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    • 专利摘要:
    It is a system comprising one or more radar sensors (210) configured to obtain radar data (212) representative of the planting line (104) in an agricultural field (102); and a controller (214) configured to determine planting line property data (216) based on radar data (212).
    公开号:BR102018009418A2
    申请号:R102018009418-1
    申请日:2018-05-09
    公开日:2018-12-04
    发明作者:Luca Ferrari；Trevor STANHOPE；Kevin Smith
    申请人:Cnh Industrial America Llc；
    IPC主号:
    专利说明:

    (54) Title: SISTEMA AGRÍCOLA (51) Int. Cl .: A01D 41/127; G01S 13/06; G01S 13/89.
    (52) CPC: A01D 41/1278; G01S 13/06; G01S 13/89.
    (30) Unionist Priority: 05/09/2017 US 15 / 590,639.
    (71) Depositor (s): CNH INDUSTRIAL AMERICA LLC.
    (72) Inventor (s): LUCA FERRARI; TREVOR STANHOPE; KEVIN SMITH.
    (57) Abstract: This is a system comprising one or more radar sensors (210) configured to obtain radar data (212) representative of the planting line (104) in an agricultural field (102); and a controller (214) configured to determine seeding line property data (216) based on the radar data (212).
    Figure 1a ζ-ίόίίίόό: ·: ·; · ^; ^
    1/20 “AGRICULTURAL SYSTEM”
    BACKGROUND OF THE INVENTION [001] Determining the properties of the planting lines that will be processed by an agricultural vehicle, such as a sprayer, can be beneficial in improving the operation of the agricultural vehicle. For example, the deviation error, which can result in the agricultural vehicle damaging the planting lines, can be reduced.
    SUMMARY OF THE INVENTION [002] According to a first aspect of the invention, a system is provided comprising:
    [003] one or more radar sensors configured to obtain planting radar data representative of the planting lines in the agricultural field; and [004] a controller configured to determine planting property data based on radar data, where the planting property data is representative of one or more planting line properties that are in a field.
    [005] Advantageously, such radar sensors can generate radar data that is better representative of the planting lines than is possible with optical sensors. This can enable an agricultural vehicle to be controlled with greater precision, which can result in a reduction of deviation errors and a reduction in damage to planting.
    [006] Planting property data can comprise planting location data, which is representative of the location of a planting line, optionally in relation to the agricultural vehicle.
    [007] The controller can be configured to determine route plan data that is representative of a route to be taken by an agricultural vehicle in the agricultural field, based on plantation property data.
    Petition 870180038455, of 05/09/2018, p. 7/37
    2/20 [008] The controller can be configured to determine vehicle control instructions for an agricultural vehicle, based on plantation property data. Vehicle control instructions can comprise vehicle driving instructions to automatically control the direction of travel of the agricultural vehicle.
    [009] The planting property data comprises: planting location data that are representative of the location of a planting line in relation to the agricultural vehicle; and / or no planting location data that are representative of the location or absence of a planting line in relation to the agricultural vehicle. The vehicle driving instructions can be to automatically control the direction of travel of the agricultural vehicle so that the planting location data and / or the no planting location data tend to a predetermined value. The predetermined value can be representative of a predetermined location in relation to the agricultural vehicle.
    [010] The controller can be configured to: determine a property confidence value associated with the planting property data, and determine vehicle control instructions also based on the property confidence value.
    [011] Vehicle control instructions may comprise vehicle speed instructions to automatically control the speed of the agricultural vehicle.
    [012] Vehicle control instructions can be configured to have an output device provide instructions to an agricultural vehicle operator to adjust an agricultural vehicle's travel speed and / or direction.
    [013] The system can additionally comprise an agricultural vehicle that is configured to be operated according to the vehicle control instructions.
    Petition 870180038455, of 05/09/2018, p. 8/37
    3/20 [014] The controller can be configured to: filter the planting radar data by applying one or more filter coefficient values to provide filtered planting radar data; define the one or more filter coefficient values based on one or more planting parameters; and determining planting property data based on filtered planting radar data.
    [015] The one or more radar sensors can be configured to obtain field radar data representative of one or more objects in and / or characteristics of an agricultural field. The controller can be configured to: determine field property data based on field radar data; and determine route plan data and / or vehicle control instructions based on: (i) planting property data, and (ii) field property data.
