KR101765746B1 - Positioning method and system for autonomous driving of agricultural unmmaned tractor using multiple low cost gps - Google Patents
Positioning method and system for autonomous driving of agricultural unmmaned tractor using multiple low cost gps Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
- G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/42—Determining position
- G01S19/421—Determining position by combining or switching between position solutions or signals derived from different satellite radio beacon positioning systems; by combining or switching between position solutions or signals derived from different modes of operation in a single system
- G01S19/423—Determining position by combining or switching between position solutions or signals derived from different satellite radio beacon positioning systems; by combining or switching between position solutions or signals derived from different modes of operation in a single system by combining or switching between position solutions derived from different satellite radio beacon positioning systems
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- A—HUMAN NECESSITIES
- A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
- A01B—SOIL WORKING IN AGRICULTURE OR FORESTRY; PARTS, DETAILS, OR ACCESSORIES OF AGRICULTURAL MACHINES OR IMPLEMENTS, IN GENERAL
- A01B79/00—Methods for working soil
- A01B79/005—Precision agriculture
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- G—PHYSICS
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- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
- G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
- G01C21/16—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
- G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/42—Determining position
- G01S19/45—Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement
- G01S19/47—Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement the supplementary measurement being an inertial measurement, e.g. tightly coupled inertial
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0276—Control of position or course in two dimensions specially adapted to land vehicles using signals provided by a source external to the vehicle
- G05D1/0278—Control of position or course in two dimensions specially adapted to land vehicles using signals provided by a source external to the vehicle using satellite positioning signals, e.g. GPS
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Abstract
The present invention relates to a method and system for estimating an autonomous traveling position of an unmanned tractor, and is capable of securing precise position data while using a low-cost multiple GPS.
According to the present invention, there is provided a method for estimating a position, comprising: obtaining coordinates of each corner of a GPS data triangle that is a triangle by receiving current coordinates from respective GPS receivers installed at three positions in an unmanned tractor; Calculating coordinates of the center of gravity of the GPS data triangle from each of the vertex coordinates of the GPS data triangle; Generating a range data triangle that is a virtual equilateral triangle in which the length between the GPS receivers is one side while the origin is a center of gravity coordinate; Parallel translation of the GPS data triangle so that the center of gravity coordinates of the GPS data triangle and the center of gravity coordinates of the range data triangle coincide; Wherein the range data triangle is rotated with respect to the center of gravity in a state where the GPS data triangle and the center of gravity coordinates of the range data triangle coincide with each other at an origin and the angle data vertices of the GPS data triangle and each corner data of the range data triangle Calculating three rotation angles at which the sum of the distances between the two is minimum; And the range data triangle rotated by the calculated three rotation angle average values is set as a New GPS data triangle and the New GPS data triangle is set such that the center of gravity of the New GPS data triangle coincides with the center of gravity coordinates of the GPS data triangle before the parallel movement Translating; And considering each of the vertex coordinates of the translated New GPS data triangle as the corrected coordinates of the GPS receiver.
Description
The present invention relates to an agricultural unmanned tractor, and more particularly, to a method and system for estimating an autonomous travel position of an unmanned tractor in which precise positional data can be acquired while utilizing low-cost multiple GPS.
Generally, tractors are being used for various purposes, such as moving loads (soil, stone, sand, etc.) to other places, directly or indirectly related to farming. Recently, efforts have been made to unmannize these tractors.
For this purpose, a navigation system for unmanned autonomous navigation or driving support, which has been developed in the past, has been used, such as a Global Positioning System (GPS) or an inertial navigation system, as is well known to those skilled in the art.
Among them, the navigation system using the satellite navigation system detects a three-dimensional position of the target by using a part of 24 GPS satellites. However, the conventional positioning apparatus using the above- There was a distance error due to factors. That is, due to the error of the satellite clock generated from the atomic clock mounted on the satellite, the error of the satellite orbital generated between the actual orbit of the satellite and the actual orbit acquired by the monitor station and the satellite altitude of the satellite (about 20,000 km) Due to the propagation delay error of the atmosphere due to the propagation delay caused by the satellite's signal passing through the ionosphere and the troposphere, and the multipath of the electromagnetic noise (noise) and radio waves generated by the receiver, It is difficult to use it for position estimation for autonomous driving of an unmanned tractor.
