Embedded-based vehicle auxiliary driving control method and system
By analyzing the vibration data and direction angle of the harvester, and dynamically adjusting the PID algorithm parameters, the problem of the harvester deviating from its direction of travel in the field was solved, achieving precise straight-line control and improving operational efficiency.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- LAIWU VOCATIONAL & TECHNICAL COLLEGE
- Filing Date
- 2026-04-13
- Publication Date
- 2026-06-16
AI Technical Summary
Existing PID algorithms are ill-suited to addressing the issue of crop harvesters deviating from their intended direction when traveling in the field due to uneven road surfaces and varying crop growth patterns. This results in low control accuracy and reduced operational efficiency.
By analyzing vertical and horizontal vibration data, driving speed and direction angle, the vertical and lateral disturbance values, crop resistance coefficient and disturbance degree are calculated, and the proportional parameters of the PID algorithm are dynamically adjusted to adapt to the complex road conditions in the field and the influence of crops, so as to achieve precise straight-line control.
It improves the straight-line control precision of harvesters under complex field conditions, enhances field operation efficiency, and reduces labor intensity and input costs.
Smart Images
Figure CN122009166B_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of driver assistance technology, specifically to an embedded-based vehicle driver assistance control method and system. Background Technology
[0002] In modern agricultural production, crop harvesters are key agricultural vehicles in the agricultural production process. Their driving status directly affects the harvesting efficiency of crops. The assisted driving of crop harvesters can control the driving status of the vehicle by sensing the surrounding environment in real time, so as to make it travel in a straight line along the field ridges, thereby reducing labor intensity, reducing input costs, and significantly improving agricultural production efficiency.
[0003] When a harvester travels in the field, the unevenness of the field ridges causes severe bumps and vibrations, resulting in lateral slippage in its travel direction. Furthermore, the varying growth density and lodging conditions of crops planted in different areas of the field cause the harvester's travel direction to deviate from the crop's harvesting direction to varying degrees. Since PID algorithms use fixed proportional parameters, they struggle to adapt to these deviations caused by different factors. This can lead to insufficient response and delayed correction when facing impacts from the field ridges, or overreaction and over-adjustment when dealing with continuous pushing from crops. Consequently, the control precision for maintaining the harvester's straight-line travel is low, impacting field operation efficiency. Summary of the Invention
[0004] To address the aforementioned technical issues, an embedded-based vehicle assisted driving control method and system are provided to resolve existing problems.
[0005] The solution to the technical problem addressed in this application is to provide an embedded-based vehicle assisted driving control method and system, comprising the following steps:
[0006] In a first aspect, embodiments of this application provide an embedded vehicle-assisted driving control method, which includes the following steps:
[0007] Acquire vertical vibration data, horizontal vibration data, travel speed, and azimuth angle of the crop harvester at each moment within each monitoring cycle;
[0008] Analyze the fluctuations of vertical and horizontal vibration data in each monitoring period, assess the impact of unevenness of the field road surface on driving obstacles and deviation from driving direction, and calculate the vertical and lateral disturbance values for each monitoring period.
[0009] After the harvester switches to the straight-line assisted driving mode, the last monitoring cycle before the switch is recorded as the reference cycle, and the monitoring cycle after the switch is recorded as the control cycle.
[0010] Based on the relative changes in vertical and lateral disturbance values between each control cycle and the reference cycle, the vertical and lateral evaluation values for each control cycle are obtained respectively.
[0011] The changes in driving speed between each control cycle and the reference cycle are analyzed. Combined with the vertical evaluation value, the impact of crops on driving resistance is evaluated, and the crop resistance coefficient for each control cycle is obtained.
[0012] By comparing the differences between the azimuth angle at different times within each control cycle and the azimuth angle at the last time of the reference cycle, and combining the lateral assessment value and the crop resistance coefficient, the impact of crop deviation on the driving direction is assessed, and the crop disturbance degree of each control cycle is determined.
[0013] Based on the differences in lateral disturbance values and crop disturbance levels between each control cycle and its adjacent control cycles, the adjustment coefficients for each control cycle are calculated, and the proportional parameters of the PID algorithm for the next control cycle are adjusted. The straight-line driving direction of the harvester is then controlled in conjunction with the PID algorithm.
[0014] Preferably, the calculation of the vertical disturbance value for each monitoring period includes:
[0015] The peak of the absolute value of the vertical vibration data at all times within each monitoring cycle is recorded as the vertical peak.
[0016] The median of the absolute values of vertical vibration data at all times within each monitoring period is obtained and denoted as the vertical threshold.
[0017] The degree of dispersion of the absolute value of vertical vibration data at all times within each monitoring cycle is calculated and denoted as vertical dispersion.
[0018] The vertical disturbance value is the product of the mean of the peak values of all vertical peaks greater than the vertical threshold within each monitoring period and the vertical dispersion.
[0019] Preferably, the calculation process for the lateral disturbance value is as follows:
[0020] The peak of the absolute value of the horizontal vibration data at all times within each monitoring period is recorded as the transverse peak.
[0021] The median of the absolute values of horizontal vibration data at all times within each monitoring period is obtained and denoted as the horizontal threshold.
[0022] The degree of dispersion of the absolute value of horizontal vibration data at all times within each monitoring cycle is calculated and denoted as the lateral dispersion.
