Pan-tilt angle adjustment method and device of unmanned aerial vehicle, electronic equipment and storage medium

Abstract

The invention discloses a holder angle adjustment method and device of an unmanned aerial vehicle, electronic equipment and a storage medium, and relates to the field of unmanned aerial vehicle-holder system multi-source sensor fusion. The method comprises the following steps: acquiring first speed information of a GNSS sensor configured on the unmanned aerial vehicle at the current moment, and first acceleration information and first angular rate information of a miniature IMU of a holder configured on the unmanned aerial vehicle at the current moment; and performing data fusion processing on the first speed information, the first acceleration information and the first angular rate information to determine angle information of the cradle head, so as to adjust the attitude of the cradle head according to the angle information. According to the invention, accurate adjustment of the attitude of the unmanned aerial vehicle holder is realized, and the problem that the target condition cannot be efficiently identified and observed due to inaccurate adjustment of the attitude of the unmanned aerial vehicle holder is solved.

Description

Technical Field

[0001] This invention relates to the field of multi-source sensor fusion in UAV-gimbal systems, and particularly to a method, apparatus, electronic device, and storage medium for adjusting the gimbal angle of a UAV. Background Technology

[0002] Drone technology has been widely used in various industries, often using images captured by drones to analyze the situation on-site and the target. For example, in power line inspections, drones can photograph target objects to detect whether they have defects. Therefore, if the position and posture of the target object in the image are not appropriate, the defects of the target object cannot be effectively detected.

[0003] Currently, the position of the target object in the shooting image and its proportion on the screen are often used to adjust the attitude of the drone gimbal to ensure accurate shooting of the target object. However, the above methods may have positional information errors, which may lead to inaccurate adjustment of the drone gimbal attitude, resulting in the inability to efficiently identify and observe the target situation. Summary of the Invention

[0004] This invention provides a method, device, electronic device, and storage medium for adjusting the gimbal angle of a drone, so as to achieve precise adjustment of the attitude of the drone gimbal.

[0005] According to one aspect of the present invention, a method for adjusting the gimbal angle of a UAV is provided, wherein the UAV is equipped with a GNSS sensor and the gimbal on the UAV is equipped with a miniature IMU, the method comprising:

[0006] Acquire the first velocity information of the GNSS sensor at the current moment, as well as the first acceleration information and first angular velocity information of the micro IMU at the current moment;

[0007] By performing data fusion processing on the first velocity information, the first acceleration information, and the first angular rate information, the angle information of the gimbal is determined, so as to adjust the gimbal attitude according to the angle information.

[0008] According to another aspect of the present invention, a gimbal angle adjustment device for a drone is provided. The drone is equipped with a GNSS sensor, and the gimbal on the drone is equipped with a miniature IMU. The device includes:

[0009] The data acquisition module is used to acquire the first velocity information of the GNSS sensor at the current moment, as well as the first acceleration information and first angular velocity information of the micro IMU at the current moment;

[0010] The data fusion module is used to determine the angle information of the gimbal by performing data fusion processing on the first velocity information, the first acceleration information and the first angular rate information, so as to adjust the gimbal attitude according to the angle information.

[0011] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0012] At least one processor; and

[0013] A memory communicatively connected to the at least one processor; wherein,

[0014] The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the gimbal angle adjustment method for a drone according to any embodiment of the present invention.

[0015] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the gimbal angle adjustment method for a drone according to any embodiment of the present invention.

[0016] The technical solution of this invention acquires the first velocity information of the GNSS sensor at the current moment, as well as the first acceleration information and the first angular rate information of the micro IMU at the current moment; so as to accurately determine the angle information of the gimbal by performing data fusion processing on the first velocity information, the first acceleration information and the first angular rate information, and adjust the gimbal attitude according to the angle information, thereby realizing the precise adjustment of the attitude of the UAV gimbal.

