Linear path control method and system suitable for limited structured environment

Abstract

The invention discloses a linear path control method and system suitable for a limited structured environment. The method comprises the steps that first relative pose information of a vehicle body and boundaries on the two sides, second movement pose information of the vehicle body and third movement mileage information of left and right wheels are obtained; preprocessing the three types of information to respectively extract at least one distance characteristic quantity, at least one angle characteristic quantity and at least one mileage characteristic quantity which represent the vehicle body path deviation; the distance characteristic quantity, the angle characteristic quantity and the mileage characteristic quantity are subjected to weighted fusion, a comprehensive path deviation quantity is generated, and the distance characteristic quantity participates in fusion based on a normalized value of the distance characteristic quantity and a preset expected distance; and with the comprehensive path deviation value as a control target, a driving signal is calculated through a closed-loop controller, and differential control is carried out on left and right driving wheels of the vehicle body so as to maintain the vehicle body to advance along a preset linear path.

Description

Technical Field

[0001] This application relates to the field of automatic navigation and control technology, and more specifically, to a high-precision straight-line path control method and system for unmanned transport platforms applicable to confined structured environments such as docks and tunnels. Background Technology

[0002] In industrial environments such as shipyards, unmanned transport platforms (such as automated painting vehicles) need to travel in a straight line along a pre-set path (e.g., between two stone pillars). However, due to mechanical errors in the vehicle itself (such as tire angle and motor performance differences) and environmental factors, vehicles are prone to deviating from the predetermined path when operating without human intervention. Taking automated painting of ship hulls as an example, path deviation can cause the painted area to be skewed, heavily relying on subsequent manual touch-ups and reducing operational efficiency.

[0003] Currently, common automatic navigation solutions such as visual navigation or LiDAR SLAM (Simultaneous Localization and Mapping) have significant limitations in the specific environment of a shipyard. The environment is usually dimly lit, and reference objects (such as cargo and equipment) frequently change, resulting in unstable visual features. LiDAR SLAM, on the other hand, requires frequent remapping, leading to high implementation costs and low efficiency. Summary of the Invention

[0004] To address the aforementioned issues, this application provides a linear path control method and system suitable for constrained structured environments, enabling stable and high-precision linear movement of unmanned transport platforms in constrained structured environments with poor lighting and unfixed reference points.

[0005] In a first aspect, embodiments of this application provide a straight path control method suitable for constrained structured environments, the method comprising:

[0006] By using a range-measuring sensor array, an onboard inertial sensor, and a wheeled odometer deployed on both sides of the vehicle body, the first relative pose information between the vehicle body and the two side boundaries, the second motion attitude information of the vehicle body itself, and the third motion mileage information of the left and right wheels are obtained respectively.

[0007] The first relative pose information, the second motion posture information, and the third motion mileage information are preprocessed to extract at least one distance feature, at least one angle feature, and at least one mileage feature representing the vehicle path deviation, respectively.

[0008] The distance feature, angle feature, and mileage feature are weighted and fused to generate a comprehensive path deviation, wherein the distance feature is fused based on its normalized value with a preset expected distance.

[0009] Using the comprehensive path deviation as the control target, the drive signal is calculated by the closed-loop controller, and differential control is implemented on the left and right drive wheels of the vehicle to maintain the vehicle traveling along a preset straight path.

[0010] Preferably, preprocessing the first relative pose information includes:

[0011] Determine whether the measurement data of each sensor in the ranging sensor array is valid, and filter out the measurement data that is determined to be invalid; the invalid data refers to data whose value indicates that the sensor has not detected the valid boundary;

[0012] Based on the remaining valid measurement data after filtering on each side, the average distance between that side and the boundary is calculated as the distance feature quantity.

[0013] Based on the difference in effective measurements of adjacent sensors on the same side and their fixed installation distance, the local tilt angle of the vehicle body relative to that side boundary is calculated, and the average of all available local tilt angles is obtained to obtain the angular characteristic quantity.

[0014] Preferably, the determination of whether the data is valid is achieved by comparing the measured data with a preset distance threshold, wherein the distance threshold is set according to the typical width of the boundary interval in the environment.

[0015] Preferably, the preprocessing of the second motion posture information includes:

[0016] A high-pass digital filter is applied to the raw angular velocity data acquired by the inertial sensor to suppress its low-frequency drift;

[0017] The filtered angular velocity signal is integrated over time, and the resulting integral value is used as the angular characteristic quantity obtained by the inertial sensor.

