Method and apparatus for positioning self-moving device, and self-moving device

Abstract

A method and apparatus for positioning a self-moving device, and a self-moving device, which belong to the technical field of positioning of a self-moving device. The self-moving device comprises a positioning sensor and a dual-antenna RTK system. The method in the present application comprises: acquiring first positioning data generated by a positioning sensor and second positioning data generated by a dual-antenna RTK system; performing fusion processing on the first positioning data and the second positioning data, so as to determine positioning data to be used; and using said positioning data as the current motion basis data of a self-moving device.

Description

Method and device for positioning self-moving equipment, and self-moving equipment

[0001] This application claims priority to the Chinese patent application filed with the China Patent Office on September 11, 2023, with application number 202311170115.0, and invention name “Self-mobile device positioning method, device and self-mobile device”, the entire contents of which are incorporated by reference into this application. Technical Field

[0002] The present application relates to the field of self-mobile device positioning, and in particular to a self-mobile device positioning method, device and self-mobile device. Background Art

[0003] The inventors realized that most existing self-moving devices use conventional single-antenna RTK (Real-time kinematic) solutions or lidar for independent positioning, or integrate positioning with modules such as wheel speed meters and IMUs (Inertial Measurement Units). When one positioning method fails, these can serve as supplementary positioning data to ensure positioning stability.

[0004] Conventional single-antenna RTK technology only provides position data and relies on the movement of the autonomous device to determine its direction, making it difficult to obtain accurate and immediate direction data. Furthermore, in complex environments, such as shadows cast by trees, roofs, and walls, strong reflections from snow, or damaged roads, single-antenna RTK and LiDAR positioning may fail, resulting in reduced positioning accuracy and compromised autonomous device safety.

[0005] The above content is only used to assist in understanding the technical solution of this application and does not constitute an admission that the above content is prior art.

[0006] Summary of the Invention

[0007] The main purpose of this application is to provide a method, device and self-moving device for positioning a self-moving device, aiming to solve the technical problem that during the driving process of existing self-moving devices, complex environmental factors interfere with the positioning data of the self-moving device, making it difficult to obtain relatively accurate position and direction data in a short period of time, and it is difficult to determine the correct direction of travel, resulting in reduced safety of the self-moving device's operation.

[0008] To achieve the above-mentioned object, the present application provides a method for positioning a self-moving device, wherein the self-moving device includes a positioning sensor and a dual-antenna RTK system; the method for positioning a self-moving device includes:

[0009] Acquire first positioning data generated by a positioning sensor and second positioning data generated by a dual-antenna RTK system;

[0010] fusing the first positioning data and the second positioning data to determine positioning data to be used;

[0011] The to-be-used positioning data is used as current movement basis data of the mobile device.

[0012] In addition, in order to achieve the above purpose, the present application also provides a positioning device, wherein the positioning device includes: a data receiving module for obtaining the first positioning data generated by the positioning sensor and the second positioning data generated by the dual-antenna RTK system; a data processing module for fusing the first positioning data and the second positioning data to determine the standby positioning data; an instruction calculation module for using the standby positioning data as the current motion basis data of the mobile device.

[0013] The present application also provides a self-moving device, wherein the self-moving device includes: a vehicle head, a vehicle body, a positioning device, a memory, one or more processors, and a self-moving device positioning program stored in the memory and executable on the one or more processors, wherein the vehicle head is detachably connected to the vehicle body, the positioning device, the memory, and the one or more processors are disposed on the vehicle body, and the self-moving device positioning program is configured to implement the following steps:

[0014] Acquire first positioning data generated by a positioning sensor and second positioning data generated by a dual-antenna RTK system;

[0015] fusing the first positioning data and the second positioning data to determine positioning data to be used;

[0016] The to-be-used positioning data is used as current movement basis data of the mobile device.

[0017] The present application uses a dual-antenna RTK system for positioning, so that when a mobile device obtains its position in various complex environments, the dual-antenna RTK system can quickly obtain the current position and direction of the mobile device, reducing the interference of environmental factors on the positioning data of the mobile device, and further improving the environmental adaptability of the mobile device. In addition, by fusing the first positioning data obtained by the positioning sensor with the second positioning data obtained by the dual-antenna RTK system, the influence of the noise generated by the positioning sensor during the positioning process on the positioning accuracy is suppressed, thereby improving the accuracy of the positioning data, avoiding problems such as collisions of the mobile device during movement, and solving the technical problem of low safety of the mobile device when traveling in complex and diverse environments.

[0018] The details of one or more embodiments of the disclosure are set forth in the accompanying drawings and the description below, and other features and advantages of the disclosure will be apparent from the description, the drawings, and the claims. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0020] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0021] FIG1 is a flow chart of a method for positioning a mobile device according to an embodiment of the present invention;

[0022] FIG2 is a schematic diagram of modules of a mobile device to which the mobile device positioning method of the present application is applied;

[0023] FIG3 is a schematic diagram of the installation position of the mobile station antenna provided in Example 1 of the method for positioning the mobile device of the present application;

[0024] FIG4 is a flow chart of a second embodiment of a method for positioning a mobile device according to the present invention;

[0025] FIG5 is a schematic diagram of the fusion processing flow provided by the mobile device positioning method of the present application;

[0026] FIG6 is a schematic diagram of the module structure of the positioning device according to an embodiment of the present application;

[0027] FIG7 is a schematic diagram of the device structure of the hardware operating environment involved in the method for positioning a mobile device in an embodiment of the present application.

