Unmanned winery workshop positioning system based on magnetic label
By laying magnetic labels and installing magnetic sensors in the winery workshop, combining Kalman filtering algorithm and inertial navigation system, the problem of insufficient accuracy and stability in complex environments of traditional positioning technology is solved, and high-precision unmanned positioning is achieved and automatic navigation improve productivity.
Patent Information
- Application Number
- CN202411499508.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-10-25
- Publication Date
- 2025-02-18
AI Technical Summary
Traditional winery workshop positioning technology is susceptible to interference in humid, temperature fluctuations and metal barriers, affecting accuracy and stability, and it is difficult to meet the needs of unmanned and efficient.
A positioning system based on magnetic labels is adopted. By laying magnetic labels on the workshop floor and equipment, and installing magnetic sensors on the automatic guide vehicle, combining Kalman filtering algorithm and inertial navigation system, high-precision positioning and automatic navigation are achieved.
Achieve stable and high-precision positioning in complex environments, suitable for long-term operation and simple maintenance, improving the unmanned and production efficiency of the winery workshop.
Smart Images
Figure CN119469106A_ABST
Abstract
Description
Technical Field
[0001] The invention belongs to the field of positioning technology, and in particular relates to an unmanned winery workshop positioning system based on magnetic tags. Background Art
[0002] With the development of industrial automation and intelligent manufacturing, unmanned production workshops have gradually become a hot topic in the industrial field. Traditional winery production workshops usually rely on manual operation, have high requirements for the environment, and have limited efficiency. In order to realize the unmanned operation of winery workshops, an accurate and reliable positioning system is needed to ensure the accurate movement and placement of equipment and materials in the production process.
[0003] Currently, common positioning technologies include laser, vision and ultrasound, but these technologies are susceptible to interference in the humid winery environment with large temperature fluctuations and metal obstacles, affecting accuracy and stability.
[0004] Therefore, a positioning system based on magnetic tags is developed to meet the unmanned and efficient needs of the winery workshop. Summary of the invention
[0005] The main purpose of the present invention is to provide an unmanned winery workshop positioning system based on magnetic tags. Through the combination of magnetic tags and magnetic sensors, in conjunction with positioning algorithms, the precise positioning and automatic navigation of the automatic guided vehicle in the winery workshop can be ensured. The main application scenario of this system is the unmanned winery workshop, which improves production efficiency through comprehensive automated management.
[0006] To achieve the above objectives, the present invention provides an unmanned winery workshop positioning system based on magnetic tags, including magnetic tags, magnetic sensors and automatic guided vehicles (AGV trolleys), wherein:
[0007] For magnetic tags, (in order to ensure that the AGV can accurately locate in real time in the workshop) they are systematically arranged on the workshop floor and (key production) equipment (the tags use high-performance permanent magnetic materials, which can maintain long-term stable magnetism under different temperature and humidity conditions, and are corrosion-resistant and adaptable to the winery workshop environment. The size of each magnetic tag is (customizable), which is large enough to ensure that the magnetic sensor can read accurately and avoid collisions caused by excessive proximity of the AGV). The layout of the magnetic tags is based on the moving range of the AGV, the workshop logistics route and the detection range of the magnetic sensor to ensure that the AGV (can) always cover at least one magnetic tag on any moving path (thereby achieving continuous and stable positioning). Each magnetic tag has a unique magnetic field code, and each magnetic tag is distinguished by different combinations of magnetic strength and polarity.
