CN110262479A - A kind of estimation of caterpillar tractor kinematics and deviation calibration method - Google Patents
A kind of estimation of caterpillar tractor kinematics and deviation calibration method Download PDFInfo
- Publication number
- CN110262479A CN110262479A CN201910448762.0A CN201910448762A CN110262479A CN 110262479 A CN110262479 A CN 110262479A CN 201910448762 A CN201910448762 A CN 201910448762A CN 110262479 A CN110262479 A CN 110262479A
- Authority
- CN
- China
- Prior art keywords
- tractor
- deviation
- crawler
- coordinate component
- model
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 42
- 238000006073 displacement reaction Methods 0.000 claims abstract description 25
- 238000001914 filtration Methods 0.000 claims abstract description 17
- 230000000694 effects Effects 0.000 claims abstract description 11
- 238000009434 installation Methods 0.000 claims abstract description 7
- 239000011159 matrix material Substances 0.000 claims description 41
- 238000005259 measurement Methods 0.000 claims description 3
- 230000007704 transition Effects 0.000 claims description 3
- 230000009897 systematic effect Effects 0.000 abstract 1
- 238000005516 engineering process Methods 0.000 description 3
- 230000002159 abnormal effect Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 230000005654 stationary process Effects 0.000 description 1
Classifications
-
- 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
-
- 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
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0223—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory involving speed control of the vehicle
-
- 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
-
- 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
- G05D1/08—Control of attitude, i.e. control of roll, pitch, or yaw
- G05D1/0891—Control of attitude, i.e. control of roll, pitch, or yaw specially adapted for land vehicles
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/11—Complex mathematical operations for solving equations, e.g. nonlinear equations, general mathematical optimization problems
- G06F17/12—Simultaneous equations, e.g. systems of linear equations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/11—Complex mathematical operations for solving equations, e.g. nonlinear equations, general mathematical optimization problems
- G06F17/13—Differential equations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/16—Matrix or vector computation, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Mathematical Physics (AREA)
- Computational Mathematics (AREA)
- Mathematical Analysis (AREA)
- Mathematical Optimization (AREA)
- Pure & Applied Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Theoretical Computer Science (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Aviation & Aerospace Engineering (AREA)
- Automation & Control Theory (AREA)
- Algebra (AREA)
- Databases & Information Systems (AREA)
- Software Systems (AREA)
- General Engineering & Computer Science (AREA)
- Operations Research (AREA)
- Computing Systems (AREA)
- Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)
Abstract
The invention discloses a kind of estimation of caterpillar tractor kinematics and deviation calibration methods, belong to agricultural machinery automatic Pilot technical field, now propose following scheme, it includes the following steps, construct caterpillar tractor kinematics model, in a practical situation, since surface relief changes, GNSS double antenna installation deviation factor, cause heading angle deviation, path trace effect is caused to be deteriorated, for simplified model, it is certain value that heading angle deviation, which can be approximately considered, choose east orientation displacement coordinate component, north orientation displacement coordinate component, tractor speed, north orientation displacement coordinate component, tractor speed, tractor course angle is measured as systematic perspective, the caterpillar tractor kalman Filtering Model of building is nonlinear model.The present invention quickly can accurately estimate the heading angle deviation due to caused by surface relief variation, GNSS antenna installation deviation etc., to compensate to course angle, improve system to the adaptability on ground.
Description
Technical Field
The invention relates to the technical field of automatic driving of agricultural machinery, in particular to a method for estimating kinematics and calibrating deviation of a crawler tractor.
Background
With the rapid development of the GNSS high-precision satellite navigation positioning technology, autopilot and information technology, modern agriculture is gradually developing towards digital agriculture and precision agriculture. The information and intelligent degree of the tractor serving as common agricultural equipment has important significance for development of precision agriculture.