[0028] The purpose, features and advantages of this application will be further explained with reference to the accompanying drawings in conjunction with the embodiments. DETAILED DESCRIPTION

[0029] To make the above-mentioned purposes, features, and advantages of the present application more clearly understood, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making any creative work are within the scope of protection of this application.

[0030] Example 1

[0031] With the continuous development of technology, the application scenarios of autonomous vehicles are becoming increasingly broad. They are no longer limited to small, stable and safe indoor environments. Instead, they are beginning to move into more complex and changing environments such as outdoor courtyards. This has led to the emergence of various types of garden robots, such as robotic lawn mowers and snow sweepers. A fast and accurate positioning system is key to ensuring the safe operation of autonomous vehicles outdoors.

[0032] During operation, autonomous devices require positioning equipment to locate them. By obtaining the specific location and orientation of the device's current movement, the next location and direction of movement can be determined to avoid safety issues such as collisions or falls. To obtain the location and orientation data of the autonomous device, a positioning sensor can be installed on the device. The sensor's measurement data can be used to determine the current location data, which includes both location and orientation data.

[0033] Conventional self-moving equipment positioning technologies include RTK technology, ultrasonic radar positioning, magnetic induction sensor induced electromagnetic field positioning and other single information positioning or multi-information fusion positioning methods. Among them, conventional single-antenna RTK can only provide position data and needs to rely on the movement of the self-moving equipment to obtain direction data. For example, in summer, when the conventional single-antenna RTK solution used by the self-moving equipment encounters obstructions such as shade, eaves, and walls during movement, the positioning accuracy will deteriorate due to the poor positioning signal of the single-antenna RTK. In winter, under the strong reflection of the snow surface, the positioning accuracy will also be affected due to the high possibility of failure of the lidar.

[0034] For some self-moving devices that need to locate themselves by installing magnetic induction sensors to sense the magnetic field of wires, the workload of arranging other sensors and wires will increase. At the same time, when positioning is required, the self-moving device needs to return to the vicinity of the wires for auxiliary positioning, which increases the workload of the self-moving device.

[0035] In addition, in snow-clearing scenarios, for safety reasons, self-moving devices need to quickly obtain directions. However, self-moving devices often have difficulty moving in snowy environments, and cannot use a single-point positioning solution without accurate directional angle data to quickly obtain body positioning data.

[0036] In summary, currently used positioning technologies are easily affected by the environment and cannot accurately obtain the current position and direction data of the mobile device in a timely manner. This will cause the mobile device to be unable to determine whether it will fall or collide with an obstacle during the next step of walking, resulting in low safety of the mobile device during movement.

[0037] Based on this, the present application proposes a first embodiment of a positioning method for an autonomous mobile device. Referring to FIG. 1 , the autonomous mobile device includes a positioning sensor and a dual-antenna RTK system. The positioning method for the autonomous mobile device includes:

[0038] Step S10, acquiring first positioning data of the positioning sensor and second positioning data generated by the dual-antenna RTK system;

[0039] It should be noted that RTK technology uses a dynamic real-time differential method for carrier phase to achieve centimeter-level positioning accuracy in real time during outdoor measurements. An RTK module contains an antenna, a computing unit, and related circuitry. An RTK system consists of a base station installed outdoors and a rover installed on a self-propelled device. Because the measurement errors between the base station and the rover are spatially correlated, the rover, upon receiving data transmitted from the base station, can calculate its own three-dimensional coordinates in a specified coordinate system in real time based on the principle of relative positioning, achieving centimeter-level positioning accuracy.

[0040] It should also be noted that the first positioning data obtained comes from the positioning sensor installed on the self-moving device, and the second positioning data obtained comes from the dual-antenna RTK system. It is understandable that in order to more quickly and accurately obtain the current position data and orientation information of the self-moving device, the dual-antenna RTK system is used for positioning, which can directly output the required positioning data, including the current position data and orientation data of the self-moving device. In other words, when obtaining the self-moving device's positioning data based on the dual-antenna RTK system, it is no longer necessary to obtain its own orientation data through the movement of the self-moving device; instead, it can be directly measured by the dual-antenna RTK system.

[0041] As an example, a positioning sensor for obtaining the first positioning data may include, for example, a wheel speed meter installed on the wheel axle of the chassis of the self-mobile device, and an IMU module installed inside the body of the self-mobile device, wherein the measurement data output by the wheel speed meter may include, for example, speed data, and the measurement data output by the IMU module may include, for example, roll angle, pitch angle and azimuth angle data.

[0042] As another example, a dual-antenna RTK system may include one base station installed outdoors in the open and two mobile stations installed on both sides of the mobile device. The measurement data output by the base station includes position data, direction data, and positioning status.

[0043] Step S20: fusing the first positioning data and the second positioning data to determine positioning data to be used;

[0044] It should be noted that the fusion processing is used to mutually correct and fuse the two collected positioning data to obtain more accurate positioning data, that is, to mutually correct and fuse the first positioning data and the second positioning data to obtain more accurate standby positioning data.

[0045] In addition, it should be noted that the raw data output by the IMU module is generally acceleration and angular velocity data containing bias and noise. During the uninterrupted positioning process, sensor noise and slippage can easily cause cumulative positioning errors in the first positioning data. For the second positioning data, although there is no cumulative error, there is noise in the short term, which can easily lead to instantaneous positioning errors. Therefore, the positioning data obtained from the positioning sensor of the mobile device and the two positioning modules of the dual-antenna RTK system are fused and calculated to correct the positioning errors caused by slippage or inaccurate wheel speed meter data. This improves the large instantaneous positioning errors of conventional single-antenna RTK and the increasing positioning errors of the wheel speed meter and IMU module in the positioning sensor over time. It also has a certain degree of noise resistance, thereby obtaining more accurate position and direction data, and the fused positioning data is used as the basis for calculating the next movement command of the mobile device.

