Front wheel steering angle calibration method, system and equipment of three-wheel robot and medium

Abstract

The invention discloses a front wheel steering angle calibration method, system and device of a three-wheel robot and a medium, and aims to solve the problems of zero drift, mechanical error accumulation and insufficient calibration reliability of an existing scheme. The method comprises the steps of monitoring the motion state of the robot in real time, and collecting speed information of left and right rear wheels when the robot is stable; calculating a front wheel steering angle instantaneous estimate based on a kinematic model, such as a three-wheel differential kinematic model; accumulating a plurality of instantaneous estimation values in a multi-stable state within a preset time period; determining a reliable estimation value through a dispersion index and a sample number; a front wheel steering angle is calibrated using a smooth fusion algorithm based on the value. According to the method, hardware does not need to be newly added, high-precision self-calibration is achieved through an inherent sensor, robustness is high, errors can be dynamically compensated, the track precision and stability of long-term operation of the robot are remarkably improved, and the method is suitable for sweeping robots, service robots and other equipment.

Description

Technical Field

[0001] This application belongs to the field of three-wheeled robot technology, and in particular relates to a method, system, device and medium for calibrating the front wheel steering angle of a three-wheeled robot. Background Technology

[0002] Three-wheeled mobile chassis have become the mainstream chassis type in service robots, robotic vacuum cleaners, and other fields due to their advantages such as simple structure, flexible steering, and controllable cost. In this structure, the accuracy of the robot's motion trajectory and operational reliability are highly dependent on the performance of the front wheel steering system—the real-time and accurate acquisition of the actual steering angle of the front wheels is the core prerequisite for achieving precise trajectory control, actively suppressing deviation, and improving overall navigation accuracy. Therefore, how to obtain the true physical steering angle of the front wheels while balancing cost and reliability has become a key technical problem that urgently needs to be solved in this field.

[0003] Currently, there are two main methods for obtaining the front wheel steering angle: the first is to indirectly obtain the steering angle by directly using a relative encoder installed on the front wheel steering motor. This method has problems such as zero-point drift and mechanical errors, and is also costly. The second method is to calculate the change in vehicle heading angle by using the speed difference between the left and right rear wheels. However, this method cannot provide information on the front wheel steering angle and cannot be directly used for front wheel calibration control. In addition, long-term operation will lead to integral accumulation error.

[0004] These existing technical solutions cannot meet the requirements of low cost, high precision, and stability, especially in achieving effective estimation and calibration of the front wheel steering angle without the need for additional hardware.

[0005] In summary, existing technologies present a dilemma: direct measurement methods can acquire steering angle signals, but their long-term reliability is insufficient; indirect calculation methods are inexpensive, but the output information does not match the control target, failing to achieve high-precision correction. Therefore, the industry urgently needs an innovative solution that, without adding additional dedicated sensors (such as front wheel absolute angle sensors or IMUs), fully utilizes existing basic sensors on the robot chassis (such as rear wheel speed sensors) to estimate the actual physical steering angle of the front wheels in real time and accurately, providing reliable feedback to the control system and fundamentally solving the problem of deviation caused by hardware drift and mechanical wear. Summary of the Invention

[0006] The technical problem to be solved by this application is to provide a method, system, device and medium for calibrating the front wheel steering angle of a three-wheeled robot, so as to solve the problems mentioned in the background art.

[0007] To address the aforementioned technical problems, this application provides the following technical solution: In a first aspect, this application provides a method for calibrating the front wheel steering angle of a three-wheeled robot, including: The motion state of the three-wheeled robot is monitored in real time, and the speed information of the left and right rear wheels of the three-wheeled robot is obtained when the three-wheeled robot enters a stable motion state. Based on the speed information of the left and right rear wheels and the kinematic model, the instantaneous estimated value of the front wheel steering angle is calculated. The kinematic model includes a three-wheel differential kinematic model or an Ackermann steering model. The motion state of the three-wheeled robot is continuously monitored, and multiple instantaneous estimates of the front wheel steering angle are obtained under multiple stable motion states within a preset first time period. Based on the dispersion index of the multiple instantaneous estimates and the number of instantaneous estimates, it is determined whether the multiple instantaneous estimates are reliable estimates; If the value is a reliable estimate, then the steering angle of the front wheel of the three-wheeled robot is calibrated based on the reliable estimate.