Tractors can be classified into wheel tractors and track tractors according to the traveling mode. Compared with a wheel type tractor, the crawler type tractor has the advantages of large contact surface, small ground pressure, good traction adhesion performance, difficulty in slipping and the like, is more suitable for operation in environments with relatively severe conditions, such as snowfields, hills, muddy fields, grasslands, plateaus and the like, and effectively fills the defects of the wheel type tractor. However, in the automatic driving process of the crawler tractor, due to the fact that a large amount of unpredictable interference exists in the actual operation environment, such as the complex situation of the farmland ground to be cultivated, the kinematic model is not accurate, the GNSS antenna mounting angle deviation, the GPS signal shielding and reflection, the system noise and the external environment noise interference and other factors, the position and posture related information of the tractor is abnormal, the automatic driving control precision of the crawler tractor is seriously influenced, the operation intensity and the economic cost of farmers are increased, and the agricultural cultivation efficiency and the land utilization rate are greatly reduced.
The invention provides a method for estimating the kinematics and calibrating the deviation of a track type tractor aiming at the problems in the automatic driving control technology of the track type tractor, so as to realize the accurate estimation of the position and attitude information of the track type tractor, enhance the adaptability and anti-interference performance of the system to the external environment and improve the operation precision of the automatic driving system of the track type tractor. The invention aims to accurately estimate the course angle deviation in real time by designing a proper estimation method, and compensate and correct the course angle so as to improve the adaptability of the system to the environment.
Disclosure of Invention
The invention aims to solve the problems of poor control effect and low operation precision of a crawler tractor caused by interference of various factors in an actual operation environment, and provides a crawler tractor kinematic estimation and deviation calibration method of anti-interference factors.
In order to achieve the purpose, the invention adopts the following technical scheme:
a method for estimating the kinematics and calibrating the deviation of caterpillar tractor includes such steps as providing a caterpillar tractor,
s1, constructing a crawler tractor kinematic model:
wherein x is the east displacement coordinate component of the crawler tractor, y is the north displacement coordinate component, v is the running speed of the crawler tractor,the track angle of the track type tractor, omega is the angular speed of the track type tractor body;
s2: because the crawler-type tractor turns to when, and left wheel, right wheel and barycenter department angular velocity are equal, so can deduce:
wherein v islTo be the running speed of the left track, vrThe running speed of the right crawler belt is shown, R is the turning radius, and b is the width of the vehicle body;
s3: the simultaneous equations can be solved to obtain:
s4: the following equations (3) and (6) can be derived:
in the formula, u is the speed difference of the left and right crawler belts, namely the control quantity;
s5: in the automatic driving operation process of the agricultural machinery, in order to ensure the crop cultivation quality, the tractor is arranged to do uniform linear motion, so that the following results can be obtained:
s6: in practical situations, the course angle deviation caused by the ground fluctuation and the GNSS dual-antenna installation deviation factors causes the path tracking effect to be poor, and in order to simplify the model, the course angle deviation can be approximately considered as a constant value, and then:
s7: through the processes of S1-S6, the constructed Kalman filtering nonlinear differential equation model of the crawler tractor is as follows:
s8: selecting an east displacement coordinate component x, a north displacement coordinate component y, a tractor speed v and a tractor course angleAnd the course angle deviation delta is used as a system state quantity, and an east displacement coordinate component x, a north displacement coordinate component y, a tractor speed v and a tractor course angle are usedAs a system view measurement, there are:
wherein X is a system estimation vector, and Z is a system observation vector;
s9: the crawler tractor kalman filtering model constructed in the S7 is a nonlinear model, and by adopting an EKF filtering algorithm and solving a Jacobian matrix, the model is linearized to obtain a corresponding system linear state space equation:
s10: discretizing the continuous system to obtain a state transition matrix phi and an observation matrix H:
s11: selecting a process noise covariance matrix Q and an observation noise covariance matrix R:
s12: initialization state vector X, covariance matrix P, and observation state vector Z:
X(0)＝E[X(0)] (19)；
P(0)＝var[X(0)] (20)；
Z(0)＝Z0 (21)；
wherein, X (0), P (0), Z (0) are the initial values of the state vector X, the covariance matrix P and the observation state vector Z respectively;
s13: kalman filter state one-step prediction:
s14: calculating a one-step prediction covariance matrix:
P(k+1|k)＝Φ(k+1|k)P(k|k)ΦT(k+1|k)+Q(k+1) (23)；
s15: computing a Kalman gain:
K(k+1)＝P(k+1|k)HT(k+1)[H(k+1)P(k+1|k)HT(k+1)+R(k+1)]-1 (24)；
s16: calculating an estimated value:
s17: updating the covariance matrix:
P(k+1)＝[I-K(k+1)H(k+1)]P(k+1|k) (26)；
wherein, in the formula (22-26), k +1 represents the next time, k represents the current time,for the system state estimator, K is the kalman gain.
