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
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 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
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 G—PHYSICS
 G05—CONTROLLING; REGULATING
 G05D—SYSTEMS FOR CONTROLLING OR REGULATING NONELECTRIC VARIABLES
 G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
 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 NONELECTRIC VARIABLES
 G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
 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 NONELECTRIC VARIABLES
 G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
 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 NONELECTRIC VARIABLES
 G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
 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; 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; 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; 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. matrixmatrix or matrixvector multiplication, matrix factorization

 G—PHYSICS
 G05—CONTROLLING; REGULATING
 G05D—SYSTEMS FOR CONTROLLING OR REGULATING NONELECTRIC VARIABLES
 G05D2201/00—Application
 G05D2201/02—Control of position of land vehicles
 G05D2201/0201—Agriculture or harvesting machine
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 present invention relates to agricultural machinery automatic Pilot technical field more particularly to a kind of estimation of caterpillar tractor kinematics and partially
Poor calibration method.
Background technique
With the rapid development of GNSS highprecision Technique of Satellite Navigation and Positioning, automatic Pilot and information technology, Modern Agriculture
Industry just gradually develops to the direction of Digital Agriculture, precision agriculture.Tractor as a kind of common agricultural equipment, it is informationbased,
Intelligence degree is of great significance to the development of precision agriculture.
Tractor can be divided into wheeled tractor and caterpillar tractor according to walking manner.Compared with wheeled tractor,
Caterpillar tractor has many advantages, such as that contact surface is big, grounding pressure is small, traction adhesion property is good, is not easy to skid, and is more suitable in item
Operation in part relatively rugged environment, such as snowfield, hillside, miriness, meadow, plateau have effectively filled up wheeled dilatory
The deficiency of machine.However in caterpillar tractor automatic Pilot operation process, since there is a large amount of in actual job environment
Uncertain interference, such as complicated state on cultivation farmland ground, kinematics model is inaccurate, GNSS antenna setting angle is inclined
The factors such as difference, GPS signal are blocked and reflected, system noise and extraneous environmental noise interfere, cause tractor position and attitude phase
Close information and occur abnormal etc., seriously affect caterpillar tractor automatic Pilot control precision, not only cause peasant household's operation intensity and
Economic cost increases, and has been greatly reduced agricultural tillage efficiency and land utilization ratio.
The present invention is directed to the problem of caterpillar tractor automatic Pilot control technology, proposes a kind of crawler type and drags
The estimation of machine drawing kinematics and deviation calibration method, to realize the accurate estimation of caterpillar tractor position and attitude information, enhancing system
The adaptability and antiinterference united to external environment improve caterpillar tractor automated driving system homework precision.The present invention
Purpose be then realtime and accurately estimate heading angle deviation, and to course angle by designing suitable estimation method
It compensates and corrects, to improve system to the adaptability of environment.
Summary of the invention
The purpose of the present invention is draw in actual job environment due to being interfered by many factors for caterpillar tractor
The problem that the control effect risen is poor, homework precision is low, the present invention propose a kind of caterpillar tractor kinematics of antiinterference factor
Estimation and deviation calibration method, the algorithm can quickly Accuracy extimate go out since surface relief variation, the installation of GNSS double antenna are inclined
Course angle error caused by the factors such as difference, and course angle is compensated, to improve the control of caterpillar tractor automatic Pilot
Adaptability of the algorithm to the various environmental disturbances factors such as ground.