[0008] Furthermore, the condition for entering a stable motion state is: The first condition is: the fluctuation value of the steering angle of the front wheel of the three-wheeled robot within a preset second time period is less than the first threshold. The second condition is: the fluctuation value of the centerline velocity of the three-wheeled robot during the second time period is less than the second threshold. When both of the above conditions are met simultaneously, the three-wheeled robot is considered to have entered a stable motion state.

[0009] Furthermore, obtaining the speed information of the left and right rear wheels of the three-wheeled robot includes: Obtain the pulse frequencies of the left and right rear wheels; Based on the pulse frequency and the wheel diameters of the left and right rear wheels, the linear velocities of the left and right rear wheels are calculated.

[0010] Furthermore, based on the speed information of the left and right rear wheels and the kinematic model, the instantaneous estimate of the front wheel steering angle is calculated using the following formula: θ_diff = function(v_L, v_R, L, d) Where θ_diff is the instantaneous estimate of the front wheel steering angle, v_L is the left rear wheel steering angle, v_R is the rear wheel track, L is the rear wheel track width, and d is the front and rear axle track width.

[0011] Further, determining whether the multiple instantaneous estimates are reliable estimates based on the dispersion index of the multiple instantaneous estimates and the number of instantaneous estimates includes: Calculate the dispersion index parameters of the plurality of instantaneous estimates, wherein the dispersion index includes at least one of variance, standard deviation, range and coefficient of variation; If the dispersion index parameter is less than a preset index parameter threshold and the number of instantaneous estimates is greater than a preset third threshold, then the multiple instantaneous estimates are considered reliable estimates.

[0012] Furthermore, the calibration of the front wheel steering angle of the three-wheeled robot based on the reliable estimate includes: Calculate the mean of the reliable estimates. The mean value and the current front wheel steering angle are weighted and fused to obtain the calibrated front wheel steering angle; The front wheel steering angle is calibrated based on the calibrated front wheel steering angle.

[0013] Furthermore, the weighted fusion of the mean and the current front wheel steering angle to obtain the calibrated front wheel steering angle is performed using the following calculation formula: θ_calibrated = (1 - α) * θ_current + α * μ Where θ_calibrated is the calibrated front wheel steering angle; α is the weighting factor; θ_current is the current front wheel steering angle; and μ is the mean.

[0014] Secondly, this application also provides a front wheel steering angle calibration system for a three-wheeled robot, comprising: The first monitoring module is used to monitor the motion state of the three-wheeled robot in real time, and to obtain the speed information of the left and right rear wheels of the three-wheeled robot when the three-wheeled robot enters a stable motion state. The estimation calculation module is used to calculate the instantaneous estimated value of the front wheel steering angle based on the speed information of the left and right rear wheels and the kinematic model. The kinematic model includes a three-wheel differential kinematic model or an Ackermann steering model. The second monitoring module is used to continuously monitor the motion state of the three-wheeled robot and obtain multiple instantaneous estimates of the front wheel steering angle corresponding to multiple stable motion states within a preset time period. The judgment module is used to determine whether the multiple instantaneous estimates are reliable estimates based on the dispersion index of the multiple instantaneous estimates and the number of instantaneous estimates; The calibration module is used to calibrate the front wheel steering angle of the three-wheeled robot based on the reliable estimate if the estimate is reliable.

[0015] Thirdly, this application also provides a computer electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the front wheel steering angle calibration method for a three-wheeled robot as described in any of the above-mentioned methods.

[0016] Fourthly, this application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the front wheel steering angle calibration method for a three-wheeled robot as described in any of the preceding claims.

[0017] The present application provides a method, system, device, and medium for calibrating the front wheel steering angle of a three-wheeled robot, the advantages of which are: 1. Low-cost solution to core technical challenges: This application does not require additional dedicated hardware such as front wheel absolute angle sensors and IMUs. It only uses the rear wheel speed sensors (originally used for speed measurement and mileage calculation) inherent in the three-wheeled robot chassis to estimate and calibrate the front wheel steering angle. This completely avoids the problems of increased hardware costs and system complexity caused by existing high-precision encoder solutions for the front wheels. It achieves high-precision control while controlling costs, and has significant economic advantages.

