CN113406964B - Motion parameter adjusting method and device, storage medium and electronic device - Google Patents
Motion parameter adjusting method and device, storage medium and electronic device Download PDFInfo
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC 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
- G05D1/0808—Control of attitude, i.e. control of roll, pitch, or yaw specially adapted for aircraft
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/10—Simultaneous control of position or course in three dimensions
- G05D1/101—Simultaneous control of position or course in three dimensions specially adapted for aircraft
- G05D1/106—Change initiated in response to external conditions, e.g. avoidance of elevated terrain or of no-fly zones
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- G06—COMPUTING; CALCULATING OR COUNTING
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- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
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Abstract
The embodiment of the invention provides a motion parameter adjusting method, a motion parameter adjusting device, a storage medium and an electronic device, wherein the method comprises the following steps: determining a first motion state of a target device; determining a measured motion parameter of a target sub-device, wherein the target sub-device is a device included in the target device, and the measured motion parameter is a parameter obtained by measurement of a target sensor; determining a confidence level of the measured motion parameter based on the first motion state; performing target filtering processing on the measured motion parameters based on the credibility to determine target motion parameters of the target sub-equipment; the control target sub-device moves based on the target motion parameter. The invention solves the problems of inaccurate regulation of the motion parameters, easy interference and high cost in the related technology, improves the accuracy of the regulation of the motion parameters and reduces the cost.
Description
Technical Field
The embodiment of the invention relates to the field of communication, in particular to a motion parameter adjusting method, a motion parameter adjusting device, a storage medium and an electronic device.
Background
When one device includes another device, the motions of the two devices often cannot be synchronized, and the following description takes an unmanned aerial vehicle and a cradle head installed in the unmanned aerial vehicle as examples:
consumer-grade attitude detection sensors are typically used to estimate the amount of detected speed deviation of the device, thereby adjusting the motion parameters of the device. However, the consumer-grade attitude detection sensor has a temperature drift characteristic in detecting angular velocity data, that is, the angular velocity deviation detected by the sensor is different under different temperatures for the same angular velocity. The pan-tilt drift phenomenon can be caused by performing closed-loop control on the velocity by taking the angular velocity as feedback. In the optimization for this characteristic of the sensor, the pan/tilt/roll directions can be estimated using the accelerometer of the attitude detection sensor itself as a reference, and the amount of speed deviation detected at that time. However, no sensor can refer to the pan direction, so that the speed deviation amount cannot be estimated, and the motion parameter is not accurately adjusted, thereby causing poor pan-tilt control stability. In the related art, the heading drift is usually restricted by using a geomagnetic sensor, however, the geomagnetic pole is easily interfered by the outside world, the restriction effect is affected, and the cost is high.
Therefore, the problems of inaccurate motion parameter adjustment, easy interference and high cost exist in the related art.
In view of the above problems in the related art, no effective solution has been proposed.
Disclosure of Invention
The embodiment of the invention provides a motion parameter adjusting method, a motion parameter adjusting device, a storage medium and an electronic device, and aims to at least solve the problems of inaccurate motion parameter adjustment, easy interference and high cost in the related technology.
According to an embodiment of the present invention, there is provided a motion parameter adjustment method including: determining a first motion state of a target device; determining a measured motion parameter of a target sub-device, wherein the target sub-device is a device included in the target device, and the measured motion parameter is a parameter obtained by measurement of a target sensor; determining a confidence level of the measured motion parameter based on the first motion state; performing target filtering processing on the measured motion parameters based on the credibility to determine target motion parameters of the target sub-device; controlling the target sub-device to move based on the target motion parameter.
According to another embodiment of the present invention, there is provided a motion parameter adjusting apparatus including: a first determination module for determining a first motion state of a target device; the second determination module is used for determining a measured motion parameter of a target sub-device, wherein the target sub-device is a device included in the target device, and the measured motion parameter is a parameter obtained by measurement of a target sensor; a third determination module for determining a trustworthiness of the measured motion parameter based on the first motion state; the filtering module is used for carrying out target filtering processing on the measured motion parameters based on the credibility so as to determine target motion parameters of the target sub-equipment; and the control module is used for controlling the target sub-equipment to move based on the target motion parameters.
According to a further embodiment of the present invention, there is also provided a computer-readable storage medium having a computer program stored thereon, wherein the computer program is arranged to, when executed, perform the steps of any of the method embodiments described above.
