acquisition module is configured to acquire a remote sensing image of the vegetation dense area;
the classification module is configured to classify the remote sensing image and determine vegetation pixels and non-vegetation pixels in the remote sensing image;
the calculation module is configured to divide the remote sensing image into a plurality of position nodes, and calculate the vegetation coverage of each position node according to the vegetation pixel and the non-vegetation pixel of the position node coverage area;
the searching module is configured to search all route nodes from all position nodes by adopting an ant colony algorithm based on the vegetation coverage;
and the planning module is configured to plan the unmanned aerial vehicle air route of the vegetation-dense area according to all the air route nodes.
With reference to the second aspect, in a first implementation manner of the second aspect, the classification module classifies the remote sensing image by using a random forest algorithm.
With reference to the second aspect, in a second implementation manner of the second aspect, the search module calculates a transition probability between two position nodes according to the vegetation coverage.
With reference to the second aspect, in a third implementation manner of the second aspect, the planning module performs curve fitting on all route nodes by using a least square curve fitting method to obtain the unmanned aerial vehicle route of the vegetation dense area.
In a third aspect, a storage medium is provided, which stores a computer program, wherein the computer program executes the vegetation-dense area route planning method according to any one of the first aspect and the first to third aspects.
In a fourth aspect, an electronic device is provided, comprising:
one or more processors;
a storage device to store one or more programs that, when executed by the one or more processors, cause an electronic device to implement the vegetation-dense area route planning method of any one of the first aspect, the first to third of the first aspect.
Has the beneficial effects that: by adopting the method and the system for planning the routes of the vegetation-dense area, the storage medium and the electronic equipment, the vegetation coverage is extracted as an influence coefficient and introduced into the transfer probability calculation of the ant colony algorithm on the basis of the high-resolution remote sensing image, and the state transfer rule of the unmanned aerial vehicle waypoint is optimized, so that the unmanned aerial vehicle flight route of the vegetation-dense area is obtained. Compared with the conventional route planning, the optimized route obtained by the method can autonomously avoid the ground environment with high coverage in the survey area and improve the probability of obtaining ground points.
Drawings
In order to more clearly illustrate the embodiments of the present invention, the drawings, which are required to be used in the embodiments, will be briefly described below. In all the drawings, the elements or parts are not necessarily drawn to actual scale.
Fig. 1 is a flowchart of a method for planning routes in a planted compact area according to an embodiment of the present invention;
FIG. 2 is a flowchart of a method for classifying remote sensing images according to an embodiment of the present invention;
fig. 3 is a flowchart of an ant colony optimization algorithm according to an embodiment of the present invention;
FIG. 4 is a remote sensing image of an acquired vegetation-dense area;
FIG. 5 is a schematic diagram of a classification result of a remote sensing image;
FIG. 6 is a sample partitioning diagram;
FIG. 7 is a schematic diagram of a sample plant coverage calculation;
FIG. 8 is a schematic diagram of location nodes constructed based on sample plant coverage points;
FIG. 9 is a comparison graph of an optimized path obtained by the path planning method of the present invention and a normal path obtained by a normal ant colony algorithm;
FIG. 10 is a flight trajectory obtained by curve fitting using the least squares method;
fig. 11 is a system block diagram of a classification system according to an embodiment of the present invention.
Detailed Description
Embodiments of the present invention will be described in detail below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and therefore are only examples, and the protection scope of the present invention is not limited thereby.
As shown in fig. 1, the method for planning the routes of the vegetation dense area comprises the following steps:
and 5, planning the unmanned aerial vehicle air route of the vegetation dense area according to the searched air route nodes.
Specifically, first, a high-resolution remote sensing image of the region to be measured may be acquired from the satellite remote sensing image, and the acquired remote sensing image is shown in fig. 4. Then, classifying each pixel in the obtained remote sensing image, and determining a vegetation pixel and a non-vegetation pixel in the remote sensing image. And then dividing the remote sensing image into a plurality of position nodes with the same size and shape according to the size and shape of the remote sensing image, and calculating the vegetation coverage of each position node according to the vegetation pixel and the non-vegetation pixel of the position node coverage area. Then, the vegetation coverage is used as an influence coefficient and introduced into the transfer probability calculation of the ant colony algorithm, and the state transfer rule of the unmanned aerial vehicle waypoint is optimized, so that the unmanned aerial vehicle route node of the vegetation dense area is searched. And finally, planning the flight route of the unmanned aerial vehicle in the vegetation dense area based on the searched route nodes. Compared with the conventional route planning, the optimized route obtained by the method can autonomously avoid the ground environment with high coverage in the survey area and improve the probability of obtaining ground points.
