Mobile encoder system and method based on ultrasonic 3D positioning

Abstract

The invention discloses a mobile encoder system and method based on ultrasonic 3D positioning, and the system comprises a mobile ultrasonic probe which is integrated with a miniature ultrasonic transmitter array and a broadband receiving sensor; the positioning base station array is arranged at the periphery of the detection area and is used for receiving ultrasonic signals; the central processing unit is used for resolving coordinates of the mobile ultrasonic probe in real time and generating a motion track; meanwhile, multipath interference and environmental noise are suppressed based on a deep learning model. Through ultrasonic 3D positioning and multi-modal data fusion, high-precision detection of the motion trail of the moving probe on the surface of an object can be achieved, a traditional mechanical encoder can be replaced, and the method has wide application prospects in the fields of industrial automation, precision manufacturing, robots and the like.

Description

Technical Field:

[0001] The present invention belongs to the field of precision measurement technology, and particularly relates to a mobile encoder system and method based on ultrasonic 3D positioning. Background Art:

[0002] In the field of industrial inspection and measurement, it is crucial to accurately measure the movement trajectory of a probe when detecting the surface of an object. Traditional measurement methods mostly use mechanical encoders. However, mechanical encoders have many limitations in practical applications.

[0003] On the one hand, mechanical encoders have mechanical wear problems. Since they rely on the relative movement of mechanical components to achieve measurement, after long-term use, the friction between mechanical components will cause wear, which will affect the measurement accuracy, shorten the service life of the equipment, and increase the maintenance cost.

[0004] On the other hand, mechanical encoders have poor environmental adaptability. In harsh environments such as high temperature, low temperature, humidity, and dust, mechanical components are easily corroded, deformed, or jammed, and cannot work properly, making it difficult to meet the measurement requirements in complex industrial environments.

[0005] In addition, mechanical encoders are complex to install. Their installation process often requires precise mechanical calibration and debugging, which requires high technical requirements for installers and takes a long time to install, which is not conducive to improving production efficiency.

[0006] With the development of industrial automation and intelligence, the demand for high-precision, non-contact, and complex-curved surface adaptable measurement is increasing. Existing mechanical encoders can no longer meet these requirements. Therefore, there is an urgent need for a measurement system and method that can detect the movement trajectory of a probe on the surface of an object in real time, with sub-millimeter accuracy, non-contact measurement, and complex-curved surface adaptability.

[0007] The information disclosed in this background art section is only intended to increase the understanding of the overall background of the present invention, and should not be regarded as an admission or any form of suggestion that this information constitutes prior art known to those of ordinary skill in the art. Summary of the Invention:

[0008] The purpose of the present invention is to provide a mobile encoder system and method based on ultrasonic 3D positioning, so as to overcome the defects in the above-mentioned prior art.

[0009] To achieve the above purpose, the present invention provides a mobile encoder system based on ultrasonic 3D positioning, including:

[0010] A mobile ultrasonic probe, which integrates a micro ultrasonic transmitter array and a broadband receiving sensor;

[0011] An array of positioning base stations is arranged around the detection area to receive ultrasonic signals;

[0012] The central processing unit is used to solve the coordinates of the mobile ultrasonic probe in real time and generate the motion trajectory; at the same time, it suppresses multipath interference and environmental noise based on the deep learning model.

[0013] Furthermore, preferably, the mobile ultrasonic probe is adsorbed on the surface of the object.

[0014] Furthermore, preferably, the mobile ultrasonic probe has a built-in inertial measurement unit, and the inertial measurement unit includes an accelerometer, a gyroscope and a magnetometer.

[0015] Furthermore, preferably, at least three positioning base stations are arranged in the detection area, and nanosecond time synchronization is achieved between the base stations through the IEEE 1588 protocol.

[0016] The present invention also provides a positioning method for a mobile encoder based on ultrasonic 3D positioning, comprising the following steps:

[0017] Base station deployment and synchronization: at least three ultrasonic receiving base stations are arranged around the detection area to form a positioning network. Nanosecond-level time synchronization is achieved between base stations through the IEEE 1588 protocol.

[0018] Probe installation and calibration: attach the mobile ultrasonic probe to the surface of the moving object, perform initial position calibration between the probe and the base station, and establish a coordinate system;

[0019] Ultrasonic signal transmission: The mobile ultrasonic probe transmits pseudo-random coded ultrasonic signals with a frequency of 40kHz, 80k Hz or 120kHz. Frequency division multiplexing (FDM) technology is used to allocate independent frequency bands for the probe to avoid signal conflicts.