Preferably, the method further comprises the step S18, and the steps S13-S17 are repeated continuously.
Preferably, the method further includes step S19, in the process of off-line analysis and debugging by using the actual data, adjusting the observation noise matrix, the process noise matrix, and the covariance matrix to achieve the desired filtering effect, and estimating the heading angle deviation meeting the actual heading angle to compensate the heading angle.
Compared with the prior art, the invention has the beneficial effects that:
(1) the method for estimating the kinematics and calibrating the deviation of the crawler tractor can quickly and accurately estimate course angle deviation caused by ground fluctuation, GNSS antenna installation deviation and the like, so that the course angle is compensated, and the adaptability of the system to the ground is improved;
(2) according to the method, the EKF filtering algorithm is adopted to filter the data source of the automatic driving control algorithm of the crawler tractor, so that the data noise is reduced, the influence of external environment interference factors and system noise on the performance of the automatic driving system of the crawler tractor is reduced, and the control precision and the system stability of the automatic driving system of the crawler tractor are improved;
(3) the method has the advantages of small calculated amount and high real-time performance, and can improve the automatic driving performance of the crawler tractor by about 25 percent in comparison.
Drawings
FIG. 1 is a track type tractor kinematics model.
Fig. 2 is a flow chart of kalman filter estimation.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments.
Referring to fig. 1-2, a method for estimating kinematics and offset calibration of a track-type tractor includes the steps of,
the first step is as follows: constructing a kinematic model of the crawler tractor:
wherein,x is the east displacement coordinate component of the crawler tractor, y is the north displacement coordinate component, v is the running speed of the crawler tractor,the track angle of the track type tractor, omega is the angular speed of the track type tractor body;
the second step is that: because the crawler-type tractor turns to when, and left wheel, right wheel and barycenter department angular velocity are equal, so can deduce:
wherein v islTo be the running speed of the left track, vrThe running speed of the right crawler belt is shown, R is the turning radius, and b is the vehicle width.
The third step: the simultaneous equations can be solved to obtain:
the fourth step: the following equations (3) and (6) can be derived:
in the formula, u is the speed difference of the left and right crawler belts, namely the control quantity;
the fifth step: in the automatic driving operation process of the agricultural machinery, in order to ensure the crop cultivation quality, the tractor is arranged to do uniform linear motion, so that the following results can be obtained:
and a sixth step: in practical situations, due to the factors of ground fluctuation and GNSS dual-antenna installation deviation, a course angle deviation is caused, which results in a poor path tracking effect, and in order to simplify the model, the course angle deviation is approximately considered to be a certain value, and then:
the invention aims to accurately estimate the course angle deviation delta in real time by designing a proper estimation method, and compensate and correct the course angle;
the seventh step: through the process, the Kalman filtering nonlinear differential equation model of the crawler tractor is constructed as follows:
eighth step: selecting an east displacement coordinate component x, a north displacement coordinate component y, a tractor speed v and a tractor course angleAnd the course angle deviation delta is used as a system state quantity, and an east displacement coordinate component x, a north displacement coordinate component y, a tractor speed v and a tractor course angle are usedAs a system view measurement, there are:
wherein, X is a system estimation vector, and Z is a system observation vector.