According to yet another embodiment of the present invention, there is also provided an electronic device, including a memory in which a computer program is stored and a processor configured to execute the computer program to perform the steps in any of the above method embodiments.
According to the invention, the first motion state of the target equipment is determined, the measured motion parameters of the target sub-equipment in the target equipment are determined, the credibility of the measured motion parameters is determined according to the first motion state, the target filtering processing is carried out on the measured motion parameters according to the credibility so as to determine the target motion parameters of the target sub-equipment, and the target sub-equipment is controlled to move according to the target motion parameters. Because the credibility of the measured motion parameter is determined according to the first motion state of the target equipment, and the target filtering processing is carried out on the target measured motion parameter according to the credibility, the target motion parameter can be accurately determined without interference, and other devices are not needed, so that the cost is saved. Therefore, the problems of inaccurate regulation of the motion parameters, easy interference and high cost in the related technology can be solved, the accuracy of the regulation of the motion parameters is improved, and the cost is reduced.
Drawings
Fig. 1 is a block diagram of a hardware configuration of a mobile terminal of a motion control method according to an embodiment of the present invention;
FIG. 2 is a flow chart of a motion control method according to an embodiment of the invention;
FIG. 3 is a schematic diagram of determining a first motion state of a target device using a first model according to an exemplary embodiment of the invention;
FIG. 4 is a flow chart of a motion control method according to an embodiment of the present invention;
fig. 5 is a block diagram of a motion control apparatus according to an embodiment of the present invention.
Detailed Description
Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings in conjunction with the embodiments.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order.
The method embodiments provided in the embodiments of the present application may be executed in a mobile terminal, a computer terminal, or a similar computing device. Taking the operation on the mobile terminal as an example, fig. 1 is a hardware structure block diagram of the mobile terminal of a motion control method according to an embodiment of the present invention. As shown in fig. 1, the mobile terminal may include one or more (only one shown in fig. 1) processors 102 (the processor 102 may include, but is not limited to, a processing device such as a microprocessor MCU or a programmable logic device FPGA), and a memory 104 for storing data, wherein the mobile terminal may further include a transmission device 106 for communication functions and an input-output device 108. It will be understood by those skilled in the art that the structure shown in fig. 1 is only an illustration, and does not limit the structure of the mobile terminal. For example, the mobile terminal may also include more or fewer components than shown in FIG. 1, or have a different configuration than shown in FIG. 1.
The memory 104 may be used to store a computer program, for example, a software program of an application software and a module, such as a computer program corresponding to the motion control method in the embodiment of the present invention, and the processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, so as to implement the method described above. The memory 104 may include high speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include memory located remotely from the processor 102, which may be connected to the mobile terminal over a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The transmission device 106 is used to receive or transmit data via a network. Specific examples of the network described above may include a wireless network provided by a communication provider of the mobile terminal. In one example, the transmission device 106 includes a Network adapter (NIC) that can be connected to other Network devices through a base station to communicate with the internet. In one example, the transmission device 106 may be a Radio Frequency (RF) module, which is used to communicate with the internet in a wireless manner.
In the present embodiment, a motion control method is provided, and fig. 2 is a flowchart of a motion control method according to an embodiment of the present invention, as shown in fig. 2, the flowchart includes the following steps:
step S202, determining a first motion state of the target device;
step S204, determining a measured motion parameter of a target sub-device, wherein the target sub-device is a device included in the target device, and the measured motion parameter is a parameter obtained by measurement of a target sensor;
step S206, determining the reliability of the measured motion parameter based on the first motion state;
step S208, performing target filtering processing on the measured motion parameters based on the credibility to determine target motion parameters of the target sub-device;
and step S210, controlling the target sub-equipment to move based on the target motion parameters.
In the above embodiment, the target device may be an unmanned aerial vehicle, and the target sub-device may be a pan/tilt head in the unmanned aerial vehicle. The target sensor can be a holder course encoder, an attitude detection sensor and the like. The target filtering process may be kalman filtering. When the target device is a drone, the first motion state may include a hover state, a motion state, and the like. The measured motion parameters can be angle data and flight motion data obtained by the measurement of a pan-tilt heading encoder, such as the heading angular velocity of a cloud platform. And determining the deviation of the course angular velocity of the target sub-equipment according to the measured course angular velocity and the motion state of the target equipment, compensating the deviation to determine a target motion parameter, and controlling the target sub-equipment to move according to the target motion parameter.