[0046] Step S30: Using the standby positioning data as current movement data of the mobile device.

[0047] It should be noted that the standby positioning data is the positioning data that is closest to the actual position and direction of the current self-mobile device. Therefore, the standby positioning data can be used as the current movement basis data of the self-mobile device to calculate the motion instructions for the next movement of the self-mobile device, that is, to obtain the position data and direction data of the next movement of the self-mobile device. Finally, the self-mobile device completes accurate and safe movement according to the motion instructions.

[0048] Exemplarily, to help understand the technical concept or technical principle of the present application, please refer to Figure 2. Figure 2 provides a module schematic diagram of a self-moving device to which a positioning method for a self-moving device is applied. Exemplarily, the self-moving device includes at least a wheel speed meter, an IMU module and an RTK system, as well as a computing unit and an actuator. The wheel speed meter measures the speed data of the self-moving device, the IMU module measures the angular direction data of the self-moving device, and the RTK system is a dual-antenna RTK system, which measures the second positioning data of the self-moving device. The computing unit calculates the distance data measured by the wheel speed meter, the angular direction data measured by the IMU module, and the second positioning data measured by the RTK system to generate a motion instruction. The actuator receives the motion instruction and executes the self-moving device to move according to the motion instruction.

[0049] This application provides a method for positioning a self-moving device using a dual-antenna RTK system. The method obtains first positioning data generated by the self-moving device's positioning sensor and second positioning data generated by the dual-antenna RTK system, fuses the generated first positioning data and second positioning data, determines standby positioning data, and finally uses the fused standby positioning data as the current motion basis data for the self-moving device. The dual-antenna RTK system enables the self-moving device to quickly and directly obtain its current position and orientation when facing various complex environments, reducing interference with the self-moving device's positioning data caused by environmental factors, thereby improving the self-moving device's ability to adapt to environmental conditions. Furthermore, because the dual-antenna RTK system can directly obtain orientation data, it eliminates the need to rely on the movement of the self-moving device to obtain orientation, further improving the safety of the self-moving device's motion. In addition, by fusing the first positioning data obtained by the positioning sensor of the mobile device with the second positioning data obtained by the dual-antenna RTK system, the influence of noise generated by the sensor or positioning device on the positioning accuracy during the positioning process is suppressed, and technical problems such as positioning failure of a single positioning sensor or positioning device during the positioning process or positioning offset due to accumulation of errors are avoided. This greatly suppresses problems such as collisions of the mobile device during movement, thereby improving the accuracy of the positioning data and solving the technical problem of low safety of the mobile device when traveling in complex and diverse environments.

[0050] In one possible implementation, the step of obtaining the first positioning data of the mobile device includes:

[0051] Step S11, obtaining the distance data collected by the wheel speed meter and the differential speed model of the driving wheel of the chassis of the self-moving device;

[0052] Step S12, determining initial position data and initial direction data based on the differential speed model and the distance data;

[0053] Step S13: Acquire the azimuth angle data output by the IMU module, and correct the initial position data and initial azimuth angle data according to the azimuth angle data to obtain the first positioning data.

[0054] It should be noted that the chassis of the self-propelled device used in this application has two active wheels that can rotate back and forth. The motion characteristics of these two wheels can be described by a two-wheel differential model. Based on the speed data output by the wheel speedometer, the distance traveled by the two wheels can be integrated to obtain the distance data traveled by the self-propelled device. This distance data is then combined with the two-wheel differential model to calculate the initial position data and initial orientation data of the self-propelled device in the navigation coordinate system. Because the wheels are likely to slip when turning, affecting positioning accuracy, the azimuth angle output by the IMU module is required for calculation. The first positioning data is determined by the two-wheel differential model, the distance data provided by the wheel speedometer, and the azimuth angle data provided by the IMU module.

[0055] In addition, it should be noted that since the raw data output by the IMU includes noise data, it will affect the positioning accuracy. Therefore, the first positioning data is more accurate in a short period of time, but sensor noise and slippage will bring about cumulative positioning errors.

[0056] In addition, it should be noted that, for the first positioning data, its positioning data exists in a navigation coordinate system. For example, the navigation coordinate system takes the position of the mobile device's body when it is turned on as the origin, the direction of the mobile device's body when it is turned on as the x-axis, the z-axis perpendicular to the horizontal plane upward, and the y-axis direction satisfies the right-hand rule.

[0057] In this embodiment, the first positioning data of the self-mobile device at the current moment, which includes position data and direction data, is calculated through the two-wheel differential model of the self-mobile device, the distance data provided by the wheel speed meter, and the direction angle data provided by the IMU module, and the measurement result of the first positioning data in a short time is relatively accurate.

[0058] In one possible implementation, the step of obtaining the second positioning data generated by the dual-antenna RTK system includes:

[0059] Step A10, obtaining the main antenna latitude and longitude data and antenna vector angle output by the dual-antenna RTK system;

[0060] Step A11, obtaining the position of the base station of the dual-antenna RTK system, and setting the position of the base station as a reference point;

[0061] It should be noted that the working principle of RTK technology is to use the first-level control point with higher position accuracy as the basic reference point, that is, the base station. The base station continuously observes the satellite, and the mobile station receives the satellite signal. At the same time, the data obtained by the base station must be effectively received by the radio transmission equipment, and the computer performs calculations and displays the specific coordinates and measurement results. During the movement of the mobile device in this solution, the base station of the dual-antenna RTK system is placed in an open and suitable location, and the location of the base station is set as the reference point. The mobile stations installed on both sides of the mobile device body obtain the longitude and latitude data of the main antenna of the mobile device based on the reception of satellite signals, and obtain the antenna vector angle based on the angle between the vector formed by the main and secondary antennas on the mobile device body and the geographic east.

