GNSS and INS based autonomous unmanned pest control device

Abstract

The present invention relates to an unmanned pest control device based on a GNSS sensor and an IMU sensor, wherein the unmanned pest control device comprises: at least two caterpillars mounted on both sides of the lower part of the main body of the unmanned pest control device to move the unmanned pest control device; a drive unit that transmits power to the caterpillars; a chemical tank mounted on the main body and containing a chemical solution; a spray unit arranged to spray the chemical solution from the rear of the main body; and a control panel for controlling the unmanned pest control device, wherein the control panel A GNSS (Global Navigation Satellite System) sensor for detecting the position and speed of the unmanned sprayer; an IMU sensor for detecting the attitude of the unmanned sprayer; a memory storing an autonomous driving path; and a processor for controlling an actuator to drive the unmanned sprayer along the autonomous driving path based on the detected position, speed, and attitude of the speed sprayer; wherein the processor of the control panel The unmanned pest control device is configured to estimate its position, speed, and attitude using GNSS sensors and IMU sensors, determine the quality of the navigation solution obtained from the GNSS sensors and IMU sensors, and control the unmanned pest control device to drive along an autonomous driving path stored in the memory according to the navigation solution.

Description

Technology Field

[0001] The present invention relates to an unmanned pest control machine, and more specifically, to an unmanned pest control machine capable of autonomous driving based on GNSS and INS. Background Technology

[0003] Generally, when cultivating crops, water or nutrients are supplied to the crops to help their growth, or pesticides such as insecticides are sprayed to eliminate pathogens and pests.

[0004] As such, sprayers are used to supply water, nutrients, etc., to crops or to spray pesticides. These sprayers include manual sprayers and power sprayers. A manual sprayer includes a tank containing the spray liquid, a spray nozzle, and a manual pump that pumps and supplies the spray liquid. The user moves on foot to the location where the spray liquid is to be sprayed and operates the manual pump to spray the liquid through the spray nozzle. A power sprayer is not significantly different from a manual sprayer, except that the tank containing the spray liquid is enlarged and a power pump is used instead of a manual pump.

[0006] In this regard, one of the agricultural machines is known as an unmanned sprayer or speed sprayer. An unmanned sprayer refers to a dedicated sprayer for orchards that is currently widely used in orchards. The sprayer mounted on the unmanned pest control machine receives power from the engine to reciprocate the plunger inside the cylinder. When the plunger rises, the liquid passes through a filter, raises the suction valve, and is sucked into the cylinder through the suction pipe. When the plunger descends, the pressure pushes up the discharge valve, causing the liquid to move to the holding chamber. The compressed liquid accumulates in the holding chamber and is discharged through the discharge pipe to spray the liquid. At this time, some of the liquid enters the air chamber, while the remainder returns to the liquid tank through the discharge port by raising the pressure regulating valve through the suction port of the pressure regulating device. Through this process, the pressure of the liquid is maintained at a constant level, and the liquid inside the tank is mixed by the liquid that has flowed back into the tank. The system operates to spray through the sprayer using the power of the engine.

[0008] Meanwhile, the current agricultural sector faces an urgent need to improve conditions due to factors such as the decline and aging of the agricultural population and rising management costs. This is emerging as a major issue not only in orchards but also in all agricultural operations, including rice cultivation. Agricultural operations, such as fruit farming and greenhouse horticulture, are labor-intensive and involve numerous tasks like pruning, compost application, weeding, fertilization, training, and harvesting, most of which are difficult to mechanize. However, in the case of such agricultural transport vehicles, there is a shortage of personnel capable of driving them directly due to the recent aging population, and there are also problems where drivers unfamiliar with these vehicles end up causing accidents.

[0010] With recent advancements in electronic technology, unmanned pest control equipment is now being operated autonomously. Generally, autonomous driving technology enables a vehicle to recognize its driving environment and navigate to a target destination on its own without driver intervention. By utilizing systems such as lane departure prevention, vehicle change control, and obstacle avoidance, the vehicle selects the optimal route and drives itself once a starting point and destination are entered. Vehicles equipped with this technology are referred to as autonomous vehicles or driverless cars. Considering the poor domestic agricultural environment and labor shortages, agricultural machinery such as autonomous tractors can be considered an essential choice for enhancing competitiveness; however, due to the agricultural landscape characterized by limited arable land and numerous sloping fields, such applications are not easily implemented in the country. Prior art literature

[0012] Republic of Korea Registered Patent Publication No. 10-1460991 (Publication Date: November 21, 2014) The problem to be solved

[0013] The present invention is an invention devised based on the aforementioned problems, and aims to provide an autonomous unmanned pest control machine based on GNSS and INS with high positional accuracy that is more convenient to use. means of solving the problem

[0015] The present invention provides an unmanned pest control device based on a GNSS sensor and an IMU sensor to solve the aforementioned problem. The unmanned pest control device comprises: at least two caterpillars mounted on both sides of the lower part of the main body of the unmanned pest control device to move the unmanned pest control device; a drive unit that transmits power to the caterpillars; a chemical tank mounted on the main body and containing a chemical solution; a spray unit arranged to spray the chemical solution from the rear of the main body; and a control panel for controlling the unmanned pest control device, wherein the control panel comprises: a GNSS (Global Navigation Satellite System) sensor for detecting the position and speed of the unmanned pest control device; an IMU sensor for detecting the attitude of the unmanned pest control device; and a memory storing an autonomous driving path. A processor that controls an actuator to drive along the autonomous driving path based on the position, speed, and attitude of the detected speed sprayer; wherein the processor of the control panel estimates the position, speed, and attitude of the unmanned sprayer using a GNSS sensor and an IMU sensor, determines the quality of a navigation solution obtained from the GNSS sensor and the IMU sensor, and controls the unmanned sprayer to drive along the autonomous driving path stored in the memory according to the navigation solution.

