KR20240031559A - Smart farm autonomous driving robot system and control method thereof - Google Patents

Abstract

The present invention relates to a smart farm self-driving robot system, and includes a sensor module that includes a vision camera and a position sensor and detects information necessary for the smart farm self-driving robot system to manage an open-air smart farm; A communication module that communicates with an external device through wireless communication, transmits various information from the smart farm autonomous robot system, and receives commands or data for controlling the smart farm autonomous robot system; It controls the overall operation of the smart farm self-driving robot system, uses the information detected through the sensor module to obtain the status of the open-air smart farm and crop growth information, and maintains the field of the open-air smart farm in optimal condition. A processor that manages and manages crops in the open-field smart farm to grow safely; and a driving module that drives the crawler wheels of the smart farm self-driving robot system under the control of the processor to travel along the path of the open-air smart farm.

Description

Smart farm autonomous robot system and control method {SMART FARM AUTONOMOUS DRIVING ROBOT SYSTEM AND CONTROL METHOD THEREOF}

The present invention relates to a smart farm self-driving robot system and a control method thereof, and more specifically, to a smart farm self-driving robot system and a control method thereof that enable management of an open-air smart farm.

In general, Smart Farm refers to a type of intelligent farm that minimizes the need for human labor by automating farming technology by combining information and communication technology (ICT).

According to this smart farm technology, the temperature, humidity, sunlight, carbon dioxide, soil, etc. of crop cultivation facilities are measured and analyzed using Internet of Things (IoT) technology, and the growth status of crops is analyzed, and the analysis results are obtained. Accordingly, crops can be grown in an optimal growth environment by running an automated system, and remote management is also possible through mobile devices such as smartphones.

It is also possible to automatically spray fertilizers and pesticides for pest control.

Meanwhile, these smart farms can be installed in various forms and at various costs depending on how the system is configured, but because they usually cost a lot of money, research has recently been conducted on open-air smart farms, which are cheaper to install than greenhouse-type smart farms. there is. The open field refers to land that is not covered by a roof, and in agriculture, it refers to rice paddies and fields without facilities.

However, in order to manage the smart farm, it is desirable to perform leveling work first. However, in the case of an open-field smart farm, the smart farm system can be installed in open fields (e.g. orchards, highland vegetables, etc.) that are not leveled due to the nature of the open field. Even if leveling is achieved, it may be transformed into a non-flat state due to the effects of wind (typhoon), rain (heavy rain), sunlight (heat wave), etc. Therefore, an autonomous robot system capable of managing such open-air smart farms is needed. It's a situation.

The background technology of the present invention is disclosed in Republic of Korea Patent No. 10-1556301 (registered on September 22, 2015, device for controlling harmful elements in crops using an unmanned automatic robot).

According to one aspect of the present invention, the present invention was created to solve the above problems, and provides a smart farm autonomous robot system and a control method thereof that enable management of an open-air smart farm. There is a purpose.

The smart farm self-driving robot system according to one aspect of the present invention includes a sensor module that includes a vision camera and a position sensor and detects information necessary for the smart farm self-driving robot system to manage an open-air smart farm; A communication module that communicates with an external device through wireless communication, transmits various information from the smart farm autonomous robot system, and receives commands or data for controlling the smart farm autonomous robot system; It controls the overall operation of the smart farm self-driving robot system, uses the information detected through the sensor module to obtain the status of the open-air smart farm and crop growth information, and maintains the field of the open-air smart farm in optimal condition. A processor that manages and manages crops in the open-field smart farm to grow safely; and a driving module that drives the crawler wheels of the smart farm self-driving robot system according to the control of the processor to travel the path of the open-air smart farm.

In the present invention, the device further includes a memory module that stores software or data for driving the smart farm autonomous robot system and data generated while performing management functions of the smart farm autonomous robot system. .

In the present invention, the processor analyzes the image captured through the vision camera based on information learned in advance, and determines the shape of the field, weeds growing in the field, fruits or tree branches that have fallen in the field, and vegetables growing in the field. And it is characterized by distinguishing the shape of the tree, the shape of the tree or branches, the shape of the fruit, the shape of the leaves, and the color.

In the present invention, in order to accurately detect the state of the field and the state of trees or crops using the vision camera of the sensor module under the control of the processor, wind is blown in the shooting direction to prevent tree branches or leaves from moving in front of the camera. It is characterized in that it further includes a wind generation module that moves out of the way so as not to obscure it.

In the present invention, according to the control of the processor, a farming equipment module for selecting one of a plurality of farming equipment modules mounted on one side of the smart farm autonomous robot system and performing a task corresponding to the farming equipment module; It is characterized by including more.

In the present invention, the agricultural tool module includes a farming tool module for leveling the land, such as a hoe or a plow; Agricultural equipment modules for weeding operations, such as scythes or mowers; A farm tool module for pruning, cutting tree branches using pruning shears or an electric saw; and agricultural implement modules for digging or covering the ground using shovels or tongs; It is characterized by including at least one of the following.

