CN108051001A - A kind of robot movement control method, system and inertia sensing control device - Google Patents
A kind of robot movement control method, system and inertia sensing control device Download PDFInfo
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- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
- G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
- G01C21/16—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
- 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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Abstract
The present invention relates to a kind of robot movement control method, including：The angular velocity data of inertial sensor is obtained, pretreatment is filtered to the angular velocity data；Quaternion differential equation is established according to the angular velocity data, the quaternion differential equation is solved using runge kutta method, obtains the attitude matrix for including object attitude angle；Object attitude angle is converted into navigational coordinate system from carrier coordinate system；Object attitude angle not in threshold range in object attitude angle in the navigational coordinate system is excluded；Robot motion is controlled according to the object attitude angle in threshold range.The present invention can utilize inertial sensor control robot to move, and have degree of precision and good online recognition effect, and universality is strong, and application prospect value is preferable.The present invention also provides a kind of robot mobile control system and inertia sensing control devices.
Description
Technical field
The present invention relates to technical field of robot control more particularly to a kind of robot movement control method, system and it is used to
Property sensing and controlling device.
Background technology
In numerous expanded applications, man-machine interaction mode is in occupation of an important role, and people and robot interactive are then
It is the important topic in robotic technology field, especially life auxiliary robot field.All the time, people find it is more natural,
The step of more humane man-machine interaction mode never stopped, and it is complicated cumbersome to control robot that can replace using gesture
Procedure operation, simply and easily operate machine people, to robot issue order, interacted with robot, it has also become research
Hot spot.
Gesture identification essence is to be intended to according to the small operation of the gesture of user to perceive the operation of user, belongs to multichannel friendship
Mutual scope, research contents are related to a series of related disciplines such as pattern-recognition, robot, image procossing, computer vision.
The research of gesture identification can not only promote the development of these subjects, but also because some elder generations of gesture motion to a certain extent
Its intrinsic advantages also has very big realistic meaning.
Gesture identification mainly has 2 kinds of methods at present, and a kind of is the Gesture Recognition of view-based access control model, and technology development is compared
Early also relative maturity, but stringent to equipment and environmental requirement, it is larger using limitation.Another kind is the hand based on inertial sensor
Gesture identification technology, the technology from environment, light influence, mainly by measuring the variation of acceleration and angular speed, so as into
Row gesture identification, but inertial sensor in gesture identification, such as judges still to deposit in this minor motion of finger there are drift error
The problem of inaccurate is judged precision is not high.
The content of the invention
The technical problems to be solved by the invention are in view of the deficiencies of the prior art, to provide a kind of robot movement controlling party
Method, system and inertia sensing control device.
The technical solution that the present invention solves above-mentioned technical problem is as follows：A kind of robot movement control method, including：
S1 obtains the angular velocity data of inertial sensor；
S2 carries out the angular velocity data pretreatment of online adaptive filtering；
S3 establishes quaternion differential equation according to the angular velocity data through online adaptive filter preprocessing, utilizes
Runge-Kutta method solves the quaternion differential equation, obtains the attitude matrix for including object attitude angle；
Object attitude angle is converted to navigational coordinate system by S4 from carrier coordinate system；
S5 arranges the object attitude angle not in threshold range in the object attitude angle in the navigational coordinate system
It removes；
S6 controls robot motion according to the object attitude angle in threshold range.
Another technical solution that the present invention solves above-mentioned technical problem is as follows：A kind of robot mobile control system, including：
Collecting unit, for obtaining the angular velocity data of inertial sensor；
Pretreatment unit, for carrying out the pretreatment of online adaptive filtering to the angular velocity data；
Processing unit, for establishing quaternary fractional differentiation according to the angular velocity data through online adaptive filter preprocessing
Equation solves the quaternion differential equation using Runge-Kutta method, obtains the attitude matrix for including object attitude angle；
Coordinate system converting unit, for object attitude angle to be converted to navigational coordinate system from carrier coordinate system；
Screening unit, for by the target not in threshold range in the object attitude angle in the navigational coordinate system
Attitude angle excludes；
Control unit, for controlling robot motion according to the object attitude angle in threshold range.