[0018] 2. Completely eliminate the hidden danger of long-term deviation: This application establishes a "rear wheel sensor redundancy and cross-verification" mechanism, which uses the rear wheel speed data to back-calculate the true physical steering angle of the front wheel in real time. It can dynamically compensate for the zero-point drift of the front wheel relative to the encoder (such as zero-point offset caused by power-on, slippage, or collision) as well as the nonlinear error caused by mechanical wear of the steering mechanism and assembly tolerance. It fundamentally solves the core defects of existing direct measurement schemes, such as the inability to self-correct errors and deviation of straight-line walking after long-term operation, and greatly improves the trajectory accuracy and stability of the robot during long-term operation.

[0019] 3. Precisely matching control requirements and improving the effectiveness of deviation correction: Unlike the existing rear wheel differential odometer solution which can only calculate the vehicle's heading angle, this application directly outputs the actuator status feedback signal required for front wheel steering angle control. It can be directly connected to the front wheel steering closed-loop control system, providing a precise control basis for actively suppressing small and continuous deviations. This effectively avoids the problems of mismatch between the heading angle calculation and the front wheel steering angle control target, and error accumulation, meeting the application requirements of high-precision straight-line maintenance and trajectory tracking. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a flowchart illustrating a method for calibrating the front wheel steering angle of a three-wheeled robot according to an embodiment of this application. Figure 2 This is a schematic diagram of the structure of a front wheel steering angle calibration system for a three-wheeled robot according to an embodiment of this application; Figure 3 This is a schematic diagram of the structure of a computer electronic device according to an embodiment of this application. Detailed Implementation

[0022] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0023] Currently, the technical solutions for obtaining the front wheel steering angle in the industry are mainly divided into two categories, but both have inherent limitations that are difficult to overcome: I. Direct Measurement Scheme Based on Front Wheel Steering Axle Sensor This solution represents the current mainstream technology. Its core logic involves integrating a high-precision relative encoder onto the drive motor (such as a servo motor or stepper motor) of the front wheel steering mechanism. By measuring the rotation angle of the motor shaft, it indirectly calculates and controls the physical steering angle of the front wheels. Its advantages lie in its clear control loop and direct response, but it suffers from the following fundamental drawbacks: System error accumulation and zero point drift: The relative encoder cannot record the absolute position. Every time the robot is powered on, or when the wheels slip or collide during operation, causing the motor to stall, the correspondence between the encoder count value and the actual physical angle (i.e., the "zero point") is easily lost or shifted. This leads to a systematic deviation between the preset steering angle and the actual steering angle. Moreover, this deviation cannot be detected and corrected by the sensor itself. Ultimately, it manifests as an irreversible deviation in straight-line walking after long-term operation of the robot. Mechanical error interference: Gear backlash, wear of connecting rod hinge points and assembly tolerances in the steering mechanism will introduce nonlinear errors between encoder readings and the actual wheel direction. The encoder cannot detect this type of mechanical transmission error, which causes the control accuracy to continuously decrease over time. Increased cost and complexity: High-precision relative encoders and their associated processing circuits significantly increase hardware costs and system complexity.

[0024] II. Indirect Heading Calculation Scheme Based on Rear Wheel Differential Odometer To circumvent the limitations of front wheel sensors, this solution abandons directly measuring the front wheel angle and instead utilizes the wheel speed pulse sensors standard on the dual rear drive wheels (originally used for speed measurement and mileage calculation). It calculates the instantaneous rotational angular velocity of the robot by measuring the speed difference between the two wheels, and then integrates this to obtain the heading angle change. Its advantage lies in requiring no additional hardware costs, but it has the following core limitations: Misalignment of the estimated object: This scheme calculates the change in the heading angle of the robot's center of mass, rather than the steering angle of the front wheels. Although there is a dynamic relationship between the front wheel steering angle and the rate of change of the vehicle's heading angle in a geometrically defined model for three-wheeled vehicles, the accuracy of this model drops significantly under actual working conditions such as ground slippage and tire deformation, resulting in a significant deviation between the estimated result and the actual steering angle of the front wheels. Unable to be directly used for correction control: Motion control systems require direct actuator state feedback from the front wheel steering angle. Simply relying on changes in the yaw angle cannot directly and accurately reverse or control the specific steering angle of the front wheels in a closed loop. Especially when actively suppressing minor, persistent deviations caused by mechanical wear and zero-point drift, this solution lacks sufficient control basis, and the error accumulates continuously during the integration process, failing to meet the requirements for high-precision straight-line maintenance.