[0062] Step A12: Obtain the second positioning data based on the latitude and longitude data of the main antenna, the antenna vector angle, and the position of the reference point.

[0063] It should be noted that the RTK antenna needs to be installed upright, for example, perpendicular to the horizontal plane, in order to be used. The initial output data of the dual-antenna RTK system includes the latitude and longitude data of the main antenna, as well as the angle between the vector formed by the two antennas and the geographic east. Based on the position of the reference point, the longitude and latitude data of the main antenna are converted into the position coordinate data of the mobile device in the geographic navigation coordinate system. Furthermore, based on the installation position of the mobile station antenna on the body of the mobile device, the antenna vector angle can be converted into the direction data of the mobile device in the geographic navigation coordinate system. The coordinate data and direction data are used to obtain the second positioning data of the mobile device body in the geographic navigation coordinate system.

[0064] For example, in order to help understand the installation position of the mobile station antenna of the present application on the body of the self-moving device, please refer to Figure 3. Figure 3 provides a schematic diagram of the installation position of the mobile station antenna on the body of the self-moving device. The dual-antenna RTK system includes an RTK main antenna and an RTK sub-antenna. The RTK main antenna and the RTK sub-antenna are respectively arranged on both sides of the center of the body of the self-moving device.

[0065] It should be noted that since the RTK antenna has a certain height relative to the ground, position deviation will occur when the fuselage is tilted. Therefore, the second positioning data needs to be corrected in real time according to the tilt angle of the RTK antenna.

[0066] Additionally, it should be noted that the second positioning data has no cumulative error, but has noise in a short period of time.

[0067] For example, it should be noted that, for the second positioning data, its positioning data exists in a geographic navigation coordinate system, wherein the geographic navigation coordinate system has a reference point as the origin, the geographic east direction as the x-axis, the geographic north direction as the y-axis, and the z-axis perpendicular to the horizontal plane upward.

[0068] In this embodiment, the dual-antenna RTK system can quickly and accurately obtain the position data and direction data of the mobile device at the current moment, that is, the second positioning data, and the long-term measurement results of the second positioning data are relatively accurate.

[0069] In one possible implementation, the step of fusing the first positioning data and the second positioning data to determine the positioning data to be used includes:

[0070] In step S21 , the first positioning data and the second positioning data are fused in a loose combination manner using an extended Kalman filter algorithm to obtain fused positioning data, and the fused positioning data is used as standby positioning data.

[0071] It should be noted that loose combination is a relatively simple combination method. In this method, the IMU module, wheel speed meter and dual-antenna RTK system work independently, and their positioning data are also settled independently.

[0072] In addition, it should be noted that the Extended Kalman Filter (EKF) algorithm is an algorithm that can fuse measurement data from multiple sensors. By fusing measurement data from different sensors, more accurate and reliable standby positioning data can be obtained.

[0073] It is understandable that both the wheel speed meter and the IMU module rely on the integration (of velocity / angular velocity) to calculate the carrier position, resulting in relatively accurate positioning results in the short term. However, due to the presence of sensor noise, this noise will also be accumulated, resulting in increasing errors in the positioning calculation results. When using a dual-antenna RTK system for positioning, the presence of noise in the dual-antenna RTK system in the short term leads to large instantaneous positioning errors. However, the dual-antenna RTK system does not have cumulative errors, and the long-term positioning results are relatively accurate. Therefore, the extended Kalman filter algorithm is used to fuse the first positioning data with the second positioning data, and the second positioning data with more accurate long-term results is used to correct the first positioning data. This corrects the positioning errors caused by slippage or inaccurate wheel speed meter data, ultimately providing more accurate standby positioning data, improving the impact of sensor noise on positioning data during the positioning process, and improving the reliability of the combined positioning system.

[0074] In addition, it should be noted that since the final positioning calculation result is expressed in the geographic navigation coordinate system, before using different types of positioning sensors to fuse positioning to determine the location data of the mobile device, the first positioning data in the navigation coordinate system needs to be converted to the geographic navigation coordinate system to facilitate the fusion calculation of the positioning data of multiple positioning sensors to improve the positioning accuracy of the movable mobile device.

[0075] In this embodiment, an extended Kalman filter algorithm is used to fuse the first positioning data and the second positioning data in a loose combination manner, and correct each other, thereby suppressing the influence of sensor noise on positioning accuracy and improving the reliability of the positioning result.

[0076] In one possible implementation, the step of fusing the first positioning data with the second positioning data to obtain fused positioning data includes:

[0077] First construct the state transfer system:

[0078] Where, X = [xy θ 1] T is the system status, x and y are the position coordinates of the mobile device in the geographic navigation coordinate system, is the state transition matrix, θ is the angle between the orientation of the mobile device and the east direction, v and w are the single-step increments of the first positioning data in position and direction, and k represents the result of the iterative calculation of the extended Kalman filter algorithm up to the kth step;

[0079] The extended Kalman filter related formula is:

[0080] Optimal state estimate covariance matrix:

[0081] Kalman filter matrix:

[0082] Update best estimate status:

[0083] Update the covariance matrix:

[0084] Where Z = [x obs y obs θ obs 1] T represents the observation quantity determined by the second positioning data, Q represents the noise covariance matrix of the state quantity determined by the first positioning data, R represents the noise covariance matrix of the observation quantity determined by the second positioning data, X k It is the fused positioning data obtained by fusion calculation.

