CN113155132A - Unmanned aerial vehicle path planning method and system for greenhouse and unmanned aerial vehicle - Google Patents
Unmanned aerial vehicle path planning method and system for greenhouse and unmanned aerial vehicle Download PDFInfo
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Abstract
The invention relates to the technical field of unmanned aerial vehicle path planning, and provides an unmanned aerial vehicle path planning method for a greenhouse. The method comprises the following steps: modeling an internal structure of the greenhouse; according to the obtained data information of the internal structure model, a preset barrier expansion algorithm, a preset ground extraction algorithm and a preset module division rule, carrying out module division on the internal structure model, obtaining nodes corresponding to each module, and numbering each node; and selecting preset initial position information and preset end position information from the numbers, and acquiring a second preset shortest route which starts from the preset initial position, traverses all nodes and returns to the preset initial position by combining a preset A-x algorithm and a preset 2-opt algorithm. By adopting the method, the unmanned aerial vehicle is used for fertilizing or spraying pesticides on the crops in the greenhouse, so that the unmanned aerial vehicle can select the shortest route for operation.
Description
Technical Field
The invention relates to the technical field of unmanned aerial vehicle path planning, in particular to an unmanned aerial vehicle path planning method and system for a greenhouse and an unmanned aerial vehicle.
Background
Greenhouse cultivation plants are a common technology in modern agriculture, and in the prior art, spraying of liquid such as pesticides and water of plants in the greenhouse is mainly carried out manually, a large amount of manpower is consumed, the labor cost is high, and the automation degree is low.
In order to solve the problems, some schemes are adopted at present, for example, an unmanned aerial vehicle sprays pesticide or water, but the unmanned aerial vehicle still needs artificial participation and artificial control in the spraying process, so that the unmanned aerial vehicle is prevented from colliding with plants or barriers in the greenhouse; or a fixed flight track is installed, and the unmanned aerial vehicle is controlled to spray pesticide or water according to the flight track. However, the method still wastes manpower, and the manual operation in the flight process occupies a large proportion, the automation degree is low, and the intellectualization is low; and adopt fixed flight orbit's mode, the cost is very high again, and when the crop of planting changes, unmanned aerial vehicle's flight route has no way to change, when needing to spray the liquid that needs, still will change the flight orbit, therefore the cost is higher. Meanwhile, the existing unmanned aerial vehicle battery is usually built-in and can not be detached, and the unmanned aerial vehicle can be continuously used after waiting for the charging to be finished after the electric quantity of the unmanned aerial vehicle is exhausted.
Therefore, how intelligent in the greenhouse will be solved, the unmanned aerial vehicle is controlled automatically to correspondingly spray or monitor, and meanwhile, the unmanned aerial vehicle convenient for battery replacement is provided.
Disclosure of Invention
The invention aims to solve the technical problem that after an unmanned aerial vehicle with high reliability avoids barriers in a greenhouse and keeps a certain height from a planted plant to fly, an optimal route can be selected to fertilize, spray pesticides and monitor vegetables in the greenhouse. In order to solve the problems, the invention provides an unmanned aerial vehicle path planning method and system for a greenhouse. The invention is realized by the following technical scheme: an unmanned aerial vehicle path planning method for a greenhouse comprises the following steps:
s1: according to a preset modeling algorithm, modeling the internal structure of the greenhouse and acquiring data information of an internal structure model;
s2: acquiring barrier expansion layout data information corresponding to the internal structure model of the greenhouse according to the acquired internal structure model data information and a preset barrier expansion algorithm;
s3: according to a preset ground extraction algorithm, ground data extraction is carried out on the obstacle expansion layout data information, and corresponding ground structure layout data information in the obstacle expansion layout data information is obtained;
s4: according to the obtained barrier expansion layout data information and a preset module division rule, carrying out module division on the internal structure model after the barrier expansion, obtaining nodes corresponding to each module, and numbering each node;
s5: selecting preset initial position information and preset end position information from the serial numbers according to the serial numbers of the modules in the step S4, and acquiring a first preset shortest route between the preset initial position and the preset end position by combining a preset A-algorithm, barrier expansion layout data information and ground structure layout data information;
s6: and acquiring a second preset shortest route which traverses all nodes after starting from the preset starting position and returns to the preset starting position by combining the serial number in the step S4, the first preset shortest route between the preset starting position and the preset end position acquired in the step S5 and a preset 2-opt algorithm.
Further, the step S2 of obtaining the obstacle dilation layout data information according to the preset obstacle dilation algorithm specifically includes the steps of:
s21: acquiring three-dimensional data information corresponding to the unmanned aerial vehicle;
s22: according to the length data information, the width data information and the height data information which are obtained and correspond to the unmanned aerial vehicle, the preset initial structure layout data information of the internal structure of the greenhouse is expanded, and the obstacle expansion layout data information of the internal structure of the greenhouse is obtained.
Further, step S22 specifically includes the steps of:
s221: performing width expansion on preset initial structure layout data information according to the acquired width information of the unmanned aerial vehicle;
s222: according to the acquired height information of the unmanned aerial vehicle and a preset height expansion algorithm, performing height expansion on the data subjected to width expansion in the step S221, and subtracting a margin coefficient from the expanded data information;
s223: and obtaining the layout data information of the expanded structure according to the expanded data obtained by subtracting the margin coefficient in the step S222, and storing the layout data information of the expanded structure in the background server.