[0020] Base station signal reception: Each base station receives the probe signal, records the arrival time ToA and signal strength RSSI, and improves the anti-multipath interference capability through Chirp spread spectrum modulation;

[0021] Time difference of arrival (TDOA) solution: calculate the distance difference between the probe and each base station based on the arrival time difference of the signal between base stations; and solve the initial 3D coordinates x, y, z of the probe by the least squares method;

[0022] Phase difference auxiliary correction, extract the phase difference of continuous wave signal, calculate the probe's tiny displacement, combine the arrival time difference result, and output high-precision 3D pose;

[0023] IMU data fusion. The mobile ultrasonic probe is built-in with an inertial measurement unit (IMU), including an accelerometer, a gyroscope, and a magnetometer, which can collect motion data in real time. The ultrasonic and IMU data are fused through Kalman filtering to suppress trajectory drift when the signal is lost.

[0024] Motion trajectory prediction. Based on the kinematic model, the short-term motion trajectory of the probe is predicted. The positioning algorithm parameters are dynamically adjusted to adapt to high-speed motion scenarios.

[0025] Non-line-of-sight (NLOS) identification. By using the signal attenuation rate and phase consistency, the direct signal and the reflected signal are distinguished, and at the same time, a deep learning model is used to dynamically identify and filter out NLOS interference.

[0026] Temperature and humidity compensation. The environmental temperature and humidity are monitored in real time, the ultrasonic wave propagation speed is corrected, and the positioning result is updated to ensure accuracy and stability.

[0027] Trajectory generation and output. The 3D pose data is converted into a motion trajectory and output to the host computer or the control system.

[0028] Furthermore, as a preference, the method for calculating the time difference of arrival (TDOA) is as follows:

[0029] Input the time differences of arrival of signals from each base station, Δt1, Δt2, Δt3,...; Output the 3D coordinates of the probe, x, y, z.

[0030] Calculate the distance difference: Δd = v * Δt, where v is the ultrasonic wave propagation speed.

[0031] Establish a system of equations:

[0032] √((x - x1) 2 +(y - y1) 2 +(z - z1) 2 ) - √((x - x0) 2 +(y - y0) 2 +(z - z0) 2 ) = Δd1√((x - x2) 2 +(y - y2) 2 +(z - z2) 2 ) - √((x - x0) 2 +(y - y0) 2 +(z - z0) 2 ) = Δd2 ...

[0034] Use the least squares method to solve the non-linear system of equations to obtain the initial 3D coordinates of the probe, x, y, z.

[0035] Furthermore, as a preference, the method for fusing ultrasonic and IMU data by Kalman filtering is as follows:

[0036] Input the ultrasonic positioning results x_us, y_us, z_us; IMU data, including acceleration a and angular velocity ω; output the fused 3D pose x, y, z, θ_x, θ_y, θ_z.

[0037] Predict the next state of the probe based on the IMU data:

[0038] x_k = x_{k - 1}+v_{k - 1}*Δt + 0.5*a*Δt 2

[0039] v_k = v_{k - 1}+a*Δt;

[0040] Use the ultrasonic positioning results as the observed values to update the state estimation.

[0041] Calculate the Kalman gain to optimize the state estimation accuracy.

[0042] Furthermore, as a preference, the method for non-line-of-sight (NLOS) identification is as follows:

[0043] Input the signal attenuation rate, phase consistency, and environmental parameters; output the signal type LOS / NLOS.

[0044] Extract the signal features: attenuation rate, phase change, and multipath components.

[0045] Use a pre-trained deep learning model to classify the signal type.

[0046] Filter out the non-line-of-sight signals and retain the direct signals for positioning.

[0047] Furthermore, as a preference, the following formula is used to correct the ultrasonic propagation speed: v = 331.4 + 0.6T + 0.0124H.

[0048] Compared with the prior art, the present invention has the following beneficial effects:

[0049] (1) The present invention uses a mobile ultrasonic probe to achieve dynamic positioning, breaking through the traditional fixed sensor layout and supporting the detection of free movement trajectories.

[0050] (2) The present invention jointly optimizes multi-modal data including ultrasonic, IMU data, and environmental parameters, and can adapt to complex industrial environments.

[0051] (3) The ultrasonic probe of the present invention can be attached to curved objects, avoiding measurement deviations caused by mechanical rigid contact and having higher measurement accuracy. Description of the Drawings:

[0052] Figure 1Schematic flowchart of the positioning method of the mobile encoder based on ultrasonic 3D positioning of the present invention;

[0053] Figure 2 Schematic diagram of the application of the present invention in the motion trajectory detection of industrial robotic arms. Specific embodiments:

[0054] The following describes the specific embodiments of the present invention in detail, but it should be understood that the protection scope of the present invention is not limited by the specific embodiments.