[0017] In the aforementioned embodiment, the IMU sensor includes an accelerometer, a gyroscope, and a magnetometer, and the processor is configured to estimate a navigation solution through INS mechanization using data from the accelerometer and gyroscope, calculate a yaw value from the magnetometer and correct INS errors, and perform position and velocity estimation and INS error correction from the GNSS sensor.

[0019] In addition, in any one of the aforementioned embodiments, the autonomous driving path is based on a navigation solution based on data acquired from the GNSS sensor and data acquired from the IMU sensor while the unmanned sprayer is driving along a preset path, and the quality of the GNSS sensor in the processor is determined by comparing a factor for determining the quality of the GNSS sensor with a preset reference value, and if the factor satisfies the preset reference value, the current position of the speed sprayer predicted through a navigation solution from a specific time prior to the current time is compared with the current position of the speed sprayer acquired through the GNSS sensor to determine whether the position of the speed sprayer acquired through the GNSS sensor is in a good state.

[0021] In addition, in any one of the aforementioned embodiments, among the quality information of the navigation data, if the ambiguity of GNSS-RTK is not fixed or the GNSS update elapsed time exceeds 1 second, it is configured to be determined as poor quality.

[0023] In addition, in any one of the aforementioned embodiments, the processor may be configured to merge autonomous driving paths by merging waypoint data generated for a portion of the generated autonomous driving path. Effects of the invention

[0025] According to the present invention, an autonomous unmanned pest control machine based on GNSS and INS with high positional accuracy can be provided. In addition, according to the present invention, an autonomous unmanned pest control machine based on GNSS and INS can be provided that can reconfigure its route even if it deviates from the route for battery replacement and chemical refilling during unmanned pest control. Brief explanation of the drawing

[0027] FIG. 1 is a schematic perspective view showing an autonomous driving unmanned pest control device according to an embodiment of the present invention; FIG. 2 is a schematic diagram showing the caterpillar and the driving unit thereof of an unmanned pest control device according to an embodiment of the present invention; FIG. 3 is a diagram exemplarily showing the internal configuration of an unmanned pest control device according to an embodiment of the present invention; FIG. 4 is a diagram illustrating the operation of estimating position, velocity, and attitude using a multi-sensor in an unmanned pest control device according to an embodiment of the present invention; FIG. 5 is a diagram showing an example of a position correction operation using multiple GNSS sensors; FIG. 6 is a flowchart illustrating the process of generating an autonomous driving path in an unmanned pest control machine according to the present invention; FIG. 7 is a diagram showing the flow of waypoint generation during the process of generating an autonomous driving path in an unmanned pest control machine according to the present invention; FIG. 8 is a flowchart showing the flow of the autonomous driving path merging process in an unmanned pest control machine according to the present invention; FIG. 9 is a flowchart illustrating an autonomous driving method using an autonomous driving path generated according to the present invention; FIG. 10 is a diagram illustrating the principle of an enclosed based line of sight guidance; Figure 11 is an explanatory diagram for explaining path resetting in an unmanned pest control machine. Specific details for implementing the invention

[0028] The advantages and features of the present invention and the methods for achieving them will become clear by referring to the embodiments described below in detail together with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below but may be implemented in various different forms.

[0030] The embodiments described herein are provided to ensure that the disclosure of the invention is complete and to fully inform those skilled in the art of the scope of the invention. The invention is defined only by the scope of the claims. Accordingly, in some embodiments, well-known components, well-known operations, and well-known techniques are not specifically described to avoid the invention being interpreted ambiguously.

[0032] Throughout the specification, the same reference numerals refer to the same components. Furthermore, the terms used (mentioned) in this specification are for describing the embodiments and are not intended to limit the invention. In this specification, the singular form includes the plural form unless specifically stated otherwise in the text. Additionally, components and operations referred to as "comprising (or comprising)" do not exclude the presence or addition of one or more other components and operations.

[0034] Unless otherwise defined, all terms used in this specification (including technical and scientific terms) may be used in a meaning commonly understood by those skilled in the art to which the present invention pertains. Additionally, terms defined in commonly used dictionaries are not to be interpreted ideally or excessively unless otherwise defined.

[0036] A preferred embodiment of the present invention will be described below with reference to the attached drawings. FIG. 1 is a schematic perspective view showing the exterior of an autonomous sprayer (1) according to the present invention. The unmanned sprayer (1) according to the present invention includes a caterpillar (10) mounted on the lower part of the main body of the unmanned sprayer (1) to move the unmanned sprayer (1), a chemical tank receiving part (20) configured to mount a chemical tank in the central part of the main body; a spraying part (30) arranged to spray chemical; and a control panel (50) installed at the front of the main body. The caterpillar (10) is formed at the lower end of each side of the main body, and a reducer is connected to each drive shaft of the caterpillar (10) on both sides, and a motor is connected to each to generate driving force for the caterpillar (10) through the reducer. Since one drive motor is provided for each caterpillar, even if the same torque is generated from each drive motor (10a), the force transmitted to the caterpillar (10) through the control of the reduction gear can be different, thereby enabling the unmanned pest control machine (1) to rotate to the left or right. A battery (10b) for operating the drive motor (10a) is placed on the upper side of the caterpillar (10) via a frame, and as the distance between the battery (50) and the drive motor (10a) is shortened, the wiring for implementing the drive motor (10a) can be simplified.