    [016] The one or more radar sensors can be associated with an agricultural vehicle, and can be configured to obtain planting radar data that is representative of planting in the agricultural field in the vicinity of the agricultural vehicle.
    [017] The system can additionally comprise the agricultural vehicle. One or more radar sensors can be positioned on the agricultural vehicle so that they have a field of view that is above the crop canopies. The one or more radar sensors are positioned on the agricultural vehicle so that they have a field of view that is below a canopy of the planting lines.
    [018] The one or more radar sensors can be selectively positioned on the agricultural vehicle in: (i) a first radar position so that they have a first field of view that is above a canopy of the planting lines; and (ii) a second radar position so that they have a second field of view that is below the canopy of the planting lines. The controller can be configured to define the position of one or more radar sensors either as the first radar position or the second radar position with
    Petition 870180038455, of 05/09/2018, p. 9/37
    4/20 based on plantation property data. Plantation property data can be representative of one or both of the canopy cover and the maturity of the crop.
    [019] A computer program can be provided, which, when run on a computer, causes the computer to configure any device, including a controller, processor, machine, vehicle or device revealed here, or perform any method revealed here. The computer program can be a software implementation, and the computer can be considered as any appropriate hardware, including a digital signal processor, a microcontroller, and a read-only memory (ROM) implementation, erasable programmable read-only memory (EPROM), or electrically erasable programmable read-only memory (EEPROM), as non-limiting examples.
    [020] The computer program can be provided on a computer-readable medium, which can be a physical computer-readable medium, such as a disk or a memory device, or can be incorporated as a temporary signal. Such a temporary signal may be a network download, including a download from the Internet.
    BRIEF DESCRIPTION OF THE DRAWINGS [021] From now on, we will describe some embodiments of the present invention by way of example and with reference to the accompanying drawings, among which:
    [022] Figure 1 a illustrates an example of an agricultural field;
    [023] Figure 1 b schematically illustrates a cross section of the planting lines;
    [024] Figure 2 schematically illustrates a system that is associated with the determination of plantation property data;
    Petition 870180038455, of 05/09/2018, p. 10/37
    5/20 [025] Figure 3 illustrates an example of planting radar data that can be obtained by a radar sensor;
    [026] Figure 4 illustrates examples of positions where a radar sensor can be positioned on an agricultural vehicle; and [027] Figure 5 schematically illustrates a system that can determine vehicle control instructions for an agricultural vehicle based on plantation property data.
    DETAILED DESCRIPTION OF THE DRAWINGS [028] Figure 1a schematically illustrates an agricultural field 102. Field 102 includes rows of planting material (planting lines) 104, which can be a vertical crop material, such as corn. Planting lines 104 are elongated lines of the products in question. Typically, field 102 contains many planting lines 104, essentially mutually parallel, upright in a field, as shown in Figures 1 a and 1 b. Planting lines 104 are spaced apart by largely consistent gaps 106.
    [029] An agricultural vehicle 130 can travel through field 102 to process planting lines 104, such that agricultural vehicle 130 comes into contact with the soil in the gaps 106 between planting lines 104, thereby preventing damage to planting lines 104. In one example, agricultural vehicle 130 is a sprayer, which includes booms extended laterally (as shown in Figure 3) to spray a product on planting lines 104.
    [030] Figure 1 b schematically illustrates a cross section of the planting lines 104; The cross section is in a plane that is transversal to the longitudinal direction of the elongated planting lines 104, which can also be in a plane that is transversal to a direction of movement of the agricultural vehicle 130 as it processes the planting lines 104. It will be appreciated that the crop in the 104 planting lines will grow over a season, and this is illustrated
    Petition 870180038455, of 05/09/2018, p. 11/37
    6/20 schematically in Figure 1b by the dotted lines that are associated with each planting line 104. Also shown in Figure 1b are the gaps 106 between the planting lines 104, along which the wheels or tracks of the agricultural vehicle 130 can be move. As the crop grows, a canopy (not shown) may develop over the gaps 106, so that, at least from above, the locations of the planting lines 104 may not be easily visible.