Another technical solution that the present invention solves above-mentioned technical problem is as follows：A kind of inertia sensing control device, including upper
State the robot mobile control system described in technical solution, the inertia sensing control device and robot wireless communication.
The beneficial effects of the invention are as follows：The problem of present invention is handled in real time for sensing data, utilizes online adaptive
Filtering method realizes online denoising, reduces influence of the noise to later stage update attitude matrix, attitude matrix is made to solve attitude angle more
Accurately；Attitude matrix is described using Quaternion Method, solves the differential equation, calculation amount is smaller, has higher precision, and can keep away
Exempt to be absorbed in " singular point "；Finger maloperation behavior is foreclosed using threshold value, completes to identify different small gesture motions；This hair
It is bright that inertial sensor control robot can be utilized to move, there is degree of precision and good online recognition effect, universality is strong, should
It is preferable with prospect value.
Description of the drawings
Fig. 1 is the schematic flow chart that the present invention one applies the robot movement control method that example provides；
Fig. 2 is the system signal process chart that one embodiment of the invention provides；
When Fig. 3 is that the finger that one embodiment of the invention provides moves up and down, the variation of pitch angle and course angle；
When Fig. 4 is that the finger that one embodiment of the invention provides moves left and right, the variation of pitch angle and course angle；
Fig. 5 is the schematic block diagram that the present invention one applies the robot mobile control system that example provides.
Specific embodiment
The principle and features of the present invention will be described below with reference to the accompanying drawings, and the given examples are served only to explain the present invention, and
It is non-to be used to limit the scope of the present invention.
Fig. 1 gives a kind of schematic flow chart of robot movement control method provided in an embodiment of the present invention.Such as Fig. 1
Shown, this method includes：
S1 obtains the angular velocity data of inertial sensor, wherein, the inertial sensor can be worn at user's finger
On；
S2 carries out the angular velocity data pretreatment of online adaptive filtering；
S3 establishes quaternion differential equation according to the angular velocity data through online adaptive filter preprocessing, utilizes
Runge-Kutta method solves the quaternion differential equation, obtains the attitude matrix for including object attitude angle；
Object attitude angle is converted to navigational coordinate system by S4 from carrier coordinate system；
S5 arranges the object attitude angle not in threshold range in the object attitude angle in the navigational coordinate system
It removes；
S6 controls robot motion according to the object attitude angle in threshold range.
In the embodiment, the problem of processing in real time for sensing data, realized using online adaptive filtering method
Line denoising；Attitude matrix is described using Quaternion Method, solves the differential equation, and calculation amount is smaller, has a higher precision, and can be with
It avoids being absorbed in " singular point "；Finger maloperation behavior is foreclosed using threshold value, completes to identify different small gesture motions；This
Invention can utilize inertial sensor control robot to move, and have degree of precision and good online recognition effect, and universality is strong,
Application prospect value is preferable.
Optionally, as one embodiment of the invention, to the pre- place of angular velocity data progress online adaptive filtering
Reason includes：
S2.1, init state amount and system self-adaption parameter.Specifically,
2.1.1, state initial value is setGeneral to be set to 3-dimensional full 0 column vector, dimension is state procedure mould
The dimension of type state vector, angular speed, angular speed first derivative and angular speed second dervative are state vector；
2.1.2, system self-adaption initial parameter values α=α0WithTake positive count, such as α0ValueValue 3,
2.1.3, auto-correlation function initial value r0(0) and r0(1) initial value is taken as
2.1.4, angular speed second dervative initial valueGenerallyTake 0.
