An intelligent following service robot integrating UWB and IMU and its control method, device and storage medium

Abstract

The present invention discloses an intelligent following service robot integrating UWB and IMU, and its control method, device and storage medium, which include a signal receiving module, a processing module and a driving module. The signal receiving module receives UWB data and IMU data emitted by the followed target, and the processing module fuses the UWB data and IMU data to perform nonlinear coordinate estimation, determine the predicted system state quantity, and drive the robot to act according to the predicted system state quantity to follow the followed target. The present invention detects the UWB data and IMU data of the followed target and fuses them, which has a low implementation cost, is less affected by the environment, and can improve the stability of the robot; nonlinear coordinate estimation is performed based on the UWB data and IMU data to determine the predicted system state quantity, and can control the robot to adapt to the nonlinear movement of the followed target, thereby improving the robot's following success rate. The present invention is widely used in the field of robotics technology.

Description

Technical Field

[0001] The present invention relates to the field of robotics technology, and in particular to an intelligent following service robot integrating UWB and IMU, and a control method, device and storage medium thereof. Background Art

[0002] Nowadays, intelligent robots are increasingly being used in various scenarios. Among them, follower service robots, with their ability to follow targets, can be applied in industrial assistance, rehabilitation training, health and epidemic prevention, and logistics and transportation. For example, in health and epidemic prevention situations, follower service robots can intelligently follow medical personnel, providing 24-hour uninterrupted personal service. They can not only assist medical personnel in performing daily item transportation tasks, thereby improving medical efficiency and convenience, but also help medical personnel share workloads, allowing them to focus more on providing high-quality medical services.

[0003] The core function of a following service robot is its ability to follow. Current technologies use devices such as lidar or cameras to locate itself and the target, thereby achieving target following. However, lidar is relatively expensive, and both are susceptible to environmental influences, reducing their effectiveness.

[0004] Explanation of terms:

[0005] UWB: Ultra Wide Band, which can be used for communication and positioning;

[0006] IMU: Inertial Measurement Unit, which consists of three single-axis accelerometers and three single-axis gyroscopes. The accelerometers detect the acceleration signals of the object in the carrier coordinate system's independent three axes, while the gyroscopes detect the angular velocity signals of the carrier relative to the navigation coordinate system. After processing these signals, the object's posture can be calculated. Summary of the Invention

[0007] In response to technical problems such as the high implementation cost of current following service robots and their susceptibility to environmental influences that reduce their effectiveness, the purpose of the present invention is to provide an intelligent following service robot that integrates UWB and IMU, as well as its control method, device and storage medium.

[0008] In one aspect, an embodiment of the present invention includes an intelligent following service robot integrating UWB and IMU, wherein the intelligent following service robot integrating UWB and IMU includes:

[0009] A signal receiving module; the signal receiving module includes a plurality of UWB base stations, the signal receiving module is used to receive UWB data sent by the followed target through each of the UWB base stations, and is used to receive IMU data sent by the followed target;

[0010] Processing module; the processing module is used to fuse the UWB data and the IMU data to perform nonlinear coordinate estimation and determine the state quantity of the prediction system;

[0011] Driving module; the driving module is used to drive the intelligent following service robot to act according to the predicted system state quantity and follow the target to be followed.

[0012] Furthermore, the fusing of the UWB data and the IMU data to perform nonlinear coordinate estimation and determine the prediction system state quantity includes:

[0013] Determining the attitude angle of the target being followed based on the IMU data;

[0014] Determine the distance value and distance change value between the intelligent following service robot and the followed target according to the UWB data;

[0015] Acquire a first system state quantity of the intelligent following service robot; the first system state quantity is the system state quantity of the intelligent following service robot at the first moment;

[0016] Determine a second system state quantity according to the first system state quantity and the posture angle; the second system state quantity is a system state quantity of the intelligent follower service robot at a second moment, and the second moment is a moment after the first moment;

[0017] Determining a difference between an observed value and a predicted value based on the first system state quantity, the attitude angle, the distance value, and the distance change value;

[0018] Determining a Jacobian equation based on the first system state quantity;

[0019] determining a Kalman gain according to the second system state quantity and the Jacobian equation;

[0020] The predicted system state quantity is determined according to the second system state quantity, the Jacobian equation, the Kalman gain, and the difference between the observed value and the predicted value.