The ninth step: the crawler tractor kalman filtering model constructed in the seventh step is a nonlinear model, the invention adopts EKF filtering algorithm, and obtains a corresponding system linear state space equation by solving Jacobian matrix and linearizing the model:
the tenth step: discretizing the continuous system to obtain a state transition matrix phi and an observation matrix H:
the eleventh step: selecting a process noise covariance matrix Q and an observation noise covariance matrix R:
process noise covariance matrix Q:
selecting an observation noise covariance matrix R: by collecting pose data east displacement coordinate component x, north displacement coordinate component y, tractor running speed v and tractor course angle of an automatic driving system of the crawler tractor in a stationary process within a period of time (10-20 min)And respectively solving the standard deviation of each group of data to obtain a system observation noise covariance matrix R, wherein n of the observation noise covariance matrix R in the running process of the tractor is 3 times when the observation noise covariance matrix R is static:
it should be noted that Q, R is not fixed and can be modified and adjusted according to the filtering effect until a satisfactory effect is achieved.
The twelfth step: by collecting the east displacement coordinate component x, the north displacement coordinate component y, the tractor speed v and the tractor course angle of the crawler tractor in the motion process (in an automatic driving mode)Controlling the quantity u and the vehicle body angular velocity omega, and taking the quantity u and the vehicle body angular velocity omega as an observation vector Z;
the thirteenth step: initialization state vector X, covariance matrix P, and observation state vector Z:
X(0)＝E[X(0)] (19)；
Z(0)＝Z0 (21)；
wherein, X (0), P (0), Z (0) are the initial values of the state vector X, covariance matrix P and observation state vector Z, respectively, and the size of P (0) will directly influence the convergence rate of the EKF algorithm;
the fourteenth step is that: kalman filter state one-step prediction:
the fifteenth step: calculating a one-step prediction covariance matrix:
P(k+1|k)＝Φ(k+1|k)P(k|k)ΦT(k+1|k)+Q(k+1) (23)；
sixteenth, step: computing a Kalman gain:
K(k+1)＝P(k+1|k)HT(k+1)[H(k+1)P(k+1|k)HT(k+1)+R(k+1)]-1 (24)；
seventeenth step: calculating an estimated value:
and eighteenth step: updating the covariance matrix:
P(k+1)＝[I-K(k+1)H(k+1)]P(k+1|k) (26)；
wherein, in the formula (22-26), k +1 represents the next time, k represents the current time,k is a kkalman gain;
the nineteenth step: continuously repeating the fourteenth step to the eighteenth step;
in the process of off-line analysis and debugging by using actual data, the observation noise matrix R, the process noise matrix Q and the covariance matrix P (0) are adjusted to achieve the expected filtering effect, and the course angle deviation conforming to the actual course angle is estimated to compensate the course angle;
the method for estimating and calibrating the kinematics of the crawler tractor is applied to the automatic driving process of the crawler tractor, and carries out online filtering, data processing and course angle deviation estimation so as to achieve better control effect;
the method for estimating the kinematics and calibrating the deviation of the crawler tractor can quickly and accurately estimate the course angle error caused by the fluctuation of the ground, thereby improving the adaptability of a control algorithm to the fluctuation of the ground, and specifically comprises the following points:
(1) the method for estimating the kinematics and calibrating the deviation of the crawler tractor can quickly and accurately estimate course angle deviation caused by ground fluctuation, GNSS antenna installation deviation and the like, so that the course angle is compensated, and the adaptability of the system to the ground is improved;
(2) according to the method, the EKF filtering algorithm is adopted to filter the data source of the automatic driving control algorithm of the crawler tractor, so that the data noise is reduced, the influence of external environment interference factors and system noise on the performance of the automatic driving system of the crawler tractor is reduced, and the control precision and the system stability of the automatic driving system of the crawler tractor are improved;
(3) the method has the advantages of small calculated amount and high real-time performance, and can improve the automatic driving performance of the crawler tractor by about 25 percent in comparison.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and the technical solutions and the inventive concepts thereof according to the present invention should be equivalent or changed within the scope of the present invention.