[0008] For magnetic sensors, multiple magnetic sensors are installed on the automatic guided vehicle and the magnetic sensors are used to detect the magnetic field information of the magnetic tags arranged in the workshop in real time (obtained through magnetic field encoding), so as to obtain the precise position information of the automatic guided vehicle. Multiple magnetic sensors form a multi-dimensional magnetic sensor array, which simultaneously senses the magnetic field in the x, y, and z directions through the multi-dimensional magnetic sensor array, ensuring that the automatic guided vehicle can achieve high-precision positioning in any moving or rotating state;
[0009] For the automated guided vehicle, the automated guided vehicle obtains the magnetic field information transmitted by the magnetic sensor and combines the Kalman filter algorithm with the inertial navigation system. By fusing the data of the magnetic tag and its own motion state information, it ensures the accuracy and stability of positioning, thereby accurately moving to the target position through the path planned by the system.
[0010] As a further preferred technical solution of the above technical solution, the magnetic field encoding is set as follows:
[0011]
[0012] Where M(x,y,z) represents the magnetic field intensity distribution of the magnetic tag in three-dimensional space, μ i is the magnetic permeability of the magnetic material, H i is the magnetic field vector for each label.
[0013] As a further preferred technical solution of the above technical solution, the output signal of the magnetic sensor is:
[0014] S(t)=[S x (t),S y (t),S z (t)]
[0015] Among them, S x (t),S y (t),S z (t) represents the magnetic field strength in the x, y, and z directions respectively. Through the multi-dimensional sensor array, comprehensive perception of magnetic field changes in different directions is achieved.
[0016] As a further preferred technical solution of the above technical solution, the magnetic sensor uses a bandpass filter to process the detected magnetic field signal in real time, and the transfer function of the bandpass filter is:
[0017]
[0018] Among them, f o is the center frequency, n is the order of the filter. The filter suppresses low-frequency and high-frequency noise signals and only retains the effective magnetic field signal generated by the magnetic tag to ensure the clarity and accuracy of the signal. According to the noise frequency characteristics in the workshop environment, f0 Dynamically adjust based on results.
[0019] As a further preferred technical solution of the above technical solution, for the automatic guided vehicle, the role of the Kalman filter algorithm is to process the noise data read by the magnetic tag and to make a smooth estimation and prediction of its own position. The framework is state estimation and observation update, where:
[0020] The state transfer equation is:
[0021] X k =AX k-1 +BU k-1 +W k
[0022] Among them, X k represents the state vector at the current moment, including position and speed; A is the state transfer matrix, describing the relationship between the position and speed of the AGV; B is the control input matrix, describing the influence of the control signal including acceleration of the AGV on the position; W k is the process noise, which represents the error in the inertial navigation system; the state transfer equation is the state X at the kth time k Make a prediction, which depends on the state X at the previous moment k-1 and the control input U at the previous moment k-1 , the state of the system at time k evolves from the state at the previous k-1 time;
[0023] The observation equation is:
[0024] Z k =HX k +V k
[0025] Among them, Z k is the observation vector, representing the position information (i.e., magnetic field information) read by the magnetic sensor; H is the observation matrix, mapping the state space to the observation space; V k is the observation noise, representing the error of the magnetic sensor data;
[0026] In the prediction step, the system first predicts the current position based on the state at the previous moment. The prediction formula is as follows:
[0027]
[0028] Indicates that at the kth moment, according to the previous state X k-1 and control input U k-1 , and the state predicted by the state transfer equation;
[0029] The error covariance is predicted to be:
[0030]
[0031] in, is the predicted error covariance matrix, indicating the uncertainty of the predicted state, P k-1 is the error covariance matrix of the previous moment, describing the confidence of the state estimate at moment k-1, and Q is the process noise covariance matrix;
[0032] Then use the current observation data Z k Update the predicted state, Kalman gain K k The calculation formula is:
[0033] K k =P k H T (HP k H T +R) -1
[0034] Among them, P k is the error covariance matrix, R is the observation noise covariance matrix;
[0035] According to the Kalman gain, the updated state estimate is:
[0036]
[0037] The error covariance is updated as:
[0038]
[0039] I represents the identity matrix. Through Kalman filtering, the system dynamically smoothes the noise of the magnetic sensor readings to provide accurate position information;
[0040] In the short period when the magnetic tag signal is missing or cannot be detected, the system uses the inertial navigation system (INS) to predict the position and attitude to ensure the continuous positioning of the AGV. The INS relies on accelerometers and gyroscopes to measure the motion state of the AGV. The INS is combined with the Kalman filter algorithm to achieve tag-free position prediction in a short period of time and correct the positioning error through magnetic sensor data. Assuming that the INS is at time t 0 The initial velocity is v 0 , the acceleration is a(t), and the position is calculated by two integrations:
[0041]
[0042] Attitude estimation of the inertial navigation system: The inertial navigation system calculates the displacement and rotation angle of the automatic guided vehicle through the three-axis accelerometer and gyroscope data of the automatic guided vehicle, and the output is:
[0043]
[0044] Where ω(t) is the angular velocity and θ(t) is the attitude angle.