[0017] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart of a method for adjusting the gimbal angle of an unmanned aerial vehicle (UAV) according to an embodiment of the present invention;

[0020] Figure 2This is a schematic diagram of the structure of a gimbal angle adjustment device for a drone according to an embodiment of the present invention;

[0021] Figure 3 This is a schematic diagram of the structure of an electronic device that implements the gimbal angle adjustment method for a drone according to an embodiment of the present invention. Detailed Implementation

[0022] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0023] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0024] Example 1

[0025] Figure 1 This is a flowchart of a method for adjusting the gimbal angle of a drone according to an embodiment of the present invention. This embodiment is applicable to situations where the gimbal angle of a drone needs to be adjusted. The method can be executed by a gimbal angle adjustment device of the drone, which can be implemented in hardware and / or software. The gimbal angle adjustment device of the drone can be configured in any electronic device with network communication function.

[0026] This invention relates to a drone equipped with a GNSS sensor, and the gimbal on the drone is equipped with a miniature IMU, or MEMS IMU. A GNSS sensor is an antenna device used to receive signals from a Global Navigation Satellite System (GNSS). An Inertial Measurement Unit (IMU) is a device that measures acceleration and angular velocity, and then calculates attitude and motion state. An IMU consists of an accelerometer and a gyroscope. The accelerometer is a sensor that measures the acceleration of an object, and the gyroscope is used to measure the angular velocity around an axis. Typically, expensive and high-precision IMUs are very large and cannot be placed on a drone. Therefore, to make the measurement effect of a miniature and less precise IMU equivalent to that of a high-precision IMU, it is necessary to combine the parameters measured by the drone with the parameter information of the miniature IMU to improve the accuracy of the gimbal's angle information, thus achieving an effect equivalent to that of an expensive IMU. Figure 1 As shown, the gimbal angle adjustment method for the UAV of the present invention includes the following process:

[0027] S110: Obtain the first velocity information of the GNSS sensor at the current moment, as well as the first acceleration information and first angular rate information of the micro IMU at the current moment.

[0028] The first acceleration information is the acceleration measurement value of the UAV at the current moment, obtained by the accelerometer of the miniature IMU. The first angular rate information is the gyroscope measurement value of the UAV at the current moment, obtained by the gyroscope of the miniature IMU; the gyroscope measurement value is the three-axis measurement value of the gyroscope. The first velocity information can be the velocity measurement value of the UAV obtained by the GNSS single antenna at the current moment.

[0029] The first velocity information can be the velocity in a preset reference coordinate system. The preset reference coordinate system can be understood as a three-dimensional spatial coordinate system, which can be any reference coordinate system related to the geodetic coordinate system; the preferred preset reference coordinate system is the North-East-Down (NED) reference coordinate system, a geographic coordinate system commonly used to describe local position and orientation. The coordinate axes of the North-East-Down (NED) reference coordinate system are defined as follows: x-axis: pointing north, i.e., along the meridian direction on the Earth's surface towards the North Pole, used to represent northward displacement in the horizontal direction. y-axis: pointing east, perpendicular to the x-axis and along the latitude direction on the Earth's surface, used to describe eastward displacement in the horizontal direction. z-axis: pointing downwards, perpendicular to the Earth's surface downwards (towards the Earth's center), used to represent vertical displacement, i.e., changes in altitude.

[0030] S120. By performing data fusion processing on the first velocity information, the first acceleration information and the first angular velocity information, the angle information of the gimbal is determined, so as to adjust the attitude of the gimbal according to the angle information.

[0031] The data fusion processing of this invention refers to the comprehensive processing of data from various types of sensors and other relevant data sources on board the UAV to obtain more accurate, complete, and reliable information, thereby supporting the UAV to make more effective operations in flight control, environmental perception, target recognition, and mission decision-making. The data fusion processing in this application involves fusing first velocity information, first acceleration information, and first angular rate information to accurately determine the angle information of the gimbal.

[0032] Data fusion processing methods may include, but are not limited to, complementary filtering, neural network fusion, and Kalman filtering algorithms.

[0033] 1) Complementary filtering can be a method of data fusion that utilizes the complementary characteristics of different sensors. For example, the accelerometer in a miniature IMU can measure the acceleration of a drone, but it suffers from noise and drift issues; while the gyroscope in a miniature IMU can measure the angular velocity of a drone, it also experiences drift over prolonged use. Complementary filtering leverages the fact that the accelerometer is more accurate in the low-frequency range and the gyroscope is more accurate in the high-frequency range, fusing the data from both. Low-pass and high-pass filters are used to process the accelerometer and gyroscope data respectively, yielding the target gyroscope zero-bias value for the drone.