[0038] FIG. 2 is a block diagram illustrating the configuration of an autonomous driving unit for an agricultural work machine (specifically, an unmanned pest control machine (1)) according to an embodiment of the present invention.

[0040] First, although the unmanned pest control device according to the embodiment of the present invention is described as a speed sprayer, the present invention is not limited thereto and may refer to various means of operation used for tasks such as plowing fields, transporting fertilizer, and transporting various crops, such as tractors, combines, cultivators, etc. To this end, the unmanned pest control device (1) may include a main body and wheels provided at the lower ends of the front and rear of the main body, respectively.

[0042] Meanwhile, the unmanned pest control machine (1) can be implemented as an unmanned machine capable of autonomously moving along a preset path even without a user on board. To this end, the unmanned pest control machine (1) may include a plurality of actuators (10a) as shown in FIG. 1, an autonomous driving sensor group (120) for detecting data necessary for autonomous driving, a memory (130) for storing paths such as waypoints, a processor (140) for controlling operations necessary for autonomous driving, and a camera (160) for monitoring the path of autonomous driving.

[0044] The actuator (110) is configured to control the autonomous driving (including manual driving) of the unmanned pest control machine (1). For example, the actuator unit (110) is configured to include an accelerator actuator, a brake actuator, and a steering angle control actuator, and is configured to increase the rotational speed of the wheel provided on the unmanned pest control machine (1) through the accelerator actuator, decrease the rotational speed of the wheel provided on the unmanned pest control machine (1) or stop the wheel through the brake actuator, or rotate the rotation axis of the wheel provided on the unmanned pest control machine (1) through the steering angle control actuator to rotate the unmanned pest control machine (1) to the left or right.

[0046] The autonomous driving sensor group (120) detects the position and movement of the unmanned pest control machine (1). To this end, the sensor (120) may include a positioning sensor (121), such as a GNSS (Global Navigation Satellite System), as shown in FIG. 2, an IMU sensor (123) for measuring the attitude and angular velocity and acceleration of the unmanned pest control machine, and a driving sensor (122).

[0048] The positioning sensor (121) can measure the position, speed, and direction angle of the unmanned pest control machine (1) using a satellite. Here, the position, speed, and direction angle of the unmanned pest control machine (1) obtained through the positioning sensor (121) can be described as the positioning solution of the GNSS sensor.

[0050] Among positioning sensors, a GNSS sensor refers to a system that calculates the coordinates of a specific location using radio waves transmitted from satellites in space. As such, because GNSS utilizes satellites, it is not constrained by time and space and can acquire position, velocity, and time information relatively stably compared to other systems.

[0052] The GNSS sensor (121) can determine its position using GNSS signals received from a satellite, receive position correction information from an RTK (Real Time Kinematic) reference station (300), and correct its position determined through the GNSS signals using the position correction information.

[0054] Meanwhile, according to an embodiment of the present invention, the GNSS sensor (121) can be implemented as a single-frequency-based low-cost GNSS sensor. That is, in the case of an unmanned pest control machine, since it moves at a relatively low speed compared to a drone, aircraft, or ship, a sufficiently accurate position can be measured using only a single frequency. Thus, according to an embodiment of the present invention, by using a single-frequency-based GNSS sensor, the position of the unmanned pest control machine (1) can be measured at a lower cost than a sensor using two or more frequencies. In this case, the RTK reference station (300) can generate position correction information to calculate a position with centimeter-level accuracy and provide it to the low-cost GNSS sensor (121).

[0056] According to an embodiment of the present invention, the GNSS sensor (121) includes a first GNSS sensor (121a) and a second GNSS sensor (121b). The first GNSS sensor (121a) is located at the center of the unmanned pest control device, and the second GNSS sensor (121b) is installed in the driving direction (forward side) of the unmanned pest control device. The driving sensor calculates the current position of the unmanned pest control device more precisely using position information received from the two GNSS sensors (121), thereby allowing for a more precise estimation of the current position and direction of the unmanned pest control device.

[0058] An Inertial Navigation System (INS) is a system that provides information for estimating the position, velocity, and attitude of a body based on inertial sensors such as an IMU and DR [Dead Reckoning]. This INS is based on Newton's laws. It can acquire acceleration and angular velocity, and by integrating them, calculate a vector of movement and a vector of the angle of movement. The above-mentioned IMU (Inertial Measurement Unit) is equipped with a gyroscope sensor and an accelerometer sensor, and unlike the GNSS sensor (121) used to calculate the absolute position of the unmanned sprayer (1), it is useful for calculating the relative position of the unmanned sprayer. The above-mentioned IMU sensor (123) can calculate the roll, pitch, and yaw values ​​of the tractor and function to correct the position when the tractor is located on a slope, thereby enabling the acquisition of more precise position data.