[0025] The technical solution of this application and how it solves the above-mentioned technical problems will be described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes will not be repeated in some embodiments. The embodiments of this application will be described below with reference to the accompanying drawings.

[0026] Please see Figure 1 The present application provides a method for calibrating the front wheel steering angle of a three-wheeled robot, which includes at least the following steps: S10. Monitor the motion state of the three-wheeled robot in real time, and when the three-wheeled robot enters a stable motion state, obtain the speed information of the left and right rear wheels of the three-wheeled robot.

[0027] In one embodiment of this application, the condition for entering a stable motion state is: The first condition is: the fluctuation value of the steering angle of the front wheel of the three-wheeled robot within a preset second time period is less than the first threshold. The second condition is: the fluctuation value of the centerline velocity of the three-wheeled robot during the second time period is less than the second threshold. When both of the above conditions are met simultaneously, the three-wheeled robot is considered to have entered a stable motion state.

[0028] Specifically, in this application, the system first needs to determine whether the robot has entered a "stable motion state" suitable for angle estimation. This determination is based on the simultaneous fulfillment of the following two conditions: The first condition is stable front wheel steering angle control: the target steering angle command value sent by the control unit to the front wheel steering motor (based on the closed-loop setpoint feedback from the encoder) changes by less than a first threshold ε_δ within a preset time period (e.g., T1 seconds). This indicates that the system is attempting to maintain a fixed steering angle.

[0029] The second condition is stable longitudinal motion speed: the variation of the vehicle's centerline speed, calculated from the speeds of the two rear wheels, within the same preset time period is less than a second threshold ε_v. This indicates that the robot is moving at a near-constant speed.

[0030] When the above conditions are met simultaneously, the system determines that the robot has entered a "quasi-steady state" motion segment. At this time, the theoretical turning angle of the front wheel and the vehicle speed are relatively constant, providing a reliable observation window for subsequent angle estimation based on wheel speed difference.

[0031] It should be noted that, to make the determination of stable motion state more accurate, the angular velocity signal of a low-cost IMU can be introduced. When the angular velocity is close to zero and consistent with the angular velocity calculated from the wheel speed difference, the reliability of the "stable motion state" criterion can be further enhanced.

[0032] It should be noted that, in one embodiment of this application, another method for determining a stable motion state can also be used, namely, a stable state can be triggered by a specific preset action. For example, the control system actively controls the robot to perform a "standard straight-line driving" or "standard radius circular motion", and data collection is forcibly performed during this preset action.

[0033] Secondly, when the three-wheeled robot enters a stable motion state, it is necessary to obtain the speed information of the robot's left and right rear wheels in that state.

[0034] In one embodiment of this application, obtaining the speed information of the left and right rear wheels of the three-wheeled robot includes: obtaining the pulse frequency of the left and right rear wheels; and calculating the linear velocity of the left and right rear wheels based on the pulse frequency and the wheel diameter of the left and right rear wheels.

[0035] Specifically, at each moment of motion determined to be stable (sampling point), the system synchronously reads the pulse frequency f_L of the left rear wheel and the pulse frequency f_R of the right rear wheel, and calculates the linear velocity v_L and v_R of the left rear wheel by combining the wheel diameters of the left and right rear wheels.

[0036] S20. Based on the speed information of the left and right rear wheels and the kinematic model, calculate the instantaneous estimated value of the front wheel steering angle, wherein the kinematic model includes: a three-wheel differential kinematic model or an Ackermann steering model.

[0037] Specifically, when the three-wheeled robot enters a stable motion state, according to a kinematic model, such as a three-wheeled differential kinematic model, under ideal conditions where slippage is ignored, an instantaneous wheel speed difference angle θ_diff can be calculated from the speed difference between the left and right rear wheels. This angle reflects the steering angle that the front wheels should possess to generate the observed wheel speed difference under the current geometric structure and kinematic constraints. The simplified expression of its calculation formula is as follows: θ_diff = function(v_L, v_R, L, d) Where θ_diff is the instantaneous estimate of the front wheel steering angle, v_L is the left rear wheel's angle, v_R is the front wheel's angle, L is the rear wheel track, and d is the front and rear axle track. This function originates from the kinematic relationship of a three-wheeled vehicle, namely, the vehicle's angular velocity ω = (v_R - v_L) / L, and the front wheel steering angle δ satisfies the geometric relationship with ω and vehicle speed V (V = (v_R + v_L) / 2): tan(δ) ≈ ω * d / V (approximate for small angles). Through this model, the measured v_L and v_R can be mapped to the estimated front wheel steering angle θ_diff.