[0045] As a further preferred technical solution of the above technical solution, the workflow of the unmanned winery workshop positioning system based on magnetic tags is as follows:
[0046] The system stores the network of magnetic tags in the entire workshop and the corresponding positions of each device. The magnetic field code of each magnetic tag is unique, indicating a specific location or path node. When the AGV is performing a task, the system sends the target location of the AGV and the magnetic tag path that the AGV needs to pass through.
[0047] When the AGV moves, the magnetic sensor reads the magnetic field information from the magnetic tag and transmits the read data to the central processing unit of the AGV. The central unit uses the Kalman filter algorithm to combine its own motion data (speed, acceleration) to predict and correct the state;
[0048] If the location information of the magnetic tag cannot be read temporarily, the system starts the inertial navigation system and calculates the current position and attitude of the automatic guided vehicle through the accelerometer and gyroscope data of the automatic guided vehicle; when the data of the magnetic tag is restored, the Kalman filter algorithm corrects the results of the inertial navigation system to ensure the accuracy and continuity of positioning.
[0049] As a further preferred technical solution of the above technical solution, in the planning of the workshop logistics route, a special termination magnetic field code will be set at the end of the path to facilitate the automatic guided vehicle to identify the current position and reach the end of the path (facilitating timely switching of working modes in complex processes).
[0050] The beneficial effects of the present invention are:
[0051] Compared with traditional visual and ultrasonic positioning, this system is based on magnetic field induction and can work stably in humid environments with metal obstacles. Magnetic tags are used in conjunction with sensors and combined with optimization algorithms to achieve high-precision positioning at the centimeter level. Magnetic tags and magnetic sensors have a long life and simple maintenance, which is suitable for long-term operation of winery workshops. The system has good scalability and can flexibly adjust the layout and functions according to changes in the workshop. BRIEF DESCRIPTION OF THE DRAWINGS
[0052] Figure 1 It is a schematic diagram of an unmanned winery workshop positioning system based on magnetic tags of the present invention. DETAILED DESCRIPTION
[0053] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art can think of other obvious variations. The basic principles of the present invention defined in the following description can be applied to other embodiments, variations, improvements, equivalents, and other technical solutions that do not deviate from the spirit and scope of the present invention.
[0054] In the preferred embodiments of the present invention, those skilled in the art should note that the winery workshop and the like involved in the present invention may be regarded as prior art.
[0055] Preferred embodiments.
[0056] like Figure 1 As shown, the present invention discloses an unmanned winery workshop positioning system based on magnetic tags, including magnetic tags, magnetic sensors and automatic guided vehicles (AGV trolleys), wherein:
[0057] For magnetic tags, (in order to ensure that the AGV can accurately locate in real time in the workshop) they are systematically arranged on the workshop floor and (key production) equipment (the tags use high-performance permanent magnetic materials, which can maintain long-term stable magnetism under different temperature and humidity conditions, and are corrosion-resistant and adaptable to the winery workshop environment. The size of each magnetic tag is (customizable), which is large enough to ensure that the magnetic sensor can read accurately and avoid collisions caused by excessive proximity of the AGV). The layout of the magnetic tags is based on the moving range of the AGV, the workshop logistics route and the detection range of the magnetic sensor to ensure that the AGV (can) always cover at least one magnetic tag on any moving path (thereby achieving continuous and stable positioning). Each magnetic tag has a unique magnetic field code, and each magnetic tag is distinguished by different combinations of magnetic strength and polarity.