    [031] Figure 2 schematically illustrates a system for determining planting property data 216, which is representative of one or more properties of a planting line in a field. The system includes one or more radar sensors / a radar system 210 that can obtain planting radar data 212 that is representative of the planting lines in the agricultural field. As will be discussed in more detail below, radar sensor 210 can be mounted on an agricultural vehicle (not shown), and can be operational while the agricultural vehicle is processing the planting lines. That is, the radar sensor 210 can have a field of view that covers parts of the culture that are to be processed.
    [032] The system also includes a controller 214 that can determine planting property data 216 based on planting radar data 212. It will be appreciated that controller 214 can be located on the farm vehicle, or remotely from the farm vehicle . For example, the functionality of controller 214 can be performed on a remote server, such as a server "in the cloud".
    [033] Advantageously, the radar waves that are generated by the radar sensor 210 are able to penetrate a crop canopy that can obscure the true center of a planting line. Therefore, the use of a radar sensor 210 can be considered beneficial when compared to optical sensor systems. This is due to the fact that the radar sensor 210 can use electromagnetic waves with a wavelength long enough, so that there is dispersion
    Petition 870180038455, of 05/09/2018, p. 12/37
    7/20 low enough through the canopy so that most of the planting line can be mapped. In this way, a more precise determination of the location of the planting line can be determined. Radar sensor 210 can therefore generate radar data 212 that is better representative of planting lines than is possible with optical sensors. In some examples, as will be described below, this can enable the agricultural vehicle to be controlled with greater precision, which can result in a reduction of deviation errors and a reduction in damage to planting.
    [034] Also advantageously, the radar sensor 210 can be used during conditions of darkness, fog or dust, which may not be possible or convenient in the case of optical systems.
    [035] The planting radar data 212 can be representative of: (i) a distance to a detected object; and (ii) a direction to that object detected from radar sensor 210. Radar sensor 210 can transmit transmitted radar signals, and receive received radar signals, which are reflected from an object, such as the line planting and / or the land / soil. By applying appropriate post-processing algorithms, it may be possible to obtain information about the distance to the object that is causing the reflections and the angle of arrival. In this way, the radar sensor 210 is able to identify objects that are capable of reflecting electromagnetic radar waves. Any of the radar sensors described here can be two-dimensional or three-dimensional radar sensors, so that they can provide two-dimensional radar data or three-dimensional radar data.
    [036] Radar data 212 can be provided as a plurality of coordinates that are representative of the locations from which received radar signals were received. In one example, radar data 212 can be provided as polar coordinates.
    [037] Depending on the type of radar and the number of receiving antennas,
    Petition 870180038455, of 05/09/2018, p. 13/37
    8/20 radar data can be used by controller 214 to generate a 3D image of the crop mass distribution as planting property data 216. For example, if radar sensor 210 includes a MIMO radar, then controller 214 you can apply algorithms (such as the MUSIC algorithm (multiple signal classification)) to determine the polar coordinates (p, &, φ) of each object that reflects electromagnetic radar waves. Then, knowing the location of installation of the radar sensor 210, the controller 214 can determine the geometric coordinates (x, y, z) of the identified objects. This is an example of how controller 214 can determine planting location data, which is representative of the location of the crop material.
    [038] As will be discussed below, planting property data 216 may also include location data for no planting, which are representative of locations where the absence of a planting line is identified, optionally in relation to the agricultural vehicle.
    [039] Figure 3 illustrates an example of 312 planting radar data that can be obtained by a three-dimensional radar sensor. The planting radar data 312 is illustrated in Figure 3 as multiple sets of three-dimensional Cartesian coordinates (x, y, z), where each coordinate represents the location from which a received radar signal was reflected. In this way, the planting radar data represents a profile that corresponds to soil 306 and crop 304 which was mapped by the radar sensor. Advantageously, as discussed above, the profile may not be representative of the less dense canopy / outer foliage that does not form the volume of a planting line, but instead appears to spread in what would be considered as the above-ground gaps between the planting lines. This may be due to the fact that the transmitted radar waves are not reflected as significantly by the canopy / outer foliage as by the central regions of the 306 planting lines. A controller can process the radar data
    Petition 870180038455, of 05/09/2018, p. 14/37
    9/20 of planting 312 to identify the locations of the 304 planting lines (planting location data), and the soil / land location 306 (no planting location data) between the 304 planting lines.