To achieve the goals above, present invention employs following technical solutions:
A kind of estimation of caterpillar tractor kinematics and deviation calibration method, include the following steps,
S1 constructs caterpillar tractor kinematics model:
Wherein, x is caterpillar tractor east orientation displacement coordinate component, and y is north orientation displacement coordinate component, and v drags for crawler type
Machine drawing travel speed,For caterpillar tractor course angle, ω is caterpillar tractor car body angular speed；
S2: since caterpillar tractor is when being turned to, angular speed is equal at revolver, right wheel and mass center, therefore can derive
Out:
Wherein, v_{l}For the travel speed for left track, v_{r}For the travel speed for right side track, R is turning radius, and b is
Width of the carbody；
S3: simultaneous equations solution can obtain:
S4: it can be released by formula (3) and (6):
In formula, u is left and right track speed differential, as control amount；
S5: in agricultural machinery automatic Pilot operation process, to guarantee crop cultivation quality, uniform rectilinear is in tractor setting
Movement, therefore can obtain:
S6: in a practical situation, since surface relief variation, GNSS double antenna installation deviation factor cause course angle inclined
Difference causes path trace effect to be deteriorated, and is simplified model, and can be approximately considered heading angle deviation is steady state value, then has:
S7: by S1S6 process, the caterpillar tractor kalman filtering nonlinear differential equation model of building is as follows:
S8: east orientation displacement coordinate component x, north orientation displacement coordinate component y, tractor speed v, tractor course angle are chosen
And heading angle deviation δ is as system state amount, it is fast with east orientation displacement coordinate component x, north orientation displacement coordinate component y, tractor
Spend v, tractor course angleIt measures, then has as systematic perspective:
Wherein, X is system estimation vector, and Z is systematic observation vector；
The caterpillar tractor kalman Filtering Model constructed in S9:S7 is nonlinear model, using EKF filtering algorithm,
By Method of Obtaining Jacobian Matrix, model is linearized to obtain corresponding system linear state space equation:
S10: discrete model construction is obtained into statetransition matrix Φ and observing matrix H:
S11: process noise covariance matrix Q and observation noise covariance matrix R is chosen:
S12: init 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)=Z_{0}(21)；
Wherein, X (0), P (0), Z (0) are respectively the initial of state vector X, covariance matrix P and observation state vector Z
Value；
S13:Kalman filter state onestep prediction:
S14: onestep prediction covariance matrix is calculated:
P (k+1  k)=Φ (k+1  k) P (k  k) Φ^{T}(k+1k)+Q(k+1) (23)；
S15: Kalman gain is calculated:
K (k+1)=P (k+1  k) H^{T}(k+1)[H(k+1)P(k+1k)H^{T}(k+1)+R(k+1)]^{1}(24)；
S16: estimated value is calculated:
S17: covariance matrix is updated:
P (k+1)=[IK (k+1) H (k+1)] P (k+1  k) (26)；
Wherein, in formula (2226), k+1 indicates that subsequent time, k indicate current time,For system state estimation
Amount, K are kalman gain.
Preferably, further include step S18, constantly repeat step S13S17.
Preferably, further include step S19, during carrying out offline analysis debugging using real data, adjust observation and make an uproar
Sound matrix, process noise matrix and covariance matrix meet actual course angle to reach desired filter effect, and estimate
Deviation compensates course angle.
Compared with prior art, the beneficial effects of the present invention are:
(1) estimation of caterpillar tractor kinematics and deviation calibration method that the present invention realizes quickly can be estimated accurately
The heading angle deviation due to caused by surface relief variation, GNSS antenna installation deviation etc. improves to compensate to course angle
Adaptability of the system to ground；
(2) present invention can be to caterpillar tractor automatic Pilot control algolithm data source by using EKF filtering algorithm
Be filtered, reduce data noise, reduce external environmental interference factor and system noise to caterpillar tractor from
The influence of dynamic control loop performance improves caterpillar tractor automated driving system control precision and system stability；
(3) calculation amount of the present invention is small, and realtime is high, in contrast, can be by caterpillar tractor automatic Pilot performance boost
25% or so.
Detailed description of the invention
Fig. 1 is caterpillar tractor kinematics model.
Fig. 2 is kalman filtering estimation flow chart.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.
Referring to Fig.12, a kind of caterpillar tractor kinematics estimation and deviation calibration method, include the following steps,
Step 1: building caterpillar tractor kinematics model:
Wherein, x is caterpillar tractor east orientation displacement coordinate component, and y is north orientation displacement coordinate component, and v drags for crawler type
Machine drawing travel speed,For caterpillar tractor course angle, ω is caterpillar tractor car body angular speed；
Step 2: angular speed is equal at revolver, right wheel and mass center, therefore can since caterpillar tractor is when being turned to
It derives:
Wherein, v_{l}For the travel speed for left track, v_{r}For the travel speed for right side track, R is turning radius, and b is
Width of the carbody.