[0060] When performing continuous measurements with GNSS, there may be no accumulation of errors, but reliability may be affected by weather conditions or the surrounding environment. Therefore, in the embodiment of the present invention, position estimation is performed by combining a GNSS sensor (121) and an IMU sensor (123). In the case of GNSS position estimation, if there are obstacles such as buildings or trees, the reception of satellite signals is not smooth, and the reliability of the data may decrease. In the case of INS using an IMU, unlike GPS such as GNSS, if the initial position is unstable, drift occurs due to the integration of noise, causing a phenomenon where error values ​​continuously accumulate. In areas where GNSS reception is not possible, the jumping phenomenon of data can be corrected by the continuity of the IMU, and the accumulation of errors in the IMU can be compensated for by the accuracy of the GNSS, so accurate and continuous position data can be acquired using GNSS and IMU.

[0062] There are various ways to represent the coordinates of an object using INS, and methods for converting between them also exist. ECIF (Earth Centered Inertial Frame) is a coordinate system similar to ECEF (Earth Centered Earth Fixed Frame) and is referred to as an i-frame. Centered on the Earth's center of gravity, the z-axis is parallel to the CTP (Conventional Terrestrial Pole), which is perpendicular to the Earth's left-right orientation; the x-axis is the direction from the equatorial plane toward the vernal equinox; and the y-axis is perpendicular to both the x and z axes. In the case of ECEF, the only difference is the direction of the x-axis relative to the Greenwich meridian on the equatorial plane.

[0064] The Local Level Frame (LLF) is a coordinate system representing the attitude and velocity of a body on the Earth's surface; the x-axis is due north, the y-axis is due east, and the z-axis is perpendicular to both the x and y directions. This can also be expressed as Ease North Up (ENU) and North East Down (NED), which are referred to as l-frames. The Wander frame is a coordinate system designed to resolve the problem of singularity and the increasing rotational force caused by Earth's rotation as the l-frame approaches the poles. The z-axis is perpendicular to the ellipsoid plane, the y-axis is an axis rotated counterclockwise by α from due north, and the x-axis is perpendicular to these y and z axes.

[0066] Finally, the b-frame, or body frame, is a coordinate system in which sensors are mounted on the fuselage and centered on the fuselage, with the center of gravity of the fuselage serving as the center of the coordinate system. The y-axis is the axis of roll direction, and the x-axis is the axis of pitch direction. Additionally, z is the direction perpendicular to x and y.

[0068] FIG. 4 is a schematic diagram illustrating an algorithm for estimating the position, speed, and attitude of an agricultural machine using the GNSS sensor (121) and IMU sensor (123) as described above. In an embodiment of the present invention, data is received from two sensors, GNSS and IMU, and the position of the unmanned pest control machine (1) is estimated using this data. The sensor data format used in each sensor is as follows.

[0070] First, GNSS utilizes the NMEA (The National Marine Electronics Association) 0183 data transmission protocol. NMEA 0183 is a standard defined by the U.S. National Marine Electronics Association for transmitting information such as time, location, and bearing, primarily used in gyroscopes, GPS, compasses, and INS. Often referred to as NMEA, the protocol for GNSS contains various data formats in ASCII format. Each format has its data content standardized for specific purposes or devices. The NMEA data formats used in this algorithm are GPGGA, GPRMC, and GPGSA. GPGGA is the most commonly used format for 3D positioning and serves as Global Positioning System Fix Data, providing information such as time, location coordinates, and Fix data indicating the quality of the GPS receiver.

[0072] This system utilizes data for UTC (Coordinated Universal Time), Fix, latitude, longitude, elevation above sea level, and the number of visible satellites. Fix is ​​data indicating the operating mode of the GNSS receiver, while elevation above sea level is expressed in meters. GPRMC stands for Recommended Minimum Data, and this system uses velocity and Course Angle. Velocity is expressed in knots, and the Course Angle is expressed in degrees relative to due north; this is used later for Heading Angle estimation. Finally, GPGSA is a format that provides GPS DOP and satellite information; this system uses Mode, which indicates whether 2D or 3D positioning is being performed, and V / H / PDOP data, which indicates the arrangement status of visible satellites.

[0074] As described above, position data (rGNSS) and velocity (vGNSS) are acquired from the GNSS sensor, and the acquired position and velocity data are combined with position data (rINS) and velocity data (vINS) obtained from the motion sensor (IMU) sensor and act as inputs (σr, σv) to an extended Kalman filter.

[0076] Meanwhile, in the extended Kalman filter, the yaw value is calculated from the magnetic field sensor (magnetometer) of the IMU sensor (123) and the calculated yaw value ( ) is a yaw value estimated in an INS mechanism based on the accelerometer and gyroscope of the IMU sensor (123) ( Combined with ) input( )becomes.

[0078] As mentioned above, the extended Kalman filter is based on the position and velocity inputs (σr, σv) input by the GNSS and INS mechanisms, and the yaw value inputs ( Process ) and the processed result, position, velocity, and error calculated by the INS mechanism ( By reflecting this in the correction of the accelerometer and gyroscope, it becomes possible to estimate the position, velocity, and attitude of the unmanned pest control aircraft more accurately and to calculate a more stable real-time navigation solution.