In the above embodiments, the aircraft is in a stationary state with the aircraft hovering, when the integral of the pan head heading axis angular velocity over time is equal to the angular data measured by the pan head heading axis encoder. Therefore, when the aircraft is in a hovering state, the angle data detected by the heading shaft encoder is required to be used as a reference value of the heading shaft angular speed to participate in the drift calculation and compensation for calculating the heading shaft angular speed. When the airplane is in a flying state, the airplane actually moves. At this time, the angle data detected by the encoder is not equal to the integral of the heading axis acceleration, so that the angle data cannot be used for the estimation and compensation of the drift of the reference amount of the heading axis angular velocity. Based on the characteristics, when the aircraft is in a hovering state, the angle reliability of the course shaft encoder is high, so that the Kalman filter parameters need to be used
The smaller the pitch, the larger the specific gravity, the more the angular velocity drift is corrected. On the contrary, the credibility of the angle of the pan-tilt encoder is low in the motion state of the airplane, and the Kalman filter parameters need to be used
And (5) enlarging and reducing the specific gravity of the station. Therefore, the flight state of the airplane can be obtained through the first step, and the airplane can be fitted
Dynamically adjusting parameters with respect to curves of aircraft motion states
The drift compensation algorithm has stronger robustness and more accurate speed drift compensation.
Optionally, the main body of the above steps may be a background processor or other devices with similar processing capabilities, and may also be a machine integrated with at least a data processing device, where the data processing device may include a terminal such as a computer, a mobile phone, and the like, but is not limited thereto.
According to the invention, the first motion state of the target equipment is determined, the measured motion parameters of the target sub-equipment in the target equipment are determined, the credibility of the measured motion parameters is determined according to the first motion state, the target filtering processing is carried out on the measured motion parameters according to the credibility so as to determine the target motion parameters of the target sub-equipment, and the target sub-equipment is controlled to move according to the target motion parameters. Because the credibility of the measured motion parameter is determined according to the first motion state of the target equipment, and the target filtering processing is carried out on the target measured motion parameter according to the credibility, the target motion parameter can be accurately determined without interference, and other devices are not needed, so that the cost is saved. Therefore, the problems of inaccurate regulation of the motion parameters, easy interference and high cost in the related technology can be solved, the accuracy of the regulation of the motion parameters is improved, and the cost is reduced.
In one exemplary embodiment, determining the first motion state of the target device comprises: acquiring a first motion parameter of the target device; analyzing the first motion parameter using a first model to determine a first motion state of the target device, wherein the first model is trained through machine learning using a plurality of sets of first training data, each of the plurality of sets of first training data including: the motion parameters and motion states of the device. In this embodiment, a first motion state of the target device may be determined through a first model, where the first model may be a DNN model, and the first motion parameter may be a motion state of the target device. When the target device is an unmanned aerial vehicle, flight state data including three-axis angular velocity (Gx, gy, gz), three-axis linear velocity (Vx, vy, vz), three-axis acceleration (Ax, ay, az), and aircraft position coordinates (Px, py, pz) of the aircraft may be acquired from the aircraft. And (3) transmitting 12 data of the airplane into a trained deep learning model, and obtaining the state Y (stable hovering or movement) of the airplane through forward reasoning. Transferring the state Y to a Kalman filter to dynamically adjust the parameters
(corresponding to the above-mentioned reliability). Confidence level can be formulated
That is, Y represents the motion state of the target device (represented by any one of data included in 0-1), and F is a predetermined parameter. Y can be represented by any data in (0,1), where the closer the data is to 1, the greater the probability that the first motion state is a motion state, and the closer the data is to 0, the smaller the probability that the first motion state is a hover state. A schematic diagram of determining a first motion state of the target device using the first model is shown in fig. 3. Wherein, the first model can be a DNN model, a CNN model and the like.