S2.2 establishes the state procedure model with system self-adaption parameter.Specifically,
2.2.1, the moving characteristic of target is described using following formula；
It is similar with Singer models, if angular speed second dervative is the time correlation random process of Non-zero MeanWhereinFor angular speed second dervative average, a (t) is zero-mean correlation of indices coloured noise model, related
Function is：
Wherein, t be arbitrary sampling instant, τ be calculation of correlation parameter, Ra(τ) represents correlation function；Represent acceleration side
Difference；α is maneuvering frequency, has reacted the variation stochastic behaviour of state；
Whitening processing is done to coloured noise a (t), is obtained
Wherein, w (t) is zero-mean white noise, and variance is
ByWithThe continuous state equation for obtaining state change is as follows：
It is sampled with cycle T, system state change meets below equation after discretization：
WhereinFor 3-dimensional state column vector,It is angular speed, angular speed first derivative and angle respectively
Speed second dervative, x (k+1) are k+1 moment state vectors, and k is sampling instant；A (k+1, k) is state-transition matrix；x(k)
For the state vector of k moment targets；U (k) matrixes in order to control；The angular speed second order started for 0 moment to k moment targets is led
Number average；W (k) is process noise, and average 0, variance is Q (k)；Containing motor-driven in the A (k+1, k), U (k) and Q (k)
Frequency alpha and angular speed second dervative varianceChange with the variation of system self-adaption parameter；State-transition matrix A (k+1,
K) expression formula such as following formula
Control the expression formula such as following formula of matrix U (k)
The expression formula of the variance Q (k) of process noise w (k) such as following formula
Wherein,
2.2.2, it is as follows to measure equation
Y (k)=H (k) x (k)+v (k) (8)
Wherein k is sampling instant, and measured values of the y (k) at y (k) moment, H (k) is calculation matrix, and x (k) is k moment targets
State vector；V (k) be Gauss measurement white noise, variance R, and with process noise w (k) independently of each other.
S2.3 carries out the target mobile status according to the state procedure model with system self-adaption parameter of foundation
Prediction obtains status predication value and state covariance predicted value.
2.3.1 according to state procedure model of the foundation with system self-adaption parameter and a step of initial value completion status
Prediction, predictive equation formula are as follows：
WhereinRepresent that the k-1 moment predicts state of the target at the k moment, k is sampling instant, and A (k, k-1) is shape
State transfer matrix；Represent target k-1 moment state estimations；U (k-1) matrixes in order to control；For from
0 moment started to the angular speed second dervative average of k-1；
2.3.2 the one-step prediction of completion status covariance according to the following formula：
P (k | k-1)=A (k, k-1) P (k-1 | k-1) AT(k, k-1)+Q (k-1) (10)
Wherein P (k | k-1) represent that the k-1 moment predicts state covariance of the target at the k moment, k is sampling instant, | it represents
Conditional operation accords with；P (k-1 | k-1) represents the estimate of the state covariance of k-1 moment targets；A (k, k-1) shifts square for state
Battle array；Q (k-1) is process noise covariance.
S2.4 moves shape according to the status predication value, measured data values and state covariance predicted value to the target
State is updated, and obtains state estimation.
2.4.1 wave filter is calculated according to the following formula according to state covariance predicted value, calculation matrix and measurement noise variance to increase
Benefit；
K (k)=P (k | k-1) HT(k)[H(k)P(k|k-1)HT(k)+R]T (11)
Wherein, K (k) is filter gain, and k is sampling instant, and P (k | k-1) represent that the k-1 moment predicts target at the k moment
State covariance, H (k) be the k moment calculation matrix, R be Gauss measurement white noise variance, HT(k) square is measured for the k moment
The transposition of battle array；
2.4.2 target current state estimate, such as following formula are calculated using status predication value and observed data value
Wherein,Represent k moment state estimations,Represent that prediction is in the shape at k moment during the k-1 moment
State, k are sampling instant, and K (k) is k moment filter gains, and y (k) is the observation at the k moment, and H (k) is the measurement at k moment
Matrix；
2.4.3 the estimate of state covariance is calculated according to the following formula；
P (k | k)=(I-K (k) H (k)) P (k | k-1) (13)
Wherein I is 3-dimensional unit matrix, and P (k | k) represents the estimate of the state covariance at k moment, and k is sampling instant, K
(k) for k moment filter gains, H (k) is the calculation matrix at k moment, and P (k | k-1) represent that the k-1 moment predicts the shape at the k moment
State covariance.
S2.5 calculates angular speed second dervative average and angular speed second dervative estimate according to the state estimation.
Angular speed second dervative average is calculated using following formula；
WhereinFor 0 to k moment angular speed second dervative averages,For the state estimation at k moment's
The third line value, k are sampling instant；And the angular speed second dervative estimate at system k-1 moment and k moment is obtained according to the following formula
WhereinFor k-1 moment state estimationsThe third line value,For k moment shapes
State is estimatedThe third line value.
