CN114227676A - Fruit picking control method and device, electronic equipment and storage medium - Google Patents
Fruit picking control method and device, electronic equipment and storage medium Download PDFInfo
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- 235000013399 edible fruits Nutrition 0.000 title claims abstract description 312
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- 238000004590 computer program Methods 0.000 claims description 8
- 238000004364 calculation method Methods 0.000 claims description 6
- 230000008569 process Effects 0.000 abstract description 4
- 210000000078 claw Anatomy 0.000 description 8
- 235000020971 citrus fruits Nutrition 0.000 description 4
- 238000004891 communication Methods 0.000 description 4
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 4
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
- B25J9/1656—Programme controls characterised by programming, planning systems for manipulators
- B25J9/1661—Programme controls characterised by programming, planning systems for manipulators characterised by task planning, object-oriented languages
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- A—HUMAN NECESSITIES
- A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
- A01D—HARVESTING; MOWING
- A01D46/00—Picking of fruits, vegetables, hops, or the like; Devices for shaking trees or shrubs
- A01D46/30—Robotic devices for individually picking crops
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J19/00—Accessories fitted to manipulators, e.g. for monitoring, for viewing; Safety devices combined with or specially adapted for use in connection with manipulators
- B25J19/02—Sensing devices
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
- B25J9/1602—Programme controls characterised by the control system, structure, architecture
- B25J9/161—Hardware, e.g. neural networks, fuzzy logic, interfaces, processor
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
- B25J9/1679—Programme controls characterised by the tasks executed
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- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
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Abstract
The invention relates to the technical field of fruit picking, in particular to a fruit picking control method, a fruit picking control device, electronic equipment and a storage medium. The fruit picking control method is applied to a control system of a picking robot, the picking robot comprises a picking arm and a plurality of collecting boxes, a weight detection device for measuring the weight of picked fruits is arranged on the picking arm, and the fruit picking control method comprises the following steps: acquiring the size information of the fruit; controlling the picking arm to pick the fruit to obtain weight information of the fruit collected by the weight detection device; calculating a density value of the fruit according to the size information and the weight information; classifying the fruits according to the density values; and controlling the picking arm to place the fruits into the collection boxes of the corresponding categories according to the classification result. The fruit picking device can distinguish the good and bad conditions of the fruits and place the fruits in a classified manner in the fruit picking process.
Description
Technical Field
The invention relates to the technical field of fruit picking, in particular to a fruit picking control method and device, electronic equipment and a storage medium.
Background
In daily life, some citrus fruits such as grapefruit, orange, etc. have dry pulp, which is difficult to distinguish from the appearance. When the existing fruit picking robot picks citrus fruits, generally, only an image recognition method can be used for classifying and storing the fruits according to the sizes of the fruits, but whether the fruits have dry pulp conditions or not can not be distinguished according to the images of the fruits, so that the citrus fruits are difficult to classify and store according to the water content conditions of the fruits while being picked automatically.
Accordingly, the prior art is in need of improvement and development.
Disclosure of Invention
The invention aims to provide a fruit picking control method, a fruit picking control device, an electronic device and a storage medium, which can distinguish the good and bad conditions of fruits and classify the fruits in the fruit picking process.
In a first aspect, the present application provides a fruit picking control method applied to a control system of a picking robot, the picking robot comprises a picking arm and a plurality of collecting boxes, a weight detecting device for measuring the weight of picked fruits is arranged on the picking arm, and the fruit picking control method comprises:
s1, acquiring size information of fruits;
s2, controlling the picking arms to pick the fruits to obtain weight information of the fruits collected by the weight detection device;
s3, calculating the density value of the fruit according to the size information and the weight information;
s4, classifying the fruits according to the density values;
s5, controlling the picking arms to place the fruits into the collecting boxes of the corresponding categories according to the classification results.
The robot picks the operation while, through calculating the density value of fruit to classify rationally according to this density value this fruit, thereby put into corresponding collecting box, realized picking the in-process of fruit and classifying according to the good and bad condition of fruit and place, effectively avoid the fruit of different grades to mix and sell.
Further, step S1 includes:
acquiring image information of the fruit;
and acquiring the size information of the fruit according to the image information.
Further, the size information is cross-sectional area information;
the step of obtaining the size information of the fruit according to the image information includes:
acquiring the area of the fruit in the image information according to the image information;
acquiring the horizontal distance between the fruit and the picking robot;
and calculating the section area information of the fruit according to the horizontal distance.