[0058] Magnetic tags are densely distributed in key areas where AGVs frequently pass, such as corners, intersections, and important nodes. This dense layout not only ensures that AGVs have high-precision positioning capabilities on complex routes, but also improves the system's response speed in dynamic environments. Magnetic tags use a unique and highly identifiable grayscale encoding method, and each tag contains independent magnetic field encoding information. AGVs can read these magnetic field codes to identify their current specific path location in real time.
[0059] In the overall design, the spacing of magnetic tags is calculated according to the size of the AGV, the workflow and the process requirements. Standard spacing is used in conventional areas, while the spacing between tags is shortened in corners and areas where the AGV frequently interacts, ensuring that the AGV can seamlessly switch navigation paths and avoid positioning errors caused by path complexity or the speed of the AGV. At the same time, each magnetic tag and magnetic field encoding has anti-interference capabilities to ensure data stability in complex environments.
[0060] For magnetic sensors, multiple magnetic sensors are installed on the automatic guided vehicle and the magnetic sensors are used to detect the magnetic field information (also known as magnetic field data, obtained through magnetic field encoding) of magnetic tags arranged in the workshop in real time, so as to obtain the precise position information of the automatic guided vehicle. Multiple magnetic sensors form a multi-dimensional magnetic sensor array, which simultaneously senses the magnetic field in the x, y, and z directions through the multi-dimensional magnetic sensor array, ensuring that the automatic guided vehicle can achieve high-precision positioning in any moving or rotating state;
[0061] For the automated guided vehicle, the automated guided vehicle obtains the magnetic field information transmitted by the magnetic sensor and combines the Kalman filter algorithm with the inertial navigation system. By fusing the data of the magnetic tag and its own motion state information, it ensures the accuracy and stability of positioning, thereby accurately moving to the target position through the path planned by the system.
[0062] Specifically, the magnetic field encoding is set as follows:
[0063]
[0064] Where M(x,y,z) represents the magnetic field intensity distribution of the magnetic tag in three-dimensional space, μ i is the magnetic permeability of the magnetic material, H i is the magnetic field vector for each label.
[0065] More specifically, the output signal of the magnetic sensor is:
[0066] S(t)=[S x (t), S y(t),S z (t)]
[0067] Among them, S x (t),S y (t),S z (t) represents the magnetic field strength in the x, y, and z directions respectively. Through the multi-dimensional sensor array, comprehensive perception of magnetic field changes in different directions is achieved.
[0068] Furthermore, (in order to ensure the elimination of noise interference in a complex workshop environment, the magnetic sensor integrates signal filtering and enhancement technology) the magnetic sensor uses a bandpass filter to process the detected magnetic field signal in real time, and the transfer function of the bandpass filter is:
[0069]
[0070] Among them, f o is the center frequency, n is the order of the filter. The filter suppresses low-frequency and high-frequency noise signals and only retains the effective magnetic field signal generated by the magnetic tag to ensure the clarity and accuracy of the signal. According to the noise frequency characteristics in the workshop environment, f 0 Dynamically adjust based on results.