The classification of the remote sensing image in step 2 will be described in detail with reference to fig. 2.
In this embodiment, a random forest algorithm may be used to classify the remote sensing images, which specifically includes:
and 2-1, drawing a classification sample. And establishing classification samples according to objects in the remote sensing image in remote sensing image processing software, wherein the classification samples are vegetation samples and non-vegetation samples.
And 2-2, creating a sample data set. And establishing a sample data set required by random forest classification by taking the remote sensing image and the classification sample as data bases.
And 2-3, training a random forest classifier and storing a classification model. And taking the sample data set as classifier training data. Wherein, the RGB value of the pixel is the classification characteristic value of the pixel. The parameters of the random forest classifier are set as follows: the number of decision trees is 100, and the number of features to be considered when finding the optimal segmentation is the square root of the number of features. Secondly, the training set and the testing set respectively account for 90% and 10% of the sample data set.
And 2-4, inputting the acquired remote sensing image of the area to be detected into a trained random forest classification model, and storing the obtained classification result data in a grid data format, wherein the attribute value of the vegetation pixel is 255 and the attribute value of the non-vegetation pixel is 0.
By adopting the classification method, through multiple adjustments and repeated experiments on the samples, the accuracy of the classification training set and the accuracy of the classification testing set respectively reach 95.38 percent and 95.05 percent, the classification precision can be used for the integral classification of the images, and the classification condition is shown in fig. 5:
the division of the location nodes in step 3 will be described in detail with reference to fig. 6 and 7.
In this embodiment, the remote sensing image may be divided into a plurality of rectangular samples having the same size and shape according to the size and shape of the remote sensing image, as shown in fig. 6. Taking each rectangular sample as a position node, as shown in fig. 7, the area of the non-vegetation area and the area of the vegetation area in the rectangular sample coverage area can be calculated according to the number of vegetation pixels and non-vegetation pixels in the rectangular sample coverage area, and then the vegetation coverage of each sample can be calculated according to the area of the non-vegetation area and the area of the vegetation area in the rectangular sample coverage area. Because the pixels contained in the rectangular sample are more, the data volume can be reduced by setting the position nodes by adopting the vegetation coverage, and the convergence time required by the ant colony algorithm is shortened. Location nodes are established on a per sample basis, and the established location nodes are shown in fig. 8.
The searching of the route node in step 4 will be described in detail below with reference to fig. 3.
In this embodiment, the ant colony algorithm may be adopted to search the route node from all the position nodes, and specifically includes:
first, various parameters, such as the number of ant colonies, pheromone volatilization factors, pheromone constants, heuristic function factors, etc., are initialized. Then, each ant is placed at a different location node, and the next location node visited by each ant is determined until each ant has visited all location nodes. In the ant access process, a next position node to be accessed can be selected by adopting a roulette method, and the transition probability of the ants from the current position node to the next position node can be calculated according to the vegetation coverage, wherein the transition probability is calculated by the following formula:
wherein, coverage is vegetation coverage, and coverage +1 is to avoid calculation error when the coverage is 0.
Is the transition probability for ant k to transition from position node i to position node j at time t. Tau is ij (t) is the amount of pheromones on the route (i, j) at time t; eta i j (t) is a heuristic function representing the expected degree of transfer of ants from location node i to location node j.
τ is (t) the pheromone quantity from position node i to position node s at time t; eta is (t) is the expected degree of location node i to location node s at time t. Alpha is an pheromone heuristic factor, the larger the value of alpha is, the higher the possibility that ants select a route which is walked before is, otherwise, the search range of the ant colony is reduced and is easy to fall into local optimum, and the general value range is [1,4 ]]. Beta is a heuristic function factor, the larger the value of beta is, the easier the ant colony is to select a local shorter route, the convergence speed of the algorithm is accelerated, and the value is generally [0,5 ]]；allowedd k Is the set of ant k allowed forward route nodes.