Claims (3)
1. A method for estimating the kinematics and calibrating the deviation of a crawler tractor is characterized by comprising the following steps,
s1, constructing a crawler tractor kinematic model:
wherein x is the east displacement coordinate component of the crawler tractor, y is the north displacement coordinate component, v is the running speed of the crawler tractor,the track angle of the track type tractor, omega is the angular speed of the track type tractor body;
s2: because the crawler-type tractor turns to when, and left wheel, right wheel and barycenter department angular velocity are equal, so can deduce:
wherein v islTo be the running speed of the left track, vrThe running speed of the right crawler belt is shown, R is the turning radius, and b is the width of the vehicle body;
s3: the simultaneous equations can be solved to obtain:
s4: the following equations (3) and (6) can be derived:
in the formula, u is the speed difference of the left and right crawler belts, namely the control quantity;
s5: in the automatic driving operation process of the agricultural machinery, in order to ensure the crop cultivation quality, the tractor is arranged to do uniform linear motion, so that the following results can be obtained:
s6: in practical situations, the course angle deviation caused by the ground fluctuation and the GNSS dual-antenna installation deviation factors causes the path tracking effect to be poor, and in order to simplify the model, the course angle deviation can be approximately considered as a constant value, and then:
s7: through the processes of S1-S6, the constructed Kalman filtering nonlinear differential equation model of the crawler tractor is as follows:
s8: selecting an east displacement coordinate component x, a north displacement coordinate component y, a tractor speed v and a tractor course angleAnd the course angle deviation delta is used as a system state quantity, and an east displacement coordinate component x, a north displacement coordinate component y, a tractor speed v and a tractor course angle are usedAs a system view measurement, there are:
wherein X is a system estimation vector, and Z is a system observation vector;
s9: the crawler tractor kalman filtering model constructed in the S7 is a nonlinear model, and by adopting an EKF filtering algorithm and solving a Jacobian matrix, the model is linearized to obtain a corresponding system linear state space equation:
s10: discretizing the continuous system to obtain a state transition matrix phi and an observation matrix H:
s11: selecting a process noise covariance matrix Q and an observation noise covariance matrix R:
s12: initialization state vector X, covariance matrix P, and observation state vector Z:
X(0)＝E[X(0)] (19)；
P(0)＝var[X(0)] (20)；
Z(0)＝Z0 (21)；
wherein, X (0), P (0), Z (0) are the initial values of the state vector X, the covariance matrix P and the observation state vector Z respectively;
s13: kalman filter state one-step prediction:
s14: calculating a one-step prediction covariance matrix:
P(k+1|k)＝Φ(k+1|k)P(k|k)ΦT(k+1|k)+Q(k+1) (23)；
s15: computing a Kalman gain:
K(k+1)＝P(k+1|k)HT(k+1)[H(k+1)P(k+1|k)HT(k+1)+R(k+1)]-1 (24)；
s16: calculating an estimated value:
s17: updating the covariance matrix:
P(k+1)＝[I-K(k+1)H(k+1)]P(k+1|k) (26)；
wherein, in the formula (22-26), k +1 represents the next time, k represents the current time,for the system state estimator, K is the kalman gain.
2. The method for estimating kinematics and misalignment calibration of a track-type tractor according to claim 1, further comprising the step of S18, wherein the steps of S13-S17 are repeated.
3. The method for estimating kinematics and calibration of a track-type tractor according to claim 2, further comprising a step S19, adjusting the observation noise matrix, the process noise matrix, and the covariance matrix to achieve a desired filtering effect during off-line analysis and debugging using actual data, and estimating a heading angle deviation corresponding to the actual heading angle to compensate for the heading angle.