[0071] Furthermore, for the AGV, the role of the Kalman filter algorithm is to process the noise data read by the magnetic tag and to make a smooth estimate and prediction of its own position. The framework is state estimation and observation update, where:
[0072] The state transfer equation is:
[0073] X k =AX k-1 +BU k-1 +W k
[0074] Among them, X k represents the state vector at the current moment, including position and speed; A is the state transfer matrix, describing the relationship between the position and speed of the AGV; B is the control input matrix, describing the influence of the control signal including acceleration of the AGV on the position; W k is the process noise, which represents the error in the inertial navigation system; the state transfer equation is the state X at the kth time k Make a prediction, which depends on the state X at the previous moment k-1 and the control input U at the previous moment k-1 , the state of the system at time k evolves from the state at the previous k-1 time;
[0075] The observation equation is:
[0076] Z k =HX k +V k
[0077] Among them, Z k is the observation vector, representing the position information (i.e., magnetic field information) read by the magnetic sensor; H is the observation matrix, mapping the state space to the observation space; V k is the observation noise, representing the error of the magnetic sensor data;
[0078] In the prediction step, the system first predicts the current position based on the state at the previous moment. The prediction formula is as follows:
[0079]
[0080] Indicates that at the kth moment, according to the previous state X k-1 and control input U k-1 , and the state predicted by the state transfer equation;
[0081] The error covariance is predicted to be:
[0082]
[0083] in, is the predicted error covariance matrix, indicating the uncertainty of the predicted state, P k-1 is the error covariance matrix of the previous moment, describing the confidence of the state estimate at moment k-1; Q is the process noise covariance matrix, representing the uncertainty in the system model, used for the influence of unobserved noise, and is the random disturbance or uncertainty of the system in state transition;
[0084] Then use the current observation data Z k Update the predicted state, Kalman gain K k The calculation formula is:
[0085] K k =P k H T (HX k H T +R) -1
[0086] Among them, P k is the error covariance matrix, R is the observation noise covariance matrix;
[0087] According to the Kalman gain, the updated state estimate is:
[0088]
[0089] The error covariance is updated as:
[0090]
[0091] I represents the unit matrix, which is equivalent to "1" in matrix operations and maintains the dimension and structure of the matrix. Through Kalman filtering, the system dynamically smoothes the noise of magnetic sensor readings and provides accurate position information;
[0092] In the short period when the magnetic tag signal is missing or cannot be detected, the system uses the inertial navigation system (INS) to predict the position and attitude to ensure the continuous positioning of the AGV. The INS relies on accelerometers and gyroscopes to measure the motion state of the AGV. The INS is combined with the Kalman filter algorithm to achieve tag-free position prediction in a short period of time and correct the positioning error through magnetic sensor data. Assuming that the INS is at time t 0 The initial velocity is v 0 , the acceleration is a(t), and the position is calculated by two integrations:
[0093]
[0094] Attitude estimation of inertial navigation system: The inertial navigation system calculates the displacement and rotation angle of the automatic guided vehicle through the three-axis accelerometer and gyroscope data of the automatic guided vehicle, and the output is:
[0095]
[0096] Where ω(t) is the angular velocity and θ(t) is the attitude angle.
[0097] Preferably, the workflow of the unmanned winery workshop positioning system based on magnetic tags is:
[0098] The system stores the network of magnetic tags in the entire workshop and the corresponding positions of each device. The magnetic field code of each magnetic tag is unique, indicating a specific location or path node. When the AGV is performing a task, the system sends the target location of the AGV and the magnetic tag path that the AGV needs to pass through.
[0099] When the AGV moves, the magnetic sensor reads the magnetic field information from the magnetic tag and transmits the read data to the central processing unit of the AGV. The central unit uses the Kalman filter algorithm to combine its own motion data (speed, acceleration) to predict and correct the state;
[0100] If the location information of the magnetic tag cannot be read temporarily, the system starts the inertial navigation system and calculates the current position and attitude of the automatic guided vehicle through the accelerometer and gyroscope data of the automatic guided vehicle; when the data of the magnetic tag is restored, the Kalman filter algorithm corrects the results of the inertial navigation system to ensure the accuracy and continuity of positioning.
[0101] Preferably, in the planning of the workshop logistics route, a special termination magnetic field code is set at the end of the path to facilitate the automatic guided vehicle to recognize the current position and that it has reached the end of the path (facilitating timely switching of working modes in complex processes).