[0021] Furthermore, determining the second system state quantity according to the first system state quantity and the attitude angle includes:

[0022] According to the formula Calculate; where, is the second system state quantity, p′x and p′ y is the position coordinate of the intelligent following service robot at the second moment, v′ x and v′ y is the speed of the intelligent following service robot at the second moment, Δt is the duration between the first moment and the second moment, θ is the posture angle, is the first system state quantity, p x and p y is the position coordinate of the intelligent following service robot at the first moment, v x and v y is the speed of the intelligent following service robot at the first moment, is the process noise.

[0023] Furthermore, determining the difference between the observed value and the predicted value based on the first system state quantity, the attitude angle, the distance value, and the distance change value includes:

[0024] According to the formula Calculate; where m is the difference between the observed value and the predicted value, ρ is the distance value, is the yaw angle in the attitude angle, is the distance change value.

[0025] Furthermore, determining the Jacobian equation according to the first system state quantity includes:

[0026] According to the formula Calculation is performed; wherein H is the Jacobian formula.

[0027] Furthermore, determining the Kalman gain according to the second system state quantity and the Jacobian equation includes:

[0028] According to the formula K=P′H T S -1 Calculate; where S = HP'H T +R, K is the Kalman gain, P' is the second system state The error covariance matrix, H T is the transposed matrix of the Jacobian formula H, and R is the measurement noise matrix.

[0029] Furthermore, determining the predicted system state quantity according to the second system state quantity, the Jacobian formula, the Kalman gain, and the difference between the observed value and the predicted value includes:

[0030] Calculation is performed according to the formula x″=x′+Km; wherein x″ is the state quantity of the prediction system.

[0031] On the other hand, an embodiment of the present invention further includes a control method for an intelligent following service robot integrating UWB and IMU, the control method comprising:

[0032] Receive UWB data and IMU data sent by the target being followed;

[0033] fusing the UWB data and the IMU data to perform nonlinear coordinate estimation to determine a predicted position of a target being followed;

[0034] According to the predicted position, the intelligent following service robot is driven to follow the target.

[0035] On the other hand, an embodiment of the present invention also includes a computer device including a memory and a processor, wherein the memory is used to store at least one program, and the processor is used to load the at least one program to execute the control method of the intelligent following service robot that integrates UWB and IMU in the embodiment.

[0036] On the other hand, an embodiment of the present invention also includes a storage medium storing a program executable by a processor, which, when executed by the processor, is used to execute the control method of the intelligent following service robot that integrates UWB and IMU in the embodiment.

[0037] The beneficial effects of the present invention are as follows: the intelligent following service robot that integrates UWB and IMU in the embodiment detects the UWB data and IMU data of the followed target and fuses them. Since the cost of UWB antennas, UWB base stations and IMU sensors is low, the implementation cost is low; compared with equipment such as lidar and cameras, the detection and transmission of UWB data and IMU data are less affected by the environment, thereby improving the stability of the robot; nonlinear coordinate estimation is performed based on UWB data and IMU data to determine the predicted system state quantity, which can control the robot to adapt to the nonlinear motion of the followed target, thereby improving the robot's following success rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 This is a structural diagram of an intelligent following service robot integrating UWB and IMU in an embodiment;

[0039] Figure 2 A schematic diagram illustrating how to calculate the distance between the robot and the target being followed and the distance change between the robot and the target being followed in an embodiment;

[0040] Figure 3 Flowchart of the control method of the intelligent following service robot in the embodiment. DETAILED DESCRIPTION

[0041] In this embodiment, the structure of the intelligent following service robot integrating UWB and IMU is as follows: Figure 1 As shown, it includes a signal receiving module, a processing module and a driving module.

[0042] Among them, the signal receiving module has a built-in single-line laser radar, a 6-axis IMU sensor and two UWB receivers (UWB base stations). When using an intelligent following service robot that integrates UWB and IMU, the target to be followed by the robot, i.e., the followed target, carries or installs a device that has a built-in 6-axis IMU sensor and a UWB transmitter. The 6-axis IMU sensor carried by the followed target measures the motion data such as the attitude angle of the followed target and generates IMU data. The UWB transmitter carried by the followed target measures the position coordinates of the followed target and generates UWB data, and sends the IMU data and UWB data to the outside. The signal receiving module in the intelligent following service robot receives the UWB data and IMU data sent by the followed target.

[0043] The data input of the processing module is connected to the signal receiving module, and the data output of the processing module is connected to the driving module. The signal receiving module sends the received UWB data and IMU data to the processing module for processing. After fusing the UWB and IMU data, the processing module performs nonlinear coordinate estimation to determine the predicted system state. It then generates control instructions corresponding to the predicted system state and sends these control instructions to the driving module. The driving module drives the intelligent following service robot to follow the target according to the predicted system state.