[0038] It should be noted that the kinematic model is not limited to the three-wheel differential kinematic model or the Ackermann steering model, but can also be other kinematic models, such as dynamic models that include tire slip factors.

[0039] S30. Continuously monitor the motion state of the three-wheeled robot and obtain multiple instantaneous estimates of the front wheel steering angle corresponding to multiple stable motion states within a preset first time period.

[0040] Specifically, by extending the data acquisition cycle and expanding the sample coverage, the accuracy of subsequent reliability assessment and calibration can be improved. The first time period is set based on the following: The first time period is preset to 1 minute (which can be dynamically adjusted according to the robot's operating scenario, such as taking a longer time when moving in a straight line and a shorter time when turning), to ensure that multiple complete and stable motion segments are covered and to avoid the influence of random errors under a single working condition.

[0041] Data acquisition and filtering mechanism: During the first time period, the robot's motion state is continuously monitored. Whenever the stable motion criterion of S10 is met again, the calculation process of S20 is repeated to obtain a new instantaneous estimate. If the motion state deviates from the stable range during the period (such as slippage, collision, or sudden change in steering angle), the data acquisition of that segment is paused. Data acquisition continues after the robot re-enters the stable state to ensure that each instantaneous estimate comes from a valid working condition.

[0042] Data storage and management: All collected instantaneous estimates are stored in a cache according to time series, and the collection time, current wheel speed, steering status and other related information corresponding to each data point are recorded to form a dataset containing N samples (N≥ minimum effective sample number, such as 20), providing a complete data foundation for subsequent statistical analysis.

[0043] S40. Based on the dispersion index of the multiple instantaneous estimates and the number of instantaneous estimates, determine whether the multiple instantaneous estimates are reliable estimates.

[0044] Specifically, step S40 includes: S401. Calculate the dispersion index parameters of the plurality of instantaneous estimates, wherein the dispersion index includes at least one of variance, standard deviation, range and coefficient of variation.

[0045] S402. If the dispersion index parameter is less than a preset index parameter threshold and the number of instantaneous estimates is greater than a preset third threshold, then the multiple instantaneous estimates are considered reliable estimates.

[0046] Specifically, in this step, a multi-level reliability verification mechanism is introduced to filter out outlier data and ensure that the estimates used for calibration have high reliability: Dispersion index calculation and selection: Statistical analysis is performed on the N instantaneous estimates accumulated by S30 to calculate the dispersion index—one or more combinations of variance (σ²), standard deviation (σ), range (the difference between the maximum and minimum values), or coefficient of variation (the ratio of standard deviation to mean). Among these, variance is the preferred index, as it directly reflects the degree of data dispersion; the smaller the variance, the better the data consistency and the less noise interference.

[0047] Reliability determination criteria: A dual verification standard is set. Multiple instantaneous estimates corresponding to the dataset are considered "reliable estimates" only if the following conditions are met simultaneously: The dispersion index meets the standard: the calculated dispersion index is less than the preset threshold (such as the variance threshold σ_th²=0.01°², which can be adjusted according to the sensor accuracy and the complexity of the working conditions), indicating that there is no obvious abnormal fluctuation in the data; It should be noted that the variance threshold can be dynamically adjusted, for example, adaptively changing based on vehicle speed, ground type (carpet / hard floor), or historical variance sequence.

[0048] Sufficient sample size: The total number of instantaneous estimates N ≥ the minimum effective sample size N_min (e.g., N_min=50, to ensure statistical significance and avoid mean bias caused by insufficient sample size).

[0049] Outlier handling (optional): If the dispersion index exceeds the threshold, the outlier removal process can be initiated—outlier data exceeding 3 times the standard deviation can be removed through residual analysis (calculating the deviation between each instantaneous estimate and the preliminary mean), and the dispersion index and sample size can be recalculated; if the judgment criteria still cannot be met after removal, the current dataset can be discarded and the process can return to S10 to start data accumulation again.

[0050] S50. If it is a reliable estimate, then the steering angle of the front wheel of the three-wheeled robot is calibrated based on the reliable estimate.