[0085] It should be noted that the main steps of the extended Kalman filter algorithm include: prediction and update, where the prediction is to predict the current state quantity X based on the state quantity and state transfer matrix F of the previous step. k , the noise covariance matrix Q of the state quantity determined by the first positioning data predicts the current covariance matrix The update is done by taking the current covariance matrix into account. The noise covariance matrix R of the observation quantity determined by the second positioning data is used to calculate the Kalman gain K, and then the state quantity X at the current moment is updated by the Kalman gain K. k and the covariance matrix P k The above steps are the steps of one iteration of the EKF algorithm, and the updated state quantity X k and the covariance matrix P k , as the initial state quantity and covariance matrix of the next iteration, the estimation result is continuously corrected by combining the first positioning data and the second positioning data until a more accurate state quantity X is obtained. k , which is the fused positioning data.

[0086] In this embodiment, the extended Kalman filter algorithm is used to fuse the first positioning data and the second positioning data to obtain fused positioning data. Through continuous prediction and updating, the positioning error caused by slipping or positioning sensor noise is corrected to obtain more accurate fused positioning data.

[0087] Example 2

[0088] Based on the first embodiment of the present application, in another embodiment of the present application, the same or similar contents as those in the above-mentioned embodiment 1 can be referred to the above introduction and will not be repeated hereafter. On this basis, please refer to FIG4 , before the step of fusing the first positioning data and the second positioning data, the method further includes:

[0089] Step S01, determining whether the second positioning data meets the preset positioning status;

[0090] Step S02: If the second positioning data meets the preset positioning status, performing the step of fusing the first positioning data and the second positioning data;

[0091] Step S03: If the second positioning data does not meet the preset positioning status, the first positioning data is used as standby positioning data.

[0092] It should be noted that the second positioning data output includes the current geographic location data and direction data of the mobile device, as well as the positioning status of the dual-antenna RTK system. The positioning status indicates the quality of the differential positioning solution, including fixed solution, floating solution, and single-point solution. When the dual-antenna RTK system is in the fixed solution or floating solution state, its positioning accuracy is generally at the centimeter level or within ten centimeters. However, when the dual-antenna RTK system is in the single-point solution state, it indicates that only the primary and secondary antennas installed on both sides of the device are operating, and its positioning accuracy is generally between 5-10 meters or even higher. Therefore, only the fixed solution or floating solution state is considered to meet the preset positioning status.

[0093] In specific implementation, if it is determined that the positioning state of the dual-antenna RTK system is in a fixed solution or floating-point solution state, the first positioning data and the second positioning data can be fused and calculated, and the result of the fusion calculation is used as the standby positioning data; if it is determined that the positioning state of the dual-antenna RTK system is not in a fixed solution or floating-point solution state, no fusion calculation is performed, and only the first positioning data is selected as the standby positioning data.

[0094] Exemplarily, in order to help understand the technical concept or technical principle of the present application, please refer to Figure 5. Figure 5 provides a flow chart of the fusion processing of the first positioning data and the second positioning data. The speed data measured by the wheel speed meter and the attitude angle data measured by the IMU module (i.e., including roll angle, pitch angle and azimuth angle data) are jointly calculated to obtain position 1 and direction 1 (i.e., the first positioning data). The main antenna longitude and latitude data and the geographic direction of the mobile device (i.e., the antenna vector angle) are measured by the dual-antenna RTK system. The position 2 and direction 2 (i.e., the second positioning data) are calculated by the mobile station antenna installation position parameters. The reliability of the second positioning data is judged. If the second positioning data is reliable, the first positioning data and the second positioning data are fused and calculated to obtain position 3 and direction 3 (i.e., the standby positioning data), and the standby positioning data is used as the basis for calculating the motion instruction of the mobile device. If the second positioning data is unreliable, the first positioning data is directly used as the basis for calculating the motion instruction of the mobile device.

[0095] In this embodiment, since the second positioning data that does not meet the preset positioning status has low accuracy, it is necessary to judge the positioning status of the second positioning data to determine whether to use the second positioning data in subsequent calculations. When the second positioning data cannot be used, only the first positioning data will be used as standby positioning data.

[0096] In one possible implementation, the step of determining whether the second positioning data meets a preset positioning state includes:

[0097] Step E01, determining whether the second positioning data is in a fixed solution state or a floating point solution state;

[0098] Step E02: If yes, determine whether the second positioning data meets the preset positioning status;

[0099] Step E03: If not, it is determined that the second positioning data does not meet the preset positioning status.

[0100] It should be noted that the positioning status is the solution result directly output by the dual-antenna RTK system and can be directly read when used.

[0101] It can be understood that if the positioning state of the second positioning data is not in a fixed solution state or a floating-point solution state, it means that the positioning accuracy of the second positioning data is several meters or even greater. At this time, the second positioning data obtained by the dual-antenna RTK system is considered unreliable, so the second positioning data is no longer used in subsequent calculations.

[0102] In this embodiment, since the positioning status of the second positioning data can intuitively determine whether the positioning data is reliable, it is necessary to judge the positioning status of the second positioning data, thereby ensuring the reliability of the standby positioning data in the calculation of subsequent motion instructions and improving the accuracy of the positioning calculation of the self-moving device.