Further, in step S3, ground data extraction is performed on the obstacle expansion layout data information, and the corresponding ground structure layout data information in the obstacle expansion layout data information is obtained specifically
The method comprises the following steps:
s31: performing space horizontal plane segmentation on the internal structure of the greenhouse, and acquiring horizontal plane data information of the internal structure of the greenhouse;
s32: acquiring preset bottom surface data information in the horizontal surface data information;
s33: expanding from a preset bottom surface of the greenhouse to a plurality of preset directions according to a preset flood filling algorithm, and expanding according to a preset step value;
s34: and extracting preset ground data information of the internal structure of the greenhouse according to the assigned value corresponding to the expanded barrier and a preset comparison algorithm.
Further, a distance transformation algorithm is further included between step S5 and step S4, and the method specifically includes the steps of:
a1: acquiring a distance matrix corresponding to an internal structure model of the greenhouse shed according to a preset distance transformation algorithm; the value of a preset position point in the distance matrix is a preset minimum distance value between the position and a preset height plane of an internal structure of the greenhouse;
a2: the method comprises the steps of assigning infinity to a blank space in an internal structure model of the greenhouse, assigning a value a to a preset ground in the internal structure model of the greenhouse, and assigning a value b to a preset obstacle in the internal structure model of the greenhouse.
Further, in step S5, the preset starting position and the preset ending position are obtained by the preset a-algorithm
The shortest distance between the two devices comprises the following steps:
s51: acquiring a preset first path between a preset starting position and a preset end position through a preset estimation cost function, wherein the preset estimation cost function is as follows:
f(n)＝g(n)+h(n)
wherein g (n) is the distance estimation cost from the preset initial position to the current point position;
h (n) is the cost value of the distance from the preset initial position to the preset end position;
f (n) is the distance estimation cost from the preset initial position to the preset end position through the current point position;
wherein the calculation formula of h (n) is as follows:
wherein (Xend, Yed) is the three-dimensional coordinate of the preset end point position, and (Xn, Yn, Zn) is the three-dimensional coordinate of the current point position.
S52: and according to the route data information from the preset initial position to the preset end position calculated in the step S51 and the distance matrix data information of the internal structure of the greenhouse acquired by combining the preset distance conversion algorithm, acquiring a second preset shortest distance for keeping the preset expansion width of the unmanned aerial vehicle between the preset initial position and the preset end position.
An unmanned aerial vehicle path planning system for a greenhouse, comprising:
the modeling module is used for modeling the internal structure of the greenhouse according to a preset modeling algorithm and acquiring data information of the internal structure model;
the barrier expansion module is used for acquiring barrier expansion layout data information corresponding to the internal structure model of the greenhouse according to the acquired internal structure model data information and a preset barrier expansion algorithm;
the ground extraction module is used for carrying out ground data extraction on the obstacle expansion layout data information according to a preset ground extraction algorithm and acquiring corresponding ground structure layout data information in the obstacle expansion layout data information;
the node numbering module is used for carrying out module division on the internal structure model after the obstacle expansion according to the acquired obstacle expansion layout data information and a preset module division rule, acquiring nodes corresponding to each module and numbering each node;
the system comprises a preset two-point distance obtaining module, a first preset shortest route and a second preset shortest route, wherein the preset two-point distance obtaining module is used for selecting preset starting position information and preset end point position information from numbers, and obtaining a first preset shortest route between a preset starting position and a preset end point position by combining a preset A-algorithm and barrier expansion layout data information and ground structure layout data information;
and the optimal traversal route acquisition module is used for acquiring a second preset shortest route which starts from the preset starting position, traverses all nodes and returns to the preset starting position according to a preset 2-opt algorithm.
Further, the barrier expansion module includes:
the three-dimensional data acquisition unit is used for acquiring three-dimensional data information corresponding to the unmanned aerial vehicle;
and the barrier expansion layout unit is used for expanding the preset initial structure layout data information of the internal structure of the greenhouse according to the acquired length data information, width data information and height data information corresponding to the unmanned aerial vehicle, and acquiring the barrier expansion layout data information of the internal structure of the greenhouse.
Further, the obstacle expanding layout unit includes:
the width expansion unit is used for performing width expansion on the preset initial structure layout data information according to the acquired width information of the unmanned aerial vehicle;
the height expansion unit is used for performing height expansion on the data subjected to width expansion according to the acquired height information of the unmanned aerial vehicle and a preset height expansion algorithm, and subtracting a margin coefficient from the expanded data information;
and the expansion data acquisition unit is used for subtracting the expanded data of the margin coefficient to acquire expanded structure layout data information and storing the expanded structure layout data information to the background server.
Further, the ground extraction module comprises:
the horizontal plane dividing unit is used for performing space horizontal plane division on the internal structure of the greenhouse, acquiring horizontal plane data information of the internal structure of the greenhouse and acquiring preset bottom plane data information in the horizontal plane data information;
the expanding unit is used for expanding from the preset bottom surface of the greenhouse to a plurality of preset directions according to a preset flood filling algorithm and expanding according to a preset step value;
and the ground data information acquisition unit is used for extracting preset ground data information of the internal structure of the greenhouse according to the assigned value corresponding to the expanded barrier and a preset comparison algorithm.
By adopting the technical scheme, the invention has the following beneficial effects:
(1) the unmanned aerial vehicle path planning method for the greenhouse can be used for carrying out corresponding modeling according to the internal structure of the greenhouse, so that the unmanned aerial vehicle path planning is carried out according to the established indoor structure model of the greenhouse.
(2) According to the unmanned aerial vehicle path planning method for the greenhouse, the built internal structure model of the greenhouse can be subjected to obstacle expansion according to the size of the unmanned aerial vehicle, so that the unmanned aerial vehicle cannot impact an obstacle in the flight process.