[0051] In one embodiment of this application, step S50 includes: S501. Calculate the mean of the reliable estimates.

[0052] S502. The mean value and the current front wheel steering angle are weighted and fused to obtain the calibrated front wheel steering angle.

[0053] S503. Based on the calibrated front wheel steering angle, calibrate the front wheel steering angle.

[0054] Specifically, this step employs a smooth and safe calibration strategy to achieve precise correction of the front wheel steering angle while ensuring the robot's operational stability. The specific process is as follows: Determining the reliable estimate: If S40 determines that the dataset is reliable, then the arithmetic mean μ of the dataset is calculated and used as the "reliable estimate" - this mean integrates the measurement results under multiple stable operating conditions, which can effectively cancel random noise and approximate the true steering angle of the front wheels.

[0055] Calibration update strategy: A smooth fusion algorithm is used to update the front wheel steering angle reference value to avoid robot jitter caused by sudden angle changes during calibration. The preferred solution is weighted average fusion. θ_calibrated = (1 - α)×θ_current + α×μ Where θ_calibrated is the calibrated front wheel steering angle, θ_current is the reference value of the front wheel steering angle currently used by the system (such as the encoder feedback value), α is the weighting factor (0<α≤1, usually taken as 0.1-0.3, μ is the mean value, realizing the gradual update of the calibration value and balancing response speed and smoothness); other fusion algorithms such as first-order low-pass filtering can also be used to ensure a smooth transition in the calibration process.

[0056] Calibration Applications and Validation: Real-time closed-loop feedback: The calibrated steering angle θ_calibrated is output to the front wheel steering control system as the actual steering angle feedback quantity. It is compared with the target steering angle to generate a correction control signal to compensate for errors caused by encoder zero drift, mechanical wear, etc. It should be noted that the updated calibration can take effect immediately, or the calibration value can be stored as an "offset lookup table" and interpolated and compensated according to the current steering angle command.

[0057] In one embodiment of this application, in order to make the calibration more accurate, a "safety confirmation" step is added before the calibration takes effect. For example, after applying the calibration value, the actual trajectory of the robot is briefly observed to see if it meets the expectations. If it does, it is confirmed; if it does not, it is rolled back.

[0058] In an optional embodiment of this application, zero-position calibration may also be included: if the calibration scenario is straight driving (target steering angle is 0°), the reliable estimated value μ is stored as the zero-position offset, and the offset is directly superimposed in subsequent operation for real-time compensation, thereby solving the straight-line deviation problem from the root. In an optional embodiment of this application, model parameter correction may also be included: feeding back reliable estimates to the kinematic model and adjusting parameters such as the equivalent wheelbase d online to make the model more closely match the actual operating state of the robot and further improve the accuracy of subsequent angle calculations.

[0059] Calibration closed-loop optimization: After calibration, continuously monitor the robot's steering accuracy and running trajectory. If the dispersion of the subsequently collected instantaneous estimates increases again or the trajectory deviates, the S10-S50 process can be repeated to achieve periodic and adaptive calibration of the front wheel steering angle, ensuring long-term operating accuracy.

[0060] The method for calibrating the front wheel steering angle of a three-wheeled robot provided in this application has the following advantages: 1. Low-cost solution to core technical challenges: This application does not require additional dedicated hardware such as front wheel absolute angle sensors and IMUs. It only uses the rear wheel speed sensors (originally used for speed measurement and mileage calculation) inherent in the three-wheeled robot chassis to estimate and calibrate the front wheel steering angle. This completely avoids the problems of increased hardware costs and system complexity caused by existing high-precision encoder solutions for the front wheels. It achieves high-precision control while controlling costs, and has significant economic advantages.

[0061] 2. Completely eliminate the hidden danger of long-term deviation: This application establishes a "rear wheel sensor redundancy and cross-verification" mechanism, which uses the rear wheel speed data to back-calculate the true physical steering angle of the front wheel in real time. It can dynamically compensate for the zero-point drift of the front wheel relative to the encoder (such as zero-point offset caused by power-on, slippage, or collision) as well as the nonlinear error caused by mechanical wear of the steering mechanism and assembly tolerance. It fundamentally solves the core defects of existing direct measurement schemes, such as the inability to self-correct errors and deviation of straight-line walking after long-term operation, and greatly improves the trajectory accuracy and stability of the robot during long-term operation.