In one exemplary embodiment, prior to determining the object motion parameters based on the confidence level and the object filter, the method further comprises: determining a first motion state function of the target sub-device based on the measured motion parameters; determining a second motion state function of the target sub-device based on the motion parameters reported by the target sub-device; and performing target processing on the first motion state function and the second motion state function to obtain processing operation included in the target filtering processing. In this embodiment, before determining the target motion parameter based on the reliability and the target filter, a first motion state function of the target sub-device may be determined according to the measured motion parameter, and a second motion state function of the target sub-device may be determined according to the motion parameter reported by the target sub-device. I.e. establishing the state space equation of the target sub-device, the first motion state function may be expressed as z = h (x) + w z (1) The second motion state function can be expressed as
Where h (x) represents the measured motion function of the target sub-device, f (x) represents the actual motion function of the target sub-device, ω x 、ω z Is a noise error. The target processing can be a one-stage Taylor expansion of a state space equation, and meanwhile, discretization is carried out to obtain standard Kalman filtering.
Wherein the kalman filtering is as follows:
P k ＝(I-K k H k )P k,k-1 ………………………………(8)
wherein the content of the first and second substances,
angular velocity of pan/tilt/heading axis, K k In order to filter the gain of the filter,
measuring the residual error of the angular velocity of rotation, z, for a pan-tilt encoder k Rotation angle, phi, obtained by pan-tilt encoder k,k-1 Is a state transition matrix (derived by partial derivation of f (x)), H k For the measurement matrix (obtained by h (x) partial derivation), P k In the form of a covariance matrix,
for the system noise (predetermined),
to measure noise (corresponding to the confidence level), I is the identity matrix. In Kalman fusion in general
A constant value is taken to be a constant value,
if the measured data is too large, more beliefs recur, the measured data cannot play a role in time;
if the measurement is too small, the measurement data can be timely played.
In one exemplary embodiment, the target filtering processing the measured motion parameters based on the confidence level to determine the target motion parameters of the target sub-device comprises: determining a metrology matrix for the target sub-device based on the first motion state function; determining a filtering gain of the measured motion parameter based on the measurement matrix, the reliability and a covariance matrix of a historical measured motion parameter, wherein the motion parameter adjusting operation is executed in a circulating manner, the historical measured motion parameter is a preset measured motion parameter when the current motion parameter adjusting operation is executed for the first time, and the historical measured motion parameter is a measured motion parameter obtained when the motion parameter adjusting operation is executed for the last time; determining a measurement error of the measured motion parameter based on a measurement matrix; determining the target motion parameter based on the filter gain and the metrology error. In this embodiment, the first motion state function z = h (x) + w may be used z And h (x) included in the measurement matrix is used for obtaining the partial derivative to obtain a measurement matrix of the target sub-device, and the filter gain of the measured motion parameter is determined according to the measurement matrix, the reliability and the covariance matrix of the historical measured motion parameter. When the motion parameter adjustment operation is performed for the first time, the target filtering is initialized, an initialized covariance matrix can be obtained, and then the initialized covariance matrix can be used for determining the filtering gain of the measured motion parameter. When the operation of the motion parameter adjustment is not performed for the first time, the covariance matrix obtained by the previous execution can be usedThe array determines the filter gain of the measured motion parameter. The residual error (i.e. the measurement error) of the rotation angular velocity measured by the pan-tilt encoder can also be determined through the measurement matrix. And determining the target motion parameters through the measurement error and the filter gain.
In one exemplary embodiment, determining the filter gain for the measured motion parameters based on the metrology matrix, the confidence level, and a covariance matrix of historical measured motion parameters comprises: determining a product of the measurement matrix, the covariance matrix and a transpose matrix of the measurement matrix; determining an inverse matrix of the product and the sum of the degrees of confidence; determining a product of the covariance matrix, a transpose of the measurement matrix, and the inverse matrix as the filter gain. In this embodiment, after determining the metrology matrix, confidence level, and covariance matrix of the historically measured motion parameters, the equations may be used
A filter gain is determined.
In an exemplary embodiment, determining the metrology error of the measured motion parameter based on a metrology matrix comprises: determining a deflection angle included in the measured motion parameter; determining a first speed included in the motion parameters reported by the target sub-device; determining a difference between the deflection angle and a first product as the measurement error, wherein the first product is a product of the measurement matrix and the first velocity. In the present embodiment, the expression can be given by
And determining the measurement error. Wherein z is k The rotation angle obtained by the pan-tilt encoder,
the motion parameter reported by the target sub-device includes a first speed, where the first speed may be an angular speed.