[0034] 2) Neural network fusion can leverage the powerful nonlinear mapping and learning capabilities of neural networks, enabling them to automatically learn complex relationships between different data sources. In UAV data fusion, a multi-layered neural network can be constructed, using data from different sensors as nodes in the input layer. Through a series of nonlinear transformations and calculations in the hidden layers, the fused result is obtained at the output layer. The neural network can be trained with a large amount of training data to adapt to different flight scenarios and data patterns.

[0035] 3) The Kalman filter algorithm is an algorithm that uses the state equation of a linear system to make the optimal estimate of the system state through the system input and output observation data.

[0036] In an embodiment of the present invention, optionally, determining the angle information of the gimbal by performing data fusion processing on the first velocity information, the first acceleration information, and the first angular velocity information may include steps A1-A4:

[0037] Step A1: Obtain the first gyroscope zero bias and the first acceleration zero bias of the gimbal at the current moment. Determine the nominal state of the gimbal at the current moment based on the first gyroscope zero bias, the first acceleration zero bias, the first velocity information, and the first angular rate information. The nominal state is the angular rate information, velocity information, gyroscope zero bias, and acceleration zero bias of the gimbal under ideal conditions.

[0038] The nominal state can be understood as the state value of the gimbal under ideal conditions.

[0039] Specifically, the second angular rate information is determined based on the zero bias and first angular rate information of the first gyroscope, which can be expressed by the following formula:

[0040] ;

[0041] Where ω represents the second angular velocity information, ω x ω y ω z This is the first angular velocity information; the first gyroscope's zero bias can be expressed as... .

[0042] Then, the second velocity information of the gimbal is determined based on the first velocity information and the first acceleration information, which can be expressed by the following formula:

[0043]

[0044] The second velocity information is three-dimensional information, namely, velocity information in three directions under the northeast-northeast reference coordinate system. , For the first speed information, This is the first acceleration information, where g is the acceleration due to gravity. Rotation matrix from the northeast reference coordinate system to the body coordinate system.

[0045] Furthermore, based on the second angular rate information, the first gyroscope zero bias, the second velocity information, and the first acceleration zero bias, the nominal state of the gimbal at the current moment is constructed.

[0046] Specifically, constructing the nominal state of the gimbal at the current moment based on the second angular rate information, the first gyroscope zero bias, the second velocity information, and the first acceleration zero bias may include: determining the quaternion of the gimbal at the current moment based on the second angular rate information, and constructing the nominal state of the gimbal at the current moment based on the quaternion of the gimbal, the first gyroscope zero bias, the second velocity information, and the first acceleration zero bias.

[0047] Among them, the quaternion of the gimbal is four-dimensional information, and the quaternion cloud of the gimbal can be represented as:

[0048]

[0049] ;

[0050] Where, ω x ω y ω z This is the first angular rate information, i.e., the three-axis measurement value of the gyroscope. α is the rotation angle generated by the three-axis rotation of the gyroscope during the time interval dt.

[0051] nominal state of the gimbal x k This can be expressed as follows:

[0052] ;

[0053] in, The first gyroscope has zero bias; The first acceleration is zero bias.

[0054] Step A2: Determine the first error state of the gimbal at the current moment, and determine the covariance matrix of the first error state. The first error state is a zero matrix with thirteen rows and one column.

[0055] Specifically, the first error state is the predicted value of the error state, which can be the difference between the actual state value of the gimbal at the current moment and the nominal state of the gimbal, i.e., the first error state. It can be represented as:

[0056] ;

[0057] in, It is the change of quaternions over time dt. The resulting change in Euler angles; This represents the change in the gyroscope's zero bias.

[0058] However, to ensure the accuracy of subsequent updates to the error state and nominal state, this application resets the first error state into a 13x1 zero matrix, i.e. .

[0059] Furthermore, determining the covariance matrix of the first error state may include steps B1-B2:

[0060] Step B1: Obtain the gyroscope measurement noise, accelerometer measurement noise, gyroscope zero-bias noise, and accelerometer zero-bias noise. Determine the first noise of the gimbal at the current moment based on the gyroscope measurement noise, accelerometer measurement noise, gyroscope zero-bias noise, and accelerometer zero-bias noise.