In the present invention, the processor analyzes images acquired through a vision camera while the smart farm autonomous robot system travels the path of the open-air smart farm, detects whether there is dug land in the open ground, and detects whether there is dug land in the open ground. When this is detected, the user is notified that there is dug land in the open field, and with the user's permission, the appropriate agricultural equipment module suitable for performing the land leveling work is selected or installed to perform the land leveling work. When the land leveling work is completed, the land leveling work is performed. , It is characterized by informing the user of the images taken before and after the operation and leaving a record in the database of the memory module.

In the present invention, the processor analyzes images acquired through a vision camera while the smart farm autonomous robot system travels the path of the open-field smart farm, detects whether weeds have grown in the open field beyond a specified standard, and detects whether the weeds in the open field have grown beyond a specified standard. When weeds growing in the field are detected, the system notifies the user that weeds are growing in the open field, receives the user's permission, selects or installs a suitable agricultural equipment module to perform the weeding operation, and performs the weeding operation. When the weeding operation is completed, the , It is characterized by informing the user of the images taken before and after the operation and leaving a record in the database of the memory module.

In the present invention, the processor analyzes images acquired through a vision camera while the smart farm autonomous robot system travels the path of the open-air smart farm, and determines the shape of the branches of trees planted in the open-air smart farm. and, as a result of analyzing the shape of the tree branch, if it is detected as a broken tree branch or a tree branch to be pruned, the tree branch can be automatically pruned by a farming equipment module mounted on the smart farm autonomous robot system. If the tree branch is a tree branch capable of automatic pruning, it notifies the user that there is a tree branch to be pruned, and, with the user's permission, selects or installs the appropriate agricultural equipment module to perform the pruning task and performs the electronic task. And, when the battery operation is completed, the images taken before and after the operation are notified to the user and records are left in the database of the memory module.

In the present invention, if the tree branch is not a tree branch capable of automatic pruning, the processor records the location of the tree containing the branch to be pruned in the form of a map in a memory module and guides the user to the recorded location. It is characterized in that it allows performing pruning work.

In the present invention, the processor analyzes images acquired through a vision camera while the smart farm self-driving robot system travels the path of the open-air smart farm, and detects a tree hanging on a branch of a tree planted in the open-air smart farm. The shape and color of the fruit are analyzed, and if unbagged fruit is detected at the current growth point of the fruit hanging from the tree branch, the location of the tree where the unbagged fruit is hanging is stored in the memory module in the form of a map. records, guides the user to the recorded location so that the user can perform the fruit bag task, and when the user performs the fruit bag task, the fruit bag task is completed for all fruits by comparing the information recorded on the map. When the fruit bag operation is completed for all fruits recorded on the map, the image captured before and after the operation is notified to the user and a record is left in the database of the memory module.

In the present invention, the processor analyzes images acquired through a vision camera while the smart farm autonomous robot system travels the path of the open-air smart farm, detects whether there is fruit that has fallen on the ground, and detects whether there is fruit that has fallen on the ground. If the fruit is rotten or the fruit has burrowed flesh, the fruit burying task is performed by selecting or installing the appropriate agricultural equipment module suitable for performing the task of burying the fruit that has fallen to the ground, and the fruit burying task is completed. When this happens, the images taken before and after the operation are notified to the user and a record is left in the database of the memory module.

In the present invention, the processor analyzes images acquired through a vision camera while the smart farm self-driving robot system travels the path of the open-air smart farm, and detects a tree hanging on a branch of a tree planted in the open-air smart farm. Analyze the shape and color of the fruit, and if the fruit hanging on the branch is one of rotten fruit, fruit with peeled flesh, or ripe fruit, record the location of the tree on which the fruit hangs in the form of a map in the memory module. , the recorded location is guided to the user so that the user can perform a fruit removal operation or a fruit harvesting operation, and when the user performs a fruit removal operation or a fruit harvesting operation, all information by type is compared with the information recorded on the map. It is checked whether the corresponding task is completed for each fruit, and when the corresponding task is completed for all fruits of each type recorded on the map, the images taken before and after the task are notified to the user and recorded in the database of the memory module. It is characterized by leaving behind.

A method of controlling a smart farm self-driving robot system according to another aspect of the present invention includes the processor of the smart farm self-driving robot system using a vision camera while the smart farm self-driving robot system is traveling on a path of an open-air smart farm. Analyzing the acquired image to detect whether there is dug ground in the open field; When dug up land is detected in the open field, notifying the user that there is dug up land in the open field, receiving permission from the user, and performing the land leveling work by selecting or installing a corresponding agricultural equipment module suitable for performing the land leveling work; And when the land leveling work is completed, notifying the user of images taken before and after the work and leaving a record in the database of the memory module.

In the present invention, after the smart farm self-driving robot system analyzes images acquired through a vision camera while driving the path of an open-air smart farm, the processor detects whether weeds in the open field have grown beyond a specified standard. steps; When weeds growing in the open field are detected, notifying the user that weeds are growing in the open field, receiving permission from the user, and performing the weeding work by selecting or installing a corresponding agricultural equipment module suitable for performing the weeding work; And when the weeding operation is completed, notifying the user of images captured before and after the operation and leaving a record in the database of the memory module.