[0055] The following gives a brief overview of one or more aspects to provide a basic understanding of these aspects. This overview is not an exhaustive survey of all contemplated aspects, and is neither intended to identify key or decisive elements of all aspects nor to attempt to define the scope of any or all aspects. Its sole purpose is to present some concepts of one or more aspects in a simplified form as a prelude to the more detailed description that follows.

[0056] The mobile encoder system based on ultrasonic 3D positioning includes:

[0057] A mobile ultrasonic probe, which integrates a micro ultrasonic transmitter array and a broadband receiving sensor; wherein the working frequency of the micro ultrasonic transmitter array is 40 kHz to 200 kHz;

[0058] The mobile ultrasonic probe may also be optionally equipped with an internal inertial measurement unit IMU, including an accelerometer, a gyroscope, and a magnetometer, for assisting dynamic motion compensation;

[0059] A positioning base station array, arranged around the detection area for receiving ultrasonic signals; at least 3 are arranged, supporting multi-frequency signal reception; the time synchronization at the nanosecond level is achieved among the base stations through the IEEE 1588 protocol;

[0060] A central processing unit, for real-time calculating the 3D coordinates of the mobile ultrasonic probe, generating a motion trajectory after fusing IMU data; and simultaneously suppressing multipath interference and environmental noise based on a deep learning model.

[0061] Wherein the mobile ultrasonic probe has a flexible contact module, and the flexible contact module has a silicone or pneumatic adaptive structure to ensure that the probe fits the curved surface.

[0062] The present invention also provides a positioning method for a mobile encoder based on ultrasonic 3D positioning, including the following steps:

[0063] Base station deployment and synchronization, arranging at least 3 ultrasonic receiving base stations around the detection area to form a positioning network, and achieving nanosecond-level time synchronization among the base stations through the IEEE 1588 protocol;

[0064] Probe installation and calibration: Adsorb the mobile ultrasonic probe on the surface of the moving object, perform the initial position calibration between the probe and the base station, and establish a coordinate system;

[0065] Ultrasonic signal transmission: The mobile ultrasonic probe transmits pseudo-random coded (such as Gold code) ultrasonic signals with frequencies of 40 kHz, 80 kHz, or 120 kHz. Using frequency division multiplexing (FDM) technology, independent frequency bands are allocated for different probes to support multi-probe collaborative work and avoid signal conflicts;

[0066] Base station signal reception: Each base station receives the probe signal, records the time of arrival (ToA) and the received signal strength indication (RSSI), and improves the anti-multipath interference ability through Chirp spread spectrum modulation;

[0067] Time difference of arrival (TDOA) calculation: Calculate the distance differences between the probe and each base station based on the time differences of signal arrival between base stations; and solve the initial 3D coordinates x, y, z of the probe by the least squares method;

[0068] Phase difference assisted correction: Extract the phase differences of continuous wave signals, calculate the micro-displacement of the probe with an accuracy of up to ±0.1 mm, and combine the time difference of arrival results to output high-precision 3D pose;

[0069] IMU data fusion: The mobile ultrasonic probe is built-in with an inertial measurement unit (IMU), including an accelerometer, a gyroscope, and a magnetometer, to collect motion data in real time; fuse ultrasonic and IMU data through Kalman filtering to suppress trajectory drift when signals are lost;

[0070] Motion trajectory prediction: Based on the kinematic model, predict the short-term motion trajectory of the probe; dynamically adjust the parameters of the positioning algorithm to adapt to high-speed motion scenarios;

[0071] Non-line-of-sight (NLOS) identification: Distinguish direct signals from reflected signals through the signal attenuation rate and phase consistency, and at the same time use a deep learning model (such as CNN) to dynamically identify and filter out NLOS interference;

[0072] Temperature and humidity compensation: Real-time monitor the environmental temperature and humidity, correct the ultrasonic propagation speed (v = 331.4 + 0.6T + 0.0124H), and update the positioning result to ensure accuracy stability;

[0073] Trajectory generation and output: Convert the 3D pose data into a motion trajectory and output it to the host computer or control system, and this output supports multiple data formats (such as CSV, ROS messages).

[0074] a. In the above steps, the method for calculating the time difference of arrival (TDOA) is as follows:

[0075] Input the time difference of arrival (TDOA) of signals from each base station, Δt1, Δt2, Δt3,...; Output the 3D coordinates of the probe, x, y, z;

[0076] Calculate the distance difference: Δd = v * Δt, where v is the ultrasonic propagation speed;

[0077] Establish a system of equations:

[0078] √((x - x1) 2 +(y - y1) 2 +(z - z1) 2 ) - √((x - x0) 2 +(y - y0) 2 +(z - z0) 2 ) = Δd1√((x - x2) 2 +(y - y2) 2 +(z - z2) 2 ) - √((x - x0) 2 +(y - y0) 2 +(z - z0) 2 ) = Δd2 ...