[0061] Specifically, the first noise Q of the gimbal can be expressed by the following formula:

[0062] ;

[0063] in, To measure noise for the gyroscope, To achieve zero bias noise for the gyroscope, For accelerometer measurement noise, This is for zero bias noise of the accelerometer.

[0064] Step B2: Determine the target state transition matrix of the first error state, and determine the covariance matrix of the first error state based on the target state transition matrix and the first noise.

[0065] Specifically, the target state transition matrix can be based on State transition matrix F R1 ,as well as, State transition matrix F V It is determined that the target state transition matrix can be represented as:

[0066] ;

[0067] ;

[0068] ;

[0069] Among them, F R1 for The state transition matrix, F V for The state transition matrix, It is the identity matrix. This is the first angular rate information, i.e., the three-axis measurement value of the gyroscope. α is the rotation angle generated by the three-axis rotation of the gyroscope during the time interval dt. This is the first acceleration information.

[0070] Furthermore, the covariance matrix of the first error state is determined based on the target state transition matrix and the first noise. It can be represented as:

[0071] .

[0072] Step A3: Determine the Kalman gain based on the covariance matrix, and determine the second error state based on the Kalman gain, the first acceleration information, and the first velocity information.

[0073] Specifically, the second noise of the gimbal at the current moment is obtained; the second noise is the measurement noise of the first acceleration information. Measurement noise of first velocity information Determine; determine the first Kalman gain based on the second noise and covariance matrix.

[0074] The second noise R of the gimbal can be expressed by the following formula:

[0075] ;

[0076] The first Kalman gain K can be expressed by the following formula:

[0077]

[0078]

[0079]

[0080] Where H is the observation matrix of acceleration relative to the error state, H x The observation of acceleration relative to the observation matrix of the nominal state, X x It is the observation matrix of the nominal state relative to the error state.

[0081] Furthermore, a second error state is determined based on the Kalman gain, the first acceleration information, and the first velocity information. It can be represented as:

[0082]

[0083] in, This represents the first velocity information of the GNSS sensor in the reference coordinate system at time k. This represents the first acceleration information of the micro IMU at time k.

[0084] Step A4: Update the nominal state based on the second error state to determine the angle information of the gimbal.

[0085] Specifically, the second error state Changes in quaternions The nominal state of the gimbal Update and determine the angle information of the gimbal. It can be represented as: .

[0086] The technical solution of this invention involves obtaining the first gyroscope zero bias and the first acceleration zero bias of the gimbal at the current moment. Based on these values, the nominal state of the gimbal at the current moment is determined. The inclusion of the gyroscope zero bias prevents inaccurate heading angle determination due to gyroscope zero bias. Furthermore, the first error state of the gimbal at the current moment is determined, along with its covariance matrix. The first error state is a 13x1 zero matrix. Then, the Kalman gain is determined based on the covariance matrix. Finally, the second error state is determined based on the Kalman gain, the first acceleration information, and the first velocity information. Finally, the nominal state is updated based on the second error state to determine the gimbal's angle information. In other words, this invention combines the first gyroscope zero bias, the first acceleration zero bias, the first velocity information, the first acceleration information, and the first angular rate information for data fusion processing, which can more accurately determine the gimbal's angle information and improve the precision of gimbal angle adjustment.

[0087] The technical solution of this invention acquires the first velocity information of the GNSS sensor at the current moment, as well as the first acceleration information and the first angular rate information of the micro IMU at the current moment; so as to accurately determine the angle information of the gimbal by performing data fusion processing on the first velocity information, the first acceleration information and the first angular rate information, and adjust the gimbal attitude according to the angle information, thereby realizing the precise adjustment of the attitude of the UAV gimbal.