S2.6 is modified the system self-adaption parameter according to the angular speed second dervative estimate.
According to the size of sampling instant k values, select update the system auto-adaptive parameter α andMethod, if k be less than or equal to 4
2.6.1 is entered step, if k enters step 2.6.2 more than 4,
2.6.1 when sampling instant k is less than or equal to 4, because sampled data is less, taken using the parameter of current statistical model
Value method, be calculated as follows system self-adaption parameter alpha and
α=α0Wherein α0For the initial value of system self-adaption parameter alpha,
IfThen take
IfThen take
IfThenTake (0,10] between arbitrary number,
Wherein,For k moment angular speed second dervative estimates, π is pi, is taken as 3.14, aMFor positive constant,
It is taken as 3, a-MFor with aMThe equal negative constant of absolute value, is taken as -3；
2.6.2 when sampling instant k be more than 4 when, be calculated as follows system self-adaption parameter alpha and
Wherein b is greater than 1 constant, rk(1) it is k moment angular speed second dervatives to back correlation function, rk-1(1)
It is k-1 moment angular speed second dervatives to back correlation function,WithRespectively k-1 moment and k moment angles speed
Spend second dervative estimate；rk(0) it is k-1 moment angular speed second dervative auto-correlation functions, rk-1(0) it is k-1 moment angular speed
Second dervative auto-correlation function；
For example, modus ponens (17) is 10 with the b in (18), that is, it is shown below：
According to system equationObtaining angular speed second dervative expires
The following single order Markov random sequence of foot：
WhereinFor the angular speed second dervative at k+1 moment,For the acceleration at k moment, β adds afterwards to be discrete
The maneuvering frequency of speed random sequence, wa(k) it is zero-mean white noise discrete series, variance isWherein
For the variance of zero-mean white noise w (t), the relation of β and α are β=e-αT；
The single order Markov time accelerates degree series to meet parameters relationship as shown below：
Wherein, rk(1) for the k moment acceleration to back auto-correlation function, rk(0) it is the acceleration auto-correlation at k moment
Function, α and β are respectively the maneuvering frequency of acceleration and its maneuvering frequency of discretization post-acceleration sequence, auto-adaptive parameter α andIt can be calculated according to the following formula：
Wherein, rk(1) for the k moment acceleration to back correlation function, rk(0) it is the acceleration auto-correlation letter at k moment
Number, ln is the Logarithmic calculation taken using e the bottom of as；α andFor system self-adaption parameter, T is the sampling interval.
S2.7 updates the shape according to the angular speed second dervative average and the revised system self-adaption parameter
State process model obtains the filtered angular velocity data of online adaptive.
Optionally, as one embodiment of the invention, quaternion differential equation is established according to the angular velocity data, is utilized
Runge-Kutta method solves the quaternion differential equation, and the attitude matrix that acquisition includes object attitude angle includes：
S3.1 establishes quaternion differential equation using filtered angular velocity data and quaternary number；
Specifically, using the angular velocity information of gyroscope in filtered inertial sensor, established using quaternary number as follows
The differential equation：
Wherein, the left side of equation is the derivative operation of quaternary number, and q (t) represents quaternary number, and symbol ο represents quaternary number multiplication,For the matrix expression of three axis angular rates；
S3.2 solves the quaternion differential equation using fourth-order Runge-Kutta method, obtains the posture by quaternion representation
Matrix, first value by updating quaternary number update object attitude angle so as to update attitude matrix；
Wherein, using the slope initial value needed for the Fourth order Runge-Kutta solution quaternion differential equation by quaternary number
Initial value determine that the initial value of the quaternary number is determined by the initial value of object attitude angle.
The above-mentioned differential equation is solved using Fourth order Runge-Kutta, slope of the iteration more needed for the new algorithm first step is initial
Value can be determined by the initial value of quaternary number, and quaternary number initial value is determined by attitude angle initial value, is calculating the quaternary of next step
After each element of number, you can substitute into the attitude matrix by quaternion representationAnd then the attitude angle letter at current time can be solved
Breath.
Wherein K is slope, and t is current time, and h is newer step-length and its usually same with sensor sample cycle phase, formula
InWhereinFor the matrix expression of three axis angular rates.