[0018] Preferably, preprocessing the third motion mileage information includes:

[0019] The raw displacement data collected by the wheel odometer is subjected to smoothing and filtering processing.

[0020] Calculate the ratio of the filtered displacement of the left and right wheels within the same time period, and use this ratio as the mileage feature to characterize the vehicle offset trend caused by the difference in the movement of the left and right wheels.

[0021] Preferably, the weighted fusion is achieved by assigning weight coefficients to the distance feature, angle feature, and mileage feature respectively, and the sum of their products with the weights is used as the comprehensive path deviation; wherein the distance feature participates in the summation by using its ratio to the preset expected distance or in a normalized form.

[0022] Secondly, embodiments of this application provide a straight path control system suitable for constrained structured environments, used to implement the method described in the first aspect, the system comprising:

[0023] The multi-source sensing unit includes a range sensor array deployed on both sides of the vehicle body, an inertial measurement unit located at the center of gravity of the vehicle body, and a wheel-type odometer installed on the drive wheels.

[0024] The data processing and fusion unit is configured as follows:

[0025] Receive data from the multi-source sensing unit;

[0026] Perform preprocessing steps to extract distance, angle, and odometry features;

[0027] Perform a weighted fusion step to generate a comprehensive path deviation.

[0028] A closed-loop control unit is configured to receive the comprehensive path deviation and generate differential control signals for adjusting the speeds of the left and right drive wheels.

[0029] Preferably, at least three ranging sensors are symmetrically arranged on each side of the vehicle body; the ranging sensors are distributed at least on the left and right sides of the vehicle body.

[0030] Thirdly, embodiments of this application provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method provided as in the first aspect or any possible implementation of the first aspect.

[0031] Fourthly, embodiments of this application provide a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the method provided as in the first aspect or any possible implementation thereof.

[0032] The linear path control method and system of the present invention, applicable to constrained structured environments, has the following beneficial effects:

[0033] 1. Strong environmental robustness: It does not rely on visual features and pre-built detailed maps, making it particularly suitable for industrial environments such as docks and tunnels with low lighting and frequently changing scene layouts.

[0034] 2. High control precision: By integrating data from laser ranging, IMU and odometer, it comprehensively utilizes absolute position, inertial attitude and internal motion information, which complement and verify each other to form a high-precision composite deviation signal, resulting in more accurate control.

[0035] 3. Low system cost: The sensors used (infrared laser, IMU, encoder) are all mature and low-cost devices, avoiding expensive lidar or high-performance computing units, which is conducive to large-scale application and promotion.

[0036] 4. Good real-time performance: The algorithm has low complexity and low computational load, which can meet the real-time requirements of unmanned platform movement control. Attached Figure Description

[0037] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0038] Figure 1 A flowchart illustrating a straight path control method suitable for constrained structured environments, provided as an embodiment of this application;

[0039] Figure 2 A schematic diagram of a linear path control system suitable for constrained structured environments is provided for embodiments of this application;

[0040] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0041] Figure 4 This is a control flowchart of the method described in this application.

[0042] Figure 5 This is a schematic diagram of a restricted structured environment. Detailed Implementation

[0043] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings.

[0044] In the following description, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance. The following description provides multiple embodiments of this application, which can be substituted or combined with each other. Therefore, this application can also be considered to include all possible combinations of the same and / or different embodiments described. Thus, if one embodiment includes features A, B, and C, and another embodiment includes features B and D, then this application should also be considered to include embodiments containing one or more other possible combinations of A, B, C, and D, even if such embodiments are not explicitly described in the following text.

[0045] The following description provides examples and does not limit the scope, applicability, or examples set forth in the claims. Changes may be made to the function and arrangement of the described elements without departing from the scope of this application. Various processes or components may be appropriately omitted, substituted, or added to the examples. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Furthermore, features described with respect to some examples may be combined into other examples.

[0046] See Figure 1 , Figure 1 This is a flowchart illustrating a straight path control method suitable for constrained structured environments provided in an embodiment of this application. In this embodiment, the method includes:

[0047] S101. By using the range sensor array, vehicle-mounted inertial sensor and wheel odometer deployed on both sides of the vehicle body, the first relative pose information between the vehicle body and the two side boundaries, the second motion attitude information of the vehicle body itself and the third motion mileage information of the left and right wheels are obtained respectively.

[0048] The subject of this application can be an unmanned transport platform that achieves straight-line path travel in a restricted structured environment based on data fusion from multiple heterogeneous sensors.