It is therefore an object of the present invention to provide an autonomous navigation system of an unmanned tractor using multiple low-cost GPS which can secure precise position data while using low-cost multiple GPS And to provide a method and system for estimating the position of a vehicle.
According to an aspect of the present invention, there is provided a method for estimating an autonomous driving position of an agricultural unmanned tractor according to the technical idea of the present invention, the method comprising the steps of: receiving current coordinates from three GPS receivers, Obtaining respective corner coordinates of the triangle GDT; Calculating the center of gravity coordinates of the GPS data triangle from each of the vertex coordinates of the GPS data triangle; Generating a range data triangle (RDT) which is a virtual equilateral triangle having the length between the GPS receivers as one side while the origin is the center of gravity coordinates; Parallel translation of the GPS data triangle so that the center of gravity coordinates of the GPS data triangle and the center of gravity coordinates of the range data triangle coincide; Wherein the range data triangle is rotated with respect to the center of gravity in a state where the GPS data triangle and the center of gravity coordinates of the range data triangle coincide with each other at an origin and the angular points of the GPS data triangle and each corner point of the range data triangle Calculating three rotation angles at which the sum of the distances between the two is minimum; And the range data triangle rotated by the calculated three rotation angle average values is set as a New GPS data triangle and the New GPS data triangle is set such that the center of gravity of the New GPS data triangle coincides with the center of gravity coordinates of the GPS data triangle before the parallel movement Parallel translation; And taking each of the coordinates of the vertex of the parallel moved New GPS data triangle as the corrected coordinates of the GPS receiver.
Here, the distance between the GPS receiver and the length of one side of the range data triangle is 1 m.
In addition, the roll, pitch, and yaw values are measured by an IMU (Inertial Measurement Unit) having a gyro sensor and an acceleration sensor, and the roll, pitch, and yaw values are reflected, Is performed.
Further, the additional correction for the corrected coordinates of the GPS receiver is performed by the following equation.
(Xa Ya Za) is the coordinate value of the GPS receiver after the additional correction, (XYZ) is the coordinate value of the GPS receiver before the additional correction, E is the transformation matrix, Value, (abh) is the distance between the GPS receiver and the center of gravity of the tractor)
In addition, an absolute position value based on the corrected coordinates of the GPS receiver and a relative position value measured by an IMU (Inertial Measurement Unit) equipped with a gyro sensor and an acceleration sensor are applied to a Kalman filter to reduce an error with respect to a position . ≪ / RTI >
The position, speed, and speed of the unmanned tractor can be calculated from the absolute position value by the corrected coordinates of the GPS receiver and the relative position value measured by the IMU (Inertial Measurement Unit) equipped with the gyro sensor and the acceleration sensor by the Kalman filter, And the posture value is estimated.
Further, the position by the Kalman filter (
In order to improve the stability of the Kalman filter, a system noise covariance matrix (Q) and a measurement noise ratio matrix (R) are optimized using a central composite design (CCD).
Meanwhile, the autonomous navigation position estimation system for an agricultural unmanned tractor according to the present invention comprises three GPS receivers which form an equilateral triangle with respect to each other and receive and provide position information at respective positions; An IMU (Inertial Measurement Unit) having a gyro sensor and an acceleration sensor to calculate roll, pitch, and yaw values; A Kalman sensor that estimates the position, velocity, and attitude of the unmanned tractor by reducing the position error using the absolute position value based on the corrected coordinates of the GPS receiver and the relative position value measured by the IMU (Inertial Measurement Unit) A filter; And a controller for estimating a position by the above-described position estimation method.
Further, the agricultural unmanned tractor according to the present invention is characterized in that it has the above-described autonomous running position estimation system of the agricultural unmanned tractor.
The method and system for autonomous navigation of an agricultural unmanned tractor according to the present invention uses a low-cost multiple GPS receiver while adjusting the amount of change of position data by rotation of a triangle based on a geometrical structure. can do.
In addition, IMU and Kalman filter are complementarily used with multiple GPS receivers to positively correct the position, thereby ensuring more precise position data.
BRIEF DESCRIPTION OF DRAWINGS FIG. 1 is a block diagram of a position estimation method and system according to an embodiment of the present invention;
Figure 2 is a sample photo of an implementation of a position estimation system according to an embodiment of the present invention.