In the present invention, after the step of analyzing images acquired through a vision camera while the smart farm autonomous robot system travels the path of the open-air smart farm, the processor Analyzing the shape of the branches; As a result of analyzing the shape of the branch, if it is detected as a broken branch or a branch to be pruned, checking whether the branch is capable of automatic pruning by a farming equipment module mounted on the smart farm autonomous robot system. ; If the tree branch is a tree branch capable of automatic pruning, notifying the user that there is a tree branch to be pruned, receiving the user's permission, and performing an electronic task by selecting or installing a corresponding agricultural equipment module suitable for performing the pruning task; And when the battery operation is completed, notifying the user of images captured before and after the operation and leaving a record in the database of the memory module.

In the present invention, if the tree branch is not a tree branch capable of automatic pruning, the processor records the location of the tree containing the branch to be pruned in the form of a map in a memory module and guides the user to the recorded location. It is characterized in that it allows performing pruning work.

In the present invention, after the step of analyzing images acquired through a vision camera while the smart farm autonomous robot system travels the path of the open-air smart farm, the processor Analyzing the shape and color of fruit hanging from branches; If unbagged fruit is detected at the current growth point of the fruit hanging from the tree branch, the location of the tree where the unbagged fruit hangs is recorded in the form of a map in the memory module, and the recorded location is sent to the user. Guiding a user to perform a fruit bag task; When the user performs the fruit bag task, comparing the information recorded in the map to check whether the fruit bag task is completed for all fruits; And when the fruit bag operation is completed for all fruits recorded in the map, notifying the user of images taken before and after the operation and leaving a record in the database of the memory module.

In the present invention, after analyzing images acquired through a vision camera while the smart farm autonomous robot system travels a path of an open-air smart farm, the processor detects whether there is fruit that has fallen on the ground; If the fruit that has fallen to the ground is rotten or has a hollow flesh, performing the fruit burying task by selecting or installing a corresponding farming tool module suitable for performing the task of burying the fruit that has fallen to the ground; And when the fruit burying operation is completed, notifying the user of images captured before and after the operation and leaving a record in the database of the memory module.

In the present invention, after the step of analyzing images acquired through a vision camera while the smart farm autonomous robot system travels the path of the open-air smart farm, the processor Analyzing the shape and color of fruit hanging from branches; If the fruit hanging on the tree branch is a rotten fruit, a fruit with peeled flesh, or a ripe fruit, the location of the tree on which the fruit is hanging is recorded in the form of a map in a memory module, and the recorded location is guided to the user. enabling a user to perform a fruit removal operation or a fruit harvesting operation; When the user performs a fruit removal task or a fruit harvest task, comparing the information recorded on the map to check whether the corresponding task is completed for all fruits of each type; And when the corresponding task is completed for all fruits of each type recorded on the map, notifying the user of the images taken before and after the task and leaving a record in the database of the memory module.

According to one aspect of the present invention, the present invention enables management of an open-air smart farm.

1 is an exemplary diagram showing the schematic configuration of a smart farm autonomous robot system according to an embodiment of the present invention.
FIG. 2 is an exemplary diagram showing the schematic shape of the smart farm autonomous robot system in FIG. 1.
Figure 3 is a flowchart illustrating a control method of a smart farm autonomous robot system according to the first embodiment of the present invention.
Figure 4 is a flowchart illustrating a control method of a smart farm autonomous robot system according to a second embodiment of the present invention.
Figure 5 is a flowchart illustrating a control method of a smart farm autonomous robot system according to a third embodiment of the present invention.
Figure 6 is a flowchart illustrating a control method of a smart farm autonomous robot system according to a fourth embodiment of the present invention.
Figure 7 is a flowchart illustrating a control method of a smart farm autonomous robot system according to a fifth embodiment of the present invention.
Figure 8 is a flowchart illustrating a control method of a smart farm autonomous robot system according to a sixth embodiment of the present invention.

Hereinafter, an embodiment of the smart farm autonomous robot system and its control method according to the present invention will be described with reference to the attached drawings.

In this process, the thickness of lines or sizes of components shown in the drawing may be exaggerated for clarity and convenience of explanation. In addition, the terms described below are terms defined in consideration of functions in the present invention, and may vary depending on the intention or custom of the user or operator. Therefore, definitions of these terms should be made based on the content throughout this specification.

FIG. 1 is an exemplary diagram showing the schematic configuration of a smart farm autonomous robot system according to an embodiment of the present invention, and FIG. 2 is an exemplary diagram showing the schematic configuration of the smart farm autonomous robot system in FIG. 1. am.

As shown in FIG. 1, the smart farm autonomous robot system according to this embodiment includes a sensor module 110, a communication module 120, a memory module 130, a processor 140, a driving module 150, It includes a wind generation module 160 and a farming equipment module 170.

The sensor module 110 detects information to enable the smart farm self-driving robot system to perform smart farm management functions through autonomous driving.