    [040] The controller can determine the planting location data and / or the no planting location data based on the 312 planting radar data in several different ways. For example:
    [041] the coordinates in the 312 planting radar data that are greater than a height threshold above an identified soil plane can be identified as planting location data;
    [042] online scanning can be performed to identify planting location data;
    [043] spatial grouping can be performed to identify planting location data;
    [044] edge detection can be performed to identify the limits between planting location data and no planting location data;
    [045] characteristic detection can be performed to identify planting location data and / or no planting location data.
    [046] In some examples, the controller may determine planting location data and / or no planting location data so that they are representative of a location in relation to the agricultural vehicle. As will be appreciated from Figure 4, as described below, these can be given location data in relation to an agricultural vehicle wheel and / or a lateral center of the agricultural vehicle, as non-limiting examples.
    [047] Planting property data can include planting area data that is representative of a cross-sectional area of a planting line. The cross section can be in a direction that is transversal to the longitudinal direction of a planting line, which can also be transversal to a direction
    Petition 870180038455, of 05/09/2018, p. 15/37
    10/20 movement of the agricultural vehicle that will process the planting line. Such a cross section is illustrated in Figure 1 b.
    [048] Planting property data can include planting width data that is representative of the lateral width of a planting line. Planting property data 216 can include planting height data that is representative of the height of a planting line.
    [049] Planting property data can include planting center data that is representative of the center of a planting line. The planting center data can be one-dimensional, in that it can be representative of a side center of the crop (from side to side, as shown in Figure 3), or a center of height of the crop (from top to bottom) , as illustrated in Figure 3). In addition, the planting center data can be two-dimensional, since it can be representative of both a lateral center of a planting line and a height center of the planting line.
    [050] Planting property data can include planting end data that is representative of the location of the end of a planting line. The planting end data can be one-dimensional, since it can be representative of the lateral ends of the planting line, or of a tall end of the planting line. In addition, the planting end data can be two-dimensional, as it can be representative of both side ends and height ends of the planting. Planting property data can also include planting profile data that is representative of the planting perimeter.
    [051] Planting property data can include planting volume data, which is representative of a planting row volume.
    [052] Returning to Figure 2, in some examples, controller 214 can filter the planting radar data 212 by applying one or more values of
    Petition 870180038455, of 05/09/2018, p. 16/37
    11/20 filter coefficient to provide filtered planting radar data (not shown). Controller 214 can define one or more filter coefficient values based on one or more planting parameters, and determine planting property data 216 based on filtered planting radar data. In some examples, planting parameters may include crop maturity, spacing of planting lines, canopy cover and type of crop. The canopy cover can be representative of a canopy property on the planting lines. For example, the size, thickness, density or location of the canopy. The values of such planting parameters can be provided as input from the user in some cases. In this way, the filtering of the received radar signals can be adjusted depending on the plant species, the spacing of the planting lines, and the maturity stage, for example, at the beginning of the season, the plants will be smaller, therefore requiring greater radar sensitivity. Therefore, more accurate 216 planting property data can be determined.
    [053] Figure 4 shows examples of positions where a radar sensor 410a, 410b can be positioned on an agricultural vehicle. In this example, the agricultural vehicle is a self-propelled sprayer. The sprayer includes booms extended laterally, one on each side of the tractor, to spray a treatment product on the planting lines. In other examples, the agricultural vehicle may be a tractor, combine harvester, cultivator or nutrient applicator. Either of these vehicles may or may not be self-propelled.
    [054] In Figure 4, a first radar sensor 410a is located at the bottom of the agricultural vehicle, so that it has a low field of view 318a level with or below the crop canopy. In this example, the first radar sensor 410a is aligned with a wheel of the agricultural vehicle with a field of view parallel or transverse to the direction of travel of the vehicle. A second sensor
    Petition 870180038455, of 05/09/2018, p. 17/37
    12/20 of radar 410b is also illustrated in Figure 4, and is located on an upper part of the agricultural vehicle so that it has a high field of view 418b above the crop canopy. In this example, the second radar sensor 410a is aligned with the center of the agricultural vehicle with a field of view parallel or transversal to the direction of travel of the vehicle. Any system disclosed here can include one or a plurality of radar sensors, which can include only one or both of the radar sensors 410a, 410b which are illustrated in Figure 4.