The area of the fruit is obtained by utilizing the image, and the actual section area of the fruit is measured and calculated according to the actual distance, so that the subsequently calculated density value is more practical, the error caused by the calculation result is effectively reduced, and the fruit is prevented from being classified wrongly.
Further, step S4 includes:
comparing the density value with a preset density threshold value, and classifying the fruit as a high-quality fruit when the density value is not lower than the preset density threshold value; classifying the fruit as a poor fruit when the density value is lower than the preset density threshold.
Through comparing with the preset density threshold value, the quality condition of the fruits can be rapidly distinguished, so that the robot can rapidly and smoothly complete picking and classifying placement in the picking process.
Further, the fruit picking control method further comprises the following steps:
acquiring the number of high-quality fruits and the number of poor-quality fruits at the current picking point;
and calculating the good and bad values of the picking points according to the number of the high-quality fruits and the number of the poor-quality fruits, and sending the good and bad values to a terminal.
The user can quickly know the specific position of the picking point with problems by receiving the collecting condition of each picking point of the planting base from the terminal, so that the user can take targeted treatment on each picking point, and the user can take care of the large-area planting base more conveniently.
Further, before step S4, the method further includes:
acquiring the variety information of the fruit;
and acquiring the preset density threshold according to the category information.
Further, step S4 includes:
comparing the density value with preset density ranges of multiple levels step by step to determine the level of the density range in which the density value falls;
and classifying the fruits according to the comparison result.
In a second aspect, the present invention also provides a fruit picking control device for use in a control system of a picking robot, the picking robot comprising a picking arm and a plurality of collecting bins, the picking arm being provided with a weight detecting device for measuring the weight of picked fruits, the device comprising:
the first acquisition module is used for acquiring the size information of the fruits;
a first control module for controlling the picking arm to pick the fruit to obtain weight information of the fruit collected by the weight detection device;
the first calculation module is used for calculating the density value of the fruit according to the size information and the weight information;
the classification module is used for classifying the fruits according to the density values;
and the second control module is used for controlling the picking arm to place the fruits into the collection boxes of the corresponding categories according to the classification result.
The robot picks and calculates the density value of the picked fruit at the same time, and determines the classification of the fruit according to the density value, so that the fruits with different qualities can be quickly distinguished, classified placement is realized according to the quality of the fruit in the fruit picking process, and the following fruit with different qualities can be distinguished and sold.
In a third aspect, the present invention provides an electronic device comprising a processor and a memory, wherein the memory stores computer readable instructions, and the computer readable instructions, when executed by the processor, perform the steps of the method as described above.
In a fourth aspect, the invention provides a storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the method as described above.
By last knowing, when this application picking robot picked, can acquire the size and the weight of fruit in real time, calculate the density of this fruit through size and weight and rationally classify according to this density value to this fruit for picking robot can carry out reasonable classification and classify and place this fruit, can effectively realize the low-quality fruit that the density is lower (that is the water content is low) and the differentiation of the high-quality fruit that the density is higher (that is the water content is high), be favorable to avoiding low-quality fruit and high-quality fruit to mix and sell.
Additional features and advantages of the application will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the practice of the application. The objectives and other advantages of the application may be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
Fig. 1 is a flowchart of a fruit picking control method according to an embodiment of the present disclosure.
Fig. 2 is a schematic structural diagram of a fruit picking control device according to an embodiment of the present application.
Fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present application without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures. Meanwhile, in the description of the present application, the terms "first", "second", and the like are used only for distinguishing the description, and are not to be construed as indicating or implying relative importance.
In some practical citrus fruit (such as pomelo, orange, etc.) planting bases, although people carefully take care of the fruits, the pulp of part of the fruits is lack of water, but the low-quality fruits cannot be effectively distinguished through the appearance, and the pulp of the fruits can be known only by peeling the fruits, so that the low-quality fruits are often mixed in the high-quality fruits, and the experience of buyers is reduced.
In order to achieve effective detection of the moisture content of the pulp in the fruit without peeling the fruit, in certain preferred embodiments, a fruit picking control method is provided, which is applied to a control system of a picking robot, the picking robot comprises a picking arm and a plurality of collecting boxes, a weight detection device for measuring the weight of the picked fruit is arranged on the picking arm, and the steps comprise:
s1, acquiring size information of fruits;
s2, controlling a picking arm to pick fruits to obtain weight information of the fruits collected by the weight detection device;
s3, calculating the density value of the fruit according to the size information and the weight information;
s4, classifying the fruits according to the density values;
and S5, controlling the picking arm to place the fruits into the collection boxes of the corresponding categories according to the classification result.