For example, the sensor module 110 includes at least a camera (vision camera) and a location sensor (eg, GPS).

The camera of the sensor module 110 is provided on one side of the smart farm autonomous robot system (e.g., the top of the rotatable wind generation module, the top or front of the main body) and captures images of the condition of the field and the branches or fruits of crops. It can be obtained.

Additionally, the position sensor (GPS) of the sensor module 110 detects the current location where the smart farm autonomous robot system is moving (i.e., the location within the smart farm).

The communication module 120 may perform a wireless communication function with an external device (e.g., a smart farm management PC, a smart farm management server, an administrator's smartphone, etc.).

For example, the external devices can receive various information from the smart farm autonomous robot system through the communication module 120, and can also transmit commands or data for controlling the smart farm autonomous robot system.

The memory module 130 stores software or data for driving the smart farm autonomous robot system.

Additionally, the memory module 130 may store data generated while performing management functions of the smart farm autonomous robot system.

The processor 140 generally controls the operation of the smart farm autonomous robot system, and obtains the status of the smart farm and crop growth information through vision inspection using the sensor module 110, and based on this, the smart farm We manage the farm in optimal condition and manage crops so that they can grow safely.

The processor 140 analyzes information (eg, image information, location information) detected through the sensor module 110.

For example, the processor 140 analyzes the image based on information learned in advance (e.g., deep learning algorithm) to determine the shape of the open field, weeds (grass) growing in the open field, and objects (e.g., fruits, trees) that have fallen on the open field. branches, animal feces, etc.), vegetables and trees, etc., and also distinguish the shape of trees or branches, the shape of fruit, the shape and color of leaves, etc.

The driving module 150 controls the wheel (e.g., crawler wheel) 151 to move forward or backward based on power supplied from a battery (not shown) under the control of the processor 140. , performs speed control and direction control.

The processor 140 controls the driving module 150 based on information detected through the sensor module 110, allowing the smart farm self-driving robot system to perform a smart farm management function through autonomous driving. .

The wind generation module 160, under the control of the processor 140, uses the camera of the sensor module 110 to accurately detect the state of the field and trees (or crops) (e.g., behind tree branches or leaves). (to take pictures of hidden fruits, etc.), spray wind in the shooting direction to move tree branches or leaves out of the way so that they do not block the front of the camera.

The processor 140 analyzes the image detected through the sensor module 110 to detect broken tree branches or pruning (i.e., to even out the outer appearance of plants, prevent overgrowth, and increase production of fruit trees, etc.). Detecting tree branches that require pruning, pruning, pruning, etc., which involves cutting and trimming side branches, etc.

In addition, in orchards, etc., fruits are wrapped in paper bags to prevent them from being damaged by birds or insects. The processor 140 analyzes the image detected through the sensor module 110 and determines the fruit wrapped in the paper bag. Separate fruit from fruit not wrapped in a paper bag.

In addition, the processor 140 distinguishes rotten fruit, peeled fruit, and ripe fruit from fruit trees (e.g., apples, pears, peaches, etc.), records the number and location of each type, and creates a map for use by the user. By notifying the user, the user can perform a fruit removal task or a fruit harvesting task.

The farming equipment module 170 is one of a plurality of farming equipment modules (i.e., a plurality of farming equipment modules required for smart farm management) mounted on one side of the smart farm autonomous robot system under the control of the processor 140. Select one and perform the task corresponding to the farm equipment module.

For example, the agricultural tool module 170 includes a farming tool module for leveling the land such as a hoe or a plow (farming tool module No. 1), a farming tool module for weeding work such as a sickle or a lawnmower (farming tool module No. 2), secateurs or an electric saw. It includes a farming tool module (No. 3 farming tool module) for pruning work by cutting tree branches, and a farming tool module (No. 4 farming tool module) for digging or covering the ground using a shovel or tongs.

However, the above-mentioned agricultural equipment module is merely described as an example, and further agricultural equipment modules that perform more diverse functions may be included.

Here, each farming equipment module included in the farming equipment module 170 is preliminarily placed on one side (e.g., bottom, top, front, rear) of the smart farm autonomous robot system according to the characteristics of the work to be performed by the farming equipment module. It can be attached or carried on the main body and used by installing (or attaching) it to a designated location when the required agricultural equipment module is selected (or switched).

Hereinafter, the control method of the smart farm autonomous robot system will be described.

-First Embodiment-

Figure 3 is a flowchart for explaining a control method of a smart farm autonomous robot system (hereinafter simply referred to as a robot system) according to the first embodiment of the present invention.

Referring to FIG. 3, the processor 140 analyzes the image acquired through the camera while the robot system according to this embodiment travels the path of the open-air smart farm (S101), and determines whether the dug-out ground is in the open ground (S102). Detect whether it exists (S103).

For example, due to the nature of open fields, the ground may be damaged by natural conditions (e.g. wind, rain, etc.).