Step 3: simultaneous equations solution can obtain:
Step 4: can be released by formula (3) and (6):
In formula, u is left and right track speed differential, as control amount；
Step 5:, to guarantee crop cultivation quality, tractor setting is done at the uniform velocity in agricultural machinery automatic Pilot operation process
Linear motion, therefore can obtain:
Step 6: in a practical situation, due to surface relief variation, GNSS double antenna installation deviation factor, causing course
Angular displacement causes path trace effect to be deteriorated, and is simplified model, and being approximately considered heading angle deviation is certain value, then has:
The purpose of the present invention is then that it is inclined can realtime and accurately to estimate course angle by designing suitable estimation method
Poor δ, and course angle is compensated and corrected；
Step 7: by the above process, the caterpillar tractor kalman that the present invention constructs filters nonlinear differential equation
Model is as follows:
Step 8: choosing east orientation displacement coordinate component x, north orientation displacement coordinate component y, tractor speed v, tractor course
AngleAnd heading angle deviation δ with east orientation displacement coordinate component x, north orientation displacement coordinate component y, is drawn as system state amount
Machine speed v, tractor course angleIt measures, then has as systematic perspective:
Wherein, X is system estimation vector, and Z is systematic observation vector.
Step 9: the caterpillar tractor kalman Filtering Model constructed in the 7th step is nonlinear model, the present invention is adopted
Model is linearized to obtain corresponding system linear state space side by Method of Obtaining Jacobian Matrix with EKF filtering algorithm
Journey:
Step 10: discrete model construction is obtained statetransition matrix Φ and observing matrix H:
Step 11: choosing process noise covariance matrix Q and observation noise covariance matrix R:
Process noise covariance matrix Q:
The selection of observation noise covariance matrix R: by acquiring in a period of time (10~20min) caterpillar tractor certainly
Dynamic control loop pose data east orientation displacement coordinate component x, north orientation displacement coordinate component y, tractor traveling in quiescing process
Speed v, tractor course angleAnd ask standard deviation to obtain systematic observation noise covariance matrix R each group of data respectively, it draws
N=3 times when the observation noise covariance matrix R of machine in the process of running is static:
It is worth noting that, Q, R for choosing are not definite value herein, it can modify and adjust according to filter effect, directly
Until reaching promising result.
Step 12: by acquisition caterpillar tractor, the east orientation (under automatic driving mode) is displaced seat during the motion
Mark component x, north orientation displacement coordinate component y, tractor speed v, tractor course angleControl amount u and car body angular velocity omega, and
As observation vector Z；
Step 13: init state vector X, covariance matrix P and observation state vector Z:
X (0)=E [X (0)] (19)；
Z (0)=Z_{0}(21)；
Wherein, X (0), P (0), Z (0) are respectively the initial of state vector X, covariance matrix P and observation state vector Z
Value, the size of P (0) will directly affect EKF convergence speed of the algorithm；
Step 14: Kalman filter state onestep prediction:
Step 15: calculating onestep prediction covariance matrix:
P (k+1  k)=Φ (k+1  k) P (k  k) Φ^{T}(k+1k)+Q(k+1) (23)；
Step 16: calculating Kalman gain:
K (k+1)=P (k+1  k) H^{T}(k+1)[H(k+1)P(k+1k)H^{T}(k+1)+R(k+1)]^{1}(24)；
Step 17: calculating estimated value:
Step 18: updating covariance matrix:
P (k+1)=[IK (k+1) H (k+1)] P (k+1  k) (26)；
Wherein, in formula (2226), k+1 indicates that subsequent time, k indicate current time,For system state estimation
Amount, K are kkalman gain；
Step 19: constantly repeating step the ten fourthe 18；
During the present invention carries out offline analysis debugging using real data, observation noise matrix R, process noise are adjusted
Matrix Q and covariance matrix P (0) meets actual heading angle deviation to course to reach desired filter effect, and estimate
Angle compensates；
The present invention estimates caterpillar tractor kinematics and calibration method is applied to caterpillar tractor automatic Pilot mistake
Journey is filtered, data processing, estimated course angular displacement, online to reach preferable control effect；
Caterpillar tractor kinematics proposed by the present invention estimation and deviation calibration method can quickly Accuracy extimate due to
Course angle error caused by surface relief changes specifically includes to improve control algolithm to the adaptability of ground fluctuations
The following:
(1) estimation of caterpillar tractor kinematics and deviation calibration method that the present invention realizes quickly can be estimated accurately
The heading angle deviation due to caused by surface relief variation, GNSS antenna installation deviation etc. improves to compensate to course angle
Adaptability of the system to ground；
(2) present invention can be to caterpillar tractor automatic Pilot control algolithm data source by using EKF filtering algorithm
Be filtered, reduce data noise, reduce external environmental interference factor and system noise to caterpillar tractor from
The influence of dynamic control loop performance improves caterpillar tractor automated driving system control precision and system stability；
(3) calculation amount of the present invention is small, and realtime is high, in contrast, can be by caterpillar tractor automatic Pilot performance boost
25% or so.
