CN111736631A - Path planning method and system of pesticide spraying robot - Google Patents
Path planning method and system of pesticide spraying robot Download PDFInfo
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- CN111736631A CN111736631A CN202010655291.3A CN202010655291A CN111736631A CN 111736631 A CN111736631 A CN 111736631A CN 202010655291 A CN202010655291 A CN 202010655291A CN 111736631 A CN111736631 A CN 111736631A
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- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
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- G05D1/10—Simultaneous control of position or course in three dimensions
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- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
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Abstract
The invention relates to the technical field of unmanned aerial vehicles, in particular to a method and a system for spraying pesticides by an unmanned aerial vehicle. The path planning method of the pesticide spraying robot comprises the following steps: acquiring all closed contour data in the current environment, determining each contour starting point according to each closed contour data to form the starting point set, and classifying data in the starting point set to form a classified set; searching calculation contour data matched with the classification set according to the classification set, and calculating a travel path in the current classification set according to the calculation contour data; and searching any basic path in the travel paths, calculating to form a second path matched with the basic path and the target path, and forming the planning path according to the basic path and the second path.
Description
Technical Field
The invention relates to the technical field of unmanned aerial vehicles, in particular to a method and a system for spraying pesticides by an unmanned aerial vehicle.
Background
At each stage of the growth process of crops, the crops are easily infected by pests, so that the normal metabolism of the crops is interfered, and a series of changes and damages occur from physiological functions to tissue structures, so that abnormal pathological phenomena such as withering, rot, spots, mildew, flowers and leaves and the like are presented on external forms, which are collectively called diseases. In order to effectively combat diseases and protect the healthy growth of crops, pesticides need to be sprayed to kill pests and prevent the yield reduction of crops. Nowadays, unmanned airborne pesticide spraying devices are already used by some farmers. But current unmanned aerial vehicle needs the route of traveling of manual control unmanned aerial vehicle when carrying out the operation of spouting medicine, and the incomplete condition of spraying often can be urged in the manual control unmanned aerial vehicle route often, has appeared this condition and still can't in time discover in addition, must pass after a period of time, just can reflect this condition.
Disclosure of Invention
The invention provides a path planning method of a pesticide spraying robot, which can automatically plan a driving path, reduce the labor cost and realize the all-round spraying of pesticide. Specifically, the method comprises the following steps:
in one aspect, a path planning method for a pesticide spraying robot includes:
acquiring all closed contour data in the current environment, determining each contour starting point according to each closed contour data to form the starting point set,
classifying the data in the starting point set to form a classified set;
searching calculation contour data matched with the classification set according to the classification set, and calculating a travel path in the current classification set according to the calculation contour data; searching any basic path in the travel paths, calculating and forming a second path matched with the basic path and the target path,
and forming the planning path according to the basic path and the second path.
Preferably, the above path planning method for a pesticide spraying robot, wherein the acquiring all closed contour data in the current environment, and determining each contour starting point according to each closed contour data to form the starting point set specifically includes:
acquiring all closed contour data in the current environment, and acquiring a coordinate parameter of each contour data;
taking any one coordinate parameter as a reference coordinate origin to obtain a first coordinate point and a second coordinate point which are closest to each coordinate parameter,
calculating the coordinate origin and the first coordinate point to form a first slope,
calculating the coordinate origin and the second coordinate point to form a second slope;
forming the starting point according to the origin of coordinates in a state that the first slope is not equal to the second slope; deleting the coordinate origin under the condition that the first slope is equal to the second slope;
and forming the starting point set according to the starting point.
Preferably, the method for planning a path of a pesticide spraying robot, wherein the classifying the data in the starting point set to form a classification set specifically includes:
reading each starting point data in the starting point set, and sequentially traversing and calculating path data from all the starting point data to other starting points to form path data;
forming basic starting point data according to the starting point data, and obtaining the starting point data matched with the maximum path of the basic starting point data in the path data to form target starting point data;
setting a father node and a child node, and carrying out initialization setting on the father node and the child node;
obtaining starting point data with the maximum distance from the target starting point data and the basic starting point data in the starting point data set to form reference starting point data; taking the target starting point data and the basic starting point data as two father nodes, and taking the reference starting point data as child nodes;
forming each of the calculated profile data from the two parent nodes and the child nodes; releasing the father node and the child node; after all the initial point data are assigned to the father node and/or the child nodes, the calculation is completed;
forming at least two of the categorizing sets from each of the calculated profile data.