(3) This an unmanned aerial vehicle route planning method for warmhouse booth can cut apart the inside horizontal plane of warmhouse booth to draw ground in the horizontal plane, ground includes ground level and stair step plane, thereby can plan that unmanned aerial vehicle carries out the flight control of direction of height.
(4) According to the unmanned aerial vehicle path planning method for the greenhouse, the model after the internal structure of the greenhouse is built is divided into modules, each module is numbered in advance, planning can be achieved for the route planning of the unmanned aerial vehicle, and whether the route planned by the unmanned aerial vehicle is correct or not can be analyzed.
(5) The unmanned aerial vehicle path planning method for the greenhouse comprises the steps of executing a 2-opt algorithm on each acquired node of an internal structure of the greenhouse to obtain an optimal combination of the unmanned aerial vehicle in the greenhouse, then respectively setting two points in the optimal path as an initial point and an end point according to the node sequence, executing an A-x algorithm once to finally obtain the optimal path length, and visualizing the optimal path length in preset software.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a first flowchart of the unmanned aerial vehicle path planning method for the greenhouse;
FIG. 2 is a flow chart of a second unmanned aerial vehicle path planning method for a greenhouse;
fig. 3 is a flow chart of the unmanned aerial vehicle path planning method for the greenhouse;
FIG. 4 is a distance transformation algorithm diagram in the unmanned aerial vehicle path planning method for greenhouses;
fig. 5 is a first structural diagram of the unmanned aerial vehicle path planning system for the greenhouse;
fig. 6 is a second structural diagram of the unmanned aerial vehicle path planning system for the greenhouse;
fig. 7 is a schematic structural diagram of a drone in an exemplary embodiment of the invention.
Detailed Description
The technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
Example 1:
the embodiment of the invention provides a path planning method for an unmanned aerial vehicle for a greenhouse, which comprises the following steps of:
s1: according to a preset modeling algorithm, modeling the internal structure of the greenhouse and acquiring data information of an internal structure model;
the method for modeling the internal structure of the greenhouse by using the three-dimensional grid data model is adopted, and the specific modeling algorithm is as follows:
the imported original house model is such a binary three-dimensional grid data model matrix. The original house model is in a space with three dimensions of X, Y, Z axes, with data elements, i.e., voxels, that can be viewed as cubic grids. The voxel values in the original house model are only two kinds, 0 and 128, representing an empty region and a non-traffic region, respectively. The non-passing area comprises barriers such as walls, roofs, the ground, stairs and furniture in the house model and is an area which does not support the unmanned aerial vehicle model to pass through at the position of the voxel. In contrast, an empty region indicates that the voxel is free of obstructions that support the passing of the model of the drone.
S2: acquiring barrier expansion layout data information corresponding to the internal structure model of the greenhouse according to the acquired internal structure model data information and a preset barrier expansion algorithm;
further, the step S2 of obtaining the obstacle dilation layout data information according to the preset obstacle dilation algorithm specifically includes the steps of:
s21: acquiring three-dimensional data information corresponding to the unmanned aerial vehicle;
s22: according to the length data information, the width data information and the height data information which are obtained and correspond to the unmanned aerial vehicle, the preset initial structure layout data information of the internal structure of the greenhouse is expanded, and the obstacle expansion layout data information of the internal structure of the greenhouse is obtained.
Further, step S22 specifically includes the steps of:
s221: performing width expansion on preset initial structure layout data information according to the acquired width information of the unmanned aerial vehicle;
s222: according to the acquired height information of the unmanned aerial vehicle and a preset height expansion algorithm, performing height expansion on the data subjected to width expansion in the step S221, and subtracting a margin coefficient from the expanded data information;
s223: and obtaining the layout data information of the expanded structure according to the expanded data obtained by subtracting the margin coefficient in the step S222, and storing the layout data information of the expanded structure in the background server.
Namely: because the unmanned aerial vehicle is used as an abstracted point in the three-dimensional grid house data model, the unmanned aerial vehicle occupies a small volume, and the unmanned aerial vehicle has a certain volume, the sizes of all boundaries and obstacle elements of the three-dimensional grid data model have certain limitation on the capability of restricting a flight track in the flight process, and obstacles (walls, the ground and other obstacles) in the model cannot well restrict the passing unmanned aerial vehicle. Therefore, the obstacle that should in the data model in the experiment outwards carries out the expansion of certain width to guarantee that unmanned aerial vehicle can pass through the passageway that can hold its self volume completely smoothly.
Because unmanned aerial vehicle's work reason, when spraying pesticide or water or control or spray the pollination inside warmhouse booth, guarantee unmanned aerial vehicle at the security of flight in-process. The safe distance from the unmanned aerial vehicle to the obstacle needs to be set according to the size of the unmanned aerial vehicle model. And the planned indoor path of the unmanned aerial vehicle is shortest.
For the unmanned aerial vehicle indoor path planning method provided herein, the following parameters need to be provided for the algorithm: length and width of the aircraft, height of the aircraft, and step size in the vertical direction.
Length and width and height of the aircraft: the algorithm simulates the flight of an unmanned plane in a house model of a three-dimensional space, so that the model of the unmanned plane is also an object with three dimensions of length, width and height. In the experiments herein, the model of the drone is defined as a cylinder with a length and width that is the diameter of the cylinder and a height that is the height of the cylinder. Thus, in the experiments herein, the length and width of the aircraft were equal.
Step length in vertical direction: the step size in the vertical direction is defined as the maximum value that the aircraft can move at each change in altitude on the vertical altitude. In the experiments herein, the step size in the vertical direction is considered to be greater than or equal to the height of one step of the staircase in the house model.
