CN110146077A - Pose of mobile robot angle calculation method - Google Patents

Pose of mobile robot angle calculation method Download PDF

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Publication number
CN110146077A
CN110146077A CN201910542491.5A CN201910542491A CN110146077A CN 110146077 A CN110146077 A CN 110146077A CN 201910542491 A CN201910542491 A CN 201910542491A CN 110146077 A CN110146077 A CN 110146077A
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mobile robot
quaternary number
pose
coordinate system
carrier
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郑凯
郑力达
魏丽娜
陈英米
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Taizhou Zhi Tong Technology Co Ltd
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Taizhou Zhi Tong Technology Co Ltd
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Priority to CN201910542491.5A priority Critical patent/CN110146077A/en
Publication of CN110146077A publication Critical patent/CN110146077A/en
Priority to PCT/CN2020/097281 priority patent/WO2020253854A1/en
Priority to ZA2021/05514A priority patent/ZA202105514B/en
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • 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
    • G01C21/165—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 combined with non-inertial navigation instruments

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  • Engineering & Computer Science (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Automation & Control Theory (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)
  • Manipulator (AREA)

Abstract

Pose of mobile robot angle calculation method of the invention, comprising: step 1, sensor raw data acquisition；Step 2, sensor raw data pretreatment；Step 3 establishes the process model based on quaternary number fusion extended Kalman filter and dominant complementary filter blending algorithm；Step 4 establishes the observation model based on quaternary number fusion extended Kalman filter and dominant complementary filter blending algorithm；Step 5, pose of mobile robot angle solve.The present invention provides a kind of pose of mobile robot angle calculation methods based on quaternary number fusion extended Kalman filter and dominant complementary filter, solve the problems, such as that existing pose of mobile robot angle calculation accuracy is not high, this method can obtain the attitude angle of higher precision, so as to preferably control the posture of mobile robot.

Description

Pose of mobile robot angle calculation method
Technical field
The present invention relates to the mobile robots including unmanned plane, and in particular to merges spreading kalman based on quaternary number The calculation method at the pose of mobile robot angle of filter and dominant complementary filter.
Background technique
Currently, robot pose estimation is mainly calculated using attitude matrix, attitude matrix can use different numbers Mode defines.And attitude matrix update method mainly has direction cosine method, Euler's horn cupping and Quaternion Method.Mobile robot The case where will appear pitch angle in crossing over blockage close to 90 °, carries out carrier of moving robot posture renewal using Quaternion Method, Can be to avoid " singular point " problem that mobile robot occurs during crossing over blockage, and Relative Euler horn cupping calculation amount is more Small, algorithm is simpler.
Existing pose of mobile robot, which calculates filter, dominant complementary filter and extended Kalman filter.It is dominant Complementary filter calculation method is simple, and operand is small, and the mobile robot high suitable for requirement of real-time carries out attitude algorithm.By It is fixed in dominant complementary filter parameter, therefore is not particularly suited for the solution of the posture in the case of motion state changes greatly.It is transporting During solving mobile robot quaternary number posture with extended Kalman filter, made using accelerometer and magnetometer data It is measured for systematic perspective, and accelerometer output is the specific force of carrier of moving robot, i.e., carrier of moving robot relative inertness is empty Between absolute acceleration and the sum of gravitational acceleration, this will cause carrier of moving robot and passes through during High acceleration motion There is very big error in the posture that accelerometer resolves.
Summary of the invention
To overcome the above-mentioned deficiency in the presence of the prior art, the present invention provides one kind to merge expansion card based on quaternary number The pose of mobile robot angle calculation method of Thalmann filter and dominant complementary filter, to solve existing mobile robot appearance The not high problem of state angle calculation accuracy, this method can obtain the attitude angle of higher precision, so as to preferably control movement The posture of robot.