As a result of the detection (S103), when dug-up land is detected in the open field (example of S103), the processor 140 notifies the user that there is dug-up land in the open field (i.e., the dug-up land in the open field is provided through the user's external device). informing the user that there is a land leveling operation), if the user is permitted to perform land leveling work (example of S104), select (or switch) or install the corresponding farm equipment module (e.g. farm equipment module No. 1) suitable for performing the land leveling work. After (or attaching) (S105), the robot system is moved to a position dug into the ground (S106).

When the robot system moves to the dug position, the processor 140 performs a ground leveling task using the corresponding farm equipment module (e.g., farm equipment module No. 1) (S107), and when the land leveling work is completed (S108) example), the images captured before and after the work are notified to the user (i.e., the work is completed through the user's external device), and a record is left in the database of the memory module 130 (S109).

-Second Embodiment-

Figure 4 is a flowchart for explaining a control method of a smart farm autonomous robot system (hereinafter simply referred to as a robot system) according to a second embodiment of the present invention.

Referring to FIG. 4, the processor 140 analyzes the image acquired through the camera while the robot system according to this embodiment travels the path of the open-air smart farm (S201) and detects weeds (grass) in the open field (S202). ) detects whether it has grown beyond the specified standard (S203).

As a result of the detection (S203), when weeds (grass) growing in the open field are detected (example of S203), the processor 140 notifies the user that weeds (grass) have grown in the open field (i.e., the user's external device (notifies that weeds are growing in the field), and when the user is permitted to perform weeding work (weed removal work) (example of S204), the corresponding agricultural equipment module suitable for performing the weeding work (e.g., agricultural equipment module No. 2) ) is selected (or switched) or mounted (or attached) (S205), and then the robot system is moved to a location for weeding work (S206).

When the robot system moves to a position to perform weeding work, the processor 140 performs weeding work using the corresponding farming tool module (e.g., farm tool module No. 2) (S207), and when the weeding work is completed (S208) Example), images captured before and after the work are notified to the user (i.e., notified that the work has been completed through the user's external device), and a record is left in the database of the memory module 130 (S209).

-Third Embodiment-

Figure 5 is a flowchart for explaining a control method of a smart farm autonomous robot system (hereinafter simply referred to as a robot system) according to a third embodiment of the present invention.

Referring to FIG. 5, the processor 140 analyzes images acquired through a camera while the robot system according to this embodiment travels the path of the open-field smart farm (S301), and determines the trees planted in the open-field smart farm. Analyze the shape of the tree branches (S302).

As a result of analyzing the shape of the tree branch (S203), if a broken tree branch or a tree branch to be pruned is detected (example of S303), the processor 140 determines that the tree branch is moved by the robot system (i.e., the robot Automatically checks whether a tree branch can be pruned (by the agricultural equipment module mounted on the system) (S304).

As a result of the check (S304), if the tree branch is a tree branch that can be automatically pruned (example of S304), the user is notified that there is a tree branch to be pruned (i.e., that there is a tree branch to be pruned through the user's external device). inform), when the user is permitted to perform pruning work (S305), after selecting (or switching) or installing (or attaching) the relevant agricultural equipment module (e.g., farm equipment module No. 3) suitable for performing the pruning work (S305) S306), move the robot system to a location for weeding work, and then perform pruning work using the corresponding farm equipment module (e.g., farm tool module No. 3) (S307). When the pruning work is completed (example in S308), The images captured before and after the work are notified to the user (that is, notified that the work has been completed through the user's external device), and a record is left in the database of the memory module 130 (S309).

Meanwhile, as a result of the check (S304), if the tree branch is not a tree branch capable of automatic pruning (No in S304), the corresponding location (i.e., the location of the tree with the branch to be pruned) is mapped to the memory module 130. It is recorded (saved) in a (map) form, and the recorded (saved) location is guided to the user (S310) so that the user can perform pruning work.

-Fourth Embodiment-

Figure 6 is a flowchart for explaining a control method of a smart farm autonomous robot system (hereinafter simply referred to as a robot system) according to a fourth embodiment of the present invention.

Referring to FIG. 6, the processor 140 analyzes images acquired through a camera while the robot system according to this embodiment travels the path of the open-field smart farm (S401), and determines the trees planted in the open-field smart farm. Analyze the form (e.g. shape and color) of the fruit attached (hanging) to the tree branch (S402).

As a result of analyzing the shape (e.g., shape and color) of the fruit hanging on the tree branch (S203), if unbagged fruit is detected at the current growth point of the fruit (example of S403), the processor 140 records (saves) the location (i.e., the location of the tree on which unbagged fruit hangs) in the form of a map in the memory module 130 (S404), and records (stores) the recorded (stored) location by the user. (S405) so that the user can perform the fruit bag task (i.e., wrapping fruit in a bag) (S406).

As described above, when the user performs the fruit bag task (i.e., wrapping fruit in a bag), the information recorded in the map is compared (contrasted) and the fruit bag task (i.e., wrapping fruit in a bag) is performed for all fruits. Check whether the wrapping task is completed (S407), and if the fruit bag task (i.e., the task of wrapping fruit in a bag) is completed for all fruits recorded on the map (example of S407), before the task /Afterward, the captured image is notified to the user (i.e., notified that the task has been completed through the user's external device), and a record is left in the database of the memory module 130 (S408).