The formula for the heuristic function is as follows:
d ij where is the distance between position node i and position node j, d ij The smaller, the transition probability pik j The larger (t) is. The vegetation cover can be seen from the above calculation formulaThe smaller the coverage, the greater the transition probability. Finally, the ants select a path with the minimum vegetation coverage, namely the path with the minimum vegetation coverage reaches the target point, and fly along the selected path to avoid the ground environment with high vegetation coverage in the survey area, so that the probability of obtaining the ground point is improved.
In this embodiment, the pheromone amount can be calculated by the following calculation formula:
τ ij (t+n)＝(1-ρ)*τ ij (t)+Δτ ij (t)，ρ∈(0，1)
wherein, Δ τ ij (t) is the pheromone sum on the flight path (i, j) after all ants complete one traversal, rho is pheromone volatility factor, m is the number of ants, tau ij (0) Is c, c is a constant value, Δ τ ij (0) The value of (2) is 0.
In this embodiment, an ant colony period model is adopted as the pheromone updating strategy of this embodiment, and the ant colony period model specifically includes:
wherein Q is the constant of pheromone carried by each ant, and Lk is the length of each ant for searching the route.
In this embodiment, the results obtained by the ant colony algorithm search are discrete route nodes, and are not suitable for the flight of the unmanned aerial vehicle. Therefore, a least square method can be adopted for curve fitting, the searched route nodes are fitted by using a continuous curve, and the curve with the best fitting effect is the final unmanned aerial vehicle route in the vegetation dense area.
The two paths in fig. 9 respectively adopt the classical ant colony algorithm and the ant colony optimization algorithm provided in this embodiment to perform the experimental result of the flight path planning. And after the searching of the route nodes is completed, performing curve fitting according to the route nodes to obtain a flight path suitable for the unmanned aerial vehicle to fly. FIG. 10 shows the flight path after fitting. And respectively counting the attribute values of the two path nodes, wherein the vegetation coverage in the corresponding sample of the common route construction node is 31.07%, the vegetation coverage under the optimized route is 12.94%, and the percentage reduction is about 18%.
The visual result of the course search shows that the ant colony optimization algorithm for the vegetation dense area can effectively solve the problem of flight path planning of the ground observation of the unmanned aerial vehicle, and the algorithm can effectively select the area with low vegetation coverage degree from local details, so that the unmanned aerial vehicle can acquire more ground information when finishing the ground observation from the starting point to the terminal point.
As shown in fig. 11, the system block diagram of the unmanned aerial vehicle route planning system for vegetation dense areas includes:
the acquisition module is configured to acquire a remote sensing image of a vegetation dense area;
the classification module is configured to classify the remote sensing image and determine vegetation pixels and non-vegetation pixels in the remote sensing image;
the calculation module is configured to divide the remote sensing image into a plurality of position nodes, and calculate the vegetation coverage of each position node according to the vegetation pixel and the non-vegetation pixel of the position node coverage area;
the searching module is configured to search all route nodes from all position nodes by adopting an ant colony algorithm based on the vegetation coverage;
and the planning module is configured to plan the unmanned aerial vehicle air route of the vegetation-dense area according to all the air route nodes.
Specifically, the acquisition module can acquire a high-resolution remote sensing image of the area to be measured. The classification module classifies all pixels in the obtained remote sensing image and determines vegetation pixels and non-vegetation pixels in the remote sensing image. The calculation module can divide the remote sensing image into a plurality of position nodes with the same size and shape according to the size and the shape of the remote sensing image, and calculate the vegetation coverage of each position node according to the vegetation pixel and the non-vegetation pixel of the position node coverage area. The searching module can introduce the vegetation coverage as an influence coefficient into the transfer probability calculation of the ant colony algorithm, optimizes the state transfer rule of the unmanned aerial vehicle waypoint, and accordingly searches out unmanned aerial vehicle route nodes of vegetation dense areas. The planning module can plan the unmanned aerial vehicle flight route of the vegetation dense area based on the searched route nodes. Compared with the conventional route planning, the optimized route obtained by the method can autonomously avoid the ground environment with high coverage in the survey area and improve the probability of obtaining ground points.
In this embodiment, the classification module classifies the remote sensing images by using a random forest algorithm, and specifically, the classification module includes a model building unit and an image classification unit. The model construction unit can establish classification samples according to objects in the images by using remote sensing image processing software, wherein the classification samples are vegetation samples and non-vegetation samples. And establishing a sample data set required by random forest classification by taking the remote sensing image and the classification sample as data bases. And the model construction unit takes the sample data set as classifier training data to train the random forest model. The image classification unit can input the acquired remote sensing image of the region to be detected into a trained random forest classification model, and the obtained classification result data can be stored in a grid data format.