Technical solution of the present invention:
Pose of mobile robot angle calculation method, the mobile robot are driven by direct current generator；The mobile robot It is equipped with main control chip and Inertial Measurement Unit, the Inertial Measurement Unit has nine axle sensors；
The solving of attitude method includes:
Step 1, sensor raw data acquisition: main control chip is passed by nine axis that I2C bus acquires Inertial Measurement Unit The data of sensor information, i.e. three-axis gyroscope, three axis accelerometer and three axle magnetometer；
Step 2, sensor raw data pretreatment: using the high-frequency noise adulterated in mean filter removal data information；
Step 3 establishes the mistake based on quaternary number fusion extended Kalman filter and dominant complementary filter blending algorithm Journey model: it takes quaternary number and 7 parameters of gyroscope drift error as system state variables, establishes non-linear system status side Journey；
Step 4 establishes the sight based on quaternary number fusion extended Kalman filter and dominant complementary filter blending algorithm Survey model: the quaternary number resolved using dominant complementary filter recycles fusion as the observed quantity of extended Kalman filter Filter estimates optimal quaternary number；
Step 5, pose of mobile robot angle solve: optimal quaternary number obtained in step 4 is substituted into quaternary number and Euler The conversion relational expression at angle calculates three attitude angles of mobile robot: roll angle, pitch angle and yaw angle.
As optimization, in the calculation method of pose of mobile robot angle above-mentioned, the step 2 specifically includes following sub-step It is rapid:
2.1, windowing process is carried out to gyroscope, accelerometer and magnetometer initial data, takes sliding window length n= 10, step-length l=1；
2.2, average value processing is carried out to the data in window, exports numerical value of the result as current time, the following institute of formula Show:
2.3, I (n-9) exit window after sub-step 2.2, I (n+1) is into window；
2.4, sub-step 2.2 and sub-step 2.3 recycle, and to the last a data are into window, and export final result.
As optimization, in the calculation method of pose of mobile robot angle above-mentioned, in the step 3:
System mode vector are as follows:
X (k)=(q0(k) q1(k) q2(k) q3(k) bgx(k) bgy(k) bgz(k))；
In above formula, q0(k)、q1(k)、q2(k)、q3It (k) is k moment quaternary number posture, bgx(k)、bgy(k)、bgz(k) respectively For the drift error of three axis of gyroscope；
System state equation is established by system mode vector are as follows:
Ask Jacobi matrix that can obtain state-transition matrix f (x (k-1), k-1) are as follows:
Wherein: Г (k) is that noise drives matrix, and W (k-1) is zero-mean white noise sequence； It respectively indicates Gyroscope three-axis measurement value；It indicates gyroscope estimated value, is subtracted each other by gyroscope measured value and drift error amount It obtains；I3×3For three-dimensional unit matrix；
As optimization, in the calculation method of pose of mobile robot angle above-mentioned, the step 4 is real according to following sub-step It is existing:
4.1, data normalization: when mobile robot is in non-accelerating state, accelerometer is under world coordinate system Gravity vector isGravity vector is after normalizationUnder carrier of moving robot coordinate system The weight component that accelerometer measures is denoted as
4.2, the gravity vector under the carrier coordinate system that the sum that basis measures is calculated calculates error compensation value acceleration Spending the gravity vector after meter normalizes under reference frame isIt can be by g by attitude matrixnIt is converted to Gravity vector under carrier coordinate system:
Vector multiplication cross is done in gravitational vectors estimation to the gravitational vectors under carrier coordinate system and under carrier coordinate system, is obtained pair The rectification building-out value of gyroscope angular velocity data:
4.3, it according to the magnetic field strength under magnetometer data and the carrier of moving robot coordinate system being calculated, calculates The size and Orientation in error compensation value magnetometer measures earth magnetic field, magnetometer output is m in carrier coordinate systemb=(mxi myi mzi)T, it is converted under world coordinate system by attitude matrix and is projected as hn=(hxi hyi hzi)T, when magnetic geographic coordinate system and When carrier of moving robot coordinate system is overlapped, magnetometer output is bb=(0 byi bzi)T, in XOY plane, bbBe projected as byi, hnBe projected as (hxi+hyi)；The estimated value for obtaining magnetic field strength is:
Field value under navigational coordinate system is obtained by the estimated value of the magnetic field strength of above formula, then recycles attitude matrix It converts and extrapolates field projection under carrier of moving robot coordinate system as wb=(wxi wyi wzi)T；According to measure and reckoning The two is done vector multiplication cross, obtains correction-compensation value by the magnetic field strength under obtained carrier of moving robot coordinate system:
4.4, error compensation: error compensation value
4.5, gyroscope angular speed error is corrected.Gyroscope angular speed numerical value is corrected using error compensation value:
4.6, quaternary number is updated using modified gyroscope angular speed numerical value, differential side is solved using single order runge kutta method Journey, and update quaternary number；The quaternion differential equation of t moment are as follows:
Obtain (t+T) moment updated quaternary number:
Systematic perspective measurement is set as:
Z (k)=(q0(k) q1(k) q2(k) q3(k))；
Wherein: q0(k)、q1(k)、q2(k)、q3(k) mobile robot obtained after resolving for dominant complementary filter algorithm carries Body quaternary number posture；
Systematic observation equation are as follows:
Ask Jacobian matrix that can obtain system measurements matrix h (x (k), k) according to above formula are as follows:
The quaternary number posture at (k-1) moment is substituted into system state equation to update to obtain k moment carrier of moving robot four First number posture state amount；Then it is by the quaternary number posture state amount at k moment and by what dominant complementary filter solved Overall view measurement substitutes into state vector and estimates equation, finally obtains the optimal quaternary number Attitude estimation of k moment carrier of moving robot:
As optimization, in the calculation method of pose of mobile robot angle above-mentioned, in the step 5, attitude angle calculating formula are as follows:
As optimization, in the calculation method of pose of mobile robot angle above-mentioned, the main control chip is STM32F103.
As optimization, in the calculation method of pose of mobile robot angle above-mentioned, the inertia unit is MPU9250.
As optimization, in the calculation method of pose of mobile robot angle above-mentioned, the message transmission rate of the I2C bus is 400kbit/s, the data sampling frequency of nine axle sensor are 500HZ.
As optimization, in the calculation method of pose of mobile robot angle above-mentioned, the every 2ms of main control unit is carried out periodically Data acquisition and pretreatment 1 time.
Compared with prior art, pose of mobile robot angle calculation method of the invention achieves following significant progress: By the posture for merging the algorithm resolving mobile robot of extended Kalman filter and dominant complementary filter based on quaternary number Angle solves the problems, such as that existing pose of mobile robot angle calculation accuracy is not high, can obtain the attitude angle of higher precision, from And it can preferably control the posture of mobile robot.
The results showed that the attitude angle fluctuation that fused filter obtains is smaller, flatness is more preferable, and to yaw angle Control errors it is stronger, attitude angle tracking effect is more preferable；When mobile robot quickly moves, speed is responded using fused filtering device Du Genggao, and attitude angle fluctuation range is smaller.
It is found, is obtained by fusion extended Kalman filter and dominant complementary filter quiet by contrast and experiment For state accuracy of attitude determination compared to extended Kalman filter, 71%, 74% and is can be improved in roll angle, pitch angle and yaw angle respectively 66%；Dynamic accuracy of attitude determination can be improved respectively compared to extended Kalman filter, roll angle, pitch angle and yaw angle 65%, 67% and 76%, significantly more advantage is shown in attitude algorithm.