    [055] The radar sensor can be associated with an agricultural vehicle in any way so that it obtains radar data that is representative of the planting lines in the agricultural field in the vicinity of the agricultural vehicle. As illustrated in Figure 4, the radar sensor 410a, 410b can have a field of view 418, 418b that is in front of the agricultural vehicle (in a direction that is parallel to the direction in which the vehicle is moving when processing the crop) , so that the radar data is representative of a planting line that is in front of the agricultural vehicle. In other examples, the radar sensor may have a field of view that is towards the side of the agricultural vehicle (in a direction that is transversal to the direction in which the vehicle is moving when processing the crop), so that the data from radar are representative of a crop that is next to the agricultural vehicle. Such an example can be used to sweep one or more parallel planting lines, which will subsequently be processed by the agricultural vehicle. That is, the planting property data can be obtained for a planting line that is different from the one currently being processed by the agricultural vehicle. This may allow future control and planning operations to be determined before the agricultural vehicle processes a parallel planting line. In yet other examples, the radar sensor may have a field of view that is behind the agricultural vehicle.
    Petition 870180038455, of 05/09/2018, p. 18/37
    13/20 [056] The one or more radar sensors can be selectively positioned on the agricultural vehicle in: (i) a first radar position so that they have a first field of view that is above a canopy of the planting lines ; or (ii) a second radar position so that they have a second field of view that is below the canopy of the planting lines. The first radar position can be on a tractor chassis or boom to see above the canopy. In some examples, a controller can define the position of one or more radar sensors, both as the first radar position and as the second radar position, based on planting property data, such as canopy coverage and maturity of the culture, for example.
    [057] In the examples where the field of view is to the side of the agricultural vehicle, the system can detect planting property data for the planting lines next to the one that is currently in front of the agricultural vehicle. This planting property data can be used to update associated information on a map, and can be integrated with GPS coordinates that are already stored for the location of adjacent planting lines. As will be discussed below, this may involve updating the route plan data. In this way, when the sprayer is on the next line, this information can be used to improve the performance of the system / vehicle driving algorithm.
    [058] In some examples, the radar sensor may be located in another vehicle (not shown), which is different from the agricultural vehicle that will process the planting lines, but which can still be considered to be associated with the agricultural vehicle, for example, due to the fact that the other vehicle can be controlled so that it follows a route that is associated with the agricultural vehicle, or that it is positioned with reference to the agricultural vehicle. The other vehicle can be a manned vehicle or an unmanned vehicle, and it can be a
    Petition 870180038455, of 05/09/2018, p. 19/37
    14/20 air or land vehicle (an unmanned aerial vehicle can be indicated as a “drone”). The use of an aerial vehicle can enable planting radar data to be obtained from a radar sensor at a relatively high altitude to obtain an overview of the field, thereby offering a wide field of view. Subsequently or as an alternative, the aerial vehicle can remain with the agricultural vehicle at a lower altitude, for example, flying above or in front of the agricultural vehicle. The collected radar data can be transmitted to the controller and / or to “the cloud”.
    [059] Figure 5 schematically illustrates a system that can determine vehicle control instructions 528 for an agricultural vehicle 530 based on plantation property data 516.
    [060] The system includes a 510 radar sensor, which can be any radar sensor described here. Radar sensor 510 provides planting radar data 512 to a controller 514. Controller 514 processes plantar radar data 512 and determines planting property data 516, which can be any type of planting property data 516 described. on here. Controller 514 also processes planting property data 516 in order to determine vehicle control instructions 528 for agricultural vehicle 530.
    [061] Vehicle control instructions 528 can include vehicle steering instructions to automatically control the direction of travel of the agricultural vehicle 530. In this way, controller 514 can determine whether the wheels of agricultural vehicle 530 are not centered in a gap between two planting lines (for example, by identifying a deviation between (i) the lateral center of a gap, as defined by the non-planting location data, for example, and (ii) the lateral center of a gap agricultural vehicle wheel 530), then controller 514 can provide vehicle control instructions 528 that cause the direction of agricultural vehicle 530 to be adjusted to
    Petition 870180038455, of 05/09/2018, p. 20/37
    15/20 center the wheels of the agricultural vehicle 530 with reference to the gap between two planting lines (for example, to reduce deviation).