[0103] Example 3

[0104] Based on the first embodiment of the present application, in another embodiment of the present application, the same or similar contents as those in the above-mentioned embodiment 1 can be referred to the above introduction and will not be repeated hereafter. On this basis, the step of obtaining the differential model of the driving wheel of the chassis of the mobile device includes:

[0105] Step F10, obtaining a preset speed value of the mobile device, a change in position and direction angle of the mobile device when turning, and a distance value between the left and right wheels of the mobile device;

[0106] In step F11 , the speeds of the left and right wheels are determined according to the preset speed value, the change values ​​of the position and direction angle, and the distance between the left and right wheels, thereby obtaining a differential speed model.

[0107] It should be noted that the differential model refers to the situation where one wheel moves faster and the other wheel moves slower during the turning process of the self-moving device. Therefore, the speeds of the left and right wheels need to be calculated separately to obtain the differential model. The preset speed value of the self-moving device is v, the change in position and angular direction of the self-moving device when turning is w, and the distance between the left and right wheels of the self-moving device is d.

[0108] In specific implementation, w=v / r+PID(angErr)+PID(posErr), wherein w=v / r+value obtained after PID operation on angle deviation+value obtained after PID operation on position deviation, wherein the angErr value controlled by PID is the angle between the vehicle body direction and the target straight line CD: theta=A(current machine heading angle)-B{CD}, and the posErr controlled by PID is the position difference between the vehicle body position and the target position (the distance relative to the center of the turning circle, the distance difference between the current position and the target position on the curve, which may be positive or negative). The right wheel speed vr=v+w*d / 2 and the left wheel speed vl=vw*d / 2 are calculated by the differential model.

[0109] In this embodiment, the calculation process of the differential model is described in detail. The differential model describes the motion model of the left and right wheels of the mobile robot. By analyzing the differential model and the distance traveled by the left and right wheels, the initial position data and direction data of the mobile device in the navigation coordinate system are calculated.

[0110] Example 4

[0111] The present application also provides a positioning device, referring to FIG6 , which includes:

[0112] The data receiving module 10 is used to obtain the first positioning data generated by the positioning sensor and the second positioning data generated by the dual-antenna RTK system;

[0113] A data processing module 20 is configured to fuse the first positioning data and the second positioning data to determine positioning data to be used;

[0114] The instruction calculation module 30 is configured to use the standby positioning data as current motion basis data of the mobile device.

[0115] In one embodiment, the positioning device further includes:

[0116] Determining whether the second positioning data meets a preset positioning status;

[0117] If the second positioning data meets the preset positioning state, performing the step of fusing the first positioning data and the second positioning data;

[0118] If the second positioning data does not meet the preset positioning status, the first positioning data is used as standby positioning data.

[0119] In one embodiment, the positioning device further includes:

[0120] determining whether the second positioning data is in a fixed solution state or a floating point solution state;

[0121] If so, determining that the second positioning data meets the preset positioning status;

[0122] If not, it is determined that the second positioning data does not conform to the preset positioning state.

[0123] In one embodiment, the positioning device further includes:

[0124] Acquiring distance data collected by the wheel speed meter and a differential speed model of the driving wheels of the chassis of the self-moving device;

[0125] determining initial position data and initial direction data according to the differential speed model and the distance data;

[0126] Obtaining direction angle data output by the IMU module, and correcting the initial position data and initial direction data according to the direction angle data to obtain the first positioning data.

[0127] In one embodiment, the positioning device includes:

[0128] Obtaining a preset speed value of the self-moving device, a change in position and direction angle of the self-moving device when turning, and a distance value between the left and right wheels of the self-moving device;

[0129] According to the preset speed value, the change value of the position and direction angle, and the distance value between the left and right wheels, the speeds of the left and right wheels are determined respectively to obtain a differential model.

[0130] In one embodiment, the positioning device further includes:

[0131] Obtaining the main antenna latitude and longitude data and antenna vector angle output by the dual-antenna RTK system;

[0132] Obtaining a position of a base station of the dual-antenna RTK system, and setting the position of the base station as a reference point;

[0133] The second positioning data is obtained according to the latitude and longitude data of the main antenna, the antenna vector angle and the position of the reference point.

[0134] In one embodiment, the positioning device further includes:

[0135] The first positioning data and the second positioning data are fused in a loose combination manner using an extended Kalman filter algorithm to obtain fused positioning data, and the fused positioning data is used as standby positioning data.

[0136] The positioning device provided by this application, using the self-moving device positioning method of the above-mentioned embodiment 1 or embodiment 2, can solve the technical problem that existing self-moving devices, during driving, have difficulty obtaining relatively accurate position and direction data in a short period of time due to interference from complex environmental factors on the self-moving device positioning data, making it difficult to determine the correct direction of travel, and thus reducing the safety of the self-moving device's movement. Compared with the prior art, the beneficial effects of the self-moving device positioning device provided by the embodiment of this application are the same as the beneficial effects of the self-moving device positioning method provided by the above-mentioned embodiment, and the other technical features of the self-moving device positioning device are the same as the features disclosed in the method of the above-mentioned embodiment, and are not further described here.

[0137] Example 5

[0138] An embodiment of the present application provides a self-moving device, which includes: a vehicle head, a vehicle body; at least one processor; and a positioning device and a memory that are communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the self-moving device positioning method in the above-mentioned embodiment 1 to control the positioning of the positioning device.