That is, in this application, the obstacle dilation algorithm geometrically dilates around and below the plane, the degree of dilation depending on the size of the aircraft, i.e., the drone herein. Specifically, the radius of expansion in the horizontal direction is equal to the size of the drone model, while the degree of downward expansion is the height of the drone model minus 1.
S3: according to a preset ground extraction algorithm, ground data extraction is carried out on the obstacle expansion layout data information, and corresponding ground structure layout data information in the obstacle expansion layout data information is obtained;
this application has guaranteed unmanned aerial vehicle as far as possible along the steady flight of a specified height through the extraction to ground. This designated height is the height of the drone from the ground. The specific algorithm is as follows: the ground position of the internal structural model of the greenhouse is first determined. Then, in the given internal structure model, there are only obstacles and non-obstacles, wherein the obstacles include the ground, stairs, walls, roofs, etc., so that it is necessary to distinguish and extract the ground (including the stairs) from other obstacles. The distinction between the ground (including stairs) and other obstacles is realized by utilizing different characteristics between the ground and other obstacles.
The specific preset ground extraction algorithm comprises the following steps:
s31: performing space horizontal plane segmentation on the internal structure of the greenhouse, and acquiring horizontal plane data information of the internal structure of the greenhouse;
s32: acquiring preset bottom surface data information in the horizontal surface data information;
s33: expanding from a preset bottom surface of the greenhouse to a plurality of preset directions according to a preset flood filling algorithm, and expanding according to a preset step value;
s34: and extracting preset ground data information of the internal structure of the greenhouse according to the assigned value corresponding to the expanded barrier and a preset comparison algorithm.
Through predetermineeing ground extraction algorithm, can realize guaranteeing unmanned aerial vehicle's flying height.
S4: according to the obtained barrier expansion layout data information and a preset module division rule, carrying out module division on the internal structure model after the barrier expansion, obtaining nodes corresponding to each module, and numbering each node;
s5: selecting preset initial position information and preset end position information from the serial numbers according to the serial numbers of the modules in the step S4, and acquiring a first preset shortest route between the preset initial position and the preset end position by combining a preset A-algorithm, barrier expansion layout data information and ground structure layout data information;
s6: and acquiring a second preset shortest route which traverses all nodes after starting from the preset starting position and returns to the preset starting position by combining the serial number in the step S4, the first preset shortest route between the preset starting position and the preset end position acquired in the step S5 and a preset 2-opt algorithm.
The unmanned aerial vehicle adopts the above technical scheme, can realize starting from any point in the model of the inner structure of the greenhouse, traverse each point one by one, visit only once and not repeat and finally return to the original point.
Further, a distance transformation algorithm is further included between step S5 and step S4, and the method specifically includes the steps of:
a1: acquiring a distance matrix corresponding to an internal structure model of the greenhouse shed according to a preset distance transformation algorithm; the value of a preset position point in the distance matrix is a preset minimum distance value between the position and a preset height plane of an internal structure of the greenhouse;
a2: the method comprises the steps of assigning infinity to a blank space in an internal structure model of the greenhouse, assigning a value a to a preset ground in the internal structure model of the greenhouse, and assigning a value b to a preset obstacle in the internal structure model of the greenhouse.
In the method, a distance matrix of a space of an internal structure model of the greenhouse is obtained by adopting a distance conversion algorithm, a value of each position point in the matrix represents a distance value at the position, namely a minimum distance value of the voxel to a plane with a specified height in the house model, and the minimum distance value is used as a consumption value passing through the voxel.
Assigning infinity to all empty spaces (except the ground, stairs and obstacles in the internal structure model) (in the actual algorithm implementation process, assigning a very large value to the empty spaces to represent infinity), and assigning two different smaller values to the ground (including the stairs) and the obstacles in the internal structure model respectively to ensure that the ground (including the stairs) and the obstacles in the internal structure model can be accurately identified in the subsequent distance transformation algorithm execution process. As shown in fig. 4.
Further, the air conditioner is provided with a fan,
in step S5, the preset a-algorithm is used to obtain the maximum distance between the preset starting position and the preset ending position
Short distance, comprising the steps of:
s51: acquiring a preset first path between a preset starting position and a preset end position through a preset estimation cost function, wherein the preset estimation cost function is as follows:
f(n)＝g(n)+h(n)
wherein g (n) is the distance estimation cost from the preset initial position to the current point position;
h (n) is the cost value of the distance from the preset initial position to the preset end position;
f (n) is the distance estimation cost from the preset initial position to the preset end position through the current point position;
wherein the calculation formula of h (n) is as follows:
wherein (Xend, Yed) is the three-dimensional coordinate of the preset end point position, and (Xn, Yn, Zn) is the three-dimensional coordinate of the current point position.
The method comprises the following specific steps:
the algorithm A is a heuristic search algorithm, and is a most effective direct search method for solving the shortest path in a static road network due to higher flexibility and adaptability, and is an effective algorithm for solving a plurality of search problems.
The A-algorithm combines Dijkstra algorithm and BFS algorithm for use, and makes up for the deficiencies. The heuristic function of the a-algorithm is as follows:
f(n)＝g(n)+h(n)
g (n) -cost estimate of the distance from the starting point to the current point n.
h (n) -cost value of distance from the current point n to the end point.
f (n) -cost estimation from the starting point, through the current point n, to the ending point.
Given a current point n, the value of h (n) is fixed and remains unchanged, while g (n) can be updated, with both the start and end points determined.