[0044] In this embodiment, the intelligent following service robot detects and fuses the UWB data and IMU data of the followed target. Since the cost of UWB antennas, UWB base stations and IMU sensors is low, the implementation cost is low. Compared with devices such as lidar and cameras, the detection and transmission of UWB data and IMU data are less affected by the environment, thereby improving the stability of the robot. Nonlinear coordinate estimation is performed based on UWB data and IMU data to determine the predicted system state quantity, which can control the robot to adapt to the nonlinear motion of the followed target, thereby improving the robot's following success rate.

[0045] In this embodiment, when the processing module fuses UWB data and IMU data to perform nonlinear coordinate estimation and determine the prediction system state quantity, the following steps may be specifically performed:

[0046] S201. Determine the attitude angle of the target being followed based on the IMU data;

[0047] S202. According to the UWB data, determine the distance value and distance change value between the intelligent following service robot and the target being followed;

[0048] S203. Obtaining a first system state of the intelligent follower service robot; the first system state is the system state of the intelligent follower service robot at the first moment;

[0049] S204. According to the first system state and attitude angle, determine the second system state; the second system state is the system state of the intelligent follower service robot at the second moment, the second moment being a moment after the first moment;

[0050] S205. Determine the difference between the observed value and the predicted value based on the first system state, attitude angle, distance value, and distance change value;

[0051] S206. Determine the Jacobian equation based on the first system state quantity;

[0052] S207. Determine the Kalman gain based on the second system state and the Jacobian equation;

[0053] S208. Determine the predicted system state quantity based on the second system state quantity, the Jacobian formula, the Kalman gain, and the difference between the observed value and the predicted value.

[0054] In step S201, the processing module can calculate the attitude angle θ of the target being followed based on the IMU data sent by the target being followed. Specifically, the attitude angle θ can be in the form of yaw angle, pitch angle, and roll angle. In this embodiment, the yaw angle is used as the attitude angle for illustration. Specifically, through the formula Calculate the yaw angle of the target being followed Where q0, q1, q2, and q3 are quaternions, and their initial values ​​are set to 1, 0, 0, and 0 respectively. You can obtain and update quaternions by following the steps below:

[0055] 1) Quaternion represents gravity vector:

[0056]

[0057] 2) Calculate the error:

[0058]

[0059] a is the linear acceleration measurement.

[0060] 3) Using error correction gyroscope:

[0061] Correction using PI integral:

[0062]

[0063]

[0064] Among them, K Pis the amplification factor, K I is the integral proportionality coefficient, is the angular velocity (in radians) measured by the gyroscope.

[0065] 4) Update quaternion:

[0066]

[0067] T is the IMU sampling frequency.

[0068] In step S202, the processing module can calculate the distance value and distance change value between the intelligent following service robot and the followed target based on the UWB data sent by the followed target. The principle of step S202 is as follows Figure 2 As shown, MS is the UWB transmitter (UWB tag) carried by the followed target, and UWB1 and UWB2 are two UWB receivers (UWB base stations) in the signal receiving module of the intelligent following service robot.

[0069] In step S202, refer to Figure 2 In the coordinate system established by the processing module, the locations of the two UWB base stations, UWB1 and UWB2, are known. When the signal sent by the UWB transmitter reaches the two located nodes, UWB1 and UWB2, the angle α1 between the line connecting UWB1 to the UWB transmitter and the reference direction can be determined, and a ray L1 can be drawn in this direction. Similarly, the angle α2 between the line connecting UWB2 to the UWB transmitter and the reference direction can also be determined, and a ray L2 can be drawn in this direction. The intersection of rays L1 and L2 is then the arrival angle of the UWB transmitter.

[0070] Assume that the coordinates of base station UWB1 are (x1, y1), the coordinates of UWB2 are (x2, y2), and the coordinates of the target being followed (UWB transmitter) are (x, y). Assuming that α1 and α2 are not 90°, the equation of the line of ray L1 is y-y1=k1(x-x1), and the equation of the line of ray L2 is y-y2=k2(x-x2), where k1=tan(α1) and k2=tan(α2). By solving the intersection of these two lines, the arrival angle position of the target being followed (UWB transmitter) can be determined. Solve for the coordinates of the intersection of rays L1 and L2:

[0071]

[0072] The solution is:

[0073]

[0074] The required x and y are the position coordinates of the target being followed (UWB transmitter).