In some embodiments, after the picking arm takes a fruit from the fruit tree when the picking robot moves to a picking point (a fruit tree), the size information of the fruit can be obtained by arranging a touch sensor on the execution end of the picking arm, so that the picking arm can calculate the spatial distance between mechanisms on the execution end when sensing that the fruit is grabbed; for example, the execution end of the picking arm is a claw-type structure, and includes 3 mechanical claws (the mechanical claw is the mechanism of the execution end of the picking arm), the 3 mechanical claws cooperatively move to grab a fruit, and each mechanical claw is provided with a touch sensor, when the touch sensors on the 3 mechanical claws all sense the fruit (that is, the 3 mechanical claws simultaneously grab the fruit), the spatial distance between the 3 mechanical claws is similar to the volume of the fruit, and the volume of the fruit (that is, the size information of the fruit) can be calculated by obtaining the position parameters of each mechanical claw.
Meanwhile, the weight detection device on the picking arm can also weigh the fruit to obtain the weight information of the fruit. The density value of the fruit can be calculated according to the size and the weight of the fruit, because the density value of the fruit is related to the moisture content of the fruit, the lower the density of the fruit is, the lower the moisture content is, the higher the density of the fruit is, and therefore, the fruit is classified by the density value, which is beneficial to accurately distinguishing high-quality fruit from low-quality fruit.
However, in practical applications, the cost of providing the tactile sensor on the executing end of the picking arm is high, and the tactile sensor is easily damaged by branches in fruit trees because the executing end often needs to extend into the fruit trees.
Thus, in certain preferred embodiments, step S1 includes:
acquiring image information of the fruit;
and acquiring the size information of the fruit according to the image information.
The picking arm or the picking robot is provided with the camera, when the picking robot reaches a picking point, the image information of all fruits on the picking point is obtained, and then the size of each fruit is identified according to the image information.
The above-mentioned size information is based on processing the image information acquired by the picking robot, and in practical applications, because the robot is kept at a distance from the fruit, the fruit image acquired by the picking robot at this distance is not actually the real size of the fruit.
Thus in a further preferred embodiment, the size information is cross-sectional area information;
the step of acquiring the size information of the fruit according to the image information comprises the following steps:
acquiring the area of the fruit in the image information according to the image information;
acquiring the horizontal distance between the fruits and the picking robot;
calculating the section area information of the fruit according to the horizontal distance of the fruit.
In this embodiment, the sectional area information includes a sectional area of the fruit, the area of the fruit is obtained from the image by the image processing technology, meanwhile, a horizontal distance of the fruit, that is, a distance between the fruit and the robot or the camera is obtained by the image (a depth image can be obtained by the camera, and the horizontal distance of the fruit is obtained according to the depth image, which is not described herein in detail in the prior art).
It should be noted that different horizontal distances correspond to different scaling ratios, for example, if the horizontal distance is 1 meter, the corresponding scaling ratio is 2; and if the horizontal distance is 0.5 m, the corresponding scaling is 0.5, and the like, the scaling corresponding to each horizontal distance can be obtained through calculation, and all corresponding relations are recorded in the database in advance for subsequent calling.
In certain embodiments, step S4 includes:
comparing the density value with a preset density threshold value, and classifying the fruit into a high-quality fruit when the density value is not lower than the preset density threshold value; and when the density value is lower than a preset density threshold value, classifying the fruit as a poor fruit. In the embodiment, by presetting a density threshold, when the density value of the fruit picked on the picking arm is not lower than the density threshold, the fruit is considered to belong to a high-quality fruit (high-quality fruit), and then the picking arm is controlled to place the fruit into a collection box for storing the high-quality fruit; on the contrary, when the density value of the fruits picked on the picking arm is lower than the density threshold value, the fruits are considered to belong to inferior fruits (low-quality fruits), and then the picking arm is controlled to place the fruits into a collecting box for storing the inferior fruits, so that the high-quality fruits and the inferior fruits are distinguished.