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910448762.0A CN110262479A (en) | 2019-05-28 | 2019-05-28 | A kind of estimation of caterpillar tractor kinematics and deviation calibration method |
| PCT/CN2019/115000 WO2020238011A1 (en) | 2019-05-28 | 2019-11-01 | Kinematics estimation and deviation calibration method for crawler-type tractor |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910448762.0A CN110262479A (en) | 2019-05-28 | 2019-05-28 | A kind of estimation of caterpillar tractor kinematics and deviation calibration method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN110262479A true CN110262479A (en) | 2019-09-20 |
Family
ID=67915572
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910448762.0A Pending CN110262479A (en) | 2019-05-28 | 2019-05-28 | A kind of estimation of caterpillar tractor kinematics and deviation calibration method |
Country Status (2)
| Country | Link |
|---|---|
| CN (1) | CN110262479A (en) |
| WO (1) | WO2020238011A1 (en) |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111532337A (en) * | 2020-05-18 | 2020-08-14 | 新乡北方车辆仪表有限公司 | Control method for comprehensive double-current electric transmission |
| CN111766879A (en) * | 2020-06-24 | 2020-10-13 | 天津大学 | Intelligent vehicle formation system based on autonomous collaborative navigation |
| WO2020238011A1 (en) * | 2019-05-28 | 2020-12-03 | 南京天辰礼达电子科技有限公司 | Kinematics estimation and deviation calibration method for crawler-type tractor |
| CN112147656A (en) * | 2020-09-09 | 2020-12-29 | 无锡卡尔曼导航技术有限公司 | GNSS double-antenna course installation angle offset estimation method |
| CN112146561A (en) * | 2020-09-09 | 2020-12-29 | 无锡卡尔曼导航技术有限公司 | Hall angle sensor installation angle offset estimation method |
| CN113900126A (en) * | 2021-12-07 | 2022-01-07 | 广东皓行科技有限公司 | Double-antenna position determination method and device |
| CN115480579A (en) * | 2022-10-13 | 2022-12-16 | 华侨大学 | Crawler-type mobile machine, and method, device and medium for tracking and controlling established track thereof |
| WO2024212464A1 (en) * | 2023-09-12 | 2024-10-17 | 江苏徐工工程机械研究院有限公司 | Method, apparatus and system for estimating coordinates of bucket tooth tip, and excavator and storage medium |
Families Citing this family (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114200925B (en) * | 2021-11-10 | 2024-05-14 | 江苏大学 | Tractor path tracking control method and system based on self-adaptive time domain model prediction |
| CN114545944B (en) * | 2022-02-24 | 2024-04-16 | 合肥工业大学 | AGV course positioning navigation method based on magnetic nail magnetic field intensity correction |
| CN115167424B (en) * | 2022-07-12 | 2024-10-01 | 上海炙云新能源科技有限公司 | Path tracking control method of intelligent agricultural machine |
| CN117270535B (en) * | 2023-09-25 | 2024-03-12 | 青岛农业大学 | Auxiliary navigation system suitable for crawler-type potato harvester and control method |
Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH03266173A (en) * | 1990-03-16 | 1991-11-27 | Nec Corp | Equation analyzer |
| WO2002051740A1 (en) * | 2000-12-27 | 2002-07-04 | Hoei Shokai Co., Ltd | Container |
| WO2008018188A1 (en) * | 2006-08-08 | 2008-02-14 | Kyoto University | Eigen value decomposing device and eigen value decomposing method |
| CN206161820U (en) * | 2016-11-04 | 2017-05-10 | 首都师范大学 | System based on extension kalman particle filter |
| CN107991110A (en) * | 2017-11-29 | 2018-05-04 | 安徽省通信息科技有限公司 | A kind of caterpillar type robot slides parameter detection method |
| CN107991060A (en) * | 2017-11-20 | 2018-05-04 | 南京航空航天大学 | Based on adaptive and iterative algorithm load distribution type fiber-optic discrimination method |
| CN108279025A (en) * | 2017-12-22 | 2018-07-13 | 中国船舶重工集团公司第七0七研究所 | A kind of fiber optic gyro compass quick accurate alignment method based on gravitation information |