[0080] Accelerometer, Gyroscope: Navigation solution estimation via INS Mechanization (0.01 seconds)

[0081] Magnetometer: Yaw calculation and INS error correction (Update interval: 0.5 seconds)

[0082] GNSS: GNSS position and velocity estimation and INS error correction (Update interval: 0.2 seconds)

[0084] FIG. 5 is a diagram illustrating the process of calculating position and velocity data of an unmanned pest control machine using a plurality of GNSS sensors as described above. As described above, the GNSS sensor (121) in the present invention consists of a first GNSS sensor (121a) and a second GNSS sensor (121b). In the case of a GNSS sensor, if the sensor does not move, for example, if the unmanned pest control machine is stationary and not moving, it is difficult to guarantee the reliability of the position and velocity data obtained from the GNSS. In order to guarantee the reliability of such a GNSS sensor, the present invention further includes an additional GNSS sensor (a second GNSS sensor (121b)). In the present invention, position and velocity information obtained from each GNSS sensor is averaged using at least two GNSS sensors, and through this, more precise position and velocity estimation becomes possible using relatively low-cost GNSS sensors. For example, in the case of the second GNSS sensor installed at the front of the unmanned pest control vehicle, since the distance value (Δd) separated from the first GNSS sensor installed at the center of the unmanned pest control vehicle is constant, it is possible to predict and correct the jumping phenomenon of GNSS data caused by errors in position coordinates when determining GNSS position coordinates.

[0086] Referring again to FIG. 5, the first GNSS sensor and the second GNSS sensor of the unmanned pest control device receive position data. At this time, the position data may be longitude and latitude data. After receiving the first GNSS sensor and the second GNSS sensor, the control unit or processor calculates the distance difference based on the position data received from the first GNSS sensor and the second GNSS sensor. Since the distance difference is a fixed value, in S13, it is determined whether the distance difference between the two data is within a predetermined error range of the distance difference.

[0088] If the distance difference in S13 is within the error range, proceed to step S18 to calculate the position average value between the first GNSS and the second GNSS, and calculate rGNSS and vGNSS based on the calculated average value.

[0090] Meanwhile, if the distance difference in S13 is within the error range, the process proceeds to step S14, in which the previously received first GNSS and second GNSS data are called to verify the received first GNSS and second GNSS data, and in the subsequent step S15, the GNSS data having an error value is determined by comparing the previous data with the current data.

[0092] In the subsequent step S16, the GNSS data determined to be an error in step S15 is corrected with GNSS data that is not an error (for example, if the first GNSS data is an error, the first GNSS data is corrected based on the distance difference with the second GNSS data, and if the second GNSS data is an error, the second GNSS data is corrected based on the distance difference with the first GNSS data).

[0094] Referring again to FIG. 3, the processor (140) includes a path control module (141) and a motor drive control module (142) as shown in FIG. 1. Although the modules are shown individually in FIG. 1, it is obvious that at least two modules can be combined to be implemented as one unit.

[0096] The processor (140) controls the overall operation of the unmanned pest control device (1). To this end, the processor (140) may be implemented as a dedicated processor (e.g., an embedded processor) for controlling the operation of the unmanned pest control device (1), or as a generic-purpose processor (e.g., a CPU or an application processor) capable of performing the operations by executing one or more software programs stored in a memory device.

[0098] In the present invention, the "path generation technology for autonomous driving" is a technology for generating a path for autonomous driving operation, which generates waypoints—an autonomous driving path—based on positioning solutions obtained by a user manually operating agricultural machinery. The autonomous driving path generation technology developed in the present invention consists of an autonomous driving path generation algorithm for a movement path and an autonomous driving path merging algorithm, as described below.

[0100] A. Autonomous driving path generation algorithm

[0101] In this invention, a location information-based autonomous driving path generation algorithm was developed to generate a path for autonomous driving by a user using location information obtained by manually operating an unmanned pest control machine. The processing steps of the location information-based autonomous driving path generation algorithm are summarized as follows.

[0103] The location information-based autonomous driving path generation algorithm performs the function of generating a waypoint-based autonomous driving path by acquiring positioning solutions for the paths the vehicle can travel.

[0105] FIG. 6 is a flowchart illustrating the processing steps of a location information-based autonomous driving path generation algorithm according to the present invention. As shown in FIG. 6, the location information-based autonomous driving path generation data is processed in the following order: ① receiving navigation solutions from a plurality of sensors (GNSS sensors and IMU sensors), ② real-time navigation solution data quality check and storage, ② waypoint data generation, and ④ waypoint data storage.

[0107] First, the step of receiving a navigation solution from a multi-sensor means that the path control module (141) receives data from the GNSS sensor (121) and IMU sensor (123) in the processor, as shown in FIG. 3, and fuses them to calculate a navigation solution.

[0109] When a user performs manual operation of an unmanned pest control machine to generate an autonomous driving path, the GNSS sensor and IMU sensor transmit sensor data to the path control module (141) at regular intervals, and the processor uses this to calculate a navigation solution.

[0111] In the subsequent step, the path control module (141) performs a real-time quality check of the navigation data. For example, the path control module (141) stores a positioning solution of good quality based on the navigation solution and quality information provided by the multi-sensor at predetermined time intervals.

[0113] In order to generate a path using a navigation solution with a similar level of navigation precision, if the navigation solution quality information is poor, a stop command is sent to the unmanned pest control motor drive (142) to temporarily stop the manual operation of the unmanned pest control machine, and if the navigation solution quality information becomes good, the stop command is released to the unmanned pest control motor drive (142) so that the manual operation of the unmanned pest control machine can continue.

[0115] The criteria for poor navigation quality information can be set as when the GNSS-RTK ambiguity number is not fixed or the elapsed time of the GNSS-RTK reference station correction signal transmission exceeds 1 second.

[0117] When the user completes manual driving of the travel path, the path control module (141) transmits a driving completion command to the unmanned pest control machine to stop the manual driving of the unmanned pest control machine and saves navigation data for waypoint data generation. The storage format of the real-time positioning data is as follows.