Detailed description of the invention
Fig. 1 is the pose of mobile robot angle that extended Kalman filter and dominant complementary filter are merged based on quaternary number Resolve functional block diagram；
Extended Kalman filter and fusion Extended Kalman filter is respectively adopted in Fig. 2 when being the static placement of mobile robot The roll angle that device and dominant complementary filter resolve；
Extended Kalman filter and fusion Extended Kalman filter is respectively adopted in Fig. 3 when being the static placement of mobile robot The pitch angle that device and dominant complementary filter resolve；
Extended Kalman filter and fusion Extended Kalman filter is respectively adopted in Fig. 4 when being the static placement of mobile robot The yaw angle that device and dominant complementary filter resolve；
Fig. 5 is that extended Kalman filter and fusion extension karr is respectively adopted in mobile robot under microinching state The roll angle that graceful filter and dominant complementary filter resolve；
Fig. 6 is that extended Kalman filter and fusion extension karr is respectively adopted in mobile robot under microinching state The pitch angle that graceful filter and dominant complementary filter resolve；
Fig. 7 is that extended Kalman filter and fusion extension karr is respectively adopted in mobile robot under microinching state The yaw angle that graceful filter and dominant complementary filter resolve；
Fig. 8 is that extended Kalman filter and fusion extension karr is respectively adopted in mobile robot under quick motion state The roll angle that graceful filter and dominant complementary filter resolve；
Fig. 9 is that extended Kalman filter and fusion extension karr is respectively adopted in mobile robot under quick motion state The pitch angle that graceful filter and dominant complementary filter resolve；
Figure 10 is that extended Kalman filter and fusion expansion card is respectively adopted in mobile robot under quick motion state The yaw angle that Thalmann filter and dominant complementary filter resolve；
Specific embodiment
With reference to the accompanying drawings and detailed description (embodiment) the present invention is further illustrated, it is described herein Specific embodiment is only used to explain the present invention, but is not intended as the foundation limited the present invention.
Referring to Fig. 1-10, in pose of mobile robot angle calculation method of the invention, the mobile robot is by direct current Machine driving；The mobile robot is equipped with main control chip and Inertial Measurement Unit, and the Inertial Measurement Unit is passed with nine axis Sensor.
Pose of mobile robot angle calculation method includes:
Step 1, sensor raw data acquisition: main control chip is passed by nine axis that I2C bus acquires Inertial Measurement Unit The data of sensor information, i.e. three-axis gyroscope, three axis accelerometer and three axle magnetometer；
Step 2, sensor raw data pretreatment: using the high-frequency noise adulterated in mean filter removal data information；
Step 3 establishes the mistake based on quaternary number fusion extended Kalman filter and dominant complementary filter blending algorithm Journey model: it takes quaternary number and 7 parameters of gyroscope drift error as system state variables, establishes non-linear system status side Journey；
Step 4 establishes the sight based on quaternary number fusion extended Kalman filter and dominant complementary filter blending algorithm Survey model: the quaternary number resolved using dominant complementary filter recycles fusion as the observed quantity of extended Kalman filter Filter estimates optimal quaternary number；
Step 5, pose of mobile robot angle solve: optimal quaternary number obtained in step 4 is substituted into quaternary number and Euler The conversion relational expression at angle calculates three attitude angles of mobile robot: roll angle, pitch angle and yaw angle.
It is a specific embodiment of the invention below:
The step 2 specifically includes following sub-step:
2.1, windowing process is carried out to gyroscope, accelerometer and magnetometer initial data, takes sliding window length n= 10, step-length l=1；
2.2, average value processing is carried out to the data in window, exports numerical value of the result as current time, the following institute of formula Show:
2.3, I (n-9) exit window after sub-step 2.2, I (n+1) is into window；
2.4, sub-step 2.2 and sub-step 2.3 recycle, and to the last a data are into window, and export final result.