    [062] In some instances, planting property data 516 may include planting location data and / or no planting location data. Controller 514 can determine driving directions for vehicle 528 to automatically control the direction of travel of agricultural vehicle 530 so that planting location data and / or no planting location data tend to a predetermined value. For example, the predetermined value may be representative of a predetermined location in relation to the agricultural vehicle, where the planting line is expected to be if the agricultural vehicle 530 is correctly aligned with the planting lines. For example, the predetermined location can be the side center of the agricultural vehicle, or it can be aligned with a wheel of the agricultural vehicle.
    [063] In some examples, controller 514 can determine the predetermined location in relation to the agricultural vehicle and / or the deviation, using a known relationship between the field of view of the radar sensor 510 and a location of installation of the radar sensor in the agricultural vehicle 530. For example, the lateral center of the field of view of the radar sensor 510 may correspond to: the lateral center of the agricultural vehicle 530 (as represented by the second radar sensor 410b in Figure 4); or the side center of a wheel of the agricultural vehicle 530 (as represented by the first radar sensor 410a in Figure 4).
    [064] In this way, the agricultural vehicle 530 can be controlled autonomously so that it processes the crop in an improved way, for example, in a way that results in reduction of the deviation error. That is, it is possible to offer vehicle guidance by processing planting radar data 512 to determine planting property data 516, which is then used to determine vehicle control instructions 528.
    Petition 870180038455, of 05/09/2018, p. 21/37
    16/20 [065] Vehicle control instructions 528 may additionally or alternatively include vehicle speed instructions to automatically control the speed of agricultural vehicle 530. For example, controller 514 can determine planting area data or volume data planting data (such as 516 planting property data) and provide vehicle speed instructions based on planting area data or planting volume data. In one example, controller 514 can provide vehicle speed instructions to automatically increase the speed of farm vehicle 530 when planting radar data 512 is representative of a decreasing value for planting area data or volume data planting, and vice versa. In some examples, controller 514 may apply an algorithm to the planting area data or the planting volume data in order to determine vehicle speed instructions. In other examples, controller 514 can use a database or lookup table to determine vehicle speed instructions based on planting area data or planting volume data.
    [066] In some examples, vehicle control instructions 528 may cause an output device (such as a display or audio device in the cabin of the agricultural vehicle 530) to provide instructions for an agricultural vehicle operator 530 to define a speed and / or direction of travel of the agricultural vehicle 530. Optionally, an unprocessed radar image and / or cluster tracking data can be provided by controller 514, and can be displayed on a suitable output device.
    [067] A guidance system for a 530 agricultural vehicle can use AB lines (optionally straight) that are defined based on the expected location of the planting lines, or it can import guidance maps into a GPS navigation system from previous operations in the field. This is expected to
    Petition 870180038455, of 05/09/2018, p. 22/37
    17/20 offer precision guidance for planting lines. However, such guidance systems may be less than ideal in several scenarios, including non-straight lines, GPS deviation, deviation from the agricultural implement (for example, due to the fact that the agricultural vehicle is on a slope), or incompatibility data between different mapping systems. In addition, as indicated above, guidance systems that rely on optical image capture sensors (such as RGB cameras and the LIDAR system) may have limited functionality when the crop canopy obstructs the sensors' ability to correctly identify the center of a planting line. On the other hand, advantageously, radar sensors can be used to capture the image of the mass of objects in your field of view.
    [068] In one or more examples, controller 514 can determine route plan data that is representative of a route to be followed by agricultural vehicle 530 in the agricultural field, based on plantation property data 516. This can be in addition, or instead of determining vehicle control instructions 528.
    [069] Route plan data may be representative of a route to be followed by agricultural vehicle 530, optionally based on planting location data and / or no planting location data. Such processing can allow a route plan to be adapted in real time while the agricultural vehicle 530 is in the field. In some examples, controller 514 can determine route plan data by modifying a previous route plan. For example, an initial route plan may include a plurality of AB lines that should be representative of the locations of the planting lines. Advantageously, controller 514 can update this initial route plan based on planting radar data to determine more accurate route plan data.
    Petition 870180038455, of 05/09/2018, p. 23/37
    18/20 [070] As discussed above, a MIMO radar sensor can be mounted on the agricultural vehicle (as in its chassis), and the radar sensor can be positioned with a field of view parallel or transverse to the planting lines. Radar reflectivity data is used to track the relative position of the detected line (s) and subsequently request corrections to a navigation system to minimize or reduce the deviation error. This can be by providing vehicle control instructions 528 to agricultural vehicle 530, or by updating the route plan data that is representative of a route to be followed by agricultural vehicle 530. Radar sensor 510 / controller 514 can communicate at high speed with a Navigation Controller, for example, via a CAN Network (CAN bus, acronym for Controller Area NetWork) and / or Ethernet in order to update route plan data or automatically control the agricultural vehicle 530.