3 is a reference diagram for explaining the calculation concept of center of triangles in the position estimation method and system according to the embodiment of the present invention.
4 is a view for explaining a GPS data triangle in a position estimation method and system according to an embodiment of the present invention.
5 is a view for explaining the concept of the GPS data triangle and the range data triangle in the position estimation method and system according to the embodiment of the present invention,
FIG. 6 is a flowchart illustrating a method of estimating a position according to an embodiment of the present invention,
7 is a view showing a position correction program in the position estimation method and system experiment according to the embodiment of the present invention.
8 is a graph showing the result of evaluation of the position error reduction algorithm in the position estimation method and the system experiment according to the embodiment of the present invention
FIG. 9 is a graph of a field test method and system experiment according to an embodiment of the present invention,
10 is a graph showing a comparison between positions before and after application of an algorithm in a position estimation method and system experiment according to an embodiment of the present invention
11 is a graph showing a comparison between directions before and after application of an algorithm in a position estimation method and a system experiment according to an embodiment of the present invention
12 is a graph showing a comparison between the position and orientation accuracy in the position estimation method and the system experiment according to the embodiment of the present invention
FIG. 13 is a graph showing a rooftop validation test and an experimental data graph in a position estimation method and system experiment according to an embodiment of the present invention.
14 is a graph showing a lateral deviation comparison graph in a position estimation method and system experiment according to an embodiment of the present invention.
15 is a graph showing a position and direction accuracy comparison graph in a position estimation method and system experiment according to an embodiment of the present invention.
16 and 17 are views for explaining a method for correcting a position error when a tractor is positioned in a tilted region in a position estimation method and system according to an embodiment of the present invention
A method and system for estimating an autonomous traveling position of an agricultural unmanned tractor according to an embodiment of the present invention will be described in detail with reference to the accompanying drawings. The present invention is capable of various modifications and various forms, and specific embodiments are illustrated in the drawings and described in detail in the text. It is to be understood, however, that the invention is not intended to be limited to the particular forms disclosed, but on the contrary, is intended to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the invention. Like reference numerals are used for like elements in describing each drawing. In the accompanying drawings, the dimensions of the structures are enlarged to illustrate the present invention, and are actually shown in a smaller scale than the actual dimensions in order to understand the schematic structure.
Also, the terms first and second, etc. may be used to describe various components, but the components should not be limited by the terms. The terms are used only for the purpose of distinguishing one component from another. For example, without departing from the scope of the present invention, the first component may be referred to as a second component, and similarly, the second component may also be referred to as a first component. On the other hand, unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Terms such as those defined in commonly used dictionaries are to be interpreted as having a meaning consistent with the contextual meaning of the related art and are to be interpreted as either ideal or overly formal in the sense of the present application Do not.
1 is a configuration diagram of an autonomous running position estimation system for an agricultural unmanned tractor according to an embodiment of the present invention.
As shown in the figure, a position estimation system according to an embodiment of the present invention includes three GPS receivers, an IMU (Inertial Measurement Unit), a Kalman filter, and a controller, The position of the GPS receiver is precisely calculated by adjusting the amount of positional data change by the rotation of the triangle based on the geometrical structure that combines the triangles created using the actual position data received by the GPS receiver, And the direction can be more precisely estimated.
According to this configuration, instead of using an expensive GPS receiver, it is possible to secure precise position data by minimizing errors while using low-cost multiple GPS.
Hereinafter, a position estimation system according to an embodiment of the present invention and a position estimation method using the same will be described in detail with reference to the above components.
The GPS receiver is provided with three GPS receivers as described above, and receives and receives position information from each of the GPS receivers in an equilateral triangle. In this case, the GPS receiver is provided in a low-cost type such as a commercially available Garmin-GPS 19x HVS receiver. According to the present invention, even if only a low-priced GPS receiver is used, precise position data can be obtained as well as a high- . This will be described in detail later.
The IMU (Inertial Measurement Unit) is provided with a gyro sensor and an acceleration sensor, and is useful for calculating a relative position of a tractor, unlike a GPS receiver used for calculating an absolute position of a tractor. The IMU calculates the roll, pitch, and yaw values of the tractor and corrects the position of the tractor when the tractor is positioned on a slope, thereby obtaining more precise position data.