[0102] Preferably, the present invention uses a combination of magnetic tags and magnetic sensors to ensure high-precision positioning effects through a network of magnetic tags with specific spacing and arrangement and a highly sensitive magnetic sensor;
[0103] Improved positioning algorithm: The improved Kalman filter algorithm is combined with magnetic tag information to achieve accurate calculation of position and path;
[0104] Visual monitoring platform: The upper computer monitoring platform realizes real-time monitoring of the automated guided vehicle and visualization of the operation path, ensuring the stability and operability of the system.
[0105] It is worth mentioning that the technical features such as the winery workshop involved in the patent application of this invention should be regarded as the prior art. The specific structure, working principle and possible control method and spatial layout method of these technical features can be selected by conventional methods in the field, and should not be regarded as the inventive point of this patent. This patent will not be further elaborated.
[0106] For those skilled in the art, it is still possible to modify the technical solutions described in the aforementioned embodiments, or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.
Claims
1. An unmanned winery workshop positioning system based on magnetic tags, characterized in that: It includes magnetic tags, magnetic sensors and automatic guided vehicles, including: For magnetic tags, they are systematically laid out on the floor and equipment of the workshop. The layout of magnetic tags is based on the moving range of the AGV, the logistics route of the workshop, and the detection range of the magnetic sensor, ensuring that the AGV is always covered with at least one magnetic tag on any moving path. Each magnetic tag has a unique magnetic field code, and each magnetic tag is distinguished by different magnetic strength and polarity combinations. For magnetic sensors, multiple magnetic sensors are installed on the automatic guided vehicle and the magnetic sensors are used to detect the magnetic field information of the magnetic tags arranged in the workshop in real time, so as to obtain the precise position information of the automatic guided vehicle. Multiple magnetic sensors form a multi-dimensional magnetic sensor array, which simultaneously senses the magnetic field in the x, y, and z directions through the multi-dimensional magnetic sensor array, ensuring that the automatic guided vehicle can achieve high-precision positioning in any moving or rotating state; For the automated guided vehicle, the automated guided vehicle obtains the magnetic field information transmitted by the magnetic sensor and combines the Kalman filter algorithm with the inertial navigation system. By fusing the data of the magnetic tag and its own motion state information, it ensures the accuracy and stability of positioning, thereby accurately moving to the target position through the path planned by the system.
2. According to claim 1, the unmanned winery workshop positioning system based on magnetic tags is characterized in that: The settings for magnetic field encoding are as follows: Where M(x,y,z) represents the magnetic field intensity distribution of the magnetic tag in three-dimensional space, μ i is the magnetic permeability of the magnetic material, H i is the magnetic field vector for each label.
3. The unmanned winery workshop positioning system based on magnetic tags according to claim 2 is characterized in that: The output signal of the magnetic sensor is: S(t)=[S x (t),S y (t),S z (t)] Among them, S x (t),S y (t),S z (t) represents the magnetic field strength in the x, y, and z directions respectively. Through the multi-dimensional sensor array, comprehensive perception of magnetic field changes in different directions is achieved.
4. The unmanned winery workshop positioning system based on magnetic tags according to claim 3 is characterized in that: The magnetic sensor uses a bandpass filter to process the detected magnetic field signal in real time. The transfer function of the bandpass filter is: Among them, f o is the center frequency, n is the order of the filter. The filter suppresses low-frequency and high-frequency noise signals and only retains the effective magnetic field signal generated by the magnetic tag to ensure the clarity and accuracy of the signal. According to the noise frequency characteristics in the workshop environment, f0 is dynamically adjusted according to the results.