[0139] As shown in FIG7 , the self-mobile device may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, a memory 1005, a positioning device 1006, a vehicle body 1007, and a vehicle head 1008. The communication bus 1002 is used to implement communication between these components. The user interface 1003 may include a display screen and an input unit such as a keyboard. Optionally, the user interface 1003 may also include a standard wired interface or a wireless interface. The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a wireless fidelity (WI-FI) interface). The memory 1005 may be a high-speed random access memory (RAM) or a stable non-volatile memory (NVM), such as a disk storage device. The memory 1005 may also be a storage device independent of the aforementioned processor 1001. Positioning device 1006 can include an RTK module and a positioning sensor. Positioning device 1006 can also optionally be a positioning device independent of processor 1001. Vehicle head 1008 is detachably connected to vehicle body 1007. Processor 1001, communication bus 1002, user interface 1003, network interface 1004, memory 1005, and positioning device 1006 are disposed on vehicle body 1007.

[0140] Those skilled in the art will appreciate that the structure shown in FIG. 7 does not limit the self-moving device, and may include more or fewer components than shown, or a combination of certain components, or a different arrangement of components.

[0141] As shown in FIG. 7 , the memory 1005 as a storage medium may include an operating system, a data storage module, a network communication module, a user interface module, and a self-mobile device positioning program.

[0142] In the self-mobile device shown in Figure 7, the network interface 1004 is mainly used for data communication with other devices; the user interface 1003 is mainly used for data interaction with the user; the processor 1001, memory 1005 and positioning device 1006 in the self-mobile device of the present application can be set in the self-mobile device, and the self-mobile device calls the self-mobile device positioning program stored in the memory 1005 through the processor 1001, and controls the positioning device 1006 to execute the self-mobile device positioning method provided in the embodiment of the present application.

[0143] The self-propelled device provided in this application, using the self-propelled device positioning method described in the above-mentioned embodiments, can resolve the technical problem that existing self-propelled devices, during driving, have difficulty obtaining relatively accurate position and direction data in a short period of time due to interference from complex environmental factors on the self-propelled device's positioning data, making it difficult to determine the correct direction of travel, and thus reducing the safety of the self-propelled device's operation. Compared to the prior art, the beneficial effects of the computer-readable storage medium provided in this embodiment of the application are the same as the beneficial effects of the self-propelled device positioning method provided in the above-mentioned embodiments 1 or 2, and are not further elaborated here.

[0144] It should be understood that various parts of the present disclosure can be implemented with hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in an appropriate manner.

[0145] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

[0146] Example 6

[0147] An embodiment of the present application further provides a computer program product, including a computer program, which implements the steps of the above-mentioned method for positioning a mobile device when executed by a processor.

[0148] The computer program product provided by this application can solve the technical problem that existing self-driving devices, during travel, have difficulty obtaining relatively accurate position and direction data in a short period of time due to interference from complex environmental factors that interfere with the positioning data of the self-driving device, making it difficult to determine the correct direction of travel, and thus reducing the safety of the self-driving device's operation. Compared with the prior art, the beneficial effects of the computer program product provided by the embodiments of this application are the same as the beneficial effects of the self-driving device positioning method provided by the above-mentioned embodiments 1, 2, or 3, and are not further described here.

[0149] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or system comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or system. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or system comprising the element.

[0150] The serial numbers of the above embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0151] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus the necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes a number of instructions for enabling a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0152] The above are only preferred embodiments of the present application and do not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A method for positioning a self-moving device, wherein: The self-moving device includes a positioning sensor and a dual-antenna RTK system; The self-mobile device positioning method comprises: Acquire first positioning data generated by a positioning sensor and second positioning data generated by a dual-antenna RTK system; fusing the first positioning data and the second positioning data to determine positioning data to be used; The standby positioning data is used as current movement basis data of the self-mobile device.

2. The method for positioning a self-moving device according to claim 1, wherein: Before the step of fusing the first positioning data and the second positioning data, the method further includes: Determining whether the second positioning data meets a preset positioning state; If the second positioning data meets the preset positioning state, executing the step of fusing the first positioning data and the second positioning data; If the second positioning data does not conform to the preset positioning state, the first positioning data is used as the standby positioning data.

3. The method for positioning a self-moving device according to claim 2, wherein: The step of determining whether the second positioning data conforms to a preset positioning state comprises: Determining whether the second positioning data is in a fixed solution state or a floating point solution state; If yes, determining that the second positioning data meets the preset positioning status; If not, it is determined that the second positioning data does not conform to the preset positioning state.

4. The method for positioning a self-moving device according to claim 1, wherein: The positioning sensor includes a wheel speed meter and an IMU module; the step of obtaining the first positioning data generated by the positioning sensor includes: Acquire the distance data collected by the wheel speed meter and the differential speed model of the driving wheels of the chassis of the self-moving device; Determining initial position data and initial direction data according to the differential speed model and the distance data; The direction angle data output by the IMU module is obtained, and the initial position data and the initial direction data are corrected according to the direction angle data to obtain the first positioning data.

5. The method for positioning a self-moving device as claimed in claim 4, wherein: The step of obtaining the differential model of the driving wheel of the chassis of the mobile device comprises: Obtaining a preset speed value of the self-moving device, a change value of the position and direction angle of the self-moving device when turning, and a distance value between the left and right wheels of the self-moving device; According to the preset speed value, the change value of the position and direction angle and the distance value between the left and right wheels, the speeds of the left and right wheels are determined respectively to obtain a differential model.

6. The method for positioning a self-moving device as claimed in claim 4, wherein: In the differential model, the right wheel speed is vr=v+w*d / 2, and the left wheel speed is vl=vw*d / 2; wherein v is the preset speed value of the self-moving device, w is the change value of the position and direction angle, and d is the distance value between the left and right wheels.