The Kalman filter receives an absolute position value based on the corrected coordinates of the GPS receiver and a relative position value measured by the IMU (Inertial Measurement Unit) to reduce a position error, , And the posture value is estimated.
The controller is based on a geometric structure that combines a triangle formed by three GPS receivers and a triangle formed by using the actual position data received by the GPS receiver and adjusts the amount of change of the position data by rotation of the triangle The position of the GPS receiver can be calculated more precisely. The controller utilizes the IMU and Kalman filter actively while proceeding with the unique position estimation method as described above, thereby reducing errors and securing more reliable position data.
Hereinafter, a position estimation method performed by the position estimation system according to an embodiment of the present invention will be described in detail.
A method of estimating a position according to an embodiment of the present invention includes obtaining coordinates of each vertex of a GPS data triangle (GDT, GPS DATA TRIANGLE), which is a triangle, by receiving current coordinates from respective GPS receivers installed at three places in an unmanned tractor Lt; / RTI > As can be seen from FIG. 2, the three GPS receivers are installed at each corner of a frame made of an equilateral triangle, and are mounted on an unmanned tractor with a regular triangle formed therebetween. However, when a triangle is shown with actual position data coordinates provided by three low-cost GPS receivers, the triangle is not a regular triangle as shown in FIG. 4 due to the reception error. This triangle is called a GPS data triangle.
Thereafter, the step of calculating the center-of-gravity coordinates of the GPS data triangle is performed from the respective vertex coordinates of the GPS data triangle. The center of gravity of the GPS data triangle is described as CG in Fig.
Then, as shown in FIG. 3, a step of generating a range data triangle (RDT), which is a virtual equilateral triangle having the length between the GPS receivers as one side while the origin is the center of gravity coordinates, is proceeded. FIG. 3 shows a process of parallel movement to the origin by using the center of gravity of the range data triangle, and a formula for calculating new coordinates at this time. In this way, the process of moving the center of gravity of the range data triangle to the origin is convenient for the calculation to be performed in the future. If the distance between the GPS receiver and the length of one side of the range data triangle is set to 1 m, the calculation becomes simpler.
Then, the GPS data triangle is moved in parallel so that the center of gravity of the GPS data triangle reaches the origin so that the center of gravity of the GPS data triangle and the center of gravity of the range data triangle coincide with each other. FIG. 4 shows a process of parallel movement of the GPS data triangle and calculation of new coordinates at this time. When the GPS data triangle is moved, as shown in FIG. 5, the GPS data triangle, which is not an equilateral triangle, The triangles overlap each other while sharing the center of gravity.
5, the range data triangle is rotated with respect to its center of gravity, and the coordinates of the center of gravity of the GPS data triangle and the range data triangle are aligned with each other, The step of rotating is continued until the sum of the distances between the vertexes of the range data triangle is minimized. Here, as the range data triangle is rotated with respect to the center of gravity coordinates, the coordinates of each corner point also change. The changed coordinates of the range data triangle vertexes can be calculated by the following equation.
Thereafter, three rotation angles when the sum of the vertexes of the GPS data triangle and the vertexes of the range data triangle are minimized are calculated, and the range when the rotation angle is rotated by the average value of the calculated rotation angle is calculated. Parallelizing the New GPS data triangle as shown in FIG. 5 so that the center of gravity of the New GPS data triangle becomes coincident with the coordinates of the center of gravity of the GPS data triangle before the parallel movement with the data triangle as a New GPS data triangle It proceeds. The coordinates of the vertices of the New GPS data triangle are also changed according to the translation of the New GPS data triangle and can be calculated by the following equation.
Then, the coordinates of each vertex of the translated New GPS data triangle can be regarded as the corrected coordinates of the GPS receiver. This makes it possible to obtain more accurate position data of the GPS receiver.
Further, in the embodiment of the present invention, a GUI program based on the position correction algorithm process shown in FIG. 6 is created to further reduce the error from the position data obtained previously. The program visually displays the position data as shown in Fig. 7, thereby enabling intuitive correction.
As shown in FIG. 8, the range of dispersion after the application of the algorithm is reduced. As shown in Table 1, the three corrected GPS position data The average distances (1.00 m, 1.04 m, and 0.94 m) were found to be almost equal to the set distance of 1 m between GPS receivers with an equilateral triangle.