Optionally, as one embodiment of the invention, object attitude angle is converted into navigational coordinate system from carrier coordinate system
Including：Attitude matrix is solved using Quaternion Method
It is as follows that attitude angle is solved according to attitude matrix
θIt is main=arcsin C32
In order to accurately determine attitude angleThe true value of θ, γ below define the domain of attitude angle, wherein bowing
The domain at the elevation angle for (- 90 °, the domains of 90 ° of) , Transverse roll angles is (- 180 °, 180 °), the domain of course angle for (0 °,
360°).θ in formulaIt is main、γIt is mainWithIt is pitch angle, roll angle and the course angle being calculated by attitude matrix respectively, θ, γ
WithRepresent the angle value being transformed into domain.
θ=θIt is main
Since the inertia component of Strapdown Inertial Navigation System is connected firmly on carrier, the output valve of sensor is to carry
Output valve under body coordinate system, so these measured values must be converted to a coordinate system convenient for navigational parameter needed for calculating
Under, i.e. navigational coordinate system, and attitude matrix is exactly each item data measured in carrier coordinate system is gone to and lead by carrier coordinate system
The transformational relation of boat coordinate system can represent the attitude angle of carrier after the attitude matrix of carrier is determined.
Fig. 2 gives system signal process flow, from attached drawing as can be seen that the data gathered from inertia sensing control device
Control command that is filtered, solving quaternary number acquisition attitude angle, finally obtain robot.
When Fig. 3 gives finger and moves up and down, the variation of pitch angle and course angle, Pitch represents pitch angle, Yaw in figure
Course angle is represented, from attached drawing as can be seen that pitch angle shows cyclically-varying, but course with the up and down motion of finger at this time
Angle variation is little.
When Fig. 4 gives finger and moves left and right, the variation of pitch angle and course angle, Pitch represents pitch angle, Yaw in figure
Course angle is represented, from attached drawing as can be seen that course angle shows cyclically-varying, but pitching with the side-to-side movement of finger at this time
Angle variation is little.
Optionally, as one embodiment of the invention, will not exist in the object attitude angle in the navigational coordinate system
Object attitude angle exclusion in threshold range includes：
Definition of gesture is respectively：Finger lifts, downwards, to four actions of right and left, by testing we have found that working as hand
When finger lifts, pitch angle angle is advanced just, to define the gesture control robot；When finger is put down, pitch angle angle is negative, is determined
The adopted gesture control robot stops；Finger to the right when, course angle angle defines the gesture control robot and turns right for just；
Finger to the left when, course angle angle be it is negative, control robot to the left.
In the process, when finger wears inertial sensor, due to the slight of finger or significantly act all can shadow
Ring the inaccuracy of finger identification, by experimental demonstration, when finger lifts (Finger_Up), pitch angle 20 degree (threshold value 1, we
Use T1Represent) (threshold value 2, we use T to 50 degree2Represent) when, for the normal range (NR) of finger movement, advance as control robot
Correct gesture；When finger is downward (Finger_Down), pitch angle is when -20 degree are spent to -50, for the normal model of finger movement
It encloses, the correct gesture stopped as control robot.When finger is turned right (Finger_Right), course angle is in 20 degree of (threshold values
3, we use T3Represent) (threshold value 4, we use T to 40 degree4Represent) when, it is the normal range (NR) that finger turns to, as control machine
The correct gesture that people turns right；When finger is turned left (Finger_Left), course angle is turned to when -20 degree are spent to -40 for finger
Normal range (NR), as control robot turn left correct gesture.
Using above-mentioned threshold value, the instruction of following control gesture can be obtained：
Wherein, Pitch represents pitch angle, and Yaw represents course angle.