In order to obtain the optimal path, the value of h (n) is smaller than or equal to the actual distance from the current point n to the end point, where h (n) is the heuristic function in the path search planning algorithm.
In general, h (n) is obtained by calculating the euclidean distance from the current point n to the end point, and is expressed as:
in the solution, the problem is expanded into a three-dimensional space coordinate system model, and h (n) is defined as the euclidean distance from the current point n to the termination point in the three-dimensional space model, that is:
(xend,yend,zend) Is the coordinate of the termination point in the three-dimensional space coordinate system, (x)n,yn,zn) Is the coordinate of the current point n in the three-dimensional space coordinate system.
The specific steps of the A-algorithm are as follows: firstly, an OpenList table and a CloseList table are created, when a path is planned, nodes to be detected are stored in the OpenList, and grids which are detected are stored in the CloseList.
Setting a preset starting position A and a preset end position B;
firstly, adding a starting point A into an OpenList table, and setting the starting point A as a father node of other 8 grids around. Searching 8 adjacent points of a starting point A, if any of the adjacent points is not in an OpenList table or a CloseList table, calculating the value f (n) of the point, putting the point A into the CloseList table, then judging whether the OpenList table is empty or not, if not, indicating that all possible path points are found before reaching the end point, failing to find the path, and terminating the algorithm; otherwise, continuously taking out a point with the minimum f (n) value from the OpenList table as the next step of the way searching. And then judging whether the point is an end point, if so, successfully searching the route, otherwise, continuously setting the point as a starting point A, and then operating the nearby points around the A.
S52: and according to the route data information from the preset initial position to the preset end position calculated in the step S51 and the distance matrix data information of the internal structure of the greenhouse acquired by combining the preset distance conversion algorithm, acquiring a second preset shortest distance for keeping the preset expansion width of the unmanned aerial vehicle between the preset initial position and the preset end position.
The unmanned aerial vehicle is designed to sequentially traverse the preset points specified in each area from any point in the space of the greenhouse by means of the route planning, each point is visited only once, obstacle avoidance is considered in the flight process, the shortest flight path is selected, and the unmanned aerial vehicle finally returns to the original point; therefore, the problem of route planning of the two preset points can be expanded to the problem of the travelers.
The method comprises the following specific steps: starting at a certain point of n preset nodes of the internal structure of the greenhouse, traversing the n nodes, traversing each node only once, and finally returning to the initial node to obtain the shortest node traversal order. Namely:
Smin＝(s1，s2，s3...，sn)
wherein s isiIs a node, d(s)i，si+1) Representing a node siAnd node si+1When the TSP problem contains n nodes, there are (n-1)! And/2 path orders.
Because a great deal of operation can be carried out when a fine algorithm is used for solving, the problem of the traveling salesman is solved by adopting a 2-opt algorithm which is also called a two-element optimization algorithm, and the specific steps are as follows:
step 1: randomly selecting a route, setting the route as a route I (A- > B- > C- > D- > E- > F- > G), and assuming that the route is the shortest path and the route is min;
step 2: randomly selecting two unconnected nodes in the route, and turning over the path between the two nodes to obtain a new path, wherein if we randomly select the node B and the node E, the new path is A- > (E- > D- > C- > B) - > F- > G, and the part is a turned path;
if the new path is shorter than the min path, setting the new path as the shortest path min, setting the counter value of 0, and returning to the step 2; otherwise, adding 1 to the counter value, when the COUNT is greater than or equal to the COUNT max, ending the algorithm, and at this moment, min is the shortest path, otherwise, returning to the step 2.
The method is used for planning the shortest path between two points through an A-x algorithm, so that the unmanned aerial vehicle can find the shortest path and safely pass through indoor barriers; while the 2-opt algorithm is used to solve the traveler problem in indoor inspection path planning. Firstly writing Python program scripts for an A-algorithm and a 2-opt algorithm respectively, in order to realize effective combination of the two algorithms, executing the 2-opt algorithm on all house nodes to obtain an optimal path combination, then respectively setting two points in the optimal path as a starting point and an end point according to the sequence of the nodes, executing the A-algorithm once to finally obtain the total optimal path length, and visualizing in Paraview software.
Example 2:
the embodiment of the invention provides a path planning system for an unmanned aerial vehicle of a greenhouse, which comprises: the modeling module is used for modeling the internal structure of the greenhouse according to a preset modeling algorithm and acquiring data information of the internal structure model;
the barrier expansion module is used for acquiring barrier expansion layout data information corresponding to the internal structure model of the greenhouse according to the acquired internal structure model data information and a preset barrier expansion algorithm;
the ground extraction module is used for carrying out ground data extraction on the obstacle expansion layout data information according to a preset ground extraction algorithm and acquiring corresponding ground structure layout data information in the obstacle expansion layout data information;
the node numbering module is used for carrying out module division on the internal structure model after the obstacle expansion according to the acquired obstacle expansion layout data information and a preset module division rule, acquiring nodes corresponding to each module and numbering each node;
the system comprises a preset two-point distance obtaining module, a first preset shortest route and a second preset shortest route, wherein the preset two-point distance obtaining module is used for selecting preset starting position information and preset end point position information from numbers, and obtaining a first preset shortest route between a preset starting position and a preset end point position by combining a preset A-algorithm and barrier expansion layout data information and ground structure layout data information;
and the optimal traversal route acquisition module is used for acquiring a second preset shortest route which starts from the preset starting position, traverses all nodes and returns to the preset starting position according to a preset 2-opt algorithm.