[0049] In this application, the confined structured environment includes, but is not limited to, docks, underground tunnels, and narrow warehouse passages. These environments share the common characteristic of having two essentially parallel, fixed physical boundaries (such as stone piers in a dock or tunnel walls), providing detectable navigational references for the unmanned platform.

[0050] To enable navigation in this environment, the "range sensor array" is specifically defined in a preferred embodiment as follows: five infrared laser rangefinders are symmetrically installed on the left and right sides of the vehicle body, and evenly distributed along the longitudinal axis of the vehicle body. The purpose of this layout is to form a continuous detection surface for lateral boundaries. Even if some lasers fail due to falling into the boundary gaps (such as the gaps between stone blocks), the remaining sensors in the array can still provide effective detection data, thereby ensuring the robustness of the system.

[0051] The vehicle's onboard inertial sensors should be sufficiently accurate; the wheel-mounted odometers should at least be divided into left and right wheels to collect relevant motion data from the left and right wheels.

[0052] In one feasible embodiment, the first relative pose information is the data collected by the ranging sensor array, the second motion attitude information is the data collected by the vehicle-mounted inertial sensor, and the third motion mileage information is the data collected by the wheel odometer.

[0053] Specifically, the first relative pose information can be composed of the original distance value sequence returned by the infrared laser rangefinder array, the second motion attitude information can be composed of the original angular velocity data collected by the inertial measurement unit (IMU), and the third motion mileage information can be composed of the original displacement pulse technology of the left and right wheels collected by the wheel odometer.

[0054] S102. Preprocess the first relative pose information, the second motion posture information and the third motion mileage information to extract at least one distance feature, at least one angle feature and at least one mileage feature representing the vehicle path deviation.

[0055] In this embodiment of the application, the above three types of information are preprocessed to extract at least one distance feature, at least one angle feature, and at least one mileage feature representing the vehicle path deviation.

[0056] Regarding the distance characteristic, in one embodiment, it refers to the arithmetic mean of all valid laser ranging values ​​on one side. This value reflects the overall distance between the vehicle body and the boundary of that side.

[0057] Regarding angular features, they are extracted from different sources. Firstly, from the laser array: based on the difference in effective measurements from adjacent sensors on the same side and their fixed installation distance A, the local tilt angle is calculated using trigonometric operations (such as the arcsine function), and then the laser offset angle α is obtained by averaging all available local tilt angles. Secondly, from the IMU: the inertial deflection angle β is obtained by integrating the filtered angular velocity.

[0058] Regarding the odometer characteristic, in one embodiment, it refers to the left and right wheel displacement ratio ε calculated after smoothing and filtering (such as mean filtering) the left and right wheel odometer data. This ratio directly reflects the vehicle's heading deviation trend caused by the difference in speed between the left and right wheels or slippage.

[0059] In one feasible embodiment, preprocessing the first relative pose information includes:

[0060] Determine whether the measurement data of each sensor in the ranging sensor array is valid, and filter out the measurement data that is determined to be invalid; invalid data refers to data whose value indicates that the sensor has not detected the valid boundary;

[0061] Based on the remaining valid measurement data after filtering on each side, the average distance between that side and the boundary is calculated as a distance feature quantity.

[0062] Based on the difference in effective measurements of adjacent sensors on the same side and their fixed installation distance, the local tilt angle of the vehicle body relative to that side boundary is calculated, and the average of all available local tilt angles is obtained to obtain the angular characteristic quantity.

[0063] The validity of the data is determined by comparing the measured data with a preset distance threshold, which is set according to the typical width of the boundary interval in the environment.

[0064] Specifically, the first relative pose information consists of the sequence of original distance values ​​returned by the infrared laser rangefinder array. In practice, the real-time measurement value of each laser rangefinder can be compared with a preset distance threshold (e.g., 4 meters).

[0065] The threshold is set based on the typical width of the boundary interval in the environment. When the measured value is greater than this threshold, it is determined that the laser beam falls into the interval, the data is invalid, and it is filtered out.

[0066] For all valid distance data remaining after filtering on one side (denoted as l1, l2, ..., ln, where 2 ≤ n ≤ 5), calculate their arithmetic mean, i.e., l = (l1 + l2 + ... + ln) / n. This average value l is the comprehensive distance feature of that side.