In one exemplary embodiment, determining a first velocity included in the motion parameters of the target sub-device comprises:determining a state transition matrix of the target subset based on the second motion state function; and determining the product of the state transition matrix and the historical first speed as the first speed, wherein the operation of motion parameter adjustment is executed in a circulating mode, the product of the state transition matrix and the historical first speed is determined as the first speed, wherein the operation of motion parameter adjustment is executed in a circulating mode, the state transition matrix is a preset transition matrix when the operation of motion parameter adjustment at the current time is executed for the first time, and the state transition matrix is obtained when the operation of motion parameter adjustment at the current time is executed for the second time. In this embodiment, a formula may be used
Representing a first velocity comprised in a motion parameter of the target sub-device, wherein k,k-1 Is a state transition matrix. When the operation of the current motion parameter adjustment is executed for the first time, the state transition matrix is a predicted transition matrix, namely a predicted transition matrix preset during initialization. When the current motion parameter adjustment is not performed for the first time, the state transition matrix is a transition matrix obtained when the motion parameter adjustment operation was performed for the previous time.
In one exemplary embodiment, determining the target motion parameter based on the filter gain and the metrology error comprises: determining a product of the filter gain and the measurement error; and determining the sum of the product of the filter gain and the measurement error and the first speed as the target motion parameter. In the present embodiment, the expression can be given by
And representing the target motion parameters, namely updating the state quantity according to the updated filter gain and the measurement error to obtain a fusion angular velocity, namely the target motion parameters.
In the above embodiment, after the target motion parameter is calculated, the target sub-device may be controlled to move according to the target motion parameter, and the current motion parameter is calculatedCovariance matrix, which can be represented by the formula P k ＝(I-K k H k )P k,k-1 And calculating a covariance matrix, calculating a filter gain based on the calculated covariance matrix, circularly executing the steps, determining a new target motion parameter, and controlling the target sub-equipment to move according to the new target motion parameter.
The following describes the motion control method in conjunction with a specific embodiment:
fig. 4 is a flowchart of a motion control method according to an embodiment of the present invention, as shown in fig. 4, the flowchart includes:
(1) Acquiring motion data of an airplane and encoder angle data of a holder;
(2) And importing the acquired airplane motion data into an unmanned plane state estimator based on a deep learning model.
(3) Obtaining whether the state of the airplane in the previous step is stable hovering or moving;
(4) Dynamically adjusting parameters based on the state of motion of the aircraft
(5) Fusing the angular data of the pan-tilt heading encoder and the angular speed of the pan-tilt heading by using a Kalman filter, and compensating the speed drift;
(6) And obtaining the course shaft angular speed after drift compensation for the control of the holder.
The detailed algorithm of the Kalman angle fusion scheme is as follows:
and establishing equations (1) and (2) of the state space.
z＝h(x)+w z ………………………………………(2)
And (3) carrying out simultaneous discretization on the Taylor expansions in the stages (1) and (2) to obtain standard Kalman filtering.
P k ＝(I-K k H k )P k,k-1 ………………………………(8)
Dynamic calculation of flight state Y of unmanned aerial vehicle by combining first part estimation
Wherein the content of the first and second substances,
angular velocity of pan/tilt/head course axis, K k In order to filter the gain of the filter,
measuring the residual error of the angular velocity of rotation, z, for a pan-tilt encoder k Rotation angle, phi, obtained by pan-tilt encoder k,k-1 Is a state transition matrix (derived by partial derivation of f (x)), H k For the measurement matrix (obtained by h (x) partial derivation), P k To assist inThe variance matrix is used to determine the variance of the received signal,
for the system noise (predetermined),
to measure noise (corresponding to the confidence level), I is the identity matrix. In Kalman fusion in general
A constant value is taken to be a constant value,
too large and more believes recur, and the measured data cannot play a role in time;
too small or too much to believe to measure, the measurement data can play a role in time.