In a further preferred embodiment, the fruit picking control method further comprises the steps of:
acquiring the number of high-quality fruits and the number of poor-quality fruits at the current picking point;
and calculating the good and bad values of the picking points according to the number of the good fruits and the number of the bad fruits, and sending the good and bad values to the terminal (the good and bad values can be called as first good and bad values).
When the picking robot picks every pair of fruits at a picking point, the number of high-quality fruits and the number of poor-quality fruits are accumulated respectively, after all fruits are picked at the picking point, the quality value of the picking point is calculated according to the number of the high-quality fruits and the number of the poor-quality fruits, and the quality value is sent to a user terminal (such as a mobile phone, a computer and the like) through a control system, so that a user can obtain the growth condition of each picking point through the terminal.
It should be noted that the quality value of the picking point can visually reflect the overall quality of the fruit at the picking point, the quality value = high quality fruit quantity divided by inferior fruit quantity, and the higher the quality value is, the greater the quantity of high quality fruit at the picking point is, thus representing that the fruit growth condition at the picking point is good; on the contrary, the lower the quality value is, the fewer the number of the high-quality fruits of the picking point is represented, so that the fruit growth condition of the picking point is represented to be poor, a user can effectively know the picking point with problems through the quality value, meanwhile, the picking point with problems can be pertinently attended, and the agricultural operation efficiency is greatly improved.
In practical application, a large-scale planting base generally plants a plurality of varieties of fruit trees, obviously, the good and bad density thresholds of the boundaries of the fruits of each variety are different, and after the picking robot finishes picking one fruit, the density threshold needs to be adjusted to accurately distinguish the good and bad fruits of the fruit.
Therefore, in a further preferred embodiment, before step S4, the method further includes:
acquiring the variety information of the fruits;
and acquiring a preset density threshold according to the category information.
The variety of the fruit is distinguished from the obtained fruit image through image processing, the corresponding density threshold value is obtained according to the variety of the fruit, and the distinguishing accuracy of the high-quality fruit and the low-quality fruit can be greatly improved by correspondingly setting reasonable density threshold values for different types of fruits.
In practical applications, when fruits are only divided into two categories (good fruits and poor fruits), there may be some fruits with density values close to or equal to the density threshold, and the judgment of the quality of such fruits varies from person to person in reality, for example, although the fruits are divided into good fruits with density values slightly higher than the density threshold, people's subjective feeling is considered as poor fruits. Therefore, in the face of this situation, some fruit growers may need to classify the fruits more carefully, for example, to classify the fruits into superior fruits, inferior fruits, etc., so that the quality classification of the fruits is closer to the subjective perception of people.
Thus, in certain preferred embodiments, step S4 includes:
comparing the density value with preset density ranges of multiple levels step by step to determine the level of the density range in which the density value falls;
and classifying the fruits according to the comparison result.
According to the embodiment, the fruits are divided more finely by setting the density ranges of multiple levels, so that good transaction experience can be ensured for both buyers and sellers.
In a further preferred embodiment, the fruit picking control method further comprises the steps of:
acquiring the number of fruits in the density range of each grade of the current picking point;
the number of fruits for each level of density range is sent to the terminal.
In practical application, after the step of allocating a weighted value to each density range in advance, acquiring the number of fruits in the category corresponding to the density ranges of all levels of the current picking point, and sending the number of fruits in the category corresponding to the density ranges of each level to the terminal, the method may further include the steps of:
and calculating the sum of the products of the fruit quantity of the corresponding category of each density range and the corresponding weighted value as the good value and the bad value of the picking point, and sending the good value and the bad value to the terminal (the good value and the bad value can be called as a second good value).
Therefore, in a further preferred embodiment, before step S4, the method further includes:
acquiring the variety information of the fruits;
according to the category information, the density ranges of all levels are acquired.
Referring to fig. 2, fig. 2 is a fruit picking control device applied to a control system of a picking robot in some embodiments of the present application, the picking robot includes a picking arm and a plurality of collecting boxes, the picking arm is provided with a weight detecting device for measuring the weight of picked fruits, the fruit picking control device is integrated in a rear end control apparatus of the fruit picking control device in the form of a computer program, and the fruit picking control device includes:
a first acquisition module 100 for acquiring the size information between the fruit and the picking robot;
a first control module 200 for controlling the picking arm to pick the fruit to obtain the weight information of the fruit collected by the weight detection device;
a first calculation module 300 for calculating a density value of the fruit according to the size information and the weight information;
a classification module 400 for classifying the fruits according to the density values;
and the second control module 500 is used for controlling the picking arms to place the fruits into the collection boxes of the corresponding categories according to the classification result.