In this embodiment, the searching module calculates a transition probability between two position nodes according to the vegetation coverage.
Specifically, the searching module can search out the position node with the minimum vegetation coverage from all the position nodes by adopting an ant colony algorithm as a route node, so that the probability that the three-dimensional laser scanning system of the unmanned aerial vehicle acquires ground points can be improved, and the number of laser foot points acquired by the three-dimensional laser scanning system of the unmanned aerial vehicle in a vegetation dense area is increased.
The searching module comprises an initialization unit, a node searching unit and a pheromone updating unit. The initialization unit may initialize various parameters of the ant colony algorithm, such as the number of ant colonies, pheromone volatilization factors, pheromone constants, heuristic function factors, and the like.
The node searching unit may place each ant at a different location node and determine a next location node that each ant visits until each ant has visited all location nodes. In the ant access process, a next position node to be accessed can be selected by adopting a roulette method, and the transition probability of the ants from the current position node to the next position node can be calculated according to the vegetation coverage, wherein the transition probability is calculated by the following formula:
wherein, coverage is vegetation coverage, and coverage +1 is to avoid calculation error when the coverage is 0.
Is the transition probability for ant k to transition from position node i to position node j at time t. Tau. ij (t) is the amount of pheromones on the flight path (i, j) at time t; eta ij (t) is a heuristic function representing the expected degree of transfer of ants from location node i to location node j.
τ is (t) is the pheromone quantity from position node i to position node s at time t; eta is (t) is the expected degree of location node i to location node s at time t. Alpha is an pheromone heuristic factor, the larger the value of alpha is, the higher the possibility that ants select a route which is walked before is, otherwise, the search range of the ant colony is reduced and is easy to fall into local optimum, and the general value range is [1,4 ]]. Beta is a heuristic function factor, the larger the value of beta is, the more easily the ant colony selects a local shorter route, the convergence speed of the algorithm is accelerated, and the value is generally [0,5 ]]；allowedd k Is the set of ant k allowed forward route nodes.
The formula for the heuristic function is as follows:
d ij in the formula isDistance between position node i and position node j, d ij The smaller, the transition probability
The larger. It can be seen from the above calculation formula that the smaller the vegetation coverage, the greater the transition probability. Finally, the ants select a path with the minimum vegetation coverage, namely the path with the minimum vegetation coverage reaches the target point, and fly along the selected path to avoid the ground environment with high vegetation coverage in the survey area, so that the probability of obtaining the ground point is improved.
The pheromone updating unit can calculate by adopting the following calculation formula:
τ ij (t+n)＝(1-ρ)*τij(t)+Δτ ij (t)，p∈(0，1)
wherein, Δ τ i j (t) is the pheromone sum on the flight path (i, j) after all ants complete one traversal, rho is pheromone volatility factor, m is the number of ants, tau ij (0) Is c, c is a constant value, Δ τ ij (0) The value of (d) is 0. And the ant colony period model is adopted as the pheromone updating strategy of the embodiment, and the ant colony period model specifically comprises the following steps:
wherein Q is the constant of pheromone carried by each ant, and Lk is the length of each ant for searching the route.
In this embodiment, the planning module performs curve fitting on all route nodes by using a least square curve fitting method to obtain the unmanned aerial vehicle route of the vegetation dense area. The results obtained by searching by the searching module are respectively discrete route nodes, and are not suitable for the flight of the unmanned aerial vehicle. Therefore, the planning module can adopt a least square method to carry out curve fitting, a continuous curve is used for fitting the route nodes searched by the searching module, and the curve with the best fitting effect is the final route of the unmanned aerial vehicle in the vegetation dense area.
A storage medium storing a computer program which executes the above vegetation-dense area route planning method.
An electronic device, comprising:
one or more processors;
a storage device to store one or more programs that, when executed by the one or more processors, cause the electronic equipment to implement the vegetation-dense en route planning method described above.
The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the present invention, and they should be construed as being included in the following claims and description.