Preferably, in the path planning method for the pesticide spraying robot, the calculation contour data matched with the classification set is searched according to the classification set, and the travel path in the current classification set is calculated according to the calculation contour data; acquiring a basic path from the travel path, and calculating and forming a matched second path according to the basic path and the target path specifically comprises:
reading the classified set data, and calculating the travel of each calculated profile data;
sorting the classified set data according to the length of the stroke of the calculated contour data to form a first reference path; taking the first reference path as a basic path;
and forming a second path according to the basic path and the target path.
Preferably, in the path planning method for the pesticide spraying robot, the target path is a maximum driving path of the unmanned aerial vehicle in a single operation state.
In another aspect, the present invention further provides a path planning system for a pesticide spraying robot, including:
a starting point set forming module for obtaining all closed contour data in the current environment, determining each contour starting point according to each closed contour data to form the starting point set,
the classification set forming module is used for classifying the data in the starting point set to form a classification set;
the path calculation forming module is used for searching calculation contour data matched with the classification set according to the classification set and calculating a travel path in the current classification set according to the calculation contour data; searching any basic path in the travel paths, calculating and forming a second path matched with the basic path and the target path,
and the path planning module is used for forming the planning path according to the basic path and the second path.
Preferably, the path planning system for pesticide spraying robot described above, wherein the starting point set forming module specifically includes:
the first acquisition unit is used for acquiring all closed contour data in the current environment and acquiring the coordinate parameter of each contour data;
the first calculation unit is used for acquiring a first coordinate point and a second coordinate point which are closest to each coordinate parameter by taking any one coordinate parameter as a reference coordinate origin, calculating the coordinate origin and the first coordinate point to form a first slope, and calculating the coordinate origin and the second coordinate point to form a second slope;
the first judging unit is used for forming the starting point according to the coordinate origin under the condition that the first slope is not equal to the second slope; deleting the coordinate origin under the condition that the first slope is equal to the second slope;
a first forming unit that forms the start point set according to the start point.
Preferably, the path planning system for a pesticide spraying robot includes a classification set forming module:
the second calculation unit reads each starting point data in the starting point set, and sequentially traverses and calculates path data from all the starting point data to other starting points to form path data;
a target starting point data forming unit, which forms basic starting point data according to the starting point data and obtains the starting point data matched with the maximum path of the basic starting point data in the path data to form target starting point data;
the initialization unit is used for setting a father node and a child node and carrying out initialization setting on the father node and the child node;
the loop calculation unit acquires starting point data with the maximum distance from the target starting point data and the basic starting point data in the starting point data set to form reference starting point data; taking the target starting point data and the basic starting point data as two father nodes, and taking the reference starting point data as child nodes; forming each of the calculated profile data from the two parent nodes and the child nodes; releasing the father node and the child node; after all the initial point data are assigned to the father node and/or the child nodes, the calculation is completed;
and the classification unit is used for forming at least two classification sets according to each calculated profile data.
Preferably, in the path planning system of the pesticide spraying robot, the path calculation and formation module specifically includes:
a third reading unit for reading the classification set data and calculating a stroke of each of the calculated profile data;
the third sorting unit sorts the classified set data according to the length of the stroke of the calculated contour data to form a first reference path; taking the first reference path as a basic path;
and the path calculation unit is used for forming a second path according to the basic path and the target path.
Preferably, in the path planning system for the pesticide spraying robot, the target path is a maximum driving path of the unmanned aerial vehicle in a single operation state.