-Fifth Embodiment-

Figure 7 is a flowchart for explaining a control method of a smart farm autonomous robot system (hereinafter simply referred to as a robot system) according to the fifth embodiment of the present invention.

Referring to FIG. 7, the processor 140 analyzes the image acquired through the camera while the robot system according to this embodiment travels the path of the open-air smart farm (S501) and determines whether fruit that has fallen on the ground is detected (S502). It detects whether the detected fruit has fallen on the ground (S503), and checks whether the detected fruit is rotten or has its flesh broken (i.e., unmarketable or inedible fruit) (S504).

As a result of the check (S504), if the fruit that fell on the ground is rotten or has a hollow fruit (example of S504), according to a preset algorithm, the processor 140 performs a task to bury the fruit that fell on the ground. After selecting (or switching) or mounting (or attaching) the corresponding farm tool module (e.g., farm tool module No. 4) suitable for performing the fruit burying task (i.e., fruit burying task) (S506), the robot system is used to perform the fruit burying task. After moving to the location, a fruit burying task is performed using the relevant farming tool module (e.g., farm tool module No. 4) (S506). When the fruit burying task is completed (example in S507), photos taken before/after the work are taken. The image is notified to the user (i.e., notified that the task has been completed through the user's external device), and a record is left in the database of the memory module 130 (S508).

-Sixth Embodiment-

Figure 8 is a flowchart for explaining a control method of a smart farm autonomous robot system (hereinafter simply referred to as a robot system) according to the sixth embodiment of the present invention.

Referring to FIG. 8, the processor 140 analyzes images acquired through a camera while the robot system according to this embodiment travels the path of the open-field smart farm (S601), and determines the trees planted in the open-field smart farm. Analyze the form (e.g. shape and color) of fruit attached (hanging) to a branch of a fruit tree (S602).

As a result of analyzing the shape (e.g., shape and color) of the fruit hanging on the tree branch (S602), if it is one of rotten fruit, fruit with peeled flesh, or ripe fruit (example of S603), the processor 140 records (stores or updates) the location (i.e., the location of the rotten fruit, the peeled fruit, or the tree on which the ripe fruit hangs) in the form of a map in the memory module 130 (S604), and the record The (saved) location is guided to the user (S605) so that the user can perform fruit removal or fruit harvesting (S606).

When the user performs fruit removal or fruit harvesting as described above, the information recorded on the map is compared and compared to all fruits by type (i.e., rotten fruit, peeled fruit, or ripe fruit). It is checked whether the corresponding task is completed (S607), and when the corresponding task is completed for all fruits of each type recorded on the map (example of S607), the images taken before and after the task are recorded. Notifies the user (that is, notifies the user that the task has been completed through the user's external device) and leaves a record in the database of the memory module 130 (S608).

As described above, in this embodiment, the smart farm self-driving robot system autonomously moves the open-air smart farm, which is cheaper than the greenhouse-type smart farm, on behalf of the user, and the open-air smart farm is exposed to natural conditions. It has the effect of improving user convenience by allowing management of crops (e.g. fruit trees) growing in the system.

As described above, the present invention has been described with reference to the embodiments shown in the drawings, but these are merely illustrative, and various modifications and equivalent embodiments can be made by those skilled in the art. You will understand the point. Therefore, the scope of technical protection of the present invention should be determined by the scope of the patent claims below. Implementations described herein may also be implemented as, for example, a method or process, device, software program, data stream, or signal. Although discussed only in the context of a single form of implementation (eg, only as a method), implementations of the features discussed may also be implemented in other forms (eg, devices or programs). The device may be implemented with appropriate hardware, software, firmware, etc. The method may be implemented in a device such as a processor, which generally refers to a processing device that includes a computer, microprocessor, integrated circuit, or programmable logic device. Processors also include communication devices such as computers, cell phones, portable/personal digital assistants (“PDAs”) and other devices that facilitate communication of information between end-users.

110: sensor module
120: communication module
130: memory module
140: processor
150: driving module
151: Caterpillar wheel
160: Wind generation module
170: Farm equipment module

Claims (20)