Further, the barrier expansion module includes:
the three-dimensional data acquisition unit is used for acquiring three-dimensional data information corresponding to the unmanned aerial vehicle;
and the barrier expansion layout unit is used for expanding the preset initial structure layout data information of the internal structure of the greenhouse according to the acquired length data information, width data information and height data information corresponding to the unmanned aerial vehicle, and acquiring the barrier expansion layout data information of the internal structure of the greenhouse.
Further, the obstacle expanding layout unit includes:
the width expansion unit is used for performing width expansion on the preset initial structure layout data information according to the acquired width information of the unmanned aerial vehicle;
the height expansion unit is used for performing height expansion on the data subjected to width expansion according to the acquired height information of the unmanned aerial vehicle and a preset height expansion algorithm, and subtracting a margin coefficient from the expanded data information;
and the expansion data acquisition unit is used for subtracting the expanded data of the margin coefficient to acquire expanded structure layout data information and storing the expanded structure layout data information to the background server.
Further, the ground extraction module comprises:
the horizontal plane dividing unit is used for performing space horizontal plane division on the internal structure of the greenhouse, acquiring horizontal plane data information of the internal structure of the greenhouse and acquiring preset bottom plane data information in the horizontal plane data information;
the expanding unit is used for expanding from the preset bottom surface of the greenhouse to a plurality of preset directions according to a preset flood filling algorithm and expanding according to a preset step value;
and the ground data information acquisition unit is used for extracting preset ground data information of the internal structure of the greenhouse according to the assigned value corresponding to the expanded barrier and a preset comparison algorithm.
By adopting the system, the corresponding modeling can be realized according to the internal structure of the greenhouse, so that the path planning of the unmanned aerial vehicle can be carried out according to the established indoor structure model of the greenhouse. Can carry out the barrier expansion to the inner structure model of the warmhouse booth who establishes according to unmanned aerial vehicle's size to realize that unmanned aerial vehicle can not bump into the barrier at the flight in-process. Can cut apart the inside horizontal plane of warmhouse booth to extract ground in the horizontal plane, ground includes ground level and stair step plane, thereby can plan that unmanned aerial vehicle carries out the flight control of direction of height. The model after the internal structure of warmhouse booth is established carries out the module division to numbering in advance every module, can not only realize planning for unmanned aerial vehicle's route planning, can also analyze whether the route of unmanned aerial vehicle planning is correct. Executing a 2-opt algorithm on each node of the obtained internal structure of the greenhouse to obtain the optimal combination of the unmanned aerial vehicle in the greenhouse, then respectively setting two points in the optimal path as a starting point and an end point according to the node sequence, executing an A-x algorithm once to finally obtain the optimal path length, and visualizing in preset software.
Example 3:
referring to fig. 7, an unmanned aerial vehicle convenient to change battery, unmanned aerial vehicle includes organism 2 and battery package 1, and battery package 1 includes battery housing 11 and locates the electric core in the battery housing 11, is equipped with the latch groove on the battery housing 11, organism 2 is equipped with the battery compartment 30 that holds battery package 1, is equipped with in the battery compartment 30 and presses pop-up mechanism, and battery compartment 30 has an export, presses pop-up mechanism to install in the one end that is relative setting with the export of battery compartment 30, and what battery package 1 can be inserted is installed in battery compartment 30, and battery package 1 realizes along with pressing the circulation that income battery compartment 30 is inside and pop-up battery compartment 30.
The pressing pop-up mechanism is similar to a ball pen type pressing pop-up mechanism and comprises a sliding base, a chamfered heart-shaped stop block is arranged on the side surface or the lower part of the sliding base, and a sliding rail is arranged along the periphery of the stop block; the sliding rail is respectively formed by sequentially and smoothly connecting a first sliding rail, a second sliding rail, a third sliding rail, a fourth sliding rail and a fifth sliding rail, wherein the first sliding rail and the fourth sliding rail are respectively positioned at two sides below the stop block, the second sliding rail and the third sliding rail are respectively positioned at two sides above the stop block, the fifth sliding rail is positioned below the first sliding rail and the fourth sliding rail, and the fifth sliding rail is connected with the fourth sliding rail into a straight line; a loop bar is arranged in the slide rail, and the other end of the loop bar is movably connected in the unmanned aerial vehicle; be provided with in unmanned aerial vehicle inside and be used for promoting the gliding elastic component from top to bottom of sliding base, the other end of elastic component is connected in sliding base's side top, side below or below. The elastic member is a compression spring when located under or on the side of the slide base. A beveled step is arranged between the fourth sliding rail and the fifth sliding rail, the fourth sliding rail is higher than the fifth sliding rail, the fifth sliding rail is smoothly communicated with the fifth sliding rail, and the top end of the loop bar is ensured to slide in a single direction according to a preset track when the loop bar is used. A first guide bulge is arranged above the second slide rail and the third slide rail, and the first guide bulge deviates to one side of the second slide rail, so that the sleeve rod slides into the third slide rail instead of the second slide rail when sliding in the slide rail; the sleeve rod is ensured to slide in the sliding rail in one way. Furthermore, the top end of the sliding rail four is provided with an inwards concave wedge-shaped slope relative to the sliding rail three, so that the loop bar can smoothly slide from the sliding rail three to the sliding rail four and cannot reversely slide. Furthermore, a wedge-shaped guide protrusion II with a guide function is arranged above the first sliding rail, and the tail end of the guide protrusion II is located above the second sliding rail, so that after the hand is loosened by pressing, the loop bar smoothly slides into a space between the second sliding rail and the third sliding rail.