In the step 3:
System mode vector are as follows:
X (k)=(q0(k) q1(k) q2(k) q3(k) bgx(k) bgy(k) bgz(k))；
In above formula, q0(k)、q1(k)、q2(k)、q3It (k) is k moment quaternary number posture, bgx(k)、bgy(k)、bgz(k) respectively For the drift error of three axis of gyroscope；
System state equation is established by system mode vector are as follows:
Ask Jacobi matrix that can obtain state-transition matrix f (x (k-1), k-1) are as follows:
Wherein: Г (k) is that noise drives matrix, and W (k-1) is zero-mean white noise sequence； It respectively indicates Gyroscope three-axis measurement value；It indicates gyroscope estimated value, is subtracted each other by gyroscope measured value and drift error amount It obtains；I3×3For three-dimensional unit matrix；
The step 4 is realized according to following sub-step:
4.1, data normalization: when mobile robot is in non-accelerating state, accelerometer is under world coordinate system Gravity vector isGravity vector is after normalizationUnder carrier of moving robot coordinate system The weight component that accelerometer measures is denoted as
4.2, the gravity vector under the carrier coordinate system that the sum that basis measures is calculated calculates error compensation value acceleration Spending the gravity vector after meter normalizes under reference frame isIt can be by g by attitude matrixnIt is converted to Gravity vector under carrier coordinate system:
Vector multiplication cross is done in gravitational vectors estimation to the gravitational vectors under carrier coordinate system and under carrier coordinate system, is obtained pair The rectification building-out value of gyroscope angular velocity data:
4.3, it according to the magnetic field strength under magnetometer data and the carrier of moving robot coordinate system being calculated, calculates The size and Orientation in error compensation value magnetometer measures earth magnetic field, magnetometer output is m in carrier coordinate systemb=(mxi myi mzi)T, it is converted under world coordinate system by attitude matrix and is projected as hn=(hxi hyi hzi)T, when magnetic geographic coordinate system and When carrier of moving robot coordinate system is overlapped, magnetometer output is bb=(0 byi bzi)T, in XOY plane, bbBe projected as byi, hnBe projected as (hxi+hyi)；The estimated value for obtaining magnetic field strength is:
Field value under navigational coordinate system is obtained by the estimated value of the magnetic field strength of above formula, then recycles attitude matrix It converts and extrapolates field projection under carrier of moving robot coordinate system as wb=(wxi wyi wzi)T；According to measure and reckoning The two is done vector multiplication cross, obtains correction-compensation value by the magnetic field strength under obtained carrier of moving robot coordinate system:
4.4, error compensation: error compensation value
4.5, gyroscope angular speed error is corrected.Gyroscope angular speed numerical value is corrected using error compensation value:
4.6, quaternary number is updated using modified gyroscope angular speed numerical value, differential side is solved using single order runge kutta method Journey, and update quaternary number；The quaternion differential equation of t moment are as follows:
Obtain (t+T) moment updated quaternary number:
Systematic perspective measurement is set as:
Z (k)=(q0(k) q1(k) q2(k) q3(k))；
Wherein: q0(k)、q1(k)、q2(k)、q3(k) mobile robot obtained after resolving for dominant complementary filter algorithm carries Body quaternary number posture；
Systematic observation equation are as follows:
Ask Jacobian matrix that can obtain system measurements matrix h (x (k), k) according to above formula are as follows:
The quantity of state quaternary number at (k-1) moment is substituted into system state equation to update to obtain k moment carrier of moving robot Quaternary number posture；Then by the status predication value at k moment and the State Viewpoint measured value generation solved by dominant complementary filter Enter system measurements matrix, finally obtain the optimal quaternary number Attitude estimation of k moment carrier of moving robot:
In the step 5, attitude angle calculating formula are as follows:
The main control chip is STM32F103.The inertia unit is MPU9250.The data of the I2C bus transmit speed Rate is 400kbit/s, and the data sampling frequency of nine axle sensor is 500HZ.The every 2ms of main control unit is carried out periodically Data acquisition and pretreatment 1 time.
Below by way of experiment, the invention will be further described:
(1) the pose of mobile robot experiment under stationary state: carrier of moving robot attitude transducer module is static It places 3 minutes in the horizontal plane, takes 2500 sampled points, be utilized respectively extended Kalman filter and fusion spreading kalman filter The two methods of wave device and dominant complementary filter calculate the attitude angle under mobile robot stationary state.
By comparison, it was found that the roll and pitch angle that are resolved using fused filtering filter and actual conditions are more coincide, Difference is smaller between yaw angle and true value, but general effect is consistent.
(2) the pose of mobile robot experiment under state at a slow speed: by carrier of moving robot attitude transducer module around three Axis does the rotation of wide-angle, takes 2500 sampled points, is utilized respectively extended Kalman filter and fusion Extended Kalman filter The two methods of device and dominant complementary filter calculate attitude angle of the mobile robot at a slow speed under state.
The roll exported by two kinds of filters and pitch angle syncretizing effect are close, and fused filtering device smoothing capability is stronger, And the yaw angle control errors ability resolved is stronger, and attitude angle dynamically track effect is more preferable.