Has the advantages that:
by adopting the calculation scheme of the invention, the environment area is decomposed into a plurality of triangles with the largest area by adopting an iterative calculation method, the triangles are scanned by taking the slope of the longest side of each triangle as the scanning slope, and for the triangles with smaller areas, a one-way scanning mode is adopted, and the triangles with larger areas are scanned by a back-and-forth two-way mode. Meanwhile, the cross-regional track is selected according to the target path, on one hand, the environment region can be comprehensively decomposed and sprayed, on the other hand, the labor cost is greatly reduced, personnel operation and control are not needed, and automatic spraying can be directly realized.
Drawings
Fig. 1 is a schematic flow chart of a path planning method for a pesticide spraying robot according to an embodiment of the present invention;
fig. 2 is a schematic flow chart of a path planning method for a pesticide spraying robot according to an embodiment of the present invention;
fig. 3 is a schematic diagram illustrating a path planning method for a pesticide spraying robot according to an embodiment of the present invention;
fig. 4 is a schematic diagram illustrating a path planning method for a pesticide spraying robot according to an embodiment of the present invention;
fig. 5 is a schematic planning diagram of a path planning method of a pesticide spraying robot according to an embodiment of the present invention.
Detailed Description
As shown in fig. 1, a path planning method for a pesticide spraying robot includes:
step S10, acquiring all closed contour data in the current environment, and determining each contour starting point according to each closed contour data to form the starting point set; the method specifically comprises the following steps:
step S110, acquiring all closed contour data in the current environment, and acquiring a coordinate parameter of each contour data;
step S120, taking any coordinate parameter as a reference coordinate origin, and acquiring a first coordinate point and a second coordinate point which are closest to each coordinate parameter, wherein the specific calculation method comprises the following steps:
wherein S isiThe distance from the reference coordinate origin to any coordinate parameter;
Xian abscissa parameter which is a reference origin of coordinates;
Yia vertical coordinate parameter which is a reference coordinate origin;
Xjan abscissa parameter which is an arbitrary origin of coordinates;
Yjis a vertical coordinate parameter of an arbitrary coordinate origin.
And forming a first coordinate point by using the two minimum acquired values in the Si values and the coordinate parameter corresponding to the minimum distance value, and forming a second coordinate point by using the coordinate parameter corresponding to the minimum distance value.
Step S130, calculating the coordinate origin and the first coordinate point to form a first slope,
step S140, calculating the coordinate origin and the second coordinate point to form a second slope;
step S150, forming the starting point according to the coordinate origin in the state that the first slope is not equal to the second slope; deleting the coordinate origin under the condition that the first slope is equal to the second slope;
and step S160, forming the starting point set according to the starting point.
Step S20, classifying the data in the starting point set to form a classified set; specifically, the method comprises the following steps of,
step S210 shown in fig. 2, reading each starting point data in the starting point set, and sequentially calculating path data from all the starting point data to other starting points in a traversal manner to obtain route data;
step S220, forming basic starting point data according to the starting point data, and obtaining the starting point data matched with the maximum path of the basic starting point data in the path data to form target starting point data; therefore, the maximum path is selected to form the target starting point, and the aim of obtaining the maximum formation through the basic starting point and the target starting point is fulfilled.
Step S230, setting a father node and a child node, and carrying out initialization setting on the father node and the child node;
step S240, obtaining the starting point data with the maximum distance from the target starting point data and the basic starting point data in the starting point data set to form reference starting point data; taking the target starting point data and the basic starting point data as father nodes, and taking the reference starting point data as child nodes; in the state of having a basic starting point and a target starting point, the reference starting point data is formed by the starting point data with the largest distance, and the maximum triangle can be formed.
In addition, any basic starting point and target starting point are deleted in the starting point set after being used twice, and the reference starting point data is deleted in the starting point data corresponding to the reference starting point data after being used once.
The steps S210 to S230 are loop steps, and the loop is ended until the maximum path is calculated for each starting point. The current environment is split into a plurality of triangles through the above steps S210 to S230. For example, as shown in fig. 3, zone 1, zone 2 … …, zone 6.