| CN108438048A (en) * | 2018-04-04 | 2018-08-24 | 上海华测导航技术股份有限公司 | A kind of novel caterpillar tractor automatic steering control system and control method |
| CN108592911A (en) * | 2018-03-23 | 2018-09-28 | 南京航空航天大学 | A kind of quadrotor kinetic model/airborne sensor Combinated navigation method |
| CN108693773A (en) * | 2018-04-04 | 2018-10-23 | 南京天辰礼达电子科技有限公司 | A kind of automatic driving of agricultural machinery landslide deviation adaptive estimation method |
| CN109240082A (en) * | 2018-08-25 | 2019-01-18 | 南京理工大学 | A kind of caterpillar mobile robot sliding formwork cloud model cross-coupling control method |
Family Cites Families (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102673569B (en) * | 2012-05-25 | 2015-10-28 | 同济大学 | Vehicle-state is calculated device, method and is used the vehicle of this device |
| CN107633141B (en) * | 2017-09-22 | 2018-08-21 | 中国水利水电科学研究院 | A kind of discrimination method of one-dimension mathematical model pumping station performance curve coefficients |
| CN107804315B (en) * | 2017-11-07 | 2019-07-16 | 吉林大学 | It is a kind of to consider to drive people's vehicle collaboration rotating direction control method that power is distributed in real time |
| CN108454628B (en) * | 2018-04-17 | 2019-06-04 | 吉林大学 | A kind of driver turns to rolling optimization control method in people's vehicle collaboration of ring |
| CN110262479A (en) * | 2019-05-28 | 2019-09-20 | 南京天辰礼达电子科技有限公司 | A kind of estimation of caterpillar tractor kinematics and deviation calibration method |
-
2019
- 2019-05-28 CN CN201910448762.0A patent/CN110262479A/en active Pending
- 2019-11-01 WO PCT/CN2019/115000 patent/WO2020238011A1/en active Application Filing
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH03266173A (en) * | 1990-03-16 | 1991-11-27 | Nec Corp | Equation analyzer |
| WO2002051740A1 (en) * | 2000-12-27 | 2002-07-04 | Hoei Shokai Co., Ltd | Container |
| WO2008018188A1 (en) * | 2006-08-08 | 2008-02-14 | Kyoto University | Eigen value decomposing device and eigen value decomposing method |
| CN206161820U (en) * | 2016-11-04 | 2017-05-10 | 首都师范大学 | System based on extension kalman particle filter |
| CN107991060A (en) * | 2017-11-20 | 2018-05-04 | 南京航空航天大学 | Based on adaptive and iterative algorithm load distribution type fiber-optic discrimination method |
| CN107991110A (en) * | 2017-11-29 | 2018-05-04 | 安徽省通信息科技有限公司 | A kind of caterpillar type robot slides parameter detection method |
| CN108279025A (en) * | 2017-12-22 | 2018-07-13 | 中国船舶重工集团公司第七0七研究所 | A kind of fiber optic gyro compass quick accurate alignment method based on gravitation information |
| CN108592911A (en) * | 2018-03-23 | 2018-09-28 | 南京航空航天大学 | A kind of quadrotor kinetic model/airborne sensor Combinated navigation method |
| CN108438048A (en) * | 2018-04-04 | 2018-08-24 | 上海华测导航技术股份有限公司 | A kind of novel caterpillar tractor automatic steering control system and control method |
| CN108693773A (en) * | 2018-04-04 | 2018-10-23 | 南京天辰礼达电子科技有限公司 | A kind of automatic driving of agricultural machinery landslide deviation adaptive estimation method |
| CN109240082A (en) * | 2018-08-25 | 2019-01-18 | 南京理工大学 | A kind of caterpillar mobile robot sliding formwork cloud model cross-coupling control method |
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2020238011A1 (en) * | 2019-05-28 | 2020-12-03 | 南京天辰礼达电子科技有限公司 | Kinematics estimation and deviation calibration method for crawler-type tractor |
| CN111532337A (en) * | 2020-05-18 | 2020-08-14 | 新乡北方车辆仪表有限公司 | Control method for comprehensive double-current electric transmission |
| CN111532337B (en) * | 2020-05-18 | 2022-01-07 | 新乡北方车辆仪表有限公司 | Control method for comprehensive double-current electric transmission |
| CN111766879A (en) * | 2020-06-24 | 2020-10-13 | 天津大学 | Intelligent vehicle formation system based on autonomous collaborative navigation |