A sensor module that includes a vision camera and a location sensor and detects information necessary for the smart farm self-driving robot system to manage the open-air smart farm;
A communication module that communicates with an external device through wireless communication, transmits various information from the smart farm autonomous robot system, and receives commands or data for controlling the smart farm autonomous robot system;
It controls the overall operation of the smart farm self-driving robot system, uses the information detected through the sensor module to obtain the status of the open-air smart farm and crop growth information, and maintains the field of the open-air smart farm in optimal condition. A processor that manages and manages crops in the open-field smart farm to grow safely; and
A smart farm self-driving robot system comprising a driving module that drives the tracked wheels of the smart farm self-driving robot system under the control of the processor to drive the path of the open-air smart farm.
The method of claim 1, further comprising a memory module that stores software or data for driving the smart farm autonomous robot system and data generated while performing management functions of the smart farm autonomous robot system. Smart farm self-driving robot system.
The method of claim 1, wherein the processor:
Based on information learned in advance, the images captured through the vision camera are analyzed to determine the shape of the open field, weeds growing in the open field, fruits or tree branches that have fallen in the open field, the shape of vegetables and trees growing in the open field, and trees or twigs. A smart farm autonomous robot system characterized by distinguishing the shape of fruit, shape of leaves, and color.
According to clause 1,
Under the control of the processor, in order to accurately detect the state of the field and the state of trees or crops using the vision camera of the sensor module, wind is sprayed in the shooting direction to prevent tree branches or leaves from blocking the front of the camera. A smart farm autonomous robot system further comprising a wind generation module.
According to clause 1,
The feature further includes a farming equipment module for selecting one of a plurality of farming equipment modules mounted on one side of the smart farm autonomous robot system under the control of the processor and performing a task corresponding to the farming equipment module. A smart farm self-driving robot system.
The method of claim 5, wherein the agricultural equipment module,
A farming tool module for leveling the land, such as a hoe or plow;
Agricultural equipment modules for weeding operations, such as scythes or mowers;
A farm tool module for pruning, cutting tree branches using pruning shears or an electric saw; and
A farm implement module for digging and covering with a shovel or tongs; A smart farm autonomous robot system comprising at least one of the following:
The method of claim 1, wherein the processor:
As the smart farm self-driving robot system drives the path of the open-air smart farm, it analyzes images acquired through a vision camera to detect whether there is a hollow ground in the open ground,
When dug up land is detected in the open field, the user is notified that there is dug up land in the open field, and, with the user's permission, the appropriate agricultural equipment module suitable for performing the land leveling work is selected or installed to perform the land leveling work,
When the land leveling work is completed, the smart farm autonomous robot system is characterized in that it informs the user of the images taken before and after the work and leaves a record in the database of the memory module.
The method of claim 1, wherein the processor:
As the smart farm autonomous robot system drives the path of the open-field smart farm, it analyzes images acquired through a vision camera to detect whether weeds have grown beyond the specified standard in the open field.
When weeds growing in the open field are detected, the user is notified that weeds are growing in the open field, and, with the user's permission, the appropriate agricultural equipment module suitable for performing the weeding work is selected or installed to perform the weeding work.
When the weeding operation is completed, the smart farm autonomous robot system is characterized in that it notifies the user of the images taken before and after the operation and leaves a record in the database of the memory module.
The method of claim 1, wherein the processor:
As the smart farm autonomous robot system drives the path of the open-air smart farm, it analyzes images acquired through a vision camera to analyze the shape of the branches of the trees planted in the open-air smart farm.
As a result of analyzing the shape of the tree branch, if it is detected as a broken tree branch or a tree branch to be pruned, it is checked whether the tree branch is capable of automatic pruning by a farming equipment module mounted on the smart farm autonomous robot system,
If the tree branch is a tree branch capable of automatic pruning, inform the user that there is a tree branch to be pruned, obtain permission from the user, select or install a corresponding agricultural equipment module suitable for performing the pruning task, and perform the electronic task,
When the pruning work is completed, the smart farm autonomous robot system is characterized in that it notifies the user of the images taken before and after the work and leaves a record in the database of the memory module.
The method of claim 9, wherein the processor:
If the above tree branch is not a tree branch capable of automatic pruning,
A smart farm autonomous robot system that records the location of trees with branches to be pruned in the form of a map on a memory module and guides the user to the recorded location so that the user can perform pruning work.
The method of claim 1, wherein the processor:
As the smart farm self-driving robot system travels the route of the open-air smart farm, it analyzes images acquired through a vision camera to analyze the shape and color of the fruit hanging from the branches of the trees planted in the open-air smart farm.
If unbagged fruit is detected at the current growth point of the fruit hanging from the tree branch, the location of the tree where the unbagged fruit hangs is recorded in the form of a map in the memory module, and the recorded location is sent to the user. Guidance so that the user can perform the fruit bag task,
When the user performs a fruit bag task, it is checked whether the fruit bag task is completed for all fruits by comparing it with the information recorded on the map, and when the fruit bag task is completed for all fruits recorded on the map, before/after the task. A smart farm self-driving robot system that notifies the user of the captured video and records it in the memory module database.
The method of claim 1, wherein the processor:
As the smart farm autonomous robot system drives the path of the open-air smart farm, it analyzes images acquired through a vision camera and detects whether there are any fruits that have fallen on the ground.