Step S250, forming each calculation contour data according to each father node and each child node; releasing the father node and the child nodes, and finishing calculation after all initial point data are assigned to the father node and/or the child nodes;
step S260, forming each calculation contour data according to each reference starting point data, the target starting point data and the basic starting point data;
and step S270, forming at least two classification sets according to each calculated contour data. As shown in fig. 2, there are illustratively provided 6 categorizing sets that match the computed profile data or triangles.
Step S30, searching the calculation contour data matched with the classification set according to the classification set, and calculating the travel path in the current classification set according to the calculation contour data; searching any basic path in the travel paths, and calculating to form a second path matched with the basic path and the target path; specifically comprises
Reading the classification set data and calculating the travel of each calculated profile data in step S310 shown in fig. 4; specifically, for a classification set with an area larger than 30 square meters, a back-and-forth two-way mode is adopted to calculate the stroke, and for a classification set with an area larger than 3 square meters and smaller than 30 square meters, a one-way mode is adopted to calculate the stroke. For example, when the area 6 is smaller than 3 square meters in area, the stroke of the area is not calculated, while the stroke is calculated in a single-pass manner for the areas 3, 4, and 5, and the stroke is calculated in a double-pass manner for the areas 1 and 2.
For the less region of area, adopt the mode of neglecting because the adoption is that unmanned aerial vehicle spraying mode sprays, unmanned aerial vehicle's height often is higher than crops 50 centimetres to 90 centimetres, and to the less region of area this moment, under the state of spraying insecticide with its adjacent region, the less region of area has been covered, so can ignore. For the area with small area, if the crops are small plants, the artificial supplementary spraying mode can be adopted, and if the crops are large plants, the supplementary spraying mode can be used for supplementing the crops. Because unmanned aerial vehicle height on the one hand, in addition on the one hand, the centralized shower nozzle of three holes that unmanned aerial vehicle's shower nozzle adopted, the aperture of shower nozzle is less relatively, at the spraying in-process, the pesticide passes through the shower nozzle and pumps, because the aperture is less in the twinkling of an eye that pumps, its spun pressure is great, this pressure one is convenient provides some holding power for unmanned aerial vehicle, and on the other hand also makes the liquid medicine export with the form of dispersing.
S320, sorting the classified set data according to the length of the stroke of the calculated contour data to form a first reference path; taking the first reference path as a basic path; the basic path is the shortest distance of the path in the current unfinished spraying area. For example, when the area 6 is finished spraying, the area with the shortest travel distance among the areas which are not finished spraying is the area 4.
And step S330, forming a second path according to the basic path and the target path. The method specifically comprises the following steps: the base path trip is subtracted from the target path trip to trip the second path. The target path is the maximum path which can be driven by the unmanned aerial vehicle in a single cruising mode. And the second path is the most suitable area selected from the remaining feasible routes after the basic route state is finished in the single-time driving process of the unmanned aerial vehicle. For example, when area 6 has finished spraying and the remaining area can finish area 2, then the second path is the path of area 2. Instead of selecting either region 4 or region 3 adjacent to region 6.
As shown in fig. 5, step S40 is to form the planned path according to the base path and the second path. And combining the basic path with the second path to route the planned path of the unmanned aerial vehicle in the current driving process.
By adopting the calculation scheme of the invention, the environment area is decomposed into a plurality of triangles, the environment area is scanned in a triangular mode, and for the triangle with a smaller area, a single-pass scanning mode is adopted, and the triangle with a larger area is scanned in a back-and-forth two-pass mode. Meanwhile, the cross-regional track is selected according to the target path, on one hand, the environment region can be comprehensively decomposed and sprayed, on the other hand, the labor cost is greatly reduced, personnel operation and control are not needed, and automatic spraying can be directly realized.
In another aspect, the present invention further provides a path planning system for a pesticide spraying robot, including:
a starting point set forming module for obtaining all closed contour data in the current environment, determining each contour starting point according to each closed contour data to form the starting point set,
the classification set forming module is used for classifying the data in the starting point set to form a classification set;
the path calculation forming module is used for searching calculation contour data matched with the classification set according to the classification set and calculating a travel path in the current classification set according to the calculation contour data; searching any basic path in the travel paths, calculating and forming a second path matched with the basic path and the target path,
and the path planning module is used for forming the planning path according to the basic path and the second path.
As a further preferred embodiment, the path planning system for pesticide spraying robot described above, wherein the starting point set forming module specifically includes:
the first acquisition unit is used for acquiring all closed contour data in the current environment and acquiring the coordinate parameter of each contour data;
the first calculation unit is used for acquiring a first coordinate point and a second coordinate point which are closest to each coordinate parameter by taking any one coordinate parameter as a reference coordinate origin, calculating the coordinate origin and the first coordinate point to form a first slope, and calculating the coordinate origin and the second coordinate point to form a second slope;
the first judging unit is used for forming the starting point according to the coordinate origin under the condition that the first slope is not equal to the second slope; deleting the coordinate origin under the condition that the first slope is equal to the second slope;
a first forming unit that forms the start point set according to the start point.
As a further preferred embodiment, the path planning system for a pesticide spraying robot described above, wherein the classification set forming module specifically includes:
the second calculation unit reads each starting point data in the starting point set, and sequentially traverses and calculates path data from all the starting point data to other starting points to form path data;
a target starting point data forming unit, which forms basic starting point data according to the starting point data and obtains the starting point data matched with the maximum path of the basic starting point data in the path data to form target starting point data;
the initialization unit is used for setting a father node and a child node and carrying out initialization setting on the father node and the child node;
the loop calculation unit acquires starting point data with the maximum distance from the target starting point data and the basic starting point data in the starting point data set to form reference starting point data; taking the target starting point data and the basic starting point data as two father nodes, and taking the reference starting point data as child nodes; forming each of the calculated profile data from the two parent nodes and the child nodes; releasing the father node and the child node; after all the initial point data are assigned to the father node and/or the child nodes, the calculation is completed;
and the classification unit is used for forming at least two classification sets according to each calculated profile data.
As a further preferred embodiment, in the path planning system for a pesticide spraying robot, the path calculation and formation module specifically includes:
a third reading unit for reading the classification set data and calculating a stroke of each of the calculated profile data;
the third sorting unit sorts the classified set data according to the length of the stroke of the calculated contour data to form a first reference path; taking the first reference path as a basic path;
and the path calculation unit is used for forming a second path according to the basic path and the target path.
As a further preferred embodiment, in the path planning system for a pesticide spraying robot, the target path is a maximum driving path of the unmanned aerial vehicle in a single operation state.
The working principle of the path planning system of the pesticide spraying robot is the same as the principle of the path planning method of the pesticide spraying robot, and the details are not repeated here.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (10)
1. A path planning method for a pesticide spraying robot is characterized by comprising the following steps:
acquiring all closed contour data in the current environment, determining each contour starting point according to each closed contour data to form the starting point set,
classifying the data in the starting point set to form a classified set;
searching calculation contour data matched with the classification set according to the classification set, and calculating a travel path in the current classification set according to the calculation contour data; searching any basic path in the travel paths, calculating and forming a second path matched with the basic path and the target path,
and forming the planning path according to the basic path and the second path.
2. The method of claim 1, wherein the step of obtaining all closed contour data in the current environment and determining each contour starting point from each closed contour data to form the set of starting points comprises:
acquiring all closed contour data in the current environment, and acquiring a coordinate parameter of each contour data;
taking any one coordinate parameter as a reference coordinate origin to obtain a first coordinate point and a second coordinate point which are closest to each coordinate parameter,
calculating the coordinate origin and the first coordinate point to form a first slope,
calculating the coordinate origin and the second coordinate point to form a second slope;
forming the starting point according to the origin of coordinates in a state that the first slope is not equal to the second slope; deleting the coordinate origin under the condition that the first slope is equal to the second slope;
and forming the starting point set according to the starting point.
3. The method of claim 1, wherein the classifying the data in the set of starting points to form a classified set specifically comprises:
reading each starting point data in the starting point set, and sequentially traversing and calculating path data from all the starting point data to other starting points to form path data;
forming basic starting point data according to the starting point data, and obtaining the starting point data matched with the maximum path of the basic starting point data in the path data to form target starting point data;
setting a father node and a child node, and carrying out initialization setting on the father node and the child node;
obtaining starting point data with the maximum distance from the target starting point data and the basic starting point data in the starting point data set to form reference starting point data; taking the target starting point data and the basic starting point data as two father nodes, and taking the reference starting point data as child nodes;
forming each of the calculated profile data from the two parent nodes and the child nodes; releasing the father node and the child node; after all the initial point data are assigned to the father node and/or the child nodes, the calculation is completed;
forming at least two of the categorizing sets from each of the calculated profile data.
4. The path planning method for the pesticide spraying robot as claimed in claim 1, wherein the calculation profile data matched with the classification set is searched according to the classification set, and the travel path in the current classification set is calculated according to the calculation profile data; acquiring a basic path from the travel path, and calculating and forming a matched second path according to the basic path and the target path specifically comprises:
reading the classified set data, and calculating the travel of each calculated profile data;
sorting the classified set data according to the length of the stroke of the calculated contour data to form a first reference path; taking the first reference path as a basic path;
and forming a second path according to the basic path and the target path.
5. The path planning method for the pesticide spraying robot as claimed in claim 4, wherein the target path is a maximum driving path of the unmanned aerial vehicle in a single running state.
6. A path planning system of a pesticide spraying robot is characterized by comprising:
a starting point set forming module for obtaining all closed contour data in the current environment, determining each contour starting point according to each closed contour data to form the starting point set,
the classification set forming module is used for classifying the data in the starting point set to form a classification set;
the path calculation forming module is used for searching calculation contour data matched with the classification set according to the classification set and calculating a travel path in the current classification set according to the calculation contour data; searching any basic path in the travel paths, calculating and forming a second path matched with the basic path and the target path,
and the path planning module is used for forming the planning path according to the basic path and the second path.
7. The path planning system of the pesticide spraying robot as claimed in claim 6, wherein the starting point set forming module specifically comprises:
the first acquisition unit is used for acquiring all closed contour data in the current environment and acquiring the coordinate parameter of each contour data;
the first calculation unit is used for acquiring a first coordinate point and a second coordinate point which are closest to each coordinate parameter by taking any one coordinate parameter as a reference coordinate origin, calculating the coordinate origin and the first coordinate point to form a first slope, and calculating the coordinate origin and the second coordinate point to form a second slope;
the first judging unit is used for forming the starting point according to the coordinate origin under the condition that the first slope is not equal to the second slope; deleting the coordinate origin under the condition that the first slope is equal to the second slope;
a first forming unit that forms the start point set according to the start point.
8. The path planning system for pesticide spraying robot as claimed in claim 6, wherein the clustering module specifically comprises:
the second calculation unit reads each starting point data in the starting point set, and sequentially traverses and calculates path data from all the starting point data to other starting points to form path data;
a target starting point data forming unit, which forms basic starting point data according to the starting point data and obtains the starting point data matched with the maximum path of the basic starting point data in the path data to form target starting point data;
the initialization unit is used for setting a father node and a child node and carrying out initialization setting on the father node and the child node;
the loop calculation unit acquires starting point data with the maximum distance from the target starting point data and the basic starting point data in the starting point data set to form reference starting point data; taking the target starting point data and the basic starting point data as two father nodes, and taking the reference starting point data as child nodes; forming each of the calculated profile data from the two parent nodes and the child nodes; releasing the father node and the child node; after all the initial point data are assigned to the father node and/or the child nodes, the calculation is completed;
and the classification unit is used for forming at least two classification sets according to each calculated profile data.
9. The path planning system of the pesticide spraying robot as claimed in claim 6, wherein the path calculation forming module specifically comprises:
a third reading unit for reading the classification set data and calculating a stroke of each of the calculated profile data;
the third sorting unit sorts the classified set data according to the length of the stroke of the calculated contour data to form a first reference path; taking the first reference path as a basic path;
and the path calculation unit is used for forming a second path according to the basic path and the target path.
10. The pesticide spraying robot path planning system of claim 9, wherein the target path is a maximum driving path of the unmanned aerial vehicle in a single running state.
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