Press pop-up mechanism still includes locking mechanical system, locking mechanical system includes the spring bolt, and the loop bar is located the junction of slide rail two and slide rail three when the battery package is inside at unmanned aerial vehicle, and the bottom surface of battery package flushes with the export terminal surface in battery compartment, the spring bolt cooperatees with the latchbolt groove on battery package surface inside with the battery package locking at unmanned aerial vehicle's battery compartment, utilizes the finger to press the battery package bottom surface when needing to be changed the battery package, and the one end of loop bar slides into to the slide rail on four from slide rail three, loosens the finger, and under the effect of elastic component, the elastic component promotes the battery package and outwards moves and pop out the battery compartment, and the end of slide rail four relative motion to slide rail five is followed to the one.
Example 4:
an unmanned aerial vehicle convenient to change battery includes: the battery pack comprises a battery shell and a battery core arranged in the battery shell, the side wall of the battery shell is provided with a positioning hole, the side wall of the machine body is provided with a battery cavity structure, the battery cavity structure comprises a battery chamber, an ejection mechanism and a locking mechanism, wherein the battery chamber can accommodate and supply the battery for drawing and inserting, the battery compartment is provided with an outlet, the ejection mechanism is arranged at one end which is opposite to the outlet of the battery compartment, the locking mechanism is used for limiting the battery pack arranged in the battery compartment, so as to limit the battery pack to be separated from the outlet of the battery bin, the battery pack can be inserted and installed in the battery bin, the ejecting mechanism is arranged near the locking mechanism and is a pressing type ejecting mechanism, so that the batteries are circularly collected into the battery compartment and ejected out of the battery compartment along with pressing;
the locking structure is arranged at one end opposite to the outlet of the battery compartment and comprises a sleeve, a sliding base and a spring bolt arranged on the sliding base, a notch is formed in the sleeve, when the spring bolt on the sliding base slides to the notch, the spring bolt is separated from a positioning hole in the battery pack, the battery pack can be moved out of the battery compartment, and when the spring bolt is far away from the notch, the spring bolt is extruded by the sleeve and then is matched with the positioning hole in the battery pack to fix the battery pack in the battery compartment;
the pop-up mechanism comprises a fixed block arranged on the sliding base, the fixed block is fixedly connected with the upper end of the sleeve, a beveled heart-shaped stop block is arranged on the side face of the fixed block, the pop-up mechanism further comprises a moving part which penetrates through the sliding base and is matched with the stop block on the fixed block, one end of the moving part is abutted to the battery pack, and the other end of the moving part is matched with the stop block. The popping mechanism further comprises an elastic part which is arranged in the fixed block and used for pushing the sliding base to slide up and down, and the elastic part drives the sliding base to move up and down, so that the matching of the spring bolt and the positioning hole in the battery pack is controlled, and the battery is locked or unlocked. The fixed block and the sliding base are provided with mutually matched connecting structures, and the fixed block can only move in a certain stroke relative to the sliding base.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (10)
1. An unmanned aerial vehicle path planning method for a greenhouse is characterized by comprising the following steps:
s1: according to a preset modeling algorithm, modeling the internal structure of the greenhouse and acquiring data information of an internal structure model;
s2: acquiring barrier expansion layout data information corresponding to the internal structure model of the greenhouse according to the acquired internal structure model data information and a preset barrier expansion algorithm;
s3: according to a preset ground extraction algorithm, ground data extraction is carried out on the obstacle expansion layout data information, and corresponding ground structure layout data information in the obstacle expansion layout data information is obtained;
s4: according to the obtained barrier expansion layout data information and a preset module division rule, carrying out module division on the internal structure model after the barrier expansion, obtaining nodes corresponding to each module, and numbering each node;
s5: selecting preset initial position information and preset end position information from the serial numbers according to the serial numbers of the modules in the step S4, and acquiring a first preset shortest route between the preset initial position and the preset end position by combining a preset A-algorithm, barrier expansion layout data information and ground structure layout data information;
s6: and acquiring a second preset shortest route which traverses all nodes after starting from the preset starting position and returns to the preset starting position by combining the serial number in the step S4, the first preset shortest route between the preset starting position and the preset end position acquired in the step S5 and a preset 2-opt algorithm.
2. The unmanned aerial vehicle path planning method for the greenhouse as claimed in claim 1, wherein the step S2 of obtaining the obstacle expansion layout data information according to the preset obstacle expansion algorithm specifically comprises the steps of:
s21: acquiring three-dimensional data information corresponding to the unmanned aerial vehicle;
s22: according to the length data information, the width data information and the height data information which are obtained and correspond to the unmanned aerial vehicle, the preset initial structure layout data information of the internal structure of the greenhouse is expanded, and the obstacle expansion layout data information of the internal structure of the greenhouse is obtained.
3. The unmanned aerial vehicle path planning method for the greenhouse as claimed in claim 2, wherein step S22 specifically includes the steps of:
s221: performing width expansion on preset initial structure layout data information according to the acquired width information of the unmanned aerial vehicle;
s222: according to the acquired height information of the unmanned aerial vehicle and a preset height expansion algorithm, performing height expansion on the data subjected to width expansion in the step S221, and subtracting a margin coefficient from the expanded data information;
s223: and obtaining the layout data information of the expanded structure according to the expanded data obtained by subtracting the margin coefficient in the step S222, and storing the layout data information of the expanded structure in the background server.