In certain embodiments, the first obtaining module 100 performs, when used for obtaining the size information of the fruit:
acquiring image information of the fruit;
and acquiring the size information of the fruit according to the image information.
In certain embodiments, the dimensional information is cross-sectional area information; the first obtaining module 100 is configured to perform, when obtaining the size information of the fruit according to the image information:
acquiring the area of the fruit in the image information according to the image information;
acquiring the horizontal distance of the fruits;
calculating the section area information of the fruit according to the horizontal distance of the fruit.
In certain embodiments, the classification module 400 is configured to perform, when classifying a fruit according to density values:
comparing the density value with a preset density threshold value, and classifying the fruit into a high-quality fruit when the density value is not lower than the preset density threshold value; and when the density value is lower than a preset density threshold value, classifying the fruit as a poor fruit.
In some embodiments, the fruit picking control device further comprises:
the second acquisition module is used for acquiring the number of high-quality fruits and the number of poor-quality fruits at the current picking point;
and the second calculation module is used for calculating the good and bad values of the picking points according to the number of the high-quality fruits and the number of the poor-quality fruits and sending the good and bad values to the terminal.
In certain embodiments, the classification module 400 is for performing, prior to classifying the fruit according to the density value:
acquiring the variety information of the fruits;
and acquiring a preset density threshold according to the category information.
In certain embodiments, the classification module 400 is configured to perform, when classifying a fruit according to density values:
comparing the density value with preset density ranges of multiple levels step by step to determine the level of the density range in which the density value falls;
and classifying the fruits according to the comparison result.
In some embodiments, the fruit picking control device further comprises:
the third acquisition module is used for acquiring the number of fruits in the density range of each grade of the current picking point;
and the first sending module is used for sending the fruit number of each level of density range to the terminal.
In certain embodiments, before the classification module 400 is used to classify the fruit according to density values, the following further steps are performed:
acquiring the variety information of the fruits;
according to the category information, the density ranges of all levels are acquired.
Referring to fig. 3, fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure, where the present disclosure provides an electronic device, including: the processor 1301 and the memory 1302, the processor 1301 and the memory 1302 being interconnected and communicating with each other via a communication bus 1303 and/or other form of connection mechanism (not shown), the memory 1302 storing a computer program executable by the processor 1301, the processor 1301 executing the computer program when the computing apparatus is running to perform the fruit picking control method in any of the alternative implementations of the embodiments of the first aspect described above to implement the following functions: acquiring the size information of the fruit; controlling a picking arm to pick fruits to obtain weight information of the fruits collected by the weight detection device; calculating the density value of the fruit according to the size information and the weight information; classifying the fruits according to the density values; and controlling the picking arms to place the fruits into the collection boxes of the corresponding categories according to the classification result.
An embodiment of the present application provides a storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the method for controlling fruit picking in any optional implementation manner of the embodiment of the first aspect is executed, so as to implement the following functions: acquiring the size information of the fruit; controlling a picking arm to pick fruits to obtain weight information of the fruits collected by the weight detection device; calculating the density value of the fruit according to the size information and the weight information; classifying the fruits according to the density values; and controlling the picking arms to place the fruits into the collection boxes of the corresponding categories according to the classification result.
The storage medium may be implemented by any type of volatile or nonvolatile storage device or combination thereof, such as a Static Random Access Memory (SRAM), an Electrically Erasable Programmable Read-Only Memory (EEPROM), an Erasable Programmable Read-Only Memory (EPROM), a Programmable Read-Only Memory (PROM), a Read-Only Memory (ROM), a magnetic Memory, a flash Memory, a magnetic disk, or an optical disk.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one logical division, and there may be other divisions when actually implemented, and for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices or units through some communication interfaces, and may be in an electrical, mechanical or other form.
In addition, units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
Furthermore, the functional modules in the embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
In this document, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions.
The above description is only an example of the present application and is not intended to limit the scope of the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (10)
1. A fruit picking control method applied to a control system of a picking robot, wherein the picking robot comprises a picking arm and a plurality of collecting boxes, the picking arm is provided with a weight detection device for measuring the weight of picked fruits, and the fruit picking control method comprises the following steps:
s1, acquiring size information of fruits;
s2, controlling the picking arms to pick the fruits to obtain weight information of the fruits collected by the weight detection device;
s3, calculating the density value of the fruit according to the size information and the weight information;
s4, classifying the fruits according to the density values;
s5, controlling the picking arms to place the fruits into the collecting boxes of the corresponding categories according to the classification results.