5. The unmanned winery workshop positioning system based on magnetic tags according to claim 4 is characterized in that: For automatic guided vehicles, the role of the Kalman filter algorithm is to process the noise data read by the magnetic tag and to make a smooth estimate and prediction of its own position. The framework is state estimation and observation update, where: The state transfer equation is: X k =AX k-1 +BU k-1 +W k Among them, X k represents the state vector at the current moment, including position and speed; A is the state transfer matrix, describing the relationship between the position and speed of the AGV; B is the control input matrix, describing the influence of the control signal including acceleration of the AGV on the position; W k is the process noise, which represents the error in the inertial navigation system; the state transfer equation is the state X at the kth time k Make a prediction, which depends on the state X at the previous moment k-1 and the control input U at the previous moment k-1 , the state of the system at time k evolves from the state at the previous k-1 time; The observation equation is: Z k =HX k +V k Among them, Z k is the observation vector, representing the position information read by the magnetic sensor; H is the observation matrix, mapping the state space to the observation space; V k is the observation noise, representing the error of the magnetic sensor data; In the prediction step, the system first predicts the current position based on the state at the previous moment. The prediction formula is as follows: Indicates that at the kth moment, according to the previous state X k-1 and control input U k-1 , and the state predicted by the state transfer equation; The error covariance is predicted to be: in, is the predicted error covariance matrix, indicating the uncertainty of the predicted state, P k-1 is the error covariance matrix of the previous moment, describing the confidence of the state estimate at moment k-1, and Q is the process noise covariance matrix; Then use the current observation data Z k Update the predicted state, Kalman gain K k The calculation formula is: K k =P k H T (HP k H T +R) -1 Among them, P k is the error covariance matrix, R is the observation noise covariance matrix; According to the Kalman gain, the updated state estimate is: The error covariance is updated as: I represents the identity matrix. Through Kalman filtering, the system dynamically smoothes the noise of the magnetic sensor readings to provide accurate position information; In the short period when the magnetic tag signal is missing or cannot be detected, the system uses the inertial navigation system to predict the position and attitude to ensure the continuous positioning of the automatic guided vehicle. The inertial navigation system relies on accelerometers and gyroscopes to measure the motion state of the automatic guided vehicle. The inertial navigation system is combined with the Kalman filter algorithm to achieve tag-free position prediction in a short period of time and correct the positioning error through magnetic sensor data. Assuming that the initial velocity of the inertial navigation system at time t0 is v0 and the acceleration is a(t), the position is calculated by two integrations: Attitude estimation of the inertial navigation system: The inertial navigation system calculates the displacement and rotation angle of the automatic guided vehicle through the three-axis accelerometer and gyroscope data of the automatic guided vehicle, and the output is: Where ω(t) is the angular velocity and θ(t) is the attitude angle.
6. The unmanned winery workshop positioning system based on magnetic tags according to claim 5 is characterized in that: The workflow of the unmanned winery workshop positioning system based on magnetic tags is as follows: The system stores the network of magnetic tags in the entire workshop and the corresponding positions of each device. The magnetic field code of each magnetic tag is unique, indicating a specific location or path node. When the AGV is performing a task, the system sends the target location of the AGV and the magnetic tag path that the AGV needs to pass through. When the AGV moves, the magnetic sensor reads the magnetic field information from the magnetic tag and transmits the read data to the central processing unit of the AGV. The central unit uses the Kalman filter algorithm and its own motion data to predict and correct the state. If the location information of the magnetic tag cannot be read temporarily, the system starts the inertial navigation system and calculates the current position and attitude of the automatic guided vehicle through the accelerometer and gyroscope data of the automatic guided vehicle; when the data of the magnetic tag is restored, the Kalman filter algorithm corrects the results of the inertial navigation system to ensure the accuracy and continuity of positioning.
7. The unmanned winery workshop positioning system based on magnetic tags according to claim 1 is characterized in that: In the planning of workshop logistics routes, a special termination magnetic field code will be set at the end of the path to help the automatic guided vehicle recognize its current position and that it has reached the end of the path.