The foregoing is only a preferred embodiment of the present invention, but scope of protection of the present invention is not limited thereto,
Anyone skilled in the art in the technical scope disclosed by the present invention, according to the technique and scheme of the present invention and its
Inventive concept is subject to equivalent substitution or change, should be covered by the protection scope of the present invention.
Claims (3)
1. a kind of caterpillar tractor kinematics estimation and deviation calibration method, which is characterized in that include the following steps,
S1 constructs caterpillar tractor kinematics model:
Wherein, x is caterpillar tractor east orientation displacement coordinate component, and y is north orientation displacement coordinate component, and v is caterpillar tractor
Travel speed,For caterpillar tractor course angle, ω is caterpillar tractor car body angular speed；
S2: since caterpillar tractor is when being turned to, angular speed is equal at revolver, right wheel and mass center, therefore can derive:
Wherein, v_{l}For the travel speed for left track, v_{r}For the travel speed for right side track, R is turning radius, and b is car body
Width；
S3: simultaneous equations solution can obtain:
S4: it can be released by formula (3) and (6):
In formula, u is left and right track speed differential, as control amount；
S5: in agricultural machinery automatic Pilot operation process, to guarantee crop cultivation quality, uniform rectilinear's fortune is done in tractor setting
It is dynamic, therefore can obtain:
S6: it in a practical situation, since surface relief variation, GNSS double antenna installation deviation factor cause heading angle deviation, leads
It causes path trace effect to be deteriorated, is simplified model, can be approximately considered heading angle deviation is steady state value, then have:
S7: by S1S6 process, the caterpillar tractor kalman filtering nonlinear differential equation model of building is as follows:
S8: east orientation displacement coordinate component x, north orientation displacement coordinate component y, tractor speed v, tractor course angle are chosenAnd
Heading angle deviation δ as system state amount, with east orientation displacement coordinate component x, north orientation displacement coordinate component y, tractor speed v,
Tractor course angleIt measures, then has as systematic perspective:
Wherein, X is system estimation vector, and Z is systematic observation vector；
The caterpillar tractor kalman Filtering Model constructed in S9:S7 is that nonlinear model is passed through using EKF filtering algorithm
Method of Obtaining Jacobian Matrix linearizes model to obtain corresponding system linear state space equation:
S10: discrete model construction is obtained into statetransition matrix Φ and observing matrix H:
S11: process noise covariance matrix Q and observation noise covariance matrix R is chosen:
S12: init 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)=Z_{0}(21)；
Wherein, X (0), P (0), Z (0) are respectively the initial value of state vector X, covariance matrix P and observation state vector Z；
S13:Kalman filter state onestep prediction:
S14: onestep prediction covariance matrix is calculated:
P (k+1  k)=Φ (k+1  k) P (k  k) Φ^{T}(k+1k)+Q(k+1) (23)；
S15: Kalman gain is calculated:
K (k+1)=P (k+1  k) H^{T}(k+1)[H(k+1)P(k+1k)H^{T}(k+1)+R(k+1)]^{1}(24)；
S16: estimated value is calculated:
S17: covariance matrix is updated:
P (k+1)=[IK (k+1) H (k+1)] P (k+1  k) (26)；
Wherein, in formula (2226), k+1 indicates that subsequent time, k indicate current time,For system state estimation amount, K is
Kalman gain.
2. a kind of caterpillar tractor kinematics estimation according to claim 1 and deviation calibration method, which is characterized in that
Further include step S18, constantly repeats step S13S17.
3. a kind of caterpillar tractor kinematics estimation according to claim 2 and deviation calibration method, which is characterized in that
Further include step S19, during carrying out offline analysis debugging using real data, adjusts observation noise matrix, process noise
Matrix and covariance matrix with reach desired filter effect, and estimate meet actual heading angle deviation to course angle into
Row compensation.
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