Optionally, as one embodiment of the invention, robot motion is controlled according to the object attitude angle in threshold range
Including：
After finger wears sensor, communicated by WIFI connection sensors with robot, utilize inertia sensing
Device gathers the movable signal of finger, and computer obtains movable signal, judges the posture of finger, then pass through move wireless
Module passes to robot, and table 1 lists " G ", " S ", " R " and " L " described 4 orders.When gesture is lifted, sensor
The movement of finger is captured, sends the judgement that computer carries out posture to, when the threshold range for meeting setting, computer is sent to machine
Device people " G " instructs, and robot makes the action of advance；When gesture is downward, sensor captures the movement of finger, sends calculating to
Machine carries out the judgement of posture, and when the threshold range for meeting setting, computer is sent to robot " S " instruction, and robot, which makes, to stop
Action only；When gesture to the right when, sensor capture finger movement, send to computer carry out posture judgement, work as satisfaction
The threshold range of setting, computer are sent to robot " R " instruction, and robot makes the action of right-hand rotation；When gesture to the left when, pass
Sensor captures the movement of finger, sends the judgement that computer carries out posture to, and when the threshold range for meeting setting, computer is sent
It is instructed to robot " L ", robot makes the action of left-hand rotation.
The present invention accurately identifies finger movement for inertial sensor, and experiment flow is carried out with the artificial example of finger control machine
Figure is as shown in Figure 1.The robot platform of our we selected typicals --- NAO robots, the robot system there are one it is complete from
Balance module, when there is instruction input, NAO can be with itself steady walking, and therefore, we only consider whether that NAO can obtain base
In the accurate order of gesture.
Gesture identification method provided by the present invention relies primarily on inertial sensor data acquisition, and passes through control machine
Device people moves completion system and realizes.First, it is respectively used for measuring the angle movement of carrier using gyroscope and accelerometer, line moves
Information carries out online adaptive filtering removal noise；Then, described using fourth-order Runge-Kutta method solution with Quaternion Method
The differential equation, the member through quaternary number reversely negate trigonometric function to calculate pitch angle and course angle；Finally, according to the shifting of finger
The adaptive recognition threshold that should determine that pitch angle and course angle of dynamic scope, so as to which the faulty operation of finger be foreclosed.
The present invention provides one kind and gathers finger small range action data by inertial sensor, data is carried out online adaptive
Filtering and noise reduction is answered to handle, builds quaternary number attitude matrix, quaternion differential equation is solved using Runge-Kutta method, updates quaternary
Number obtains real-time attitude angle so as to update attitude matrix, and due to identification finger small range action, given threshold excludes hand
Refer to the situation of maloperation.
Above in association with Fig. 1 to 4, it is described in detail and robot movement control method is provided according to embodiments of the present invention.Below
With reference to Fig. 5, the embodiment of the present invention is described in detail, robot mobile control system is provided.
Fig. 5 gives a kind of schematic block diagram of robot mobile control system provided in an embodiment of the present invention.Such as
Shown in Fig. 5, which includes：Collecting unit 510, pretreatment unit 520, processing unit 530, coordinate system converting unit 540 are sieved
Menu member 550.
Collecting unit 510 obtains the angular velocity data of inertial sensor, wherein, the inertial sensor can be worn at use
On the finger of family；Pretreatment unit 520 carries out the angular velocity data pretreatment of online adaptive filtering；Processing unit 530
Quaternion differential equation is established according to the angular velocity data through online adaptive filter preprocessing, utilizes Runge-Kutta method
The quaternion differential equation is solved, obtains the attitude matrix for including object attitude angle；Coordinate system converting unit 540 is by target appearance
State angle is converted to navigational coordinate system from carrier coordinate system；Screening unit 550 is by the targeted attitude in the navigational coordinate system
Object attitude angle in angle not in threshold range excludes；Control unit 560 is controlled according to the object attitude angle in threshold range
Robot motion.
In the embodiment, the problem of processing in real time for sensing data, realized using online adaptive filtering method
Line denoising；Attitude matrix is described using Quaternion Method, solves the differential equation, and calculation amount is smaller, has a higher precision, and can be with
It avoids being absorbed in " singular point "；Finger maloperation behavior is foreclosed using threshold value, completes to identify different small gesture motions；This
Invention can utilize inertial sensor control robot to move, and have degree of precision and good online recognition effect, and universality is strong,
Application prospect value is preferable.
The system is realized mainly in combination with solving of attitude and adaptive threshold analysis, is first passed through adaptive-filtering and is measured
The denoising of data, then the strap-down inertial navigation system attitude algorithm algorithm based on quaternary number, completion include quaternary number appearance
State matrix, navigational parameter extraction are with calculating, primary condition gives and calculated with primary data, and passes through more newly arriving not for attitude matrix
It is disconnected to calculate course, posture, speed and location information；Then Threshold Analysis means realize that noise suppressed and maloperation are sentenced
Not, detect in real time, identify gesture minute movement situation, then complete to robot mobile control accordingly.