4. The unmanned aerial vehicle path planning method for the greenhouse as claimed in claim 1, wherein the step S3 of performing ground data extraction on the obstacle expansion layout data information and acquiring corresponding ground structure layout data information in the obstacle expansion layout data information specifically comprises the steps of:
s31: performing space horizontal plane segmentation on the internal structure of the greenhouse, and acquiring horizontal plane data information of the internal structure of the greenhouse;
s32: acquiring preset bottom surface data information in the horizontal surface data information;
s33: expanding from a preset bottom surface of the greenhouse to a plurality of preset directions according to a preset flood filling algorithm, and expanding according to a preset step value;
s34: and extracting preset ground data information of the internal structure of the greenhouse according to the assigned value corresponding to the expanded barrier and a preset comparison algorithm.
5. The unmanned aerial vehicle path planning method for the greenhouse as claimed in claim 1, wherein a distance transformation algorithm is further included between step S5 and step S4, and the method specifically includes the steps of:
a1: acquiring a distance matrix corresponding to an internal structure model of the greenhouse shed according to a preset distance transformation algorithm; the value of a preset position point in the distance matrix is a preset minimum distance value between the position and a preset height plane of an internal structure of the greenhouse;
a2: the method comprises the steps of assigning infinity to a blank space in an internal structure model of the greenhouse, assigning a value a to a preset ground in the internal structure model of the greenhouse, and assigning a value b to a preset obstacle in the internal structure model of the greenhouse.
6. The unmanned aerial vehicle path planning method for the greenhouse as claimed in claim 5, wherein the step S5 of obtaining the shortest distance between the preset starting position and the preset ending position by a preset a-algorithm comprises the steps of:
s51: acquiring a preset first path between a preset starting position and a preset end position through a preset estimation cost function, wherein the preset estimation cost function is as follows:
f(n)＝g(n)+h(n)
wherein g (n) is the distance estimation cost from the preset initial position to the current point position;
h (n) is the cost value of the distance from the preset initial position to the preset end position;
f (n) is the distance estimation cost from the preset initial position to the preset end position through the current point position;
wherein the calculation formula of h (n) is as follows:
h(n)＝√(Xend-Xn)2+(Yend-Yn)2+(Zend-Zn)2
wherein (Xend, Yed) is the three-dimensional coordinate of the preset end point position, and (Xn, Yn, Zn) is the three-dimensional coordinate of the current point position.
S52: and according to the route data information from the preset initial position to the preset end position calculated in the step S51 and the distance matrix data information of the internal structure of the greenhouse acquired by combining the preset distance conversion algorithm, acquiring a second preset shortest distance for keeping the preset expansion width of the unmanned aerial vehicle between the preset initial position and the preset end position.
7. The utility model provides an unmanned aerial vehicle path planning system for warmhouse booth which characterized in that includes:
the modeling module is used for modeling the internal structure of the greenhouse according to a preset modeling algorithm and acquiring data information of the internal structure model;
the barrier expansion module is used for acquiring barrier expansion layout data information corresponding to the internal structure model of the greenhouse according to the acquired internal structure model data information and a preset barrier expansion algorithm;
the ground extraction module is used for carrying out ground data extraction on the obstacle expansion layout data information according to a preset ground extraction algorithm and acquiring corresponding ground structure layout data information in the obstacle expansion layout data information;
the node numbering module is used for carrying out module division on the internal structure model after the obstacle expansion according to the acquired obstacle expansion layout data information and a preset module division rule, acquiring nodes corresponding to each module and numbering each node;
the system comprises a preset two-point distance obtaining module, a first preset shortest route and a second preset shortest route, wherein the preset two-point distance obtaining module is used for selecting preset starting position information and preset end point position information from numbers, and obtaining a first preset shortest route between a preset starting position and a preset end point position by combining a preset A-algorithm and barrier expansion layout data information and ground structure layout data information;
and the optimal traversal route acquisition module is used for acquiring a second preset shortest route which starts from the preset starting position, traverses all nodes and returns to the preset starting position according to a preset 2-opt algorithm.
8. The unmanned aerial vehicle path planning system for a greenhouse of claim 7, wherein the barrier dilation module comprises:
the three-dimensional data acquisition unit is used for acquiring three-dimensional data information corresponding to the unmanned aerial vehicle;
and the barrier expansion layout unit is used for expanding the preset initial structure layout data information of the internal structure of the greenhouse according to the acquired length data information, width data information and height data information corresponding to the unmanned aerial vehicle, and acquiring the barrier expansion layout data information of the internal structure of the greenhouse.
9. The unmanned aerial vehicle path planning system for a greenhouse of claim 8, wherein the barrier expansion layout unit comprises:
the width expansion unit is used for performing width expansion on the preset initial structure layout data information according to the acquired width information of the unmanned aerial vehicle;
the height expansion unit is used for performing height expansion on the data subjected to width expansion according to the acquired height information of the unmanned aerial vehicle and a preset height expansion algorithm, and subtracting a margin coefficient from the expanded data information;
and the expansion data acquisition unit is used for subtracting the expanded data of the margin coefficient to acquire expanded structure layout data information and storing the expanded structure layout data information to the background server.
10. An unmanned aerial vehicle, comprising: the method comprises the following steps: organism (2) and battery package (1), battery package (1) includes battery housing (11) and locates electric core in battery housing (11), the lateral wall of battery housing (11) is provided with the latch groove, unmanned aerial vehicle is equipped with battery compartment (30) that can hold and supply battery to take out and insert, be equipped with in battery compartment (30) and press pop-up mechanism, battery compartment (30) have an export, press pop-up mechanism install with battery compartment (30) export is the one end of relative setting, what battery package (1) can be pulled out installs in battery compartment (30), battery package (1) is along with pressing the circulation and realizing income battery compartment (30) inside and popping out battery compartment (30).
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