[0075] In step S202, the target being followed and the robot are particle-processed. The position coordinates (x, y) of the target being followed (UWB transmitter) are known, and the processing module can obtain the coordinates of the robot by positioning the robot itself, so the distance value between the intelligent following service robot and the target being followed can be calculated. In this embodiment, the distance value between the intelligent following service robot and the target being followed calculated at time k (first moment) is recorded as ρ, and the distance value between the intelligent following service robot and the target being followed calculated at time k+1 (second moment) is recorded as ρ′, and the time length between time k (first moment) and time k+1 (second moment) is Δt. Then, from time k (first moment) to time k+1 (second moment), the distance value between the intelligent following service robot and the target being followed and the distance change value are

[0076] In step S203, the processing module detects the position and speed of the intelligent following service robot at time k (first moment) and obtains the position coordinate p of the intelligent following service robot at time k (first moment). x and p y and speed v x and v y These parameters constitute the system state quantity of the intelligent following service robot at time k (the first moment), namely the first system state quantity

[0077]

[0078] In this embodiment, the target being followed may perform nonlinear motion, so an extended Kalman filter algorithm can be used to filter the system state quantity to obtain a more accurate state quantity. The implementation steps of the extended Kalman filter algorithm include steps S204-S208.

[0079] In step S204, the processing module performs the following operations based on the first system state quantity: and attitude angle θ (specifically, attitude angle θ can be the yaw angle ), establish the robot's kinematic model Thus, the system state quantity of the intelligent following service robot at time k+1 (second time) is calculated, that is, the second system state quantity

[0080] In the kinematic model of step S204, is the state transfer matrix, so the kinematic model can also be expressed as P k ′=FP k-1 F T +Q. Among them, Represents process noise. In actual calculation, Q can be set to 0.

[0081] In step S205, according to the first system state quantity The position coordinate p in x 、p y and speed v x 、v y , attitude angle θ (specifically can be taken as yaw angle ), distance value ρ and distance change value According to the formula The difference m between the observed value and the predicted value is calculated, where

[0082] In this embodiment, since the function h(x′) is nonlinear, the function h(x′) is expanded at the origin of the Taylor formula and the higher-order terms above the second order are ignored to obtain the Jacobian formula:

[0083]

[0084] It can be seen from the calculation formula of Jacobian formula H that in step S206, the first system state quantity The position coordinate p in x 、p y and speed v x 、v y The Jacobian formula H is obtained by calculating the same parameters.

[0085] In step S207, the formula K=P′H T S -1 The Kalman gain K is calculated. Where S = HP′H T +R, P' is the second system state quantity The error covariance matrix, H T is the transposed matrix of the Jacobian formula H, R is the measurement noise matrix, and the measurement noise matrix R is the intrinsic parameter of the sensor.

[0086] In step S208, based on the second system state quantity x′, Jacobian formula H, Kalman gain K, and the difference m between the observed value and the predicted value, the predicted system state quantity x″ is calculated according to the formula x″=x′+Km, and at the same time, it is calculated according to the formula P”=(1-KH)P', where P” is the error covariance matrix corresponding to the predicted system state quantity x″.

[0087] By executing steps S201-S208, the extended Kalman filter algorithm is used to filter the system state quantity, thereby obtaining an accurate predicted system state quantity x". The dimensions of the parameters included in the predicted system state quantity x" are the same as those of the first system state quantity and the second system state quantity. The predicted system state quantity x" can indicate the action that the robot will perform, so that the robot can realize the position coordinates and speed and other parameters corresponding to the predicted system state quantity x", so that the robot can cope with the nonlinear movement of the followed target and can effectively follow the followed target.

[0088] In this embodiment, the processing module generates control instructions based on the predicted system state x″ and sends the control instructions to the driving module, thereby controlling the movement of components such as the motor in the driving module and driving the robot to achieve parameters such as the position coordinates and speed corresponding to the predicted system state x″. For example, the processing module can set a navigation point based on the position coordinates in the predicted system state x″, calculate and plan the optimal global path from the robot's current position to the navigation point, plan a local path based on the optimal global path, generate control instructions, and control the driving module to drive the robot along the local path.

[0089] In this embodiment, refer to Figure 3 The control method of the intelligent following service robot integrating UWB and IMU includes the following steps:

[0090] S1. Receive UWB data and IMU data sent by the target being followed;

[0091] S2. Fuse UWB data and IMU data to perform nonlinear coordinate estimation and determine the predicted position of the target being followed;

[0092] S3. Drive the intelligent following service robot to follow the target based on the predicted position.