The embodiment of the present invention also provides a kind of inertia sensing control device, is moved including the robot described in above-mentioned technical proposal
Autocontrol system, the inertia sensing control device and robot wireless communication.
It is apparent to those skilled in the art that for convenience of description and succinctly, the dress of foregoing description
The specific work process with unit is put, may be referred to the corresponding process in preceding method embodiment, details are not described herein.
In several embodiments provided herein, it should be understood that disclosed apparatus and method can pass through it
Its mode is realized.For example, the apparatus embodiments described above are merely exemplary, for example, the division of unit, is only
A kind of division of logic function, can there is an other dividing mode in actual implementation, for example, multiple units or component can combine or
Person is desirably integrated into another system or some features can be ignored or does not perform.
The unit illustrated as separating component may or may not be physically separate, be shown as unit
Component may or may not be physical location, you can be located at a place or can also be distributed to multiple networks
On unit.Some or all of unit therein can be selected to realize the mesh of the embodiment of the present invention according to the actual needs
's.
In addition, each functional unit in each embodiment of the present invention can be integrated in a processing unit, it can also
It is that unit is individually physically present or two or more units integrate in a unit.It is above-mentioned integrated
The form that hardware had both may be employed in unit is realized, can also be realized in the form of SFU software functional unit.
If integrated unit is realized in the form of SFU software functional unit and is independent production marketing or in use, can
To be stored in a computer read/write memory medium.Based on such understanding, technical scheme substantially or
Saying all or part of the part contribute to the prior art or the technical solution can be embodied in the form of software product
Out, which is stored in a storage medium, is used including some instructions so that a computer equipment
(can be personal computer, server or the network equipment etc.) performs all or part of each embodiment method of the present invention
Step.And foregoing storage medium includes：It is USB flash disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random
Access various Jie that can store program code such as memory (RAM, Random Access Memory), magnetic disc or CD
Matter.
The foregoing is merely presently preferred embodiments of the present invention, is not intended to limit the invention, it is all the present invention spirit and
Within principle, any modifications, equivalent replacements and improvements are made should all be included in the protection scope of the present invention.
Claims (10)
1. a kind of robot movement control method, which is characterized in that including：
S1 obtains the angular velocity data of inertial sensor；
S2 carries out the angular velocity data pretreatment of online adaptive filtering；
S3 establishes quaternion differential equation according to the angular velocity data through online adaptive filter preprocessing, using Long Ge-
Ku Tafa solves the quaternion differential equation, obtains the attitude matrix for including object attitude angle；
Object attitude angle is converted to navigational coordinate system by S4 from carrier coordinate system；
S5 excludes the object attitude angle not in threshold range in the object attitude angle in the navigational coordinate system；
S6 controls robot motion according to the object attitude angle in threshold range.
2. according to the method described in claim 1, it is characterized in that, the S2 includes：
S2.1, init state amount and system self-adaption parameter, the quantity of state include angular speed, angular speed first derivative and angle
Speed second dervative；
S2.2 establishes the state procedure model with system self-adaption parameter；
S2.3 predicts the quantity of state according to the state procedure model with system self-adaption parameter of foundation, obtains
Status predication value and state covariance predicted value；
S2.4 is updated the quantity of state according to the status predication value, measured data values and state covariance predicted value,
Obtain state estimation；
S2.5 calculates angular speed second dervative average and angular speed second dervative estimate according to the state estimation；
S2.6 is modified the system self-adaption parameter according to the angular speed second dervative estimate；
S2.7 updates the state mistake according to the angular speed second dervative average and the revised system self-adaption parameter
Journey model obtains the filtered angular velocity data of online adaptive.