(3) the pose of mobile robot experiment under fast state: enable carrier of moving robot attitude transducer module around three Axis does quick high frequency time rotation, takes 2500 sampled points, is utilized respectively extended Kalman filter and fusion spreading kalman filter Two kinds of algorithms of wave device and dominant complementary filter calculate the attitude angle under mobile robot fast state.
From can be seen that in Fig. 8-10, the attitude angle fluctuation that fused filter obtains is smaller, and flatness is more preferable, and right The control errors of yaw angle are stronger, and attitude angle tracking effect is more preferable.When mobile robot quickly moves, fused filtering device is utilized Response speed is higher, and attitude angle fluctuation range is smaller.
By contrast and experiment, the static state obtained by fusion extended Kalman filter and dominant complementary filter is fixed Roll angle 71%, pitch angle 74% has been respectively increased compared to extended Kalman filter in appearance precision, and yaw angle 66% is dynamically determined Appearance precision improves roll angle 65%, pitch angle 67%, yaw angle 76%, in attitude algorithm compared to extended Kalman filter In show significantly more advantage.
The foregoing is merely illustrative of the preferred embodiments of the present invention, is not intended to limit the invention, all in essence of the invention Made any modifications, equivalent replacements, and improvements etc., should all be included in the protection scope of the present invention within mind and principle.

Claims (9)

1. pose of mobile robot angle calculation method, the mobile robot are driven by direct current generator；It is characterized by: the shifting Mobile robot is equipped with main control chip and Inertial Measurement Unit, and the Inertial Measurement Unit has nine axle sensors；
The solving of attitude method includes:
Step 1, sensor raw data acquisition: main control chip acquires nine axle sensors of Inertial Measurement Unit by I2C bus The data of information, i.e. three-axis gyroscope, three axis accelerometer and three axle magnetometer；
Step 2, sensor raw data pretreatment: using the high-frequency noise adulterated in mean filter removal data information；
Step 3 establishes the process mould based on quaternary number fusion extended Kalman filter and dominant complementary filter blending algorithm Type: it takes quaternary number and 7 parameters of gyroscope drift error as system state variables, establishes non-linear system status equation；
Step 4 establishes the observation mould based on quaternary number fusion extended Kalman filter and dominant complementary filter blending algorithm Type: the quaternary number resolved using dominant complementary filter recycles fused filtering as the observed quantity of extended Kalman filter Device estimates optimal quaternary number；
Step 5, pose of mobile robot angle solve: optimal quaternary number obtained in step 4 is substituted into quaternary number and Eulerian angles Conversion relational expression calculates three attitude angles of mobile robot: roll angle, pitch angle and yaw angle.
2. pose of mobile robot angle according to claim 1 calculation method, which is characterized in that the step 2 is specifically wrapped Include following sub-step:
2.1, windowing process is carried out to gyroscope, accelerometer and magnetometer initial data, takes sliding window length n=10, walked Long l=1；
2.2, average value processing is carried out to the data in window, exports numerical value of the result as current time, formula is as follows:
2.3, I (n-9) exit window after sub-step 2.2, I (n+1) is into window；
2.4, sub-step 2.2 and sub-step 2.3 recycle, and to the last a data are into window, and export final result.
3. pose of mobile robot angle according to claim 2 calculation method, which is characterized in that in the step 3:
System mode vector are as follows:
X (k)=(q0(k) q1(k) q2(k) q3(k) bgx(k) bgy(k) bgz(k))；
In above formula, q0(k)、q1(k)、q2(k)、q3It (k) is k moment quaternary number posture, bgx(k)、bgy(k)、bgzIt (k) is respectively top The drift error of three axis of spiral shell instrument；
System state equation is established by system mode vector are as follows:
Ask Jacobi matrix that can obtain state-transition matrix f (x (k-1), k-1) are as follows:
Wherein: Г (k) is that noise drives matrix, and W (k-1) is zero-mean white noise sequence； Respectively indicate gyro Instrument three-axis measurement value；It indicates gyroscope estimated value, subtracts each other to obtain by gyroscope measured value and drift error amount； I3×3For three-dimensional unit matrix.