[0067] Trigonometric calculations are performed based on the difference (ln–l(n-1)) between the effective measurements of two adjacent laser rangefinders on the same side and the fixed installation distance A between the two sensors. In a preferred embodiment, this trigonometric calculation is performed using an arcsine function, i.e., a single local tilt angle is asin((ln-ln-1) / A). Subsequently, the arithmetic mean of all available local tilt angles is calculated to obtain the final angular characteristic α.

[0068] In one feasible embodiment, preprocessing the second motion posture information includes:

[0069] A high-pass digital filter is applied to the raw angular velocity data acquired by the inertial sensor to suppress its low-frequency drift;

[0070] The filtered angular velocity signal is integrated over time, and the resulting integral value is used as the angular characteristic quantity obtained by the inertial sensor.

[0071] The high-pass digital filter is implemented using a first-order high-pass filter algorithm.

[0072] Specifically, high-pass digital filtering is used to process IMU angular velocity data to eliminate low-frequency noise such as temperature drift and zero drift. First-order high-pass filtering is a preferred implementation, and its discrete form can be expressed as: y_n = k * y_{n-1} + k * (x_n - x_{n-1}), where k = 1 / (1 + 2πf_cT_s), y_n is the current filter output value, y_{n-1} is the previous filter output value, x_n is the current angular velocity sample value, x_{n-1} is the previous angular velocity sample value, f_c is the cutoff frequency (e.g., 0.01 seconds), and T_s is the sampling period (e.g., 0.1 Hz).

[0073] The filtered angular velocity signal y_n is integrated over time, i.e., Δβ=y_n*Δt, and the integration results are accumulated. The resulting integral value β is the angular characteristic quantity (inertial deflection angle) obtained by the inertial sensor.

[0074] In one feasible embodiment, preprocessing the third motion mileage information includes:

[0075] The raw displacement data collected by the wheel odometer is smoothed and filtered.

[0076] Calculate the ratio of the filtered displacement of the left and right wheels within the same time period, and use this ratio as a mileage feature to characterize the vehicle offset trend caused by the difference in the movement of the left and right wheels.

[0077] In a specific implementation, the smoothing process is specifically implemented using a mean filtering algorithm in a preferred embodiment, such as a three-point moving average: d_n = (d_{n-1} + d_n + d_{n+1}) / 3, where d_n is the filtered displacement value.

[0078] The mileage characteristic is obtained by calculating the ratio of the displacement of the left and right wheels after filtering, i.e., ε = d_left / d_right. This ratio ε is the mileage characteristic, which can effectively characterize the trend of vehicle heading deviation caused by the difference in speed of the left and right wheels or slippage.

[0079] S103. The distance feature, angle feature and mileage feature are weighted and fused to generate a comprehensive path deviation, wherein the distance feature participates in the fusion based on its normalized value with the preset expected distance.

[0080] Specifically, weighted fusion is achieved in the following way: weight coefficients are assigned to distance features, angle features, and mileage features respectively, and the sum of their products with the weights is used as the comprehensive path deviation; among them, the distance features participate in the summation by their ratio to the preset expected distance or in normalized form.

[0081] In the embodiments of this application, a weight coefficient (a, b, c, d) can be assigned to each feature, and their weighted sum is used as the comprehensive path deviation e. That is: e = a * (l / l_set) + b * α + c * β + d * ε. Where l is the distance feature, and l_set is the preset expected distance.

[0082] It should be clarified that the distance feature quantity, including l, can be (l_left + l_right) / 2 or a one-sided value, depending on the control strategy.

[0083] Among them, the weighting coefficients a, b, c, and d are configurable normal numbers, and their specific values ​​can be determined during on-site debugging based on the actual vehicle's power characteristics and sensor accuracy.

[0084] S104. Taking the comprehensive path deviation as the control target, the drive signal is calculated by the closed-loop controller, and differential control is implemented on the left and right drive wheels of the vehicle to maintain the vehicle moving along the preset straight path.

[0085] In the embodiments of this application, the comprehensive path deviation e is fed into a PID controller. The PID controller calculates the control quantity used to adjust the speed of the left and right wheels based on the proportional, integral, and derivative terms of the deviation e. This control quantity ultimately acts on the platform's drive system, causing a speed difference between the left and right wheels, thereby correcting the vehicle's direction and bringing the deviation e close to zero.

[0086] See Figure 4 Based on the method of this application, after the system starts up, the sensors begin to work in parallel, forming a complete closed loop of multi-source information acquisition and processing, as detailed below:

[0087] The multi-source data acquisition phase includes:

[0088] An array of infrared laser rangefinders evenly distributed on both sides of the vehicle continuously measures the distance to the side boundaries (such as stone blocks), generating a sequence of raw distance data.