    [071] In the various examples, planting property data can represent one or more of the following parameters:
    [072] (a) a desired radius of curvature, to define how sharp the curve that the 530 agricultural vehicle should make in order to properly align with the planting lines. This can be used to define the vehicle's driving instructions;
    [073] (b) deviation correction (fine and precise adjustment of the AB line). This can be used to define the route plan data.
    [074] (c) lateral deviations from the detected line (s). This can be used to define the route plan data, for example, by applying a deviation to a plurality of, and optionally, all AB lines that are represented by the route plan data. In addition, this can be used to define the vehicle's driving instructions;
    [075] (d) direction of travel in relation to the line (s) (for example,
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    19/20 angle / degrees deviating left / right from the parallel lines. This can be used to define the vehicle's driving instructions;
    [076] Optionally, controller 214 can determine a property confidence value associated with the planting property data, and determine vehicle control instructions and / or route plan data also based on the property confidence value. . In this way, a level of confidence in the accuracy of the data / signal quality for each of the detected planting line (s) can be taken into account by the controller 214 when determining how the instructions will be defined. vehicle control and / or route plan data, if this will be done. For example, if the vehicle passes through a region of the field with an absence of planting lines or a high population of weeds that degrades the quality of planting radar data so that planting property data is compromised, the value property confidence would indicate this and the vehicle's navigation would return to a GPS or operator control. This is an example of a scenario in which the use of a property trust value can be beneficial. A characteristic / line / edge / threshold algorithm used to differentiate lines can include a metric to assess the soundness / reliability of line detection. For example, for edge detection, gradient-based edge detector artifacts, such as spurious responses, can be quantitatively identified using methods such as pattern matching (template matching).
    [077] In some examples, any radar sensor revealed here can obtain field radar data, which is representative of one or more objects in and / or characteristic of an agricultural field. Such objects / features may include ditches, telegraph poles and rocks, as non-limiting examples The radar sensor can obtain radar data in the field in addition to, or instead of, planting radar data. A controller can then process the data from
    Petition 870180038455, of 05/09/2018, p. 25/37
    20/20 field radar in order to determine field property data that are representative of the presence of any such objects / features that are in the field, in order to apply a collision prevention security system, for example. In some cases, the controller can determine vehicle control instructions based on field ownership data in order to determine vehicle speed instructions to automatically stop or slow the agricultural vehicle in advance of the detected object / feature. In some instances, the controller may also have an output device (such as a display or audio device) provide information to an agricultural vehicle operator 630 that is representative of the detected object / feature. Optionally, the controller can determine vehicle driving instructions to automatically direct the agricultural vehicle 630 around the detected object / feature in the field.
    [078] One or more of the systems described here can offer an autonomous or complementary guidance solution to determine the deviation error by directly detecting the deviation of a planting line in relation to the vehicle, and requesting corrections to the controller navigation. In particular, systems may be applicable for sprayers to determine a desired deviation from the vehicle's continuous conveyor to the planting lines.
    [079] It will be appreciated that any of the control operations disclosed here, such as setting the speed or direction of travel of the sprayer or an associated tractor, can be performed by comparing the data with one or more limit values, applying an algorithm to the data, or using a query table / database to determine a control value based on the received / determined data.
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    1/4
    权利要求:
    Claims (20)
    [1]
    1. System, CHARACTERIZED for understanding:
    one or more radar sensors configured to obtain planting radar data representative of the planting lines in the agricultural field; and a controller configured to determine planting property data based on radar data, where the planting property data is representative of one or more planting line properties that are in a field.
    [2]
    2. System, according to claim 1, CHARACTERIZED by the fact that planting property data comprises planting location data, which are representative of the location of a planting line.
    [3]
    3. System, according to claim 1, CHARACTERIZED by the fact that the planting property data comprise planting location data, which are representative of the location of a planting line in relation to the agricultural vehicle.
    [4]
    4. System, according to claim 1, CHARACTERIZED by the fact that the controller is configured to determine route plan data that are representative of a route to be followed by an agricultural vehicle in the agricultural field, based on the property data planting.