| CN112147656A (en) * | 2020-09-09 | 2020-12-29 | 无锡卡尔曼导航技术有限公司 | GNSS double-antenna course installation angle offset estimation method |
| CN112146561A (en) * | 2020-09-09 | 2020-12-29 | 无锡卡尔曼导航技术有限公司 | Hall angle sensor installation angle offset estimation method |
| CN112147656B (en) * | 2020-09-09 | 2021-05-04 | 无锡卡尔曼导航技术有限公司 | GNSS double-antenna course installation angle offset estimation method |
| CN113900126A (en) * | 2021-12-07 | 2022-01-07 | 广东皓行科技有限公司 | Double-antenna position determination method and device |
| CN115480579A (en) * | 2022-10-13 | 2022-12-16 | 华侨大学 | Crawler-type mobile machine, and method, device and medium for tracking and controlling established track thereof |
| WO2024212464A1 (en) * | 2023-09-12 | 2024-10-17 | 江苏徐工工程机械研究院有限公司 | Method, apparatus and system for estimating coordinates of bucket tooth tip, and excavator and storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| WO2020238011A1 (en) | 2020-12-03 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110262479A (en) | A kind of estimation of caterpillar tractor kinematics and deviation calibration method | |
| Bevly et al. | Cascaded Kalman filters for accurate estimation of multiple biases, dead-reckoning navigation, and full state feedback control of ground vehicles | |
| US10512204B1 (en) | Heading measurement compensation for GNSS navigation | |
| CN105987696A (en) | Low-cost vehicle automatic driving design realization method | |
| Li et al. | Review of research on agricultural vehicle autonomous guidance | |
| CN111238471B (en) | Sideslip angle estimation method and estimator suitable for agricultural machine linear navigation | |
| CN112050809B (en) | Wheel type odometer and gyroscope information fusion unmanned vehicle directional positioning method | |
| CN110530361B (en) | Steering angle estimator based on agricultural machinery double-antenna GNSS automatic navigation system | |
| US20120072075A1 (en) | Steering control device of autonomous vehicle, autonomous vehicle having the same and steering control method of autonomous vehicle | |
| Oksanen et al. | Guidance system for agricultural tractor with four wheel steering | |
| WO2019173769A1 (en) | Kalman filter for an autonomous work vehicle system | |
| CN110716565A (en) | Track vehicle navigation track tracking control system | |
| CN112146561B (en) | Hall angle sensor installation angle offset estimation method | |
| CN110007667A (en) | A kind of crawler tractor and its path tracking control method and system | |
| Miao et al. | Steering angle adaptive estimation system based on GNSS and MEMS gyro | |
| CN112326990A (en) | Method and system for measuring speed of working vehicle | |
| Bevly | High speed, dead reckoning, and towed implement control for automatically steered farm tractors using GPS | |
| Bevly et al. | Evaluation of a blended dead reckoning and carrier phase differential GPS Systemfor control of an off-road vehicle | |
| CN112147656B (en) | GNSS double-antenna course installation angle offset estimation method | |
| Gartley et al. | Online estimation of implement dynamics for adaptive steering control of farm tractors | |
| JP2005250696A (en) | System and method for controlling autonomous travelling of vehicle | |
| CN115167424B (en) | Path tracking control method of intelligent agricultural machine | |
| CN113721609B (en) | Track tracking control method for 4WID high-clearance sprayer under sideslip condition | |
| Li et al. | Development of the automatic navigation system for combine harvester based on GNSS | |
| EP3432109A1 (en) | System and method for self-tuning of an agricultural tractor and implement |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20190920 |
|
| RJ01 | Rejection of invention patent application after publication |