[0093] Among them, step S1 can be performed by the signal receiving module of the intelligent following service robot integrating UWB and IMU in this embodiment, step S2 can be performed by the processing module, and step S3 can be performed by the driving module. By executing steps S1-S3, the intelligent following service robot integrating UWB and IMU in this embodiment can be controlled, thereby achieving the technical effect of the intelligent following service robot integrating UWB and IMU in this embodiment.

[0094] A computer program that executes the control method of the intelligent following service robot that integrates UWB and IMU in this embodiment can be written and written into a storage medium or a computer device. When the computer program is read out and run, the control method of the intelligent following service robot that integrates UWB and IMU in this embodiment is executed, thereby achieving the same technical effect as the control method of the intelligent following service robot that integrates UWB and IMU in the embodiment.

[0095] It should be noted that, unless otherwise specified, when a feature is referred to as being "fixed" or "connected" to another feature, it may be directly fixed or connected to the other feature, or it may be indirectly fixed or connected to the other feature. In addition, the descriptions of up, down, left, right, etc. used in this disclosure are only relative to the relative positional relationship of the components of the present disclosure in the accompanying drawings. The singular forms of "a", "said" and "the" used in this disclosure are also intended to include the plural forms, unless the context clearly indicates otherwise. In addition, unless otherwise defined, all technical and scientific terms used in this embodiment have the same meaning as those generally understood by those skilled in the art. The terms used in the description of this embodiment are only for describing specific embodiments and are not intended to limit the present invention. The term "and / or" used in this embodiment includes any combination of one or more related listed items.

[0096] It should be understood that, although the present disclosure may adopt the term first, second, third etc. to describe various elements, these elements should not be limited to these terms.These terms are only used to distinguish the elements of the same type from each other.For example, without departing from the scope of the present disclosure, the first element may also be referred to as the second element, and similarly, the second element may also be referred to as the first element.The use of any and all examples or exemplary language ("for example", "such as" etc.) provided by the present embodiment is only intended to better illustrate embodiments of the present invention, and unless otherwise required, the scope of the present invention will not be limited.

[0097] It should be appreciated that embodiments of the present invention can be implemented or practiced by computer hardware, a combination of hardware and software, or by computer instructions stored in a non-transitory computer-readable memory. The methods can be implemented in a computer program using standard programming techniques, including a non-transitory computer-readable storage medium configured with a computer program, wherein the storage medium so configured causes the computer to operate in a specific and predefined manner, according to the methods and figures described in the specific embodiments. Each program can be implemented in a high-level procedural or object-oriented programming language to communicate with the computer system. However, if desired, the program can be implemented in assembly or machine language. In any case, the language can be a compiled or interpreted language. In addition, the program can be run on a programmed application-specific integrated circuit for this purpose.

[0098] In addition, the operations of the processes described in this embodiment may be performed in any suitable order, unless otherwise indicated in this embodiment or otherwise clearly contradicted by the context. The processes described in this embodiment (or variations and / or combinations thereof) may be performed under the control of one or more computer systems configured with executable instructions, and may be implemented as code (e.g., executable instructions, one or more computer programs, or one or more applications) that is executed collectively on one or more processors, by hardware, or a combination thereof. The computer program includes a plurality of instructions that can be executed by one or more processors.

[0099] Furthermore, the methods can be implemented in any type of computing platform that is operably connected to a suitable computer, including but not limited to a personal computer, a minicomputer, a mainframe, a workstation, a network or distributed computing environment, a separate or integrated computer platform, or in communication with a charged particle tool or other imaging device, etc. Various aspects of the present invention can be implemented as machine-readable code stored on a non-transitory storage medium or device, whether removable or integrated into a computing platform, such as a hard disk, an optical read and / or write storage medium, RAM, ROM, etc., so that it can be read by a programmable computer, and when the storage medium or device is read by the computer, it can be used to configure and operate the computer to perform the processes described herein. In addition, the machine-readable code, or portions thereof, can be transmitted over a wired or wireless network. When such media includes instructions or programs that implement the steps described above in conjunction with a microprocessor or other data processor, the invention described in this embodiment includes these and other different types of non-transitory computer-readable storage media. When programmed according to the methods and techniques of the present invention, the present invention also includes the computer itself.

[0100] The computer program can be applied to input data to perform the functions described in the present embodiment, thereby converting the input data to generate output data that is stored in a non-volatile memory. The output information can also be applied to one or more output devices such as a display. In a preferred embodiment of the present invention, the converted data represents a physical and tangible object, including a specific visual depiction of the physical and tangible object produced on the display.