    [5]
    5. System, according to claim 1, CHARACTERIZED by the fact that the controller is configured to determine vehicle control instructions for an agricultural vehicle, based on planting property data.
    [6]
    6. System, according to claim 5, CHARACTERIZED by the fact that the vehicle control instructions comprise vehicle driving instructions to automatically control the direction of travel of the agricultural vehicle.
    [7]
    7. System, according to claim 6, CHARACTERIZED by the fact
    Petition 870180038455, of 05/09/2018, p. 27/37
    2/4 of which:
    planting property data comprises:
    planting location data that are representative of the location of a planting line in relation to the agricultural vehicle; and / or no planting location data that are representative of the location of an absence of a planting line in relation to the agricultural vehicle;
    and the vehicle driving instructions are to automatically control the direction of travel of the agricultural vehicle so that the planting location data and / or the no planting location data tend to a predetermined value.
    [8]
    8. System, according to claim 7, CHARACTERIZED by the fact that the predetermined value is representative of a predetermined location in relation to the agricultural vehicle.
    [9]
    9. System, according to claim 5, CHARACTERIZED by the fact that the controller is configured to:
    determine a property confidence value associated with the planting property data, and determine vehicle control instructions also based on the property confidence value.
    [10]
    10. System according to claim 5, CHARACTERIZED by the fact that vehicle speed instructions comprise vehicle speed instructions to automatically control the speed of the agricultural vehicle.
    [11]
    11. System according to claim 5, CHARACTERIZED by the fact that vehicle control instructions are configured to cause an output device to provide instructions to an agricultural vehicle operator to
    Petition 870180038455, of 05/09/2018, p. 28/37
    3/4 define a speed and / or direction of travel of the agricultural vehicle.
    [12]
    12. The system, according to claim 5, CHARACTERIZED by the fact that the system additionally comprises an agricultural vehicle that is configured to be operated according to the vehicle control instructions.
    [13]
    13. System, according to claim 1, CHARACTERIZED by the fact that the controller is configured for:
    filter planting radar data by applying one or more filter coefficient values to provide filtered planting radar data;
    define the one or more filter coefficient values based on one or more planting parameters; and determine planting property data based on filtered planting radar data.
    [14]
    14. The system, according to claim 1, CHARACTERIZED by the fact that the one or more radar sensors are configured to obtain field radar data representative of one or more objects in and / or characteristics of an agricultural field; and the controller is configured to:
    determine field property data based on field radar data; and determine route plan data and / or vehicle control instructions based on: (i) planting property data, and (ii) field property data.
    [15]
    15. The system, according to claim 1, CHARACTERIZED by the fact that the one or more radar sensors are associated with an agricultural vehicle, and is configured to obtain planting radar data that are representative of the plantation in the surrounding agricultural field of the agricultural vehicle.
    [16]
    16. The system, according to claim 15, CHARACTERIZED by
    Petition 870180038455, of 05/09/2018, p. 29/37
    4/4 additionally understand the agricultural vehicle, and the fact that the one or more radar sensors are positioned on the agricultural vehicle so that they have a field of view that is above a canopy of the planting lines.
    [17]
    17. The system, according to claim 15, CHARACTERIZED by additionally comprising the agricultural vehicle, and by the fact that the one or more radar sensors are positioned on the agricultural vehicle so that they have a field of view that is below a canopy planting lines.
    [18]
    18. The system, according to claim 15, CHARACTERIZED by the fact that the one or more radar sensors are selectively positioned on the agricultural vehicle in: (i) a first radar position so that they have a first field of view that it is above a canopy of the planting lines; and (ii) a second radar position so that they have a second field of view that is below the canopy of the planting lines.
    [19]
    19. The system according to claim 18, CHARACTERIZED by the fact that the controller is configured to define the position of the one or more radar sensors both as the first radar position and as the second radar position based on the data of planting property.
    [20]
    20.System, according to claim 19, CHARACTERIZED by the fact that the planting property data are representative of one or both of the canopy cover and the agricultural crop maturity.
    Petition 870180038455, of 05/09/2018, p. 30/37
    1/4
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    法律状态:
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    优先权:
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    US15/590,639|US10531603B2|2017-05-09|2017-05-09|Agricultural system|
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