7. The method for positioning a self-moving device according to claim 1, wherein: The step of acquiring the second positioning data generated by the dual-antenna RTK system comprises: Obtaining the main antenna longitude and latitude data and antenna vector angle output by the dual-antenna RTK system; Obtaining the position of a base station of the dual-antenna RTK system, and setting the position of the base station as a reference point; The second positioning data is obtained according to the latitude and longitude data of the main antenna, the antenna vector angle and the position of the reference point.

8. The method for positioning a self-moving device according to any one of claims 1 to 5, wherein: The step of fusing the first positioning data and the second positioning data to determine the positioning data to be used comprises: The extended Kalman filter algorithm is adopted to fuse the first positioning data with the second positioning data in a loose combination manner to obtain fused positioning data, and the fused positioning data is used as standby positioning data.

9. The method for positioning a self-moving device as claimed in claim 8, wherein: The step of adopting the extended Kalman filter algorithm to fuse the first positioning data with the second positioning data in a loose combination manner to obtain fused positioning data comprises: First construct the state transfer system: Where, X = [xy θ 1] T is the system status, x and y are the position coordinates of the mobile device in the geographic navigation coordinate system, is the state transfer matrix, θ is the angle between the orientation of the mobile device and the east direction, v and w are the single-step increments of the first positioning data in position and direction, and k represents the result of iterative calculation by the extended Kalman filter algorithm to the kth step; The extended Kalman filter related formula is: The optimal state estimate covariance matrix: Kalman filter matrix: Update best estimate status: Update the covariance matrix: Where Z = [x obs y obs θ obs 1] T represents the observed value determined by the second positioning data, Q represents the The noise covariance matrix of the state quantity determined by the first positioning data, R represents the noise covariance matrix of the observation quantity determined by the second positioning data, X k It is the fused positioning data obtained by fusion calculation.

10. The method for positioning a self-moving device according to any one of claims 1 to 9, wherein: The dual-antenna RTK system includes a base station installed in an open outdoor area and two mobile stations installed on both sides of the body of the self-moving device.

11. A positioning device, wherein: The positioning device comprises: A data receiving module, used to obtain first positioning data generated by the positioning sensor and second positioning data generated by the dual-antenna RTK system; A data processing module, used for fusing the first positioning data and the second positioning data to determine the positioning data to be used; The instruction calculation module is used to use the standby positioning data as the current movement basis data of the mobile device.

12. The positioning device according to claim 11, wherein: The positioning device is also used for: Determining whether the second positioning data meets a preset positioning state; If the second positioning data meets the preset positioning state, executing the step of fusing the first positioning data and the second positioning data; If the second positioning data does not conform to the preset positioning state, the first positioning data is used as the standby positioning data.

13. The positioning device according to claim 12, wherein: The positioning device is also used for: Determining whether the second positioning data is in a fixed solution state or a floating point solution state; If yes, determining that the second positioning data meets the preset positioning status; If not, it is determined that the second positioning data does not conform to the preset positioning state.

14. The positioning device according to claim 11, wherein: The positioning sensor includes a wheel speed meter and an IMU module; the data receiving module is also used for: Acquire the distance data collected by the wheel speed meter and the differential speed model of the driving wheels of the chassis of the self-moving device; Determining initial position data and initial direction data according to the differential speed model and the distance data; The direction angle data output by the IMU module is obtained, and the initial position data and the initial direction data are corrected according to the direction angle data to obtain the first positioning data.

15. A self-propelled device, wherein: The self-moving device comprises: a vehicle head, a vehicle body, a positioning device, a memory, one or more processors, and a self-moving device positioning program stored in the memory and executable on the one or more processors, the vehicle head is detachably connected to the vehicle body, the positioning device, the memory, and the one or more processors are arranged on the vehicle body, the positioning device comprises a positioning sensor and a dual-antenna RTK system, and the self-moving device positioning program is configured to implement the following steps: Acquire first positioning data generated by a positioning sensor and second positioning data generated by a dual-antenna RTK system; fusing the first positioning data and the second positioning data to determine positioning data to be used; The standby positioning data is used as current movement basis data of the self-mobile device.

16. The self-moving device according to claim 15, wherein: The positioning sensor includes a wheel speed meter and an IMU module.

17. The self-moving device according to claim 15, wherein: The self-mobile device positioning program is configured to further implement the following steps: Determining whether the second positioning data meets a preset positioning state; If the second positioning data meets the preset positioning state, executing the step of fusing the first positioning data and the second positioning data; If the second positioning data does not conform to the preset positioning state, the first positioning data is used as the standby positioning data.

18. The self-moving device according to claim 17, wherein: The self-mobile device positioning program is configured to further implement the following steps: Determining whether the second positioning data is in a fixed solution state or a floating point solution state; If yes, determining that the second positioning data meets the preset positioning status; If not, it is determined that the second positioning data does not conform to the preset positioning state.

19. The self-moving device according to claim 16, wherein: The self-mobile device positioning program is configured to further implement the following steps: Acquire the distance data collected by the wheel speed meter and the differential speed model of the driving wheels of the chassis of the self-moving device; Determining initial position data and initial direction data according to the differential speed model and the distance data; The direction angle data output by the IMU module is obtained, and the initial position data and the initial direction data are corrected according to the direction angle data to obtain the first positioning data.

20. The self-moving device according to claim 19, wherein: The self-mobile device positioning program is configured to further implement the following steps: Obtaining a preset speed value of the self-moving device, a change value of the position and direction angle of the self-moving device when turning, and a distance value between the left and right wheels of the self-moving device; According to the preset speed value, the change value of the position and direction angle and the distance value between the left and right wheels, the speeds of the left and right wheels are determined respectively to obtain a differential model.

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