[0118] [Table 1] Real-time positioning storage format

[0119]

[0120] In the subsequent step, waypoints for autonomous driving are generated using stored navigation data. In this stage, location data for a high-quality autonomous driving path obtained by the user through manual operation of the unmanned sprayer is utilized. Waypoint data generation creates waypoints representing the autonomous driving path for the movement route by using stored real-time navigation data, distances between adjacent points to determine stopping points, and angle reference values ​​to determine turning sections.

[0122] FIG. 7 is a flowchart illustrating the flow of generating waypoints, which are autonomous driving paths. In this flowchart, when real-time positioning data stored sequentially is input (S110), the distance between the previous and current positions is calculated (S111). In step S113 for determining whether to stop, if the distance between adjacent points is smaller than a reference value, the average position of the previous and current positions is calculated (S210), and the average position is switched to the previous position (S211). In step S113, if the distance is larger than the reference value, a straight line is generated using the previous and current positions (S114).

[0124] In addition, in step S117 for determining whether there is a rotation section, if the distance between the previously generated straight line and the currently generated straight line is smaller than the angle reference value for determining whether there is a rotation section, the previously generated straight line is converted to the currently generated straight line (S118), and if it is larger than the angle reference value, the current position is saved as a waypoint (S214).

[0126] The waypoint data storage stage stores network data containing information on the accessibility between waypoints, which are part of the autonomous driving path. The storage formats for waypoint data and network data are as shown in the following table.

[0128] [Table 2] Waypoint Data Storage Format

[0129]

[0131] [Table 3] Network Data Storage Formats

[0132]

[0134] B. Autonomous driving path merging algorithm

[0135] In the present invention, the generation of autonomous driving paths has a limitation in that it is not possible to obtain all paths for the actual work environment at once, as waypoints are generated using data obtained by manually operating an unmanned sprayer to create location-information-based autonomous driving paths for the user's movement route. To compensate for this limitation, the present invention has developed an autonomous driving path merging algorithm capable of merging waypoint data generated for some paths. The autonomous driving path merging algorithm can be classified into a work path merging algorithm and a detour path merging algorithm.

[0137] FIG. 8 is a flowchart illustrating an autonomous driving path merging algorithm. As shown in FIG. 8, the work path merging algorithm first merges work path waypoints in the following manner when the user inputs the work path waypoints to be merged sequentially in step S301.

[0139] After the work path waypoints are entered in step S301, in step S302, the geodetic coordinates (latitude, longitude, and ellipsoidal elevation) of all entered work path waypoints are converted into coordinates based on the NED (North East Down) coordinate system, relative to the starting point of the work path.

[0141] In the subsequent step S303, the existence of an intersection between the input work path waypoints is determined. The existence of an intersection is determined by the straight line generated from adjacent waypoints using an intersection determination algorithm. The intersection determination algorithm was implemented using Line-Line intersection: https: / / en.wikipedia.org / wiki / Line%E2%80%93line_intersection.

[0143] If an intersection exists, the first input waypoint extracts the working path from the first waypoint to the waypoint containing the intersection, and the next input waypoint extracts the path from the waypoint containing the intersection to the end point waypoint, and they are merged. If no intersection exists, the distance between the end point of the first input waypoint and the start point of the next input waypoint is calculated; if it is within a certain threshold, the two working paths are merged, and if it exceeds the threshold, they are not merged. Once the waypoint merging is complete, the waypoint index is redefined sequentially starting from the start point and stored according to the waypoint output format and network data storage format.

[0145] The detour path merging algorithm performs the role of generating the final autonomous driving path waypoints by merging the work path waypoints and detour path waypoints. The NED coordinate transformation and intersection determination of the detour path merging algorithm are identical to those of the work path merging algorithm, but it differs in that it assigns different waypoint index starting numbers to distinguish between the detour path and the work path.

[0147] Next, we will explain the autonomous driving method.

[0148] FIG. 9 is a flowchart illustrating the flow of an autonomous driving operation method according to the present invention. As shown in FIG. 9, the autonomous driving method according to the present invention calculates motor drive (142) control parameters using the autonomous driving path generation method described above, that is, a navigation solution calculated by a multi-sensor and waypoints which are autonomous driving paths, and then transmits them to the motor drive to follow the autonomous driving path. The autonomous driving technology includes the steps of: ① defining a path for autonomous driving (S401), ② determining whether to operate autonomous driving (S402), ③ waypoint switching (S403), ④ determining a target point (S404), and ⑤ calculating driving control parameters (405).

[0150] In step S401, the path definition for autonomous driving involves finding the nearest waypoint location of the autonomous driving path based on the current location when the autonomous driving operation technology is executed, and extracting waypoints for driving to the final endpoint based on the nearest waypoint. The above process is performed only once when autonomous driving is executed, and autonomous driving is performed using the generated and extracted waypoints.

[0152] In step S402, the determination of whether to operate autonomous driving is performed by examining the quality of the navigation solution calculated from the multi-sensor. In this invention, autonomous driving is defined to be performed when the elapsed time for updating the GNSS-RTK measurement value with a fixed ambiguity among the quality information of the multi-sensor is within 2 seconds and the positional precision is within 0.5m. If the above threshold values ​​are exceeded, the vehicle is stopped by transmitting 0 to the left and right track speeds to the motor drive.

[0154] In step S403, waypoint switching determines whether to continue using the current waypoint or switch to the next waypoint. Since the unmanned sprayer moves between consecutive waypoints along the autonomous driving path during autonomous driving, waypoints need to be switched appropriately based on the current location to enhance the stability of autonomous driving. Additionally, as the tracked unmanned sprayer is capable of turning in place, it is necessary to first determine whether the current waypoint is a point for turning in place when switching waypoints.