[0088] Example 2

[0089] Figure 2 This is a schematic diagram of a gimbal angle adjustment device for a drone provided in an embodiment of the present invention. This embodiment is applicable to situations where the gimbal angle of a drone needs to be adjusted. The gimbal angle adjustment device can be implemented in hardware and / or software, and can be configured in any electronic device with network communication capabilities. The drone is equipped with a GNSS sensor, and the gimbal on the drone is equipped with a miniature IMU. For example... Figure 2 As shown, the gimbal angle adjustment device for the drone includes:

[0090] The data acquisition module 210 is used to acquire the first velocity information of the GNSS sensor at the current moment, as well as the first acceleration information and first angular velocity information of the micro IMU at the current moment;

[0091] The data fusion module 220 is used to determine the angle information of the gimbal by performing data fusion processing on the first velocity information, the first acceleration information and the first angular rate information, so as to adjust the gimbal attitude according to the angle information.

[0092] Based on the above embodiments, optionally, the data acquisition module includes a nominal state determination unit, a covariance matrix determination unit, an error state determination unit, and an angle information determination unit; the nominal state determination unit is used to acquire the first gyroscope zero bias and the first acceleration zero bias of the gimbal at the current moment, and determine the nominal state of the gimbal at the current moment based on the first gyroscope zero bias, the first acceleration zero bias, the first velocity information, and the first angular rate information; the nominal state is the angular rate information, velocity information, gyroscope zero bias, and acceleration zero bias of the gimbal under ideal conditions; the covariance matrix determination unit is used to determine the first error state of the gimbal at the current moment, and determine the covariance matrix of the first error state, the first error state being a 13x1 zero matrix; the error state determination unit is used to determine the Kalman gain based on the covariance matrix, and determine the second error state based on the Kalman gain, the first acceleration information, and the first velocity information; the angle information determination unit is used to update the nominal state based on the second error state to determine the angle information of the gimbal.

[0093] Based on the above embodiments, optionally, the nominal state determination unit is used to: determine second angular rate information based on the first gyroscope zero bias and the first angular rate information; determine second velocity information of the gimbal based on the first velocity information and the first acceleration information; and construct the nominal state of the gimbal at the current moment based on the second angular rate information, the first gyroscope zero bias, the second velocity information and the first acceleration zero bias.

[0094] Based on the above embodiments, optionally, the covariance matrix determination unit is used to: acquire gyroscope measurement noise, accelerometer measurement noise, gyroscope zero-bias noise, and accelerometer zero-bias noise; determine the first noise of the gimbal at the current moment based on the gyroscope measurement noise, the accelerometer measurement noise, the gyroscope zero-bias noise, and the accelerometer zero-bias noise; determine the target state transition matrix of the first error state; and determine the covariance matrix of the first error state based on the target state transition matrix and the first noise.

[0095] Based on the above embodiments, optionally, the error state determination unit includes a Kalman gain determination subunit, which is used to: obtain the second noise of the gimbal at the current moment; the second noise is determined by the measurement noise of the first acceleration information and the measurement noise of the first velocity information; and determine the first Kalman gain according to the second noise and the covariance matrix.

[0096] Based on the above embodiments, optionally, the nominal state determination unit is further configured to: determine the quaternion of the gimbal at the current moment according to the second angular rate information, and construct the nominal state of the gimbal at the current moment according to the quaternion of the gimbal, the first gyroscope zero bias, the second velocity information and the first acceleration zero bias.

[0097] Optionally, based on the above embodiments, the first velocity information is the velocity in a preset reference coordinate system.

[0098] The gimbal angle adjustment device for UAVs provided in this embodiment of the invention can execute the gimbal angle adjustment method for UAVs provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method.

[0099] Example 3

[0100] According to embodiments of this disclosure, this disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0101] Figure 3 A schematic diagram of an electronic device is shown that can be used to implement the gimbal angle adjustment method for a drone according to embodiments of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0102] like Figure 3 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An I / O interface 15 is also connected to the bus 14; the I / O interface is an input / output interface.

[0103] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0104] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as the gimbal angle adjustment method for a drone.

[0105] In some embodiments, the UAV gimbal angle adjustment method can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 10 via read-only memory (ROM) 12 and / or communication unit 19. When the computer program is loaded into random access memory (RAM) 13 and executed by processor 11, one or more steps of the UAV gimbal angle adjustment method described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to perform the UAV gimbal angle adjustment method by any other suitable means (e.g., by means of firmware).