4. pose of mobile robot angle according to claim 3 calculation method, which is characterized in that the step 4 is according to such as Lower sub-step is realized:
4.1, data normalization: when mobile robot is in non-accelerating state, gravity of the accelerometer under world coordinate system Vector isGravity vector is after normalizationAccelerate under carrier of moving robot coordinate system The weight component measured is spent to be denoted as
4.2, the gravity vector under the carrier coordinate system that the sum that basis measures is calculated, calculates error compensation value accelerometer Under reference frame normalize after gravity vector beIt can be by g by attitude matrixnBe converted to carrier Gravity vector under coordinate system:
Vector multiplication cross is done in gravitational vectors estimation to the gravitational vectors under carrier coordinate system and under carrier coordinate system, is obtained to gyro The rectification building-out value of instrument angular velocity data:
4.3, according to the magnetic field strength under magnetometer data and the carrier of moving robot coordinate system being calculated, error is calculated The size and Orientation in offset magnetometer measures earth magnetic field, magnetometer output is m in carrier coordinate systemb=(mxi myi mzi)T, It is converted under world coordinate system by attitude matrix and is projected as hn=(hxi hyi hzi)T, when magnetic geographic coordinate system and movement When robot carrier coordinate system is overlapped, magnetometer output is bb=(0 byi bzi)T, in XOY plane, bbBe projected as byi, hn Be projected as (hxi+hyi)；The estimated value for obtaining magnetic field strength is:
Field value under navigational coordinate system is obtained by the estimated value of the magnetic field strength of above formula, then recycles attitude matrix conversion Extrapolating the field projection under carrier of moving robot coordinate system is wb=(wxi wyi wzi)T；It is obtained according to measure and reckoning Carrier of moving robot coordinate system under magnetic field strength, the two is done into vector multiplication cross, obtains correction-compensation value:
4.4, error compensation: error compensation value
4.5, gyroscope angular speed error is corrected.Gyroscope angular speed numerical value is corrected using error compensation value:
4.6, quaternary number is updated using modified gyroscope angular speed numerical value, the differential equation is solved using single order runge kutta method, And update quaternary number；The quaternion differential equation of t moment are as follows:
Obtain (t+T) moment updated quaternary number:
Systematic perspective measurement is set as:
Z (k)=(q0(k) q1(k) q2(k) q3(k))；
Wherein: q0(k)、q1(k)、q2(k)、q3(k) carrier of moving robot four obtained after being resolved for dominant complementary filter algorithm First number posture；
Systematic observation equation are as follows:
Ask Jacobian matrix that can obtain system measurements matrix h (x (k), k) according to above formula are as follows:
The quaternary number posture at (k-1) moment is substituted into system state equation to update to obtain k moment carrier of moving robot quaternary number Posture state amount；Then by the k moment quaternary number posture state amount and the systematic perspective that is solved by dominant complementary filter Measurement substitutes into state vector and estimates equation, finally obtains the optimal quaternary number Attitude estimation of k moment carrier of moving robot:
5. pose of mobile robot angle according to claim 4 calculation method, which is characterized in that in the step 5, posture Angle calculating formula are as follows:
6. pose of mobile robot angle according to any one of claims 1 to 5 calculation method, it is characterised in that: The main control chip is STM32F103.
7. pose of mobile robot angle according to claim 6 calculation method, it is characterised in that: the inertia unit is MPU9250。
8. pose of mobile robot angle according to claim 6 calculation method, it is characterised in that: the number of the I2C bus It is 400kbit/s according to transmission rate, the data sampling frequency of nine axle sensor is 500HZ.
9. pose of mobile robot angle according to claim 6 calculation method, it is characterised in that: the main control unit is every 2ms carries out periodic data acquisition and pretreatment 1 time.
CN201910542491.5A 2019-06-21 2019-06-21 Pose of mobile robot angle calculation method Pending CN110146077A (en)

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PCT/CN2020/097281 WO2020253854A1 (en) 2019-06-21 2020-06-20 Mobile robot posture angle calculation method
ZA2021/05514A ZA202105514B (en) 2019-06-21 2021-08-03 Attitude angle solution method for mobile robot

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