[0156] Whether a position rotation point exists can be defined as shown in the formula below, and waypoint transitions are processed in the following order.

[0158] 1) Calculate the angle between the two lines generated by the current waypoint and adjacent before and after waypoints.

[0159] 2) If the angle between two lines is greater than the reference angle for rotation in place, it is defined as a point of rotation in place.

[0160]

[0161] Here, n is the currently selected waypoint index, WPn North wa WPn East and are the position coordinates of the current waypoint in the north and east directions, respectively, based on the NED (North East Down) coordinate system with the current location of the unmanned pest control machine as the origin, and r represents the rotation reference angle that determines whether it is a rotation point in place.

[0163] If the currently selected waypoint is a point for rotation in place, the system switches to the next waypoint if the difference between the current yaw of the unmanned sprayer and the azimuth angles generated between the current waypoint and the next waypoint is within a certain angle; conversely, the current waypoint is maintained.

[0165] If the currently selected waypoint is not a rotation point in place, the waypoint is switched if the distance between the current location of the unmanned pest control machine and the currently selected waypoint is within a certain radius as shown in the following formula, and the current waypoint is maintained if the opposite is true.

[0166]

[0167] Here, R WP represents the reference radius for determining whether to switch waypoints.

[0169] FIG. 10 is an explanatory diagram for explaining the principle of Enclosed based Line of Sight (LOS) Guidance. As shown in FIG. 10, in the target point determination step (S404), a location point to move to at the next time is set based on the current vehicle position and the selected waypoint.

[0171] The target point determination method applied the "Enclosed based Line of Sight (LOS) Guidance (Jensen, TM Waypoint-following guidance based on feasibility algorithms, Master dissertation, Norwegian University of Science, Norway, 2011.)" method, which determines the target point by finding the intersection of a straight line generated from the current waypoint and the previous waypoint with a circle generated using a reference radius (R) with the current vehicle's position as the center of the circle.

[0173] If the distance between the current vehicle location and the current waypoint is smaller than the reference radius (R), the target point is selected as the coordinates of the current waypoint.

[0175] Next, the control parameter calculation step (S405) will be explained.

[0176] In the control parameter calculation step, the speed values ​​of the left and right tracks required to reach the target point at the next time are calculated separately for rotation in place and movement.

[0178] In the case of rotation in place, where the distance between the current location of the unmanned sprayer and the current waypoint is smaller than the radius (R), the magnitudes of the left and right track speeds are set to constant values, and the signs of the left and right track speeds are assigned differently depending on the direction of rotation. For example, in the case of a left rotation in place, the sign of the left track speed is positive, and the sign of the right track speed is negative.

[0180] When moving, the distance between the current location of the unmanned sprayer and the current waypoint location is greater than the radius (R), and the target angle (α) for moving to the target point. TP ) and target speed (V TP ) is calculated through the following formula.

[0181]

[0183] Here, TP North and TP Wast is the position coordinates of the current target point in the north and east directions, expressed based on the NED (North-East-Down) coordinate system with the current location of the unmanned sprayer as the origin, ψ is the current vehicle's Yaw, Vmin and Vmax are the minimum vehicle speed and maximum vehicle speed, respectively, and σ is a reference value for decelerating according to the rotation angle of the unmanned sprayer.

[0185] Once the target angle and target speed are calculated, the left and right track speeds for moving to the target point are calculated.

[0187]

[0188] Here, v Left wa v Right represents the left and right track speeds, respectively, is the distance between the left and right tracks, and Δt is the control time interval (seconds).

[0190] FIG. 11 is a diagram illustrating the path redefinition flow in an unmanned pest control vehicle. In the case of an autonomous unmanned pest control vehicle, when an event such as battery replacement or nutrient solution shortage occurs during unmanned operation, the unmanned operation is stopped to create a breakpoint, and after returning to the starting point and performing tasks such as battery replacement and nutrient solution replenishment, it is necessary to move from the starting point back to the breakpoint and redefine the work path from the breakpoint. To this end, the path control module (141) requires an algorithm to save the breakpoint, find a return path from the breakpoint to the starting point, find a path to move from the starting point to the breakpoint, and redefine the work path from the breakpoint to the target point. This can be achieved through the redefinition of the work path based on the shortest distance algorithm using work path waypoints and overall path data.

[0192] As described above, the present invention has explained a specific method for generating a path for autonomous driving of an unmanned pest control machine and controlling autonomous driving. Various embodiments of the present disclosure may be implemented as software including instructions stored on a machine-readable storage media (e.g., a computer).

[0194] The device is a device capable of calling a command stored from a storage medium and operating according to the called command, and may include an electronic device (e.g., electronic device (A)) according to the disclosed embodiments. When the command is executed by a processor, the processor may perform a function corresponding to the command directly or by using other components under the control of the processor.

[0196] Instructions may include code generated or executed by a compiler or interpreter. Device-readable storage media may be provided in the form of non-transitory storage media. Here, 'non-transitory' means only that the storage medium does not contain a signal and is tangible, and does not distinguish whether data is stored semi-permanently or temporarily on the storage medium.