[0080] Use the least squares method to solve the non - linear system of equations to obtain the initial 3D coordinates of the probe, x, y, z.

[0081] b. The method of fusing ultrasonic and IMU data using Kalman filter is as follows:

[0082] Input the ultrasonic positioning results, x_us, y_us, z_us; IMU data, including acceleration a and angular velocity ω; Output the fused 3D pose, x, y, z, θ_x, θ_y, θ_z;

[0083] State prediction: Predict the state of the probe at the next moment based on IMU data:

[0084] x_k = x_{k - 1}+v_{k - 1}*Δt + 0.5*a*Δt 2

[0085] v_k = v_{k - 1}+a*Δt;

[0086] Measurement update: Use the ultrasonic positioning results as the observation value to update the state estimate;

[0087] Error covariance update: Calculate the Kalman gain to optimize the state estimation accuracy.

[0088] c. The method of non - line - of - sight (NLOS) identification is as follows:

[0089] Input the signal attenuation rate, phase consistency, and environmental parameters; Output the signal type, LOS / NLOS;

[0090] Extract signal features: attenuation rate, phase change, multipath components;

[0091] Use a pre-trained deep learning model (such as CNN) to classify signal types;

[0092] Filter out non-line-of-sight signals and retain direct signals for positioning.

[0093] The specific applications are as follows:

[0094] Embodiment 1: Application in the motion trajectory detection of industrial robotic arms

[0095] Deploy base stations: Install 4 ultrasonic receiving base stations around the working area of the robotic arm;

[0096] Install a probe: Adsorb a mobile ultrasonic probe on the surface of the end effector of the robotic arm;

[0097] Trajectory detection: The ultrasonic probe emits 80kHz Gold code ultrasonic waves, the base stations receive and record the signal arrival time, the central processing unit calculates the TDOA, and combines the IMU data to output the 3D trajectory of the end, with an accuracy of ±0.15mm;

[0098] Dynamic calibration: Filter out metal reflection interference through the NLOS recognition algorithm, reducing the error by 40%.

[0099] Embodiment 2: Application in the flexible curved surface contour scanning

[0100] The probe fits the curved surface: Use a pneumatic flexible module to adaptively fit the surface of the automotive covering part (flexible curved surface);

[0101] Continuous motion detection: The probe moves at a speed of 10cm / s, and the phase difference algorithm generates the curved surface point cloud in real time;

[0102] Data output: Compare with the CAD model to detect the assembly error, with an accuracy of ±0.2mm.

[0103] Through ultrasonic 3D positioning and multi-modal data fusion, the present invention can achieve high-precision detection of the motion trajectory of a mobile probe on the surface of an object, can replace traditional mechanical encoders, and has broad application prospects in the fields of industrial automation, precision manufacturing, robotics, etc.

[0104] The foregoing description of the specific exemplary embodiments of the present invention is for purposes of illustration and exemplification. These descriptions are not intended to limit the invention to the precise forms disclosed, and it is apparent that many modifications and variations are possible in light of the above teachings. The purpose of selecting and describing the exemplary embodiments is to explain the specific principles of the present invention and its practical applications, so that those skilled in the art can implement and utilize the various different exemplary embodiments of the present invention, as well as various different selections and modifications. The scope of the present invention is intended to be defined by the claims and their equivalents.

Claims

1. A mobile encoder system based on ultrasonic 3D positioning, characterized in that, Including: A mobile ultrasonic probe, which integrates a micro ultrasonic transmitter array and a broadband receiving sensor; A positioning base station array, arranged around the detection area for receiving ultrasonic signals; A central processing unit, which is used to calculate the coordinates of the mobile ultrasonic probe in real time and generate a motion trajectory; at the same time, it suppresses multipath interference and environmental noise based on a deep learning model.

2. The mobile encoder system based on ultrasonic 3D positioning according to claim 1, characterized in that, The mobile ultrasonic probe is adsorbed on the surface of an object.

3. The mobile encoder system based on ultrasonic 3D positioning according to claim 1, wherein The mobile ultrasonic probe is built-in with an inertial measurement unit, and the inertial measurement unit includes an accelerometer, a gyroscope, and a magnetometer.

4. The mobile encoder system based on ultrasonic 3D positioning according to claim 1, characterized in that, At least 3 positioning base stations are arranged in the detection area, and the base stations achieve nanosecond-level time synchronization through the IEEE 1588 protocol.