[0062] 3. Precisely matching control requirements and improving the effectiveness of deviation correction: Unlike the existing rear wheel differential odometer solution which can only calculate the vehicle's heading angle, this application directly outputs the actuator status feedback signal required for front wheel steering angle control. It can be directly connected to the front wheel steering closed-loop control system, providing a precise control basis for actively suppressing small and continuous deviations. This effectively avoids the problems of mismatch between the heading angle calculation and the front wheel steering angle control target, and error accumulation, meeting the application requirements of high-precision straight-line maintenance and trajectory tracking.

[0063] Please see Figure 2 This application also provides a front wheel steering angle calibration system 200 for a three-wheeled robot, comprising: The first monitoring module 201 is used to monitor the motion state of the three-wheeled robot in real time, and to obtain the speed information of the left and right rear wheels of the three-wheeled robot when the three-wheeled robot enters a stable motion state. The estimation calculation module 202 is used to calculate the instantaneous estimated value of the front wheel steering angle based on the speed information of the left and right rear wheels and the kinematic model. The kinematic model includes a three-wheel differential kinematic model and an Ackermann steering model. The second monitoring module 203 is used to continuously monitor the motion state of the three-wheeled robot and obtain multiple instantaneous estimates of the front wheel steering angle corresponding to multiple stable motion states within a preset time period. The judgment module 204 is used to determine whether the multiple instantaneous estimates are reliable estimates based on the dispersion index of the multiple instantaneous estimates and the number of instantaneous estimates; The calibration module 205 is used to calibrate the front wheel steering angle of the three-wheeled robot based on the reliable estimate if it is a reliable estimate.

[0064] Please see Figure 3 This application also provides a computer electronic device 300, including a memory 303 and a processor 302. The memory 303 stores a computer program, and the processor 302 executes the computer program to implement the steps of the front wheel steering angle calibration method for the three-wheeled robot described in any of the above claims.

[0065] Specifically, the computer electronic device 300 includes a transceiver 301, a bus interface, and a processor 302. The processor 302 is used to monitor the motion state of the three-wheeled robot in real time, and when the three-wheeled robot enters a stable motion state, acquire the speed information of the left and right rear wheels of the three-wheeled robot; calculate the instantaneous estimated value of the front wheel steering angle based on the speed information of the left and right rear wheels and a kinematic model, wherein the kinematic model includes a three-wheel differential kinematic model or an Ackermann steering model; continuously monitor the motion state of the three-wheeled robot, and acquire multiple instantaneous estimated values ​​of the front wheel steering angle corresponding to multiple stable motion states within a preset first time period; determine whether the multiple instantaneous estimated values ​​are reliable estimates based on the dispersion index and the number of instantaneous estimated values; if they are reliable estimates, calibrate the front wheel steering angle of the three-wheeled robot based on the reliable estimates.

[0066] In this embodiment of the application, the computer electronic device 300 further includes a memory 303. Figure 3 In this context, the bus architecture can include any number of interconnected buses and bridges, specifically linking various circuits of one or more processors 302 (represented by processor 302) and various circuits of memory 303 (represented by memory 303). The bus architecture can also link various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and therefore will not be described further herein. The bus interface provides an interface. The transceiver 301 can be multiple elements, including transmitters and receivers, providing a unit for communicating with various other devices over a transmission medium. The processor 302 is responsible for managing the bus architecture and general processing, and the memory 303 can store data used by the processor 302 during operation.

[0067] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the three-wheeled robot front wheel steering angle calibration method described above.

[0068] In this embodiment, the computer-readable storage medium can be a non-volatile storage medium or a volatile storage medium. For example, the computer storage medium may include, but is not limited to, various media capable of storing program code, such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0069] In all examples shown and described herein, any specific values ​​should be interpreted as merely exemplary and not as limitations; therefore, other examples of exemplary embodiments may have different values.

[0070] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0071] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings show the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that, in alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and combinations of blocks in the block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0072] In addition, the functional modules or units in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0073] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a terminal device (which may be a smartphone, personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application.