[0198] According to one embodiment, the method according to the various embodiments disclosed in the present invention may be provided as included in a computer program product. The computer program product may be traded between a seller and a buyer as a product. The computer program product may be distributed online in the form of a device-readable storage medium (e.g., compact disc read-only memory (CD-ROM)) or through an application store (e.g., Play Store™). In the case of online distribution, at least a portion of the computer program product may be temporarily stored or temporarily created in a storage medium such as the memory of a manufacturer's server, an application store's server, or a relay server.

[0200] Each component (e.g., module or program) according to various embodiments may be composed of a single or multiple entities, and some of the aforementioned sub-components may be omitted, or other sub-components may be further included in various embodiments. Generally or additionally, some components (e.g., module or program) may be integrated into a single entity to perform the same or similar functions as those performed by each of the respective components prior to integration. The operations performed by the module, program, or other components according to various embodiments may be executed sequentially, in parallel, iteratively, or heuristically, or at least some operations may be executed in a different order, omitted, or other operations added.

[0202] The above description is merely an illustrative explanation of the technical concept of the present invention, and those skilled in the art to which the present invention pertains will be able to make various modifications and variations within the scope of the essential characteristics of the present invention. Accordingly, the embodiments disclosed in this invention are for illustrative purposes only and are not intended to limit the technical concept of the present invention, and the scope of the technical concept of the present invention is not limited by such embodiments.

[0204] Therefore, the scope of protection of the present invention should be interpreted by the claims below rather than being limited by the embodiments described above, and all technical ideas within the equivalent scope should be interpreted as being included in the scope of rights of the present invention. Explanation of the symbols

[0206] 1 : Unmanned pest control machine 110 : Actuator section 120 : Sensor 130 : Memory 140 : Processor

Claims

Claim 1 In an unmanned pest control device based on GNSS sensors and IMU sensors, the unmanned pest control device comprises: at least two caterpillars mounted on both sides of the lower part of the main body of the unmanned pest control device to move the unmanned pest control device; a drive unit that transmits power to the caterpillars; a chemical tank mounted on the main body and containing a chemical liquid; and a spray unit arranged to spray the chemical liquid at the rear of the main body. and includes a control panel for controlling the unmanned sprayer, wherein the control panel includes a GNSS sensor group consisting of a first GNSS (Global Navigation Satellite System) sensor and a second GNSS sensor for detecting the position and speed of the unmanned sprayer; an IMU sensor for detecting the attitude of the unmanned sprayer; a memory storing an autonomous driving path; and a processor for controlling an actuator to drive the unmanned sprayer along the autonomous driving path based on the detected position, speed, and attitude of the speed sprayer; and the processor of the control panel estimates the position, speed, and attitude of the unmanned sprayer using the GNSS sensor group and the IMU sensor, and determines the quality of the navigation solution obtained from the GNSS sensor group and the IMU sensor;The unmanned pest control device is configured to be controlled to drive along an autonomous driving path stored in the memory according to the above navigation solution, the first GNSS sensor is installed at the front of the unmanned pest control device, and the second GNSS sensor is installed at the center of the unmanned pest control device, and the processor of the control panel receives position data including longitude and latitude data from the first GNSS sensor and the second GNSS sensor, and after receiving the position data from the first GNSS sensor and the second GNSS sensor, the processor calculates the distance difference based on the position data received from the first GNSS sensor and the second GNSS sensor, determines whether the distance difference is within a predetermined error range of the distance difference, and if the distance difference is within the error range, calculates the average position value between the first GNSS sensor and the second GNSS sensor and calculates rGNSS and vGNSS based on the calculated average value, and if the distance difference is outside the error range, the previously received first GNSS and second GNSS to verify the position data received from the first GNSS sensor and the second GNSS sensor An unmanned pest control device based on a GNSS sensor and an IMU sensor, characterized by operating to call GNSS position data, determine GNSS data containing error values ​​by comparing previous data and current data, correct the GNSS data determined to be erroneous based on GNSS data without error, and calculate the average position value between a first GNSS sensor and a second GNSS sensor based on the corrected GNSS data. Claim 2 An unmanned pest control device based on a GNSS sensor and an IMU sensor according to claim 1, wherein the IMU sensor comprises an accelerometer, a gyroscope, and a magnetometer, and the processor is configured to estimate a navigation solution through INS mechanization using data from the accelerometer and gyroscope, calculate a yaw value from the magnetometer and correct INS errors, and perform position and velocity estimation and INS error correction from a GNSS sensor group. Claim 3 An unmanned pest control machine based on GNSS sensors and IMU sensors according to claim 1, wherein the autonomous driving path is based on a navigation solution derived from data acquired from the GNSS sensor group and data acquired from the IMU sensor while the unmanned pest control machine is driving along a preset path, and the quality of the GNSS sensor in the processor is determined by comparing a factor for determining the quality of the GNSS sensor with a preset reference value, and if the factor satisfies the preset reference value, the current position of the speed sprayer predicted through a navigation solution from a specific time prior to the current time is compared with the current position of the speed sprayer acquired through the GNSS sensor to determine whether the position of the speed sprayer acquired through the GNSS sensor is in a good state. Claim 4 An unmanned pest control device based on a GNSS sensor and an IMU sensor according to claim 3, characterized in that, among the quality information of navigation data, if the ambiguity number of GNSS-RTK is not fixed or the elapsed time of a GNSS update exceeds 1 second, it is determined to be of poor quality. Claim 5 An unmanned pest control device based on GNSS sensors and IMU sensors according to claim 1, characterized in that the processor is configured to merge waypoint data generated for a portion of a generated autonomous driving path.

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