Claims (10)
1. A vegetation dense area route planning method is characterized by comprising the following steps:
obtaining a remote sensing image of a vegetation dense area;
classifying the remote sensing image, and determining vegetation pixels and non-vegetation pixels in the remote sensing image;
dividing the remote sensing image into a plurality of same position nodes, and calculating the vegetation coverage of each position node according to the vegetation pixel and the non-vegetation pixel of the position node coverage area;
based on the vegetation coverage, searching a course node from all position nodes by adopting an ant colony algorithm;
and planning the unmanned aerial vehicle air route of the vegetation dense area according to the searched air route nodes.
2. The vegetation-dense area route planning method according to claim 1, wherein the remote sensing images are classified using a random forest algorithm.
3. The vegetation-dense area route planning method according to claim 1, wherein in searching for the route node, a transition probability between two position nodes is calculated according to the vegetation coverage.
4. The vegetation-dense area route planning method according to claim 1, characterized in that a least square curve fitting method is adopted to perform curve fitting on all route nodes to obtain unmanned aerial vehicle routes of the vegetation-dense area.
5. An unmanned aerial vehicle route planning system for a vegetation dense area is characterized by comprising:
the acquisition module is configured to acquire a remote sensing image of the vegetation dense area;
the classification module is configured to classify the remote sensing image and determine vegetation pixels and non-vegetation pixels in the remote sensing image;
the calculation module is configured to divide the remote sensing image into a plurality of position nodes, and calculate the vegetation coverage of each position node according to the vegetation pixel and the non-vegetation pixel of the position node coverage area;
the searching module is configured to search all route nodes from all position nodes by adopting an ant colony algorithm based on the vegetation coverage;
and the planning module is configured to plan the unmanned aerial vehicle air route of the vegetation-dense area according to all the air route nodes.
6. The vegetation-dense unmanned aerial vehicle course planning system of claim 5, wherein the classification module classifies the remote sensing images using a random forest algorithm.
7. The vegetation-dense area unmanned aerial vehicle route planning system of claim 5, wherein the search module calculates a transition probability between two location nodes from the vegetation coverage.
8. The vegetation-dense area unmanned aerial vehicle route planning system of claim 5, wherein the planning module performs curve fitting on all route nodes by using a least square curve fitting method to obtain unmanned aerial vehicle routes of the vegetation-dense area.
9. A storage medium storing a computer program, wherein the computer program performs the vegetation-dense airline planning method according to any one of claims 1 to 4.
10. An electronic device, comprising:
one or more processors;
a storage device to store one or more programs that, when executed by the one or more processors, cause an electronic device to implement the vegetation-dense route planning method of any of claims 1-4.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210765900.XA CN115344057B (en) | 2022-06-30 | 2022-06-30 | Vegetation dense area route planning method, system, storage medium and electronic equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210765900.XA CN115344057B (en) | 2022-06-30 | 2022-06-30 | Vegetation dense area route planning method, system, storage medium and electronic equipment |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115344057A true CN115344057A (en) | 2022-11-15 |
| CN115344057B CN115344057B (en) | 2023-09-19 |
Family
ID=83948380
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210765900.XA Active CN115344057B (en) | 2022-06-30 | 2022-06-30 | Vegetation dense area route planning method, system, storage medium and electronic equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115344057B (en) |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101160103A (en) * | 2005-01-18 | 2008-04-09 | MSq有限公司 | System and method for treating biological tissue with a plasma gas discharge |
| CN102012528A (en) * | 2010-11-23 | 2011-04-13 | 北京理工大学 | Hyperspectral remote sensing oil-gas exploration method for vegetation sparse area |
| CN106650991A (en) * | 2016-09-27 | 2017-05-10 | 中国矿业大学(北京) | Path planning based on analog annealing ant colony algorithm |