[0089] An inertial measurement unit (IMU) located at the vehicle's center of mass continuously measures the vehicle's three-axis angular velocities, generating raw angular velocity data.

[0090] The wheel-type odometers on the left and right drive wheels continuously measure the displacement pulses generated by the rotation of the wheels, producing raw displacement data.

[0091] The parallel data preprocessing and feature extraction stages include:

[0092] On the laser data processing side, the following processes are included:

[0093] Validity assessment and filtering: The control unit reads data from each laser sensor on both sides. For any data point, if its value is greater than a preset distance threshold (e.g., 4 meters), it is determined to be invalid and filtered out.

[0094] Distance feature extraction: For the remaining valid data (the number n satisfies 2≤n≤5) after filtering on each side, calculate its arithmetic mean l, which is used as the distance feature of that side.

[0095] Angular feature extraction: Based on the difference (l_i-l_{i-1}) between two adjacent effective laser ranging values ​​on the same side and their fixed installation distance A, the local tilt angle is calculated by arcsine function, and the laser offset angle α on that side is obtained by averaging all available local tilt angles.

[0096] On the IMU data processing side, the following processes are included:

[0097] High-pass filtering: Apply a first-order high-pass digital filter (parameters: cutoff frequency f_c, sampling period T_s) to the raw angular velocity data x_n acquired by the IMU to output a clean angular velocity signal y_n.

[0098] Angular feature extraction: The filtered angular velocity y_n is integrated over time (Δβ=y_n*Δt) and accumulated to obtain the inertial deflection angle β.

[0099] On the mileage technology data processing side, the following processes are included:

[0100] Smoothing Filter: Apply mean filtering (such as three-point moving average) to the raw displacement data of the left and right wheel odometers to obtain smoothed displacement values ​​d_left and d_right.

[0101] Mileage feature extraction: Calculate the ratio of the smooth displacement of the left and right wheels within the same control cycle, ε=d_left / d_right, as the driving offset.

[0102] In the weighted fusion stage, the multiple features obtained after the above preprocessing are substituted into the weighted error formula: e = a * (l_actual / l_set) + b * α + c * β + d * ε, where l_actual can be the average of the distance features on both sides or a one-sided value, depending on the control strategy. l_set is the preset expected distance. a, b, c, and d are pre-tuned weight coefficients. The calculated e is the comprehensive path deviation.

[0103] During the closed-loop control execution phase, the comprehensive path deviation e is used as the control target and input to the PID controller. The PID controller calculates the differential control signal for adjusting the speed of the left and right drive wheels in real time based on the proportional, integral, and derivative terms of the deviation e.

[0104] The control signal acts on the platform's drive system (such as a motor driver), creating a precise speed difference between the left and right wheels, thereby generating a torque to correct the vehicle's yaw. Under the action of this corrective torque, the vehicle's direction of travel is dynamically adjusted, causing the overall path deviation e to gradually decrease and approach zero, thus ensuring that the platform can travel stably along a preset straight path (i.e., maintaining a set distance l from the side boundaries).

[0105] The above four stages cycle continuously during system operation (e.g., at a frequency of 100Hz), forming a high-frequency, high-precision real-time closed-loop control system that can effectively resist path deviation caused by mechanical errors, uneven ground, and internal and external disturbances.

[0106] Please see Figure 5 Taking an unmanned transport platform that performs automatic painting operations between dock piers as an example, the complete implementation process of the present invention will be described in detail.

[0107] Specific application scenarios and platform configurations are as follows:

[0108] The platform is an electric four-wheeled vehicle, 1.2 meters wide. Five infrared laser rangefinders are mounted on each side of the vehicle's longitudinal centerline, with an adjacent sensor spacing A = 0.5 meters. A six-axis IMU (MPU-6050) is mounted at the center of the vehicle. Each of the two rear drive wheels is equipped with a photoelectric encoder (accuracy 500 pulses / revolution) as a wheel-type odometer. The driving path is flanked by concrete stone blocks, with a desired distance l_set = 1.5 meters.

[0109] The control system parameters are set as follows:

[0110] Laser data processing: The invalid data distance threshold is set to 4.0 meters.

[0111] IMU data processing: High-pass filter cutoff frequency f_c=0.1Hz, sampling period T_s=0.01s.