In addition, as shown in Table 2, the CEP and 2DRMS results of the center-of-gravity position data using the three GPSs were 22.51 cm and 53.98 cm, respectively, which were about 6 times smaller than those of the single GPS (133.8 cm and 348.9 cm).
(cm)
(cm)
(cm)
And the mean position error was greatly decreased from 59.02cm to 0.17cm on the X axis and from 17.41cm to 17.21cm on the Y axis. Fig. 8 shows the position detection result of the running state, and the position data of the newly improved center of gravity showed more stabilization than the position data to which the algorithm was applied. However, it still shows the reference path and offset, and by using the correction value applying the attitude sensor separately to reduce the offset, the absolute position is corrected and finally, the single GPS error problem is greatly improved through stable data reception.
Furthermore, in the position estimation method according to the embodiment of the present invention, it is possible to reduce and stabilize the error based on the Kalman filter by using the IMU which provides the relative position value in addition to the multiple GPS receivers providing the absolute position values.
location(
Furthermore, when it is not necessary to consider the z direction when moving the unmanned tractor, it is possible to simplify the 2D system model as follows.
In order to apply the Kalman filter, a nonlinear 2D system model can be switched as follows.
Here, the transition matrix of the above system model is as follows,
Can be represented by a simple matrix H using the following linearization measurement model equation.
The measurement sensitivity matrix is as follows.
<Validation experiment using optimization condition of Q, R value>
The system noise covariance matrix (Q) and the measurement noise covariance matrix (R) were applied to the GPS / IMU integrated system developed in this study, and the validation test was conducted on the school ground as shown in the left side of FIG. The running speed is about 3km / h and the experimental data before and after applying the algorithm are analyzed. The right side of Fig. 9 shows experimental data.
FIG. 10 compares the positions before and after the application of the algorithm. FIG. 11 shows the comparison between the directions before and after the application of the algorithm, and the results of the entire experiment are shown in the straight portion and the turning portion. After applying the algorithm, the data (red) was smoother than the data before the algorithm (black), and the reference path (gray) and offset were also found to be much resolved. As shown in FIG. 11, It was relatively stable.
As shown in Fig. 12 showing the accuracy of the position and direction, the position RMSE and the heading RMSE of the three-state single GPS, the multiple GPS, and the multiple GPS + Kalman filter are compared with each other. As a result, the position RMSE of the straight portion is 2.649 m And the position RMSE of the turning part was improved from 1.559m to 42.9cm.
On the other hand, we applied the optimized Q and R values to the GPS / IMU integrated system and proceeded with the validation test on the building roof as shown on the left side of FIG. At this time, the running speed is about 3 km / h and analyzed by using experimental data before and after applying the algorithm.
On the right side of FIG. 13, the entire experimental result is shown, and the data after the algorithm application is almost similar to the reference path (gray), and it can be confirmed that the data is improved before the application of the algorithm. As shown in FIG. 14, it was confirmed that the lateral deviation after the application of the algorithm (red) was maintained at about 50 cm within 1 m and decreased from before the application of the algorithm (blue).
Also, as shown in FIG. 15, the position RMSE and heading RMSE of the three-state single GPS and multiple GPS + Kalman filter were decreased from 2.71 m to 64 cm, and the heading RMSE was also decreased from 8.997 to 3.618 , Respectively.
On the other hand, as described above, the role of the IMU (Inertial Measurement Unit) will be described with reference to FIG. 16 and FIG. As shown in FIGS. 16 and 17, the IMU complements the GPS receiver to obtain more precise position data by calculating the roll, pitch, and yaw values of the unmanned tractor when the unmanned tractor is located on a slope. Since the GPS receiver is usually installed on the unmanned tractor cabin, there is a problem that an error occurs in reception of the position due to the roll pitch on the slope. To compensate for these errors, it is necessary to calibrate the GPS receiver using the roll pitch of the IMU. The following equation can be used for this.
(Xa Ya Za) is the coordinate value of the GPS receiver after the additional correction, (XYZ) is the coordinate value of the GPS receiver before the additional correction, E is the transformation matrix, Value, (abh) is the distance between the GPS receiver and the center of gravity of the tractor)
As described above, the position estimation method and system according to the embodiment of the present invention adjusts the amount of change of the position data by the rotation of the triangle based on the geometrical structure while using a low-cost multiple GPS receiver, while complementing IMU and Kalman filter And more accurate position data can be secured by positively performing the position correction.