The method comprises the following specific steps of fusing the angular data of the pan-tilt course encoder and the angular speed of the pan-tilt course by using a Kalman filter, and compensating the speed drift:
1) Kalman initialization-P, K and state prediction (i.e., state transition matrix) initialization;
2) Acquiring an aircraft motion state Y output by an aircraft state estimator;
3) Calculating a prediction covariance by utilizing a P, K numerical value, namely formula (7), and simultaneously acquiring a course angular speed fed back by an actual attitude sensor, namely formula (4);
4) Calculating measurement noise by using motion state Y of airplane
-equation (9) to obtain the dynamics
Updating the filtering gain through a formula (6), and simultaneously inputting the angle data of the heading encoder of the holder into a formula (5) to update the measurement error;
5) Updating the state quantity by using the updated filter gain and the measurement error to obtain a fused angular velocity-formula (3);
6) Computing a covariance matrix, equation (8);
7) Repeating the steps 2) to 6) after updating the angle data of the holder heading encoder and the angle speed data of the holder heading shaft;
8) And obtaining the course axis angular speed after compensating the temperature drift.
In the foregoing embodiment, the current aircraft motion state is estimated using a deep learning model by utilizing aircraft flight data. And using a tripod head course encoder to obtain the angle and the speed detected by the attitude detection sensor on the tripod head to carry out Kalman fusion, and dynamically switching along with different motions of the airplane to realize dynamic adjustment of the angle obtained by the encoder and the trust degree of the attitude detection sensor so as to obtain the drift-compensated course axis speed. Dynamically changing parameters of a fitted curve according to flight conditions during Kalman fusion
And further, fusion gain is dynamically adjusted, and algorithm adaptability is improved. And applying the compensated drift course speed to an image stability augmentation system to improve the image stability. The problem of environmental interference in a scene is solved, and the speed drift compensation of the heading axis of the holder can be realized. The problem that the course axis has no angle ring and drifts is solved, and the stability of the picture is improved. And the control of the holder is more stable by calculating the drift problem caused by the defects of the compensation sensor. In addition, the invention does not need any additional sensor, thereby reducing the cost.
Through the above description of the embodiments, those skilled in the art can clearly understand that the method according to the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but the former is a better implementation mode in many cases. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which is stored in a storage medium (e.g., ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal device (e.g., a mobile phone, a computer, a server, or a network device) to execute the method according to the embodiments of the present invention.
In this embodiment, a motion control device is further provided, and the motion control device is used to implement the above embodiments and preferred embodiments, which have already been described and will not be described again. As used below, the term "module" may be a combination of software and/or hardware that implements a predetermined function. Although the means described in the embodiments below are preferably implemented in software, an implementation in hardware, or a combination of software and hardware is also possible and contemplated.
Fig. 5 is a block diagram of a motion control apparatus according to an embodiment of the present invention, as shown in fig. 5, the apparatus including:
a first determining module 502 for determining a first motion state of a target device;
a second determining module 504, configured to determine a measured motion parameter of a target sub-device, where the target sub-device is a device included in the target device, and the measured motion parameter is a parameter obtained by measurement by a target sensor;
a third determining module 506, configured to determine a reliability of the measured motion parameter based on the first motion state;
a filtering module 508, configured to perform target filtering processing on the measured motion parameters based on the confidence level to determine target motion parameters of the target sub-device;
a control module 510 for controlling the target sub-device to move based on the target motion parameter.
In an exemplary embodiment, the first determination module 502 may determine the first motion state of the target device by: acquiring a first motion parameter of the target device; analyzing the first motion parameter using a first model to determine a first motion state of the target device, wherein the first model is trained through machine learning using a plurality of sets of first training data, each of the plurality of sets of first training data including: the motion parameters and motion states of the device.
In one exemplary embodiment, the apparatus may be configured to determine a first motion state function of the target subset based on the measured motion parameters prior to determining target motion parameters based on the confidence level and a target filter; determining a second motion state function of the target sub-device based on the motion parameters reported by the target sub-device; and performing target processing on the first motion state function and the second motion state function to obtain processing operation included in the target filtering processing.
In an exemplary embodiment, the apparatus may perform target filtering processing on the measured motion parameters based on the confidence level to determine target motion parameters of the target sub-device by: determining a metrology matrix for the target sub-device based on the first motion state function; determining a filtering gain of the measured motion parameter based on the measurement matrix, the reliability and a covariance matrix of a historical measured motion parameter, wherein the motion parameter adjusting operation is executed in a circulating manner, the historical measured motion parameter is a preset measured motion parameter when the current motion parameter adjusting operation is executed for the first time, and the historical measured motion parameter is a measured motion parameter obtained when the motion parameter adjusting operation is executed for the last time; determining a measurement error of the measured motion parameter based on a measurement matrix; determining the target motion parameter based on the filter gain and the metrology error.