[0112] Odometer data processing: Three-point moving average filtering is used.

[0113] Data fusion weights: After on-site debugging, a=1.0, b=0.5, c=0.8, d=0.3 were set.

[0114] PID controller parameters: tuned using the Ziegler-Nichols method, set to Kp=2.0, Ki=0.05, Kd=0.1.

[0115] The complete running process example is as follows:

[0116] The platform starts and enters automatic navigation mode, and is expected to travel in a straight line along the center line of the stone pier.

[0117] Within a certain control period (t=k):

[0118] Laser readings on the right: [4.5m, 1.65m, 1.63m, 1.68m, 4.2m] (4.5m and 4.2m are greater than the threshold and are invalid). Laser readings on the left: [1.49m, 1.48m, 1.47m, 1.50m, 1.49m] (all are valid).

[0119] The original value of the IMU angular velocity is x_n = 0.05 rad / s.

[0120] The original displacements obtained by converting the mileage pulse counts of the left and right wheels are: d_left_raw=10.2mm, d_right_raw=10.5mm.

[0121] Parallel preprocessing and feature extraction of the above three types of data:

[0122] Laser data (right side): After filtering out invalid values, the valid data are [1.65m, 1.63m, 1.68m], n=3.

[0123] The distance characteristic l_right = (1.65 + 1.63 + 1.68) / 3 ≈ 1.653m.

[0124] The specific value of the angular characteristic α_right is [asin((1.63-1.65) / 0.5)+asin((1.68-1.63) / 0.5)] / 2, that is, [asin(-0.04)+asin(0.1)] / 2≈[-0.040+0.100] / 2=0.030rad.

[0125] Laser data (left): All valid, n=5.

[0126] The distance characteristic l_left = (1.49 + 1.48 + 1.47 + 1.50 + 1.49) / 5 = 1.486m.

[0127] The angular characteristic α_left is close to 0 rad after calculation.

[0128] IMU data: After high-pass filtering and calculation (assuming y_{n-1}=0, x_{n-1}=0.048), we get y_n≈0.002rad / s. Integrating, we get the inertial deflection angle increment Δβ=0.002*0.01=2e-5rad, which is accumulated to β. In this example, β≈0.01rad.

[0129] Odometer data: The smoothed d_left = (10.1 + 10.2 + 10.3) / 3 = 10.2 mm, and the smoothed d_right = (10.4 + 10.5 + 10.6) / 3 = 10.5 mm. Therefore, the odometer characteristic ε = 10.2 / 10.5 ≈ 0.971.

[0130] Data weighting and fusion: Let l_actual = (l_left + l_right) / 2 = (1.486 + 1.653) / 2 ≈ 1.570m. Let α = (α_left + α_right) / 2 ≈ (0 + 0.030) / 2 = 0.015rad.

[0131] Substituting into the error formula: e = 1.0 * (1.570 / 1.5) + 0.5 * 0.015 + 0.8 * 0.01 + 0.3 * 0.971.

[0132] By calculation, e≈1.047+0.0075+0.008+0.291=1.354.

[0133] Closed-loop control and execution:

[0134] The PID controller receives a positive deviation e = 1.354. Calculations show that the output control requires increasing the speed of the left wheel (and / or decreasing the speed of the right wheel) to generate a leftward corrective torque, thereby driving the system to execute the command and adjust the speeds of the left and right wheels.

[0135] In the next control cycle (t=k+1), the sensor collects data again and repeats the above steps. Under continuous closed-loop correction, the platform gradually adjusts to the left, making l_actually close to l_set, and α, β, ε close to ideal values ​​(0, 0, 1), ultimately achieving stable straight-line driving.

[0136] This specific example demonstrates that the method of the present invention can comprehensively utilize information from multiple sensors. Even when some sensor data fails (right-side laser) or there is an internal deviation (odometer shows the right wheel is slightly faster), it can accurately calculate the comprehensive path deviation and achieve precise path tracking through closed-loop control.

[0137] The following will be combined with the appendix Figure 2 This paper provides a detailed description of the linear path control system suitable for constrained structured environments provided in the embodiments of this application. It should be noted that the appendix... Figure 2 The linear path control system shown is suitable for constrained structured environments and is used to execute the present application. Figure 1 The methods shown in the embodiments are for illustrative purposes only, illustrating the parts relevant to the embodiments of this application. For specific technical details not disclosed, please refer to this application. Figure 1 The example shown.