[0074] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A method for calibrating the front wheel steering angle of a three-wheeled robot, characterized in that, include: The motion state of the three-wheeled robot is monitored in real time, and the speed information of the left and right rear wheels of the three-wheeled robot is obtained when the three-wheeled robot enters a stable motion state. Based on the speed information of the left and right rear wheels and the kinematic model, the instantaneous estimated value of the front wheel steering angle is calculated. The kinematic model includes a three-wheel differential kinematic model or an Ackermann steering model. The motion state of the three-wheeled robot is continuously monitored, and multiple instantaneous estimates of the front wheel steering angle are obtained under multiple stable motion states within a preset first time period. Based on the dispersion index of the multiple instantaneous estimates and the number of instantaneous estimates, it is determined whether the multiple instantaneous estimates are reliable estimates; If the value is a reliable estimate, then the steering angle of the front wheel of the three-wheeled robot is calibrated based on the reliable estimate.

2. The front wheel steering angle calibration method according to claim 1, characterized in that, The conditions for entering a stable motion state are: The first condition is: the fluctuation value of the steering angle of the front wheel of the three-wheeled robot within a preset second time period is less than the first threshold. The second condition is: the fluctuation value of the centerline velocity of the three-wheeled robot during the second time period is less than the second threshold. When both of the above conditions are met simultaneously, the three-wheeled robot is considered to have entered a stable motion state.

3. The front wheel steering angle calibration method according to claim 1, characterized in that, The process of obtaining the speed information of the left and right rear wheels of the three-wheeled robot includes: Obtain the pulse frequencies of the left and right rear wheels; Based on the pulse frequency and the wheel diameters of the left and right rear wheels, the linear velocities of the left and right rear wheels are calculated.

4. The front wheel steering angle calibration method according to claim 1, characterized in that, Based on the speed information of the left and right rear wheels and the aerodynamic model, the instantaneous estimate of the front wheel steering angle is calculated using the following formula: θ_diff = function(v_L, v_R, L, d) Where θ_diff is the instantaneous estimate of the front wheel steering angle, v_L is the left rear wheel steering angle, v_R is the rear wheel track, L is the rear wheel track width, and d is the front and rear axle track width.

5. The front wheel steering angle calibration method according to claim 1, characterized in that, The step of determining whether the multiple instantaneous estimates are reliable estimates based on the dispersion index of the multiple instantaneous estimates and the number of instantaneous estimates includes: Calculate the dispersion index parameters of the plurality of instantaneous estimates, wherein the dispersion index includes at least one of variance, standard deviation, range and coefficient of variation; If the dispersion index parameter is less than a preset index parameter threshold and the number of instantaneous estimates is greater than a preset third threshold, then the multiple instantaneous estimates are considered reliable estimates.

6. The front wheel steering angle calibration method according to claim 1, characterized in that, The calibration of the front wheel steering angle of the three-wheeled robot based on the reliable estimate includes: Calculate the mean of the reliable estimates. The mean value and the current front wheel steering angle are weighted and fused to obtain the calibrated front wheel steering angle; The front wheel steering angle is calibrated based on the calibrated front wheel steering angle.

7. The front wheel steering angle calibration method according to claim 6, characterized in that, The weighted fusion of the mean and the current front wheel steering angle is used to obtain the calibrated front wheel steering angle, and the following calculation formula is used: θ_calibrated = (1 - α) * θ_current + α * μ Where θ_calibrated is the calibrated front wheel steering angle; α is the weighting factor; θ_current is the current front wheel steering angle; and μ is the mean.

8. A front wheel steering angle calibration system for a three-wheeled robot, characterized in that, include: The first monitoring module is used to monitor the motion state of the three-wheeled robot in real time, and to obtain the speed information of the left and right rear wheels of the three-wheeled robot when the three-wheeled robot enters a stable motion state. The estimation calculation module is used to calculate the instantaneous estimated value of the front wheel steering angle based on the speed information of the left and right rear wheels and the kinematic model. The kinematic model includes a three-wheel differential kinematic model or an Ackermann steering model. The second monitoring module is used to continuously monitor the motion state of the three-wheeled robot and obtain multiple instantaneous estimates of the front wheel steering angle corresponding to multiple stable motion states within a preset time period. The judgment module is used to determine whether the multiple instantaneous estimates are reliable estimates based on the dispersion index of the multiple instantaneous estimates and the number of instantaneous estimates; The calibration module is used to calibrate the front wheel steering angle of the three-wheeled robot based on the reliable estimate if the estimate is reliable.

9. A computer electronic device, characterized in that, The device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of the front wheel steering angle calibration method for the three-wheeled robot according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the front wheel steering angle calibration method for the three-wheeled robot according to any one of claims 1-7.

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