[0101] The above description is merely a preferred embodiment of the present invention. The present invention is not limited to the aforementioned embodiments. As long as the technical effects of the present invention are achieved by the same means, any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention. Within the scope of protection of the present invention, various modifications and variations of the technical solutions and / or implementation methods are possible.

Claims

1. An intelligent following service robot integrating UWB and IMU, characterized in that: The intelligent following service robot integrating UWB and IMU includes: A signal receiving module; the signal receiving module includes a plurality of UWB base stations, the signal receiving module is used to receive UWB data sent by the followed target through each of the UWB base stations, and is used to receive IMU data sent by the followed target; Processing module; the processing module is used to fuse the UWB data and the IMU data to perform nonlinear coordinate estimation and determine the state quantity of the prediction system; Driving module; the driving module is used to drive the intelligent following service robot to act according to the predicted system state quantity and follow the target to be followed; The fusing of the UWB data and the IMU data to perform nonlinear coordinate estimation and determine a prediction system state quantity includes: Determining the attitude angle of the target being followed based on the IMU data; Determine the distance value and distance change value between the intelligent following service robot and the followed target according to the UWB data; Acquire a first system state quantity of the intelligent following service robot; the first system state quantity is the system state quantity of the intelligent following service robot at the first moment; Determine a second system state quantity according to the first system state quantity and the posture angle; the second system state quantity is a system state quantity of the intelligent follower service robot at a second moment, and the second moment is a moment after the first moment; Determining a difference between an observed value and a predicted value based on the first system state quantity, the attitude angle, the distance value, and the distance change value; Determining a Jacobian equation based on the first system state quantity; determining a Kalman gain according to the second system state quantity and the Jacobian equation; Determining the predicted system state quantity according to the second system state quantity, the Jacobian formula, the Kalman gain, and the difference between the observed value and the predicted value; The determining of the second system state quantity according to the first system state quantity and the attitude angle includes: According to the formula Calculate; where, is the second system state quantity, and is the position coordinate of the intelligent following service robot at the second moment, and is the speed of the intelligent following service robot at the second moment, is the duration between the first moment and the second moment, is the attitude angle, is the first system state quantity, and is the position coordinate of the intelligent following service robot at the first moment, and is the speed of the intelligent following service robot at the first moment, is the process noise.

2. The intelligent following service robot integrating UWB and IMU according to claim 1 is characterized in that: The determining, based on the first system state quantity, the attitude angle, the distance value, and the distance change value, of a difference between an observed value and a predicted value includes: According to the formula Calculate; where, is the difference between the observed value and the predicted value, is the distance value, is the yaw angle in the attitude angle, is the distance change value.

3. The intelligent following service robot integrating UWB and IMU according to claim 2 is characterized in that: Determining the Jacobian equation according to the first system state quantity includes: According to the formula Calculate; where, is the Jacobi formula.

4. The intelligent following service robot integrating UWB and IMU according to claim 3 is characterized in that: The determining of the Kalman gain according to the second system state quantity and the Jacobian equation includes: According to the formula Calculate; where, , is the Kalman gain, is the second system state quantity The error covariance matrix of The Jacobi formula The transposed matrix of is the measurement noise matrix.

5. The intelligent following service robot integrating UWB and IMU according to claim 4 is characterized in that: The step of determining the predicted system state quantity according to the second system state quantity, the Jacobian equation, the Kalman gain, and the difference between the observed value and the predicted value includes: According to the formula Calculate; where, is the state quantity of the predicted system.

6. A control method for an intelligent following service robot integrating UWB and IMU, wherein the control method is executed by the intelligent following service robot integrating UWB and IMU according to any one of claims 1 to 5, and is characterized in that: The control method includes: Receive UWB data and IMU data sent by the target being followed; fusing the UWB data and the IMU data to perform nonlinear coordinate estimation to determine a predicted position of a target being followed; According to the predicted position, the intelligent following service robot is driven to follow the target.

7. A computer device, characterized in that: The invention comprises a memory and a processor, wherein the memory is used to store at least one program, and the processor is used to load the at least one program to execute the control method of the intelligent following service robot integrating UWB and IMU as claimed in claim 6.

8. A computer-readable storage medium storing a program executable by a processor, characterized in that: The program executable by the processor is used to execute the control method of the intelligent following service robot integrating UWB and IMU as claimed in claim 6 when executed by the processor.

Images