In an exemplary embodiment, the apparatus may determine the filter gain of the measured motion parameter based on the metrology matrix, the confidence level, and a covariance matrix of historical measured motion parameters by: determining a product of the measurement matrix, the covariance matrix and a transpose matrix of the measurement matrix; determining an inverse matrix of the product and the sum of the degrees of confidence; determining a product of the covariance matrix, a transpose of the measurement matrix, and the inverse matrix as the filter gain.
In an exemplary embodiment, the apparatus may determine the metrology error of the measured motion parameter based on a metrology matrix by: determining a deflection angle included in the measured motion parameter; determining a first speed included in the motion parameters reported by the target sub-device; determining a difference between the deflection angle and a first product as the measurement error, wherein the first product is a product of the measurement matrix and the first velocity.
In an exemplary embodiment, the apparatus may determine the first velocity included in the motion parameters of the target sub-device by: determining a state transition matrix of the target subset based on the second motion state function; and determining the product of the state transition matrix and the historical first speed as the first speed, wherein the operation of motion parameter adjustment is executed circularly, the state transition matrix is a preset transition matrix when the operation of current motion parameter adjustment is executed for the first time, and the state transition matrix is a transition matrix obtained when the operation of motion parameter adjustment is executed for the last time.
In an exemplary embodiment, the apparatus may determine the target motion parameter based on the filter gain and the metrology error by: determining a product of the filter gain and the measurement error; and determining the sum of the product of the filter gain and the measurement error and the first speed as the target motion parameter.
It should be noted that the above modules may be implemented by software or hardware, and for the latter, the following may be implemented, but not limited to: the modules are all positioned in the same processor; alternatively, the modules are respectively located in different processors in any combination.
Embodiments of the present invention also provide a computer-readable storage medium having a computer program stored thereon, wherein the computer program is arranged to perform the steps of any of the above-mentioned method embodiments when executed.
In an exemplary embodiment, the computer-readable storage medium may include, but is not limited to: various media capable of storing computer programs, such as a usb disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic disk, or an optical disk.
Embodiments of the present invention also provide an electronic device comprising a memory having a computer program stored therein and a processor arranged to run the computer program to perform the steps of any of the above method embodiments.
In an exemplary embodiment, the electronic apparatus may further include a transmission device and an input/output device, wherein the transmission device is connected to the processor, and the input/output device is connected to the processor.
For specific examples in this embodiment, reference may be made to the examples described in the above embodiments and exemplary embodiments, and details of this embodiment are not repeated herein.
It will be apparent to those skilled in the art that the various modules or steps of the invention described above may be implemented using a general purpose computing device, they may be centralized on a single computing device or distributed across a network of computing devices, and they may be implemented using program code executable by the computing devices, such that they may be stored in a memory device and executed by the computing device, and in some cases, the steps shown or described may be performed in an order different than that described herein, or they may be separately fabricated into various integrated circuit modules, or multiple ones of them may be fabricated into a single integrated circuit module. Thus, the present invention is not limited to any specific combination of hardware and software.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the principle of the present invention should be included in the protection scope of the present invention.
Claims (9)
1. A method of motion parameter adjustment, comprising:
determining a first motion state of a target device;
determining a measured motion parameter of a target sub-device, wherein the target sub-device is a device included in the target device, and the measured motion parameter is a parameter obtained by measurement of a target sensor;
determining a confidence level of the measured motion parameter based on the first motion state;
performing target filtering processing on the measured motion parameters based on the credibility to determine target motion parameters of the target sub-device;
controlling the target sub-device to move based on the target motion parameter;
prior to determining target motion parameters based on the confidence level and a target filter, the method further comprises: determining a first motion state function of the target sub-device based on the measured motion parameters; determining a second motion state function of the target sub-device based on the motion parameters reported by the target sub-device; performing target processing on the first motion state function and the second motion state function to obtain processing operation included in the target filtering processing;
performing target filtering processing on the measured motion parameters based on the confidence level to determine target motion parameters of the target sub-device comprises: determining a metrology matrix for the target sub-device based on the first motion state function; determining a filtering gain of the measured motion parameter based on the measurement matrix, the reliability and a covariance matrix of a historical measured motion parameter, wherein the motion parameter adjusting operation is executed in a circulating manner, the historical measured motion parameter is a preset measured motion parameter when the current motion parameter adjusting operation is executed for the first time, and the historical measured motion parameter is a measured motion parameter obtained when the motion parameter adjusting operation is executed for the last time; determining a measurement error of the measured motion parameter based on a measurement matrix; determining the target motion parameter based on the filter gain and the metrology error.