| CN107219860A (en) * | 2017-07-31 | 2017-09-29 | 内蒙古智牧溯源技术开发有限公司 | A kind of unmanned plane rang management system and method |
| CN109240304A (en) * | 2018-10-15 | 2019-01-18 | 南京林业大学 | A kind of precision planting system and method |
| CN114219966A (en) * | 2021-11-11 | 2022-03-22 | 中国科学院地理科学与资源研究所 | Method for extracting plant coverage and computer readable storage medium |
| WO2022077954A1 (en) * | 2020-10-14 | 2022-04-21 | 国防科技大学 | Unmanned aerial vehicle path planning method based on two charging modes |
-
2022
- 2022-06-30 CN CN202210765900.XA patent/CN115344057B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101160103A (en) * | 2005-01-18 | 2008-04-09 | MSq有限公司 | System and method for treating biological tissue with a plasma gas discharge |
| CN102012528A (en) * | 2010-11-23 | 2011-04-13 | 北京理工大学 | Hyperspectral remote sensing oil-gas exploration method for vegetation sparse area |
| CN106650991A (en) * | 2016-09-27 | 2017-05-10 | 中国矿业大学(北京) | Path planning based on analog annealing ant colony algorithm |
| CN107219860A (en) * | 2017-07-31 | 2017-09-29 | 内蒙古智牧溯源技术开发有限公司 | A kind of unmanned plane rang management system and method |
| CN109240304A (en) * | 2018-10-15 | 2019-01-18 | 南京林业大学 | A kind of precision planting system and method |
| WO2022077954A1 (en) * | 2020-10-14 | 2022-04-21 | 国防科技大学 | Unmanned aerial vehicle path planning method based on two charging modes |
| CN114219966A (en) * | 2021-11-11 | 2022-03-22 | 中国科学院地理科学与资源研究所 | Method for extracting plant coverage and computer readable storage medium |
Non-Patent Citations (3)
| Title |
|---|
| 布升强;梅淼;李琼琼;杨家富;王大明;: "森林防火机器人轨迹寻踪技术研究", 森林工程, no. 03 * |
| 徐小杰;陈盛德;周志艳;兰玉彬;罗锡文;: "植保无人机主要性能指标测评方法的分析与思考", 农机化研究, no. 12 * |
| 陈盛德;兰玉彬;李继宇;徐小杰;王志国;彭斌;: "植保无人机航空喷施作业有效喷幅的评定与试验", 农业工程学报, no. 07 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115344057B (en) | 2023-09-19 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107690840B (en) | Unmanned plane vision auxiliary navigation method and system | |
| CN110609557B (en) | Unmanned vehicle mixed path planning method | |
| WO2020164092A1 (en) | Image processing method and apparatus, moveable platform, unmanned aerial vehicle and storage medium | |
| CN113916243B (en) | Vehicle positioning method, device, equipment and storage medium for target scene area | |
| CN111666855B (en) | Animal three-dimensional parameter extraction method and system based on unmanned aerial vehicle and electronic equipment | |
| WO2020103110A1 (en) | Image boundary acquisition method and device based on point cloud map and aircraft | |
| Emmi et al. | A hybrid representation of the environment to improve autonomous navigation of mobile robots in agriculture | |
| Noordermeer et al. | Predicting and mapping site index in operational forest inventories using bitemporal airborne laser scanner data | |
| Vivaldini et al. | UAV route planning for active disease classification | |
| Akshya et al. | Graph-based path planning for intelligent UAVs in area coverage applications | |
| Aguiar et al. | Localization and mapping on agriculture based on point-feature extraction and semiplanes segmentation from 3D LiDAR data | |
| CN113096181B (en) | Method and device for determining equipment pose, storage medium and electronic device | |
| CN114119920A (en) | Three-dimensional point cloud map construction method and system | |
| JP5771866B2 (en) | Area survey support system, area survey support method, and area survey support device | |
| KR101877900B1 (en) | 3d flight route creating system and method by predicting battery consumption | |
| Williams et al. | Three-dimensional segmentation of trees through a flexible multi-class graph cut algorithm (MCGC) | |
| CN115909096A (en) | Unmanned aerial vehicle cruise pipeline hidden danger analysis method, device and system | |
| Prokop et al. | Neuro-heuristic pallet detection for automated guided vehicle navigation | |
| CN115344057B (en) | Vegetation dense area route planning method, system, storage medium and electronic equipment | |
| CN117289301A (en) | Air-ground unmanned platform collaborative path planning method under unknown off-road scene | |
| CN115019216A (en) | Real-time ground object detection and positioning counting method, system and computer | |
| JP2019046401A (en) | Land leveling work object area identification system | |
| CN113052871A (en) | Target detection and automatic tracking algorithm based on intelligent selection strategy | |
| Flewelling | Forest inventory predictions from individual tree crowns: regression modeling within a sample framework | |
| Liang et al. | Forest in situ observations through a fully automated under-canopy unmanned aerial vehicle |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |