KR102522477B1 - System and method for generating optimized flight path using drug spraying drone - Google Patents

Abstract

One embodiment of the present invention, provided is a system for generating an optimized path using a medicine spraying drone. The system for generating an optimized path using a medicine spraying drone comprises: a drone which sprays medicine in a work area; a database which stores information on a medicine reach range calculated in advance based on flight information about the drone, environmental information about the work area, and variable information about the type of the medicine or a type of a spray nozzle; a measurement sensor which is installed on the drone to measure at least one among the wind direction, wind speed, and temperature; a LiDAR which is installed on the drone to identify surrounding obstacles or check the work area; and a processor which derives an optimal path of the drone for spraying the medicine to the work area by considering the information about the medicine reach range stored in the database. According to the present invention, real-time environmental changes are collected using wind direction and wind speed sensors mounted on the drone.

Description

Optimal path generation system and method using drug spraying drone {SYSTEM AND METHOD FOR GENERATING OPTIMIZED FLIGHT PATH USING DRUG SPRAYING DRONE}

The present invention relates to a system and method for generating an optimal route for drone flight, and more particularly, to a system and method for generating an optimal route using a drug spraying drone for pest control of crops.

In general, agricultural chemical spraying work for improving quality and increasing yield by pest control of crops is performed by diluting the chemical in water and putting it in a sprayer and spraying it directly by the operator.

This conventional drug spraying method has a problem that a large number of manpower is addicted to the medicine and can harm their health, there is a problem that it is difficult to find manpower as the rural manpower is slowing down, and the crops must be stepped on in the process of spraying the medicine. Because of this, there is a problem that damage occurs to crops.

In addition, since it is difficult and difficult to work while entering the farmland and dragging a heavy chemical hose, as a way to more conveniently perform the spraying operation, there is a method of spraying the drug while remotely controlling it using an unmanned aerial vehicle (drone) is being carried out

Currently, drug dispensing drones used in the fields of agriculture, forestry, firefighting, etc., perform missions in a passive manner in response to environmental changes while maintaining a predetermined route and altitude.

However, there is a variable in the actual operating environment in which drug spraying is required, such that it is not possible to spray enough drugs in the area where drug spraying is required in the current method, and there is also a problem that the drug reaches an unintended range. Therefore, there is a demand for an algorithm that predicts the drug reach range in consideration of such field variables and injects the drug accordingly.

The technical problem to be achieved by the present invention is to provide an optimal route generation system and method that can obtain the same real-time optimal route calculation effect reflecting environmental changes without directly mounting a high-performance computing device on a drone.

The technical problem to be achieved by the present invention is not limited to the above-mentioned technical problem, and other technical problems not mentioned can be clearly understood by those skilled in the art from the description below. There will be.

In order to achieve the above technical problem, an embodiment of the present invention is an optimal path generation system using a drug spraying drone, a drone that sprays drugs in a work area, flight information about the drone, and environmental information about the work area A database for storing information on the drug reach range calculated in advance based on variable information on the drug type or injection nozzle type, and a measurement sensor installed in the drone to measure at least one of wind direction, wind speed, and temperature; Deriving the optimal path of the drone for dispensing drugs in the work area in consideration of LIDAR installed in the drone to identify nearby obstacles or check the work area, and information on the drug reach range stored in the database Provides an optimal path generation system using a drug spraying drone including a processor for

In an embodiment of the present invention, the flight information includes at least one of speed and altitude of the drone, and the environment information includes at least one of wind direction, wind speed, temperature, and slope slope in the work area. information may be included.

In an embodiment of the present invention, the processor receives obstacle information about an obstacle identified from the lidar and work area information related to the structure of the work area, and wind direction, wind speed, and temperature measured from the measurement sensor. Measurement information related to can be delivered.

In an embodiment of the present invention, the processor compares the work area information received from the lidar and the measurement information received from the measurement sensor with the environmental information stored in the database, and the work area information and the measurement information An optimal path of the drone may be derived in consideration of the reach of the drug according to the corresponding environmental information.

In an embodiment of the present invention, the drone flies along the optimal path derived from the processor and sprays the drug, and the processor periodically receives measurement information on wind direction and speed measured from the measurement sensor. In addition, the optimum path of the drone may be re-derived in consideration of environmental information stored in the database according to whether wind direction change and wind speed change according to a predetermined period are equal to or greater than a threshold value.

In order to achieve the above technical problem, another embodiment of the present invention is a drug reach range in each condition based on flight information about drones, environmental information about work areas, and variable information about drug types or injection nozzle types. Building a database by performing flight and drug dispensing simulations, obtaining information on a work area by the drone performing a preliminary flight, and a processor considering the information on the work area. The step of deriving the first optimal path of the drone, the step of distributing the drug while the drone flies along the first optimal path, the step of collecting measurement information on the wind direction and speed by the measurement sensor, and the processor , Comparing the measurement information collected from the measurement sensor with the environment information stored in the database to derive a secondary optimal route for the drone.

In an embodiment of the present invention, the flight information includes at least one of speed and altitude of the drone, and the environment information includes at least one of wind direction, wind speed, temperature, and slope slope in the work area. information may be included.

In an embodiment of the present invention, the obtaining of information on the work area may include identifying obstacles around the work area by a lidar installed in the drone and collecting information about the slope of the work area. can include

In an embodiment of the present invention, in the step of deriving the first optimal path of the drone, information on the type of drug and the type of spray nozzle is input, obstacle identification information collected from the lidar and information on the slope slope Upon receiving the input, a first-order optimal path of the drone may be derived.

In an embodiment of the present invention, the step of deriving the second optimal path of the drone includes periodically receiving measurement information on wind direction and wind speed measured from the measurement sensor, and changing wind direction and speed according to a predetermined period. Depending on whether is greater than or equal to a threshold value, a secondary optimal path of the drone may be derived by considering environmental information stored in the database.

According to an embodiment of the present invention, a pre-calculated database is obtained in consideration of variables such as speed, altitude, wind direction, wind speed, and temperature of the drone, real-time environmental changes are collected using the wind direction and wind speed sensors mounted on the drone, and real-time By extracting calculated values close to the environmental monitoring results from the database, it is possible to design the optimal route for the drone in real time. This enables the same real-time optimal path calculation effect that reflects environmental changes without directly mounting a high-performance computing device on the drone.

The effects of the present invention are not limited to the above effects, and should be understood to include all effects that can be inferred from the description of the present invention or the configuration of the invention described in the claims.

1 is a block diagram schematically showing the configuration of an optimal path generating system using a drug spraying drone according to an embodiment of the present invention.
2 is a reference diagram illustrating the need to calculate an optimal flight path according to environmental changes.
3 is a flowchart illustrating an optimal path generation method using a drug spraying drone according to an embodiment of the present invention over time.

Hereinafter, the present invention will be described with reference to the accompanying drawings. However, the present invention may be embodied in many different forms and, therefore, is not limited to the embodiments described herein. And in order to clearly explain the present invention in the drawings, parts irrelevant to the description are omitted, and similar reference numerals are attached to similar parts throughout the specification.

Throughout the specification, when a part is said to be "connected (connected, contacted, combined)" with another part, this is not only "directly connected", but also "indirectly connected" with another member in between. "Including cases where In addition, when a part "includes" a certain component, it means that it may further include other components without excluding other components unless otherwise stated.

Terms used in this specification are only used to describe specific embodiments, and are not intended to limit the present invention. Singular expressions include plural expressions unless the context clearly dictates otherwise. In this specification, terms such as "include" or "have" are intended to indicate that there is a feature, number, step, operation, component, part, or combination thereof described in the specification, but one or more other features It should be understood that the presence or addition of numbers, steps, operations, components, parts, or combinations thereof is not precluded.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings.

1 is a block diagram schematically showing the configuration of an optimal path generating system using a drug spraying drone according to an embodiment of the present invention.

Referring to Figure 1, the optimal path generating system according to an embodiment of the present invention is composed of a drone 100, a measurement sensor 200, a lidar 300, a processor 400, and a database 500. can

2 is a reference diagram illustrating the need to calculate an optimal flight path according to environmental changes.

Currently, drug dispensing drones used in fields such as agriculture, forestry, and firefighting perform missions in a passive manner in response to environmental changes while maintaining a set route and altitude. However, the actual operating environment in which drug injection is required has a variable called wind, so as shown in the left figure of FIG. As shown in the right figure of FIG. 2, in a windy environment, in the current method, it is not possible to spray enough chemicals in the area where the chemicals need to be sprayed, and the chemicals reach an unintended range, so the efficiency is very low.

The drone 100 flies over the work area and can spray the medicine contained in the loaded drug container over the crops in the work area.

The measurement sensor 200 is a sensor installed in the drone 100 and can measure at least one of wind direction, wind speed, and temperature around the drone 100 while it flies. For example, the measurement sensor 200 according to an embodiment of the present invention may be implemented as a sensor integrating functions of a wind direction sensor, a wind speed sensor, and a temperature sensor.

Lidar 300 is a sensor installed in the drone 100 and can identify obstacles around the drone 100 or structural features such as obstacles and slope slopes in a work area. The lidar 300 emits a laser pulse, and as the emitted laser pulse is reflected from a nearby object and returns, using the arrival time of the reflected laser pulse, the distance between the lidar and the object is measured, and the distance between the lidar and the object is measured accordingly. You can image the surrounding structure.

The database 500 may store information about the drug reach range calculated in advance based on flight information about the drone 100, environmental information about the work area, and variable information about the drug type or injection nozzle type.

Here, the flight information may be information including at least one of speed and altitude of the drone, and environment information may include at least one of wind direction, wind speed, temperature, and slope slope in the work area. .

According to an embodiment of the present invention, the flight information stored in the database 500, the environmental information, and the information on the drug reach range according to the variable information are for each condition of each flight information and for each condition according to each environmental information, In addition, the database 500 of the present invention can be constructed through the above method with information obtained by performing a simulation on the drug reach range for each condition according to each variable information.

The processor 400 may derive an optimal route of the drone 100 for distributing the drug in the work area in consideration of the information on the reach of the drug stored in the database 500 .

The processor 400 receives obstacle information about the obstacle identified by the lidar 300 and work area information related to the structure of the work area, and measures information related to wind direction, wind speed, and temperature measured from the measurement sensor 200. Receive and compare the received work area information, obstacle information, and measurement information with the environmental information and flight information stored in the database 500 to check the drug reach according to the matching environmental information and flight information, and accordingly, the drone The optimal path of (100) can be derived.

3 is a flowchart illustrating an optimal path generation method using a drug spraying drone according to an embodiment of the present invention over time.

First, in step S310, based on flight information about the drone, environmental information about the work area, and variable information about the type of drug or injection nozzle type, flight and injection simulations are performed for the reach of the drug in each condition. build a database

That is, in step S310, the database considers flight information such as speed and altitude of the drone, environmental information such as wind direction, wind speed, temperature, slope slope of the work area, and variable information such as drug type and spray nozzle, It can be built according to the pre-calculation of the reach of the drug to the work area through simulation of flight and drug spraying under conditions.

In step S320, the drone 100 conducts a preliminary flight to map the work area to be sprayed with the drug. In addition, during the preliminary flight of the drone 100 in step S320, the temperature, wind direction, and wind speed measured by the measurement sensor 200 mounted on the drone 100 are measured, and the lidar 300 measures the area around the work area. Obstacles are identified and work area information about the slope of the work area is acquired.

In step S330, the processor 400 receives information (variable information) on the selected drug type and injection nozzle type, and in step S320, the measurement information obtained from the measurement sensor 200 and the task obtained from the lidar 300 Receive area information. Accordingly, the processor 400 compares the input variable information, measurement information, and work area information with the information stored in the database 500, and checks the drug reach range corresponding to the condition according to the input information. In addition, the processor 400 may derive the first optimal path of the drone 100 in consideration of the confirmed drug reach.

In step S340, the drone 100 flies over the work area according to the first optimal path derived from the processor 400 and sprays the medicine.

In step S350, the measurement sensor 200 collects measurement information on the wind direction and speed in real time while the drone 100 is spraying and flying along the first optimal route, and transmits the collected measurement information to the processor 400. convey

In step S360, the processor 400 determines whether a change in wind direction and a change in wind speed according to a predetermined cycle is equal to or greater than a predetermined threshold through measurement information transmitted in real time from the measurement sensor 200.

If the change in wind direction and wind speed according to the predetermined cycle determined by the processor 400 is equal to or greater than the threshold value, it is determined that the climate change is severe, and in step S370, the processor 400 detects from the measurement sensor 200 in step S350. Based on the measured measurement information, a second optimal path may be derived by referring to the database 500 again.

In more detail, the processor 400 may separate and manage wind direction change and wind speed change data according to a predetermined period from among the wind direction change and wind speed change data transmitted in real time on a periodic basis. . That is, the processor 400 needs to compare data on a plurality of changes in wind direction and wind speed for each period with a predetermined threshold value. Since there are a plurality of data to be referred to by the processor 400, the processor 400 must compare the plurality of data. After calculating the average value of , it may be implemented to determine whether to derive a second optimal path by comparing the calculated average value with the threshold value.

Then, in step S380, the drone 100 may carry out a drug dispensing flight according to the secondary optimal path derived from the processor 400.

And according to another embodiment of the present invention, if the change in the wind direction and the change in wind speed according to the predetermined period is lower than the threshold as a result of the determination of the processor 400 in step S360, it is determined that the climate change is not severe, and separate The operation can be terminated without deriving the second-order optimal path of .

According to another embodiment of the present invention, while the drone 100 is flying the drug spraying along the second optimal path, the measurement sensor 200 measures the wind direction and speed again, so that the wind direction change and the wind speed change Information about the data may be transmitted to the processor 400 . Accordingly, the processor 400 determines whether the transferred wind direction change and wind speed change are greater than or equal to a second threshold set higher than the threshold value, and if the transferred wind direction change and wind speed change are greater than or equal to the second threshold value, the nth order By deriving an optimal path, the drone 100 may be implemented to fly a drug spray according to the derived n-th optimal path.

In this case, a method of comparing wind direction change and wind speed change with a second threshold value for deriving the nth-order optimal path may also be applied in the same manner as the threshold value comparison method in the process of deriving the second-order optimal path.

In the present invention, it has been exemplified and described as a method for deriving the optimal flight path of drones for spraying pesticides for preventing pests and diseases in agriculture and forests, but is not limited thereto and can be applied to seeding, vinyl greenhouse shaders, artificial pollination, etc., and responds to marine pollution. It can also be used in fields other than agriculture, such as spraying emulsifiers in cities and spraying firefighting chemicals.

In addition, it is obvious that various unmanned aerial vehicles such as unmanned helicopters, multi-copters, and fixed-wing unmanned aerial vehicles can be applied in addition to drones according to the database (DB) secured through simulation.

The above description of the present invention is for illustrative purposes, and those skilled in the art can understand that it can be easily modified into other specific forms without changing the technical spirit or essential features of the present invention. will be. Therefore, the embodiments described above should be understood as illustrative in all respects and not limiting. For example, each component described as a single type may be implemented in a distributed manner, and similarly, components described as distributed may be implemented in a combined form.

The scope of the present invention is indicated by the following claims, and all changes or modifications derived from the meaning and scope of the claims and equivalent concepts should be interpreted as being included in the scope of the present invention.

100: drone
200: measurement sensor
300: Lidar
400: processor
500: database (DB)

Claims (10)

In the optimal path generation system using a drug spraying drone,
A drone that sprays chemicals in the work area;
A database for storing information on the drug reach range calculated in advance based on flight information about the drone, environmental information about the work area, and variable information about the type of drug or spray nozzle type;
A measurement sensor installed in the drone to measure at least one of wind direction, wind speed, and temperature;
A lidar installed on the drone to identify nearby obstacles or to check a work area;
A processor for deriving an optimal path of the drone for spraying the drug in the work area in consideration of the information on the reach of the drug stored in the database;
the processor,
Receive obstacle information about the obstacle identified from the lidar and work area information related to the structure of the work area, and receive measurement information related to wind direction, wind speed, and temperature measured from the measurement sensor,
the processor,
Compare the work area information transmitted from the lidar and the measurement information received from the measurement sensor with the environmental information stored in the database, and consider the drug reach according to the work area information and environmental information corresponding to the measurement information Deriving the first optimal path of the drone,
The drone,
Flying according to the first optimal path derived from the processor and spraying the drug,
The measuring sensor,
While the drone flies along the first optimal path, it collects measurement information on wind direction and speed in real time,
the processor,
The measurement information on the wind direction and wind speed measured by the measurement sensor is periodically received, and the drone's secondary data is measured in consideration of the environmental information stored in the database according to whether the wind direction change and wind speed change according to the predetermined period are above the threshold value. An optimal route is derived again, but prior to the derivation of the second optimal route, an average value of data on changes in a plurality of wind directions and wind speeds measured for each predetermined period is calculated, and the calculated average value is compared with the predetermined threshold value Determining whether to derive the second optimal path,
The drone,
When the processor derives the second optimal route, characterized in that the drug is sprayed again while flying according to the derived second optimal route.
According to claim 1,
The flight information includes at least one of speed and altitude of the drone,
The environmental information includes at least one information of wind direction, wind speed, temperature, and slope slope in the work area.
delete delete delete Based on the flight information of the drone, environmental information about the work area, and variable information about the type of drug or spray nozzle type, the database is built as flight and drug injection simulations are performed for the drug reach range in each condition. steps,
Acquiring information on a work area by performing a preliminary flight of the drone;
Deriving, by a processor, a first optimal path of the drone in consideration of the information on the work area;
The drone flying along the first optimal path and dispensing a drug;
Collecting measurement information about wind direction and speed by a measurement sensor;
Deriving, by the processor, a second optimal path of the drone by comparing the measurement information collected from the measurement sensor with environmental information stored in the database;
Obtaining information about the work area,
A lidar installed in the drone identifies obstacles around the work area and collects information about the slope of the work area,
The step of deriving the first optimal path of the drone,
Receive information about the type of drug and the type of injection nozzle, receive obstacle identification information and slope slope information collected from the LIDAR, and derive the first optimal path for the drone,
The step of deriving the second optimal path of the drone,
The measurement information on the wind direction and wind speed measured by the measurement sensor is periodically received, and the drone's secondary data is measured in consideration of the environmental information stored in the database according to whether the wind direction change and wind speed change according to the predetermined period are above the threshold value. An optimal route is derived, but prior to deriving the second optimal route, an average value of data on changes in a plurality of wind directions and wind speeds measured for each predetermined period is calculated, and the calculated average value is compared with the predetermined threshold value to determine the optimal path. An optimal route generation method using a drug spraying drone, characterized in that it determines whether or not to derive a secondary optimal route.
According to claim 6,
The flight information includes at least one of speed and altitude of the drone,
The environmental information includes at least one information of wind direction, wind speed, temperature, and slope slope in the work area.
delete delete delete

Images