[0138] Please see Figure 2 , Figure 2 This is a schematic diagram of a linear path control system suitable for constrained structured environments, provided in an embodiment of this application. (See attached diagram.) Figure 2 As shown, the system includes:

[0139] The multi-source sensing unit 201 includes a range sensor array deployed on both sides of the vehicle body, an inertial measurement unit located at the center of gravity of the vehicle body, and a wheel-type odometer installed on the drive wheels.

[0140] The data processing and fusion unit 202 is configured as follows:

[0141] Receive data from the multi-source sensing unit;

[0142] Perform preprocessing steps to extract distance, angle, and odometry features;

[0143] Perform a weighted fusion step to generate a comprehensive path deviation.

[0144] The closed-loop control unit 203 is configured to receive the comprehensive path deviation and generate differential control signals for adjusting the speeds of the left and right drive wheels.

[0145] Among them, at least three ranging sensors are symmetrically arranged on each side of the vehicle body; the ranging sensors can be infrared laser sensors / rangefinders.

[0146] It is feasible to have ranging sensors distributed at least on the left and right sides of the vehicle body, such as Figure 5 As shown, five laser rangefinders are deployed on one side; depending on actual needs, rangefinders can also be deployed on other locations such as the front and rear of the vehicle to meet the vehicle's movement requirements in confined environments.

[0147] Those skilled in the art will clearly understand that the technical solutions of the embodiments of this application can be implemented by means of software and / or hardware. In this specification, "unit" and "module" refer to software and / or hardware that can independently complete or cooperate with other components to complete a specific function, wherein the hardware may be, for example, a field-programmable gate array (FPGA), an integrated circuit (IC), etc.

[0148] Each processing unit and / or module in the embodiments of this application can be implemented by an analog circuit that implements the functions described in the embodiments of this application, or by software that executes the functions described in the embodiments of this application.

[0149] See Figure 3 It shows a schematic diagram of the structure of an electronic device according to an embodiment of this application, which can be used to implement... Figure 1 The method in the illustrated embodiment. (As shown) Figure 3 As shown, the electronic device 300 may include: at least one central processing unit 301, at least one network interface 304, user interface 303, memory 305, and at least one communication bus 302.

[0150] The communication bus 302 is used to enable communication between these components.

[0151] The user interface 303 may include a display screen and a camera. Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0152] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0153] The central processing unit 301 may include one or more processing cores. The central processing unit 301 connects to various parts within the electronic device 300 using various interfaces and lines. It executes various functions of the terminal and processes data by running or executing instructions, programs, code sets, or instruction sets stored in the memory 305, and by calling data stored in the memory 305. Optionally, the central processing unit 301 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The central processing unit 301 may integrate one or a combination of several of the following: a central processing unit (CPU), a graphics processing unit (GPU), and a modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content required for display on the screen; and the modem handles wireless communication. It is understood that the modem may also not be integrated into the central processing unit 301 and may be implemented as a separate chip.

[0154] The memory 305 may include random access memory (RAM) or read-only memory. Optionally, the memory 305 may include non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory 305 may also be at least one storage device located remotely from the aforementioned central processing unit 301. Figure 3 As shown, the memory 305, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and program instructions.

[0155] exist Figure 3In the illustrated electronic device 300, the user interface 303 is mainly used to provide an input interface for the user and to acquire user input data; while the central processing unit 301 can be used to call the linear path control application program suitable for constrained structured environments stored in the memory 305, and specifically perform the following operations:

[0156] By using a range-measuring sensor array, an onboard inertial sensor, and a wheeled odometer deployed on both sides of the vehicle body, the first relative pose information between the vehicle body and the two side boundaries, the second motion attitude information of the vehicle body itself, and the third motion mileage information of the left and right wheels are obtained respectively.

[0157] The first relative pose information, the second motion posture information, and the third motion mileage information are preprocessed to extract at least one distance feature, at least one angle feature, and at least one mileage feature representing the vehicle path deviation, respectively.

[0158] The distance feature, angle feature, and mileage feature are weighted and fused to generate a comprehensive path deviation. The distance feature is fused based on its normalized value with the preset expected distance.

[0159] Using the comprehensive path deviation as the control target, the drive signal is calculated by the closed-loop controller, and differential control is implemented on the left and right drive wheels of the vehicle to maintain the vehicle traveling along the preset straight path.