If the fruit that has fallen on the ground is rotten or has a hollowed-out flesh, perform the fruit burying task by selecting or installing a corresponding agricultural tool module suitable for performing the task of burying the fruit that has fallen on the ground,
When the fruit burying task is completed, the smart farm autonomous robot system is characterized in that it informs the user of the images taken before and after the task and leaves a record in the database of the memory module.
The method of claim 1, wherein the processor:
As the smart farm self-driving robot system travels the route of the open-air smart farm, it analyzes images acquired through a vision camera to analyze the shape and color of the fruit hanging from the branches of the trees planted in the open-air smart farm.
If the fruit hanging on the tree branch is a rotten fruit, a fruit with peeled flesh, or a ripe fruit, the location of the tree on which the fruit is hanging is recorded in the form of a map in a memory module, and the recorded location is guided to the user. Allows the user to perform fruit removal tasks or fruit harvesting tasks,
When the user performs a fruit removal task or a fruit harvest task, it is compared with the information recorded on the map to check whether the corresponding task is completed for all fruits of each type,
A smart farm autonomous robot system, characterized in that when the corresponding task is completed for all fruits of each type recorded on the map, the images captured before and after the task are notified to the user and a record is left in the database of the memory module.
The processor of the smart farm autonomous robot system,
Analyzing images acquired through a vision camera while the smart farm autonomous robot system drives the path of the open-air smart farm, and detecting whether there is dug ground in the open ground;
When dug up land is detected in the open field, notifying the user that there is dug up land in the open field, receiving permission from the user, and performing the land leveling work by selecting or installing a corresponding agricultural equipment module suitable for performing the land leveling work; and
When the land leveling work is completed, notifying the user of the images taken before and after the work and leaving a record in the database of the memory module. A control method of a smart farm autonomous robot system comprising a step.
The method of claim 14, after the step of analyzing images acquired through a vision camera while the smart farm autonomous robot system travels the path of the open-air smart farm,
Detecting, by the processor, whether weeds have grown above a specified standard in the field;
When weeds growing in the open field are detected, notifying the user that weeds are growing in the open field, receiving permission from the user, and performing the weeding work by selecting or installing a corresponding agricultural equipment module suitable for performing the weeding work; and
When the weeding operation is completed, notifying the user of images captured before and after the operation and leaving a record in the database of the memory module.
The method of claim 14, after the step of analyzing images acquired through a vision camera while the smart farm autonomous robot system travels the path of the open-air smart farm,
The processor,
Analyzing the shape of branches of trees planted in an open-air smart farm;
As a result of analyzing the shape of the branch, if it is detected as a broken branch or a branch to be pruned, checking whether the branch is capable of automatic pruning by a farming equipment module mounted on the smart farm autonomous robot system. ;
If the tree branch is a tree branch capable of automatic pruning, notifying the user that there is a tree branch to be pruned, receiving the user's permission, and performing an electronic task by selecting or installing a corresponding agricultural equipment module suitable for performing the pruning task; and
When the pruning operation is completed, notifying the user of the images captured before and after the operation and leaving a record in the database of the memory module.
According to clause 16,
If the above tree branch is not a tree branch capable of automatic pruning,
The processor,
A control method for a smart farm autonomous robot system, comprising recording the location of a tree with branches to be pruned in the form of a map on a memory module and guiding the user to the recorded location so that the user can perform the pruning task. .
The method of claim 14, after the step of analyzing images acquired through a vision camera while the smart farm autonomous robot system travels the path of the open-air smart farm,
The processor,
Analyzing the shape and color of fruit hanging on the branches of trees planted in an open-air smart farm;
If unbagged fruit is detected at the current growth point of the fruit hanging from the tree branch, the location of the tree where the unbagged fruit hangs is recorded in the form of a map in the memory module, and the recorded location is sent to the user. Guiding a user to perform a fruit bag task;
When the user performs the fruit bag task, comparing the information recorded in the map to check whether the fruit bag task is completed for all fruits; and
Smart farm autonomous driving further comprising: notifying the user of images taken before and after the task when the fruit bag task is completed for all fruits recorded on the map and leaving a record in the database of the memory module. Control method of robotic system.
The method of claim 14, after the step of analyzing images acquired through a vision camera while the smart farm autonomous robot system travels the path of the open-air smart farm,
The processor,
Detecting whether fruit has fallen on the ground;
If the fruit that has fallen to the ground is rotten or has a hollow flesh, performing the fruit burying task by selecting or installing a corresponding farming tool module suitable for performing the task of burying the fruit that has fallen to the ground; and
When the fruit burying task is completed, notifying the user of the images captured before and after the task and leaving a record in the database of the memory module.
The method of claim 14, after the step of analyzing images acquired through a vision camera while the smart farm autonomous robot system travels the path of the open-air smart farm,
The processor,
Analyzing the shape and color of fruit hanging on the branches of trees planted in an open-air smart farm;
If the fruit hanging on the tree branch is a rotten fruit, a fruit with peeled flesh, or a ripe fruit, the location of the tree on which the fruit is hanging is recorded in the form of a map in a memory module, and the recorded location is guided to the user. enabling a user to perform a fruit removal operation or a fruit harvesting operation;
When the user performs a fruit removal task or a fruit harvest task, comparing the information recorded on the map to check whether the corresponding task is completed for all fruits of each type; and
When the corresponding task is completed for all fruits of each type recorded on the map, notifying the user of the images taken before and after the task and leaving a record in the database of the memory module; a smart farm characterized by further comprising: Control method for self-driving robot system.

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