5. A positioning method for a mobile encoder based on ultrasonic 3D positioning, characterized in that, Including the following steps: Base station deployment and synchronization, arranging at least 3 ultrasonic receiving base stations around the detection area to form a positioning network, and the base stations achieve nanosecond-level time synchronization through the IEEE 1588 protocol; Probe installation and calibration, adsorbing the mobile ultrasonic probe on the surface of an object, performing initial position calibration of the probe and the base station, and establishing a coordinate system; Ultrasonic signal transmission, the mobile ultrasonic probe transmits a pseudo-random coded ultrasonic signal, with frequencies of 40kHz, 80kHz, or 120kHz. The frequency division multiplexing (FDM) technology is used to allocate independent frequency bands for the probe to avoid signal conflicts; Base station signal reception, each base station receives the probe signal, records the time of arrival (ToA) and the received signal strength indication (RSSI), and improves the anti-multipath interference ability through Chirp spread spectrum modulation; Time difference of arrival (TDOA) calculation, calculating the distance difference between the probe and each base station according to the time difference of signal arrival between the base stations; and solving the initial 3D coordinates x, y, z of the probe through the least squares method; Phase difference assisted correction, extracting the phase difference of the continuous wave signal, calculating the micro displacement of the probe, and combining the time difference of arrival result to output a high-precision 3D pose; IMU data fusion, the mobile ultrasonic probe is built-in with an inertial measurement unit (IMU), including an accelerometer, a gyroscope, and a magnetometer, which real-time collects motion data; the ultrasonic and IMU data are fused through Kalman filtering to suppress the trajectory drift when the signal is lost; Motion trajectory prediction, based on a kinematic model, predicting the short-term motion trajectory of the probe; dynamically adjusting the positioning algorithm parameters to adapt to high-speed motion scenarios; Non-line-of-sight (NLOS) identification, distinguishing the direct signal and the reflected signal through the signal attenuation rate and phase consistency, and at the same time using a deep learning model to dynamically identify and filter out NLOS interference; Temperature and humidity compensation, real-time monitoring of the environmental temperature and humidity, correcting the ultrasonic propagation speed, and updating the positioning result to ensure accuracy stability; Trajectory generation and output, converting the 3D pose data into a motion trajectory and outputting it to the upper computer or control system.

6. The positioning method of the mobile encoder based on ultrasonic 3D positioning according to claim 5, characterized in that: The method for calculating the time difference of arrival (TDOA) is as follows: Input the time differences of arrival of signals at each base station Δt1, Δt2, Δt3,...; output the 3D coordinates x, y, z of the probe; Calculate the distance difference: Δd = v * Δt, where v is the ultrasonic propagation speed; Establish a system of equations: √((x - x1) 2 +(y - y1) 2 +(z - z1) 2 ) - √((x - x0) 2 +(y - y0) 2 +(z - z0) 2 ) = Δd1√((x - x2) 2 +(y - y2) 2 +(z - z2) 2 ) - √((x - x0) 2 +(y - y0) 2 +(z - z0) 2 ) = Δd2 ... Use the least squares method to solve the non-linear system of equations to obtain the initial 3D coordinates x, y, z of the probe.

7. The positioning method of the mobile encoder based on ultrasonic 3D positioning according to claim 5, characterized in that: The method for fusing ultrasonic and IMU data using Kalman filter is as follows: Input the ultrasonic positioning results x_us, y_us, z_us; IMU data, including acceleration a and angular velocity ω; Output the fused 3D pose x, y, z, θ_x, θ_y, θ_z; Predict the state of the probe at the next moment based on the IMU data: x_k = x_{k - 1}+v_{k - 1}*\Delta t + 0.5*a*\Delta t 2 v_k = v_{k - 1}+a*Δt; Use the ultrasonic positioning results as the observation value to update the state estimation; Calculate the Kalman gain to optimize the state estimation accuracy.

8. The positioning method of the mobile encoder based on ultrasonic 3D positioning according to claim 5, characterized in that: The method for non-line-of-sight (NLOS) identification is as follows: Input the signal attenuation rate, phase consistency, and environmental parameters; Output the signal type LOS / NLOS; Extract signal features: attenuation rate, phase change, and multipath components; Use a pre-trained deep learning model to classify the signal type; Filter out the non-line-of-sight signals and retain the direct signals for positioning.

9. The positioning method of the mobile encoder based on ultrasonic 3D positioning according to claim 5, characterized in that: The following formula is used to correct the ultrasonic propagation speed: v = 331.4 + 0.6T+0.0124H.

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