[0160] This application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method. The computer-readable storage medium may include, but is not limited to, any type of disk, including floppy disks, optical disks, DVDs, CD-ROMs, microdrives, as well as magneto-optical disks, ROMs, RAMs, EPROMs, EEPROMs, DRAMs, VRAMs, flash memory devices, magnetic cards or optical cards, nanosystems (including molecular memory ICs), or any type of medium or device suitable for storing instructions and / or data.

[0161] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0162] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0163] In the several embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some service interfaces; indirect couplings or communication connections between devices or units may be electrical or other forms.

[0164] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0165] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0166] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a 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. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0167] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, which may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, etc.

[0168] The foregoing description is merely an exemplary embodiment of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure. Those skilled in the art will readily conceive of embodiments of this disclosure upon considering the specification and practicing the disclosure herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not described herein. The specification and embodiments are to be considered exemplary only, and the scope and spirit of this disclosure are defined by the claims.

Claims

1. A method for controlling straight paths in constrained structured environments, characterized in that, The method includes: By using a range-measuring sensor array, an onboard inertial sensor, and a wheeled odometer deployed on both sides of the vehicle body, the first relative pose information between the vehicle body and the two side boundaries, the second motion attitude information of the vehicle body itself, and the third motion mileage information of the left and right wheels are obtained respectively. The first relative pose information, the second motion posture information, and the third motion mileage information are preprocessed to extract at least one distance feature, at least one angle feature, and at least one mileage feature representing the vehicle path deviation, respectively. The distance feature, angle feature, and mileage feature are weighted and fused to generate a comprehensive path deviation, wherein the distance feature is fused based on its normalized value with a preset expected distance. Using the comprehensive path deviation as the control target, the drive signal is calculated by the closed-loop controller, and differential control is implemented on the left and right drive wheels of the vehicle to maintain the vehicle traveling along a preset straight path.

2. The method according to claim 1, characterized in that, Preprocessing of the first relative pose information includes: Determine whether the measurement data of each sensor in the ranging sensor array is valid, and filter out the measurement data that is determined to be invalid; the invalid data refers to data whose value indicates that the sensor has not detected the valid boundary; Based on the remaining valid measurement data after filtering on each side, the average distance between that side and the boundary is calculated as the distance feature quantity. Based on the difference in effective measurements of adjacent sensors on the same side and their fixed installation distance, the local tilt angle of the vehicle body relative to that side boundary is calculated, and the average of all available local tilt angles is obtained to obtain the angular characteristic quantity.

3. The method according to claim 2, characterized in that, The determination of whether the data is valid is achieved by comparing the measured data with a preset distance threshold, wherein the distance threshold is set according to the typical width of the boundary interval in the environment.

4. The method according to claim 1, characterized in that, Preprocessing of the second motion posture information includes: A high-pass digital filter is applied to the raw angular velocity data acquired by the inertial sensor to suppress its low-frequency drift; The filtered angular velocity signal is integrated over time, and the resulting integral value is used as the angular characteristic quantity obtained by the inertial sensor.

5. The method according to claim 1, characterized in that, Preprocessing of the third motion mileage information includes: The raw displacement data collected by the wheel odometer is subjected to smoothing and filtering processing. Calculate the ratio of the filtered displacement of the left and right wheels within the same time period, and use this ratio as the mileage feature to characterize the vehicle offset trend caused by the difference in the movement of the left and right wheels.

6. The method according to claim 1, characterized in that, The weighted fusion is achieved by assigning weight coefficients to the distance feature, angle feature, and mileage feature, and summing the products of these coefficients with their respective weights as the comprehensive path deviation; wherein the distance feature participates in the summation by using its ratio to the preset expected distance or in a normalized form.

7. A linear path control system suitable for constrained structured environments, characterized in that, The system for implementing the method as described in any one of claims 1 to 6 comprises: The multi-source sensing unit includes a range sensor array deployed on both sides of the vehicle body, an inertial measurement unit located at the center of gravity of the vehicle body, and a wheel-type odometer installed on the drive wheels. The data processing and fusion unit is configured as follows: Receive data from the multi-source sensing unit; Perform preprocessing steps to extract distance, angle, and odometer features; Perform a weighted fusion step to generate a comprehensive path deviation. A closed-loop control unit is configured to receive the comprehensive path deviation and generate differential control signals for adjusting the speeds of the left and right drive wheels.

8. The system according to claim 7, characterized in that, At least three ranging sensors are symmetrically arranged on each side of the vehicle body; the ranging sensors are distributed at least on the left and right sides of the vehicle body.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1-6.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1-6.

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