3. according to the method described in claim 2, it is characterized in that, the S3 includes：
S3.1 establishes quaternion differential equation using filtered angular velocity data and quaternary number；
S3.2 solves the quaternion differential equation using fourth-order Runge-Kutta method, obtains the posture square by quaternion representation
Battle array, first value by updating quaternary number update object attitude angle so as to update attitude matrix；
Wherein, using the slope initial value needed for the Fourth order Runge-Kutta solution quaternion differential equation by the first of quaternary number
Initial value determines that the initial value of the quaternary number is determined by the initial value of object attitude angle.
It is 4. according to the method described in claim 3, it is characterized in that, described micro- using the Runge-Kutta method solution quaternary number
Divide equation, obtain as follows by the attitude matrix of quaternion representation：
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<msub>
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<mn>2</mn>
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<mi>K</mi>
<mn>2</mn>
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Wherein, K is slope, and t is current time, and h is newer step-length, in formulaFor three shaft angles speed
The matrix expression of degree, q (t) represent quaternary number.
5. method according to any one of claims 1 to 4, which is characterized in that the object attitude angle include pitch angle and
Course angle；The absolute value of the threshold range of the pitch angle is 20 to 50 degree, and the threshold range of the course angle is 20
Degree is to 40 degree.
6. a kind of robot mobile control system, which is characterized in that including：
Collecting unit, for obtaining the angular velocity data of inertial sensor；
Pretreatment unit, for carrying out the pretreatment of online adaptive filtering to the angular velocity data；
Processing unit, for establishing quaternary fractional differentiation side according to the angular velocity data through online adaptive filter preprocessing
Journey solves the quaternion differential equation using Runge-Kutta method, obtains the attitude matrix for including object attitude angle；
Coordinate system converting unit, for object attitude angle to be converted to navigational coordinate system from carrier coordinate system；
Screening unit, for by the targeted attitude not in threshold range in the object attitude angle in the navigational coordinate system
Angle excludes；
Control unit, for controlling robot motion according to the object attitude angle in threshold range.
7. system according to claim 6, which is characterized in that the pretreatment unit is specifically used for：
Init state amount and system self-adaption parameter, the quantity of state include angular speed, angular speed first derivative and angular speed
Second dervative；
Establish the state procedure model with system self-adaption parameter；
The quantity of state is predicted according to the state procedure model with system self-adaption parameter of foundation, it is pre- to obtain state
Measured value and state covariance predicted value；
The quantity of state is updated according to the status predication value, measured data values and state covariance predicted value, is obtained
State estimation；
Angular speed second dervative average and angular speed second dervative estimate are calculated according to the state estimation；
The system self-adaption parameter is modified according to the angular speed second dervative estimate；
The state procedure mould is updated according to the angular speed second dervative average and the revised system self-adaption parameter
Type obtains the filtered angular velocity data of online adaptive.
8. system according to claim 7, which is characterized in that the processing unit is specifically used for：
Quaternion differential equation is established using filtered angular velocity data and quaternary number；
The quaternion differential equation is solved using fourth-order Runge-Kutta method, the attitude matrix by quaternion representation is obtained, passes through
First value of update quaternary number updates object attitude angle so as to update attitude matrix；
Wherein, using the slope initial value needed for the Fourth order Runge-Kutta solution quaternion differential equation by the first of quaternary number
Initial value determines that the initial value of the quaternary number is determined by the initial value of object attitude angle.
9. according to claim 6 to 8 any one of them system, which is characterized in that described using described in Runge-Kutta method solution
Quaternion differential equation obtains as follows by the attitude matrix of quaternion representation：
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</mrow>
</mrow>
Wherein, K is slope, and t is current time, and h is newer step-length, in formulaFor three shaft angles speed
The matrix expression of degree, q (t) represent quaternary number.
10. a kind of inertia sensing control device, which is characterized in that moved including claim 6 to 9 any one of them robot
Control system, the inertia sensing control device and robot wireless communication.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711232485.7A CN108051001B (en) | 2017-11-30 | 2017-11-30 | Robot movement control method and system and inertial sensing control device |
Applications Claiming Priority (1)
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| CN113496165A (en) * | 2020-04-01 | 2021-10-12 | 北京海益同展信息科技有限公司 | User gesture recognition method and device, hand intelligent wearable device and storage medium |
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| CN114102600B (en) * | 2021-12-02 | 2023-08-04 | 西安交通大学 | Multi-space fusion human-machine skill migration and parameter compensation method and system |
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