2. The fruit picking control method according to claim 1, wherein step S1 includes:
acquiring image information of the fruit;
and acquiring the size information of the fruit according to the image information.
3. The fruit picking control method according to claim 2, wherein the size information is cross-sectional area information;
the step of obtaining the size information of the fruit according to the image information includes:
acquiring the area of the fruit in the image information according to the image information;
acquiring the horizontal distance between the fruit and the picking robot;
and calculating the section area information of the fruit according to the horizontal distance.
4. The fruit picking control method according to claim 1, wherein step S4 includes:
comparing the density value with a preset density threshold value, and classifying the fruit as a high-quality fruit when the density value is not lower than the preset density threshold value; classifying the fruit as a poor fruit when the density value is lower than the preset density threshold.
5. The fruit picking control method according to claim 4, further comprising the steps of:
acquiring the number of high-quality fruits and the number of poor-quality fruits at the current picking point;
and calculating the good and bad values of the picking points according to the number of the high-quality fruits and the number of the poor-quality fruits, and sending the good and bad values to a terminal.
6. The fruit picking control method according to claim 4, wherein before the step S4, the method further comprises:
acquiring the variety information of the fruit;
and acquiring the preset density threshold according to the category information.
7. The fruit picking control method according to claim 1, wherein step S4 includes:
comparing the density value with preset density ranges of multiple levels step by step to determine the level of the density range in which the density value falls;
and classifying the fruits according to the comparison result.
8. A fruit picking control device applied to a control system of a picking robot, the picking robot comprises a picking arm and a plurality of collecting boxes, a weight detecting device for measuring the weight of picked fruits is arranged on the picking arm, and the fruit picking control device is characterized by comprising:
the first acquisition module is used for acquiring the size information of the fruits;
a first control module for controlling the picking arm to pick the fruit to obtain weight information of the fruit collected by the weight detection device;
a first calculation module for calculating a density value of the fruit according to the size information and the weight information;
the classification module is used for classifying the fruits according to the density values;
and the second control module is used for controlling the picking arm to place the fruits into the collection boxes of the corresponding categories according to the classification result.
9. An electronic device comprising a processor and a memory, said memory storing computer readable instructions which, when executed by said processor, perform the steps of the fruit picking control method according to any of claims 1-7.
10. A storage medium having stored thereon a computer program, wherein the computer program, when executed by a processor, performs the steps of the fruit picking control method according to any of claims 1-7.
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| CN101019484A (en) * | 2007-03-06 | 2007-08-22 | 江苏大学 | Terminal executor of fruit and vegetable picking robot |
| JP2008206438A (en) * | 2007-02-26 | 2008-09-11 | Iseki & Co Ltd | Fruit harvesting robot |
| CN109618670A (en) * | 2019-03-01 | 2019-04-16 | 南通理工学院 | Intelligent fruit and vegetable picking and sorting robot |
| CN111274877A (en) * | 2020-01-09 | 2020-06-12 | 重庆邮电大学 | CNN-based intelligent strawberry picking robot control system |
| CN112425373A (en) * | 2020-12-02 | 2021-03-02 | 陕西中建建乐智能机器人股份有限公司 | Kiwi fruit picking and sorting robot and kiwi fruit sorting method thereof |
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- 2021-12-15 CN CN202111532951.XA patent/CN114227676B/en active Active
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2008206438A (en) * | 2007-02-26 | 2008-09-11 | Iseki & Co Ltd | Fruit harvesting robot |
| CN101019484A (en) * | 2007-03-06 | 2007-08-22 | 江苏大学 | Terminal executor of fruit and vegetable picking robot |
| CN109618670A (en) * | 2019-03-01 | 2019-04-16 | 南通理工学院 | Intelligent fruit and vegetable picking and sorting robot |
| CN111274877A (en) * | 2020-01-09 | 2020-06-12 | 重庆邮电大学 | CNN-based intelligent strawberry picking robot control system |
| CN112425373A (en) * | 2020-12-02 | 2021-03-02 | 陕西中建建乐智能机器人股份有限公司 | Kiwi fruit picking and sorting robot and kiwi fruit sorting method thereof |
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