While the present invention has been particularly shown and described with reference to exemplary embodiments thereof, it is to be understood that the invention is not limited to the disclosed exemplary embodiments. It is clear that the present invention can be suitably modified and applied in the same manner. Therefore, the above description does not limit the scope of the present invention, which is defined by the limitations of the following claims.
Claims (10)
Obtaining coordinates of each of the vertexes of the GPS data triangle, which is a triangle, by receiving current coordinates from respective GPS receivers installed at three places so as to form an equilateral triangle in the unmanned tractor;
Calculating the center of gravity coordinates of the GPS data triangle from each of the vertex coordinates of the GPS data triangle;
Generating a range data triangle that is a virtual equilateral triangle having a length between one side of the GPS receiver while the origin is a center of gravity;
Parallel translation of the GPS data triangle so that the center of gravity coordinates of the GPS data triangle and the center of gravity coordinates of the range data triangle coincide;
Wherein the range data triangle is rotated with respect to the center of gravity in a state where the GPS data triangle and the center of gravity coordinates of the range data triangle coincide at the origin and the range data triangle is rotated separately for each of the vertexes, Calculating a rotation angle? 1 ,? 2 ,? 3 such that each corner of the GPS data triangle is a minimum distance from corresponding vertices of the GPS data triangle;
When the vertex coordinates of the range data triangle changed by the rotation angles? 1 ,? 2 ,? 3 are calculated by Equation (1) below,
&Quot; (1) "
(Where r is the distance from the origin to the vertex)
Rotating the triangle of the initial state of the range in which the GPS data was not rotated by an average value of the calculated rotation angles? 1 ,? 2 ,? 3 to create a New GPS data triangle having new coordinates, Moving the New GPS data triangle in parallel so as to coincide with the coordinates of the center of gravity of the GPS data triangle before movement, and obtaining coordinates of the vertices of the parallel moved New GPS data triangle by Equation (2);
&Quot; (2) "
Considering each of the vertex coordinates of the translated New GPS data triangle as the corrected coordinates of the GPS receiver;
After the step of translating the New GPS data triangle, the roll, pitch, and yaw values are measured by an IMU (Inertial Measurement Unit) equipped with a gyro sensor and an acceleration sensor, and the roll, pitch, Further correction is performed for each vertex coordinate of the parallel moved New GPS data triangle, and further correction for each vertex coordinate of the parallel moved New GPS data triangle is performed by Equation (3) below.
&Quot; (3) "
(Xa Ya Za) is the coordinate value of the GPS receiver after the additional correction, (XYZ) is the coordinate value of the parallel moved New GPS data triangle, which is the coordinate value of the GPS receiver before the additional correction, E is the transformation matrix, ) Is the roll, pitch, and yaw values obtained from the IMU, (abh) is the distance between the GPS receiver and the center of gravity of the tractor)
A method for estimating an autonomous driving position of an agricultural unmanned tractor.
Wherein the distance between the GPS receiver and the length of one side of the range data triangle is 1 m.
In an additional correction for each vertex coordinate of the parallel moved New GPS data triangle, an absolute position value of the parallel moved New GPS data triangle and an IMU (Inertial Measurement Unit) equipped with a gyro sensor and an acceleration sensor Wherein the error of each vertex coordinate of the parallel moved New GPS data triangle is reduced by applying a relative position value measured by the Kalman filter to the Kalman filter.
A position, a velocity, and an attitude value of the unmanned tractor from the absolute position value by the corrected coordinates of the GPS receiver and the relative position value measured by the IMU (Inertial Measurement Unit) equipped with the gyro sensor and the acceleration sensor by the Kalman filter And estimating an autonomous position of the tractor for agricultural use.
The position by the Kalman filter (
Wherein the method comprises the steps of:
An IMU (Inertial Measurement Unit) having a gyro sensor and an acceleration sensor to calculate roll, pitch, and yaw values;
A Kalman sensor that estimates the position, velocity, and attitude of the unmanned tractor by reducing the position error using the absolute position value based on the corrected coordinates of the GPS receiver and the relative position value measured by the IMU (Inertial Measurement Unit) A filter;
A system for estimating an autonomous traveling position of an agricultural unmanned tractor, comprising a controller for estimating a position by the position estimation method according to any one of claims 1, 2, and 5 to 7.
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