[0106] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), system-on-a-chip (SoCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0107] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0108] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0109] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0110] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0111] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0112] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0113] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for adjusting the gimbal angle of a drone, characterized in that, The drone is equipped with a GNSS sensor, and the gimbal on the drone is equipped with a miniature IMU. The method includes: Acquire the first velocity information of the GNSS sensor at the current moment, as well as the first acceleration information and first angular velocity information of the micro IMU at the current moment; By performing data fusion processing on the first velocity information, the first acceleration information, and the first angular rate information, the angle information of the gimbal is determined, so as to adjust the gimbal attitude according to the angle information.

2. The method for adjusting the gimbal angle of a UAV according to claim 1, characterized in that, The angle information of the gimbal is determined by performing data fusion processing on the first velocity information, the first acceleration information, and the first angular rate information, including: The first gyroscope zero bias and the first acceleration zero bias of the gimbal at the current moment are obtained. The nominal state of the gimbal at the current moment is determined based on the first gyroscope zero bias, the first acceleration zero bias, the first velocity information and the first angular rate information. The nominal state is the angular rate information, velocity information, gyroscope zero bias and acceleration zero bias of the gimbal under ideal conditions. Determine the first error state of the gimbal at the current moment, and determine the covariance matrix of the first error state; The Kalman gain is determined based on the covariance matrix, and the second error state is determined based on the Kalman gain, the first acceleration information, and the first velocity information. The angle information of the gimbal is determined by updating the nominal state based on the second error state.

3. The method for adjusting the gimbal angle of a UAV according to claim 2, characterized in that, The nominal state of the gimbal at the current moment is determined based on the first gyroscope zero bias, the first acceleration zero bias, the first velocity information, and the first angular rate information, including: The second angular rate information is determined based on the first gyroscope zero bias and the first angular rate information; The second speed information of the gimbal is determined based on the first speed information and the first acceleration information; Based on the second angular rate information, the first gyroscope zero bias, the second velocity information, and the first acceleration zero bias, the nominal state of the gimbal at the current moment is constructed.

4. The method for adjusting the gimbal angle of a UAV according to claim 3, characterized in that, Determining the covariance matrix of the first error state includes: The measurement noise of the gyroscope, the measurement noise of the accelerometer, the zero-bias noise of the gyroscope, and the zero-bias noise of the accelerometer are obtained. Based on the measurement noise of the gyroscope, the measurement noise of the accelerometer, the zero-bias noise of the gyroscope, and the zero-bias noise of the accelerometer, the first noise of the gimbal at the current moment is determined. Determine the target state transition matrix of the first error state, and determine the covariance matrix of the first error state based on the target state transition matrix and the first noise.

5. The method for adjusting the gimbal angle of a UAV according to claim 4, characterized in that, Determining the first Kalman gain based on the covariance matrix includes: The second noise of the gimbal at the current moment is obtained; the second noise is determined by the measurement noise of the first acceleration information and the measurement noise of the first velocity information. The first Kalman gain is determined based on the second noise and the covariance matrix.

6. The method for adjusting the gimbal angle of a UAV according to claim 3, characterized in that, Based on the second angular rate information, the first gyroscope bias, the second velocity information, and the first acceleration bias, the nominal state of the gimbal at the current moment is constructed, including: The quaternion of the gimbal at the current moment is determined based on the second angular rate information. The nominal state of the gimbal at the current moment is constructed based on the quaternion of the gimbal, the first gyroscope zero bias, the second velocity information, and the first acceleration zero bias.

7. The method for adjusting the gimbal angle of a UAV according to any one of claims 1-6, characterized in that, The first velocity information is the velocity in a preset reference coordinate system.

8. A gimbal angle adjustment device for a drone, characterized in that, The drone is equipped with a GNSS sensor, and the gimbal on the drone is equipped with a miniature IMU. The device includes: The data acquisition module is used to acquire the first velocity information of the GNSS sensor at the current moment, as well as the first acceleration information and first angular velocity information of the micro IMU at the current moment; The data fusion module is used to determine the angle information of the gimbal by performing data fusion processing on the first velocity information, the first acceleration information and the first angular rate information, so as to adjust the gimbal attitude according to the angle information.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the gimbal angle adjustment method for the UAV according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the gimbal angle adjustment method for the UAV as described in any one of claims 1-7.

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