2. The method of claim 1, wherein determining the first motion state of the target device comprises:
acquiring a first motion parameter of the target device;
analyzing the first motion parameter using a first model to determine a first motion state of the target device, wherein the first model is trained through machine learning using a plurality of sets of first training data, each of the plurality of sets of first training data including: the motion parameters and motion states of the device.
3. The method of claim 1, wherein determining a filter gain for the measured motion parameter based on the metrology matrix, the confidence level, and a covariance matrix of historical measured motion parameters comprises:
determining a product of the measurement matrix, the covariance matrix and a transpose matrix of the measurement matrix;
determining an inverse matrix of the product and the sum of the degrees of confidence;
determining a product of the covariance matrix, a transpose of the measurement matrix, and the inverse matrix as the filter gain.
4. The method of claim 1, wherein determining a metrology error of the measured motion parameter based on a metrology matrix comprises:
determining a deflection angle included in the measured motion parameter;
determining a first speed included in the motion parameters reported by the target sub-device;
determining a difference between the deflection angle and a first product as the measurement error, wherein the first product is a product of the measurement matrix and the first velocity.
5. The method of claim 4, wherein determining a first velocity included in the motion parameters of the target sub-device comprises:
determining a state transition matrix of the target subset based on the second motion state function;
and determining the product of the state transition matrix and the historical first speed as the first speed, wherein the operation of motion parameter adjustment is executed circularly, the state transition matrix is a preset transition matrix when the operation of current motion parameter adjustment is executed for the first time, and the state transition matrix is a transition matrix obtained when the operation of motion parameter adjustment is executed for the last time.
6. The method of claim 5, wherein determining the target motion parameter based on the filter gain and the metrology error comprises:
determining a product of the filter gain and the measurement error;
and determining the sum of the product of the filter gain and the measurement error and the first speed as the target motion parameter.
7. A motion parameter adjustment device, comprising:
the device comprises a first determination module, a second determination module and a third determination module, wherein the first determination module is used for determining a first motion state of target equipment;
the second determining module is used for determining a measured motion parameter of a target sub-device, wherein the target sub-device is a device included in the target device, and the measured motion parameter is a parameter obtained through measurement of a target sensor;
a third determination module for determining a trustworthiness of the measured motion parameter based on the first motion state;
the filtering module is used for carrying out target filtering processing on the measured motion parameters based on the credibility so as to determine target motion parameters of the target sub-equipment;
the control module is used for controlling the target sub-equipment to move based on the target motion parameters;
the device is also for, prior to determining a target motion parameter based on the confidence level and a target filter: determining a first motion state function of the target sub-device based on the measured motion parameters; determining a second motion state function of the target sub-device based on the motion parameters reported by the target sub-device; performing target processing on the first motion state function and the second motion state function to obtain processing operation included in the target filtering processing;
the filtering module performs target filtering processing on the measured motion parameters based on the credibility in the following manner to determine target motion parameters of the target sub-device: determining a metrology matrix for the target sub-device based on the first motion state function; determining a filtering gain of the measured motion parameter based on the measurement matrix, the reliability and a covariance matrix of a historical measured motion parameter, wherein the motion parameter adjusting operation is executed in a circulating manner, the historical measured motion parameter is a preset measured motion parameter when the current motion parameter adjusting operation is executed for the first time, and the historical measured motion parameter is a measured motion parameter obtained when the motion parameter adjusting operation is executed for the last time; determining a measurement error of the measured motion parameter based on a measurement matrix; determining the target motion parameter based on the filter gain and the metrology error.
8. A computer-readable storage medium, in which a computer program is stored, wherein the computer program is arranged to perform the method of any of claims 1 to 6 when executed.
9. An electronic device comprising a memory and a processor, wherein the memory has stored therein a computer program, and wherein the processor is arranged to execute the computer program to perform the method of any of claims 1 to 6.
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