[0023] The lateral disturbance value is the product of the mean of the peak values of all lateral peaks greater than the lateral threshold within each monitoring period and the lateral dispersion.
[0024] Preferably, obtaining the vertical evaluation value and the lateral evaluation value for each control cycle includes:
[0025] The vertical evaluation value is the ratio of the vertical disturbance value between each control cycle and the reference cycle;
[0026] The lateral evaluation value is the ratio of the lateral disturbance value between each control period and the reference period.
[0027] Preferably, obtaining the crop resistance coefficient for each control cycle includes:
[0028] Calculate the ratio between the average driving speed at all times within each control cycle and the average driving speed at all times within the reference cycle, denoted as the relative ratio, and perform a negative mapping on the relative ratio;
[0029] The crop resistance coefficient is the normalized result of the ratio of the negative mapping result to the vertical evaluation value.
[0030] Preferably, determining the crop disturbance degree for each control cycle includes:
[0031] The direction angle at the last moment within the reference cycle is recorded as the straight-ahead direction angle; the difference between the direction angle and the straight-ahead direction angle at each moment within each control cycle is recorded as the deviation angle at each moment within each control cycle.
[0032] The absolute value of the mean of the deviation angles at all times within each control cycle is calculated as the ratio of the preset maximum angle, and recorded as the deviation amount; the ratio of the deviation amount to the lateral evaluation value is used as the crop deviation degree for each control cycle.
[0033] The crop disturbance degree is the product of the crop deviation degree and the crop resistance coefficient.
[0034] Preferably, the calculation of the adjustment coefficient for each control cycle includes:
[0035] The normalized result of the ratio of the horizontal assessment value to the crop disturbance degree in each control period was used as the dominant factor in each control period.
[0036] The adjustment coefficient is the difference between the dominant factor in each control cycle and the previous control cycle.
[0037] Preferably, after switching to straight-line assisted driving mode, the first Each control cycle corresponds to the adjusted proportional parameter. The calculation formula is: ,in, For the first The proportional parameter for each control cycle This refers to the proportional parameters for the first control cycle after switching to straight-line assisted driving mode. For the first Adjustment coefficient for each control cycle.
[0038] Preferably, the process of obtaining the proportional parameter of the first control cycle is as follows: using the Ziegler-Nichols method, the proportional parameter of the PID algorithm corresponding to the first control cycle is tuned to obtain the proportional parameter of the first control cycle.
[0039] Secondly, embodiments of this application also provide an embedded vehicle-assisted driving control system, including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor executes the computer program to implement the steps of any of the above-described embedded vehicle-assisted driving control methods.
[0040] This application has at least the following beneficial effects:
[0041] This application analyzes the fluctuations in vertical vibration data within a monitoring period and calculates the vertical disturbance value for each monitoring period. Its advantage lies in considering the impact of unevenness on the harvester's travel, obtaining vertical evaluation values for each control period. Furthermore, the relative change in vertical disturbance values between the control period and the reference period reflects the degree of deterioration in road unevenness compared to before the mode switch, thus assessing the impact of road unevenness on the harvester's travel resistance. The application also calculates the lateral disturbance value for each monitoring period, which takes into account the impact of road unevenness on the harvester's travel direction. The degree of deviation impact was assessed; lateral evaluation values for each control period were obtained. The beneficial effect of this assessment lies in reflecting the significant difference between the degree of lateral deviation caused by road surface unevenness within the control period and before mode switching, through the relative change of lateral disturbance values between the control period and the reference period. This evaluated the impact of uneven road surface on the harvester's deviation from its travel direction. Crop drag coefficients for each control period were also obtained. The beneficial effect of this assessment lies in considering the changes in the harvester's travel speed, reflecting the overall resistance experienced by the harvester, and further evaluating the impact of crop resistance on the harvester's travel after deducting the influence of uneven road surface on the harvester's travel resistance. The degree of influence of the force is used to initially illustrate the risk of the harvester deviating due to crops; the crop interference degree for each control cycle is determined, which is beneficial because it considers the deviation of the harvester's heading angle, deducts the impact of unevenness of the field surface on the harvester's travel direction deviation, and combines the crop resistance coefficient to comprehensively assess the interference of crops on the harvester's straight-line travel, indicating the extent to which the current deviation is caused by crops, and comprehensively reflecting the risk of the harvester's travel direction deviation caused by crops; the adjustment coefficient for each control cycle is calculated, which is beneficial because it considers the unevenness of the field surface relative to the deviation of the harvester's travel direction caused by crops. By dynamically adjusting the proportional parameters of the PID algorithm based on the proportion of impacts, when the unevenness of the field ridges causes a high proportion of deviations in the driving direction, the proportional parameters of the PID algorithm are increased to provide strong control in response to sudden impacts and promptly restore the harvester's straight driving direction. Conversely, when crops cause a high proportion of deviations in the driving direction, the proportional parameters of the PID algorithm are decreased to provide stable control against continuous pushing and avoid over-adjustment. This allows for real-time control of the harvester's driving direction, improving the control accuracy for maintaining straight-line driving and ensuring precise straight-line control of the harvester under complex field conditions, thereby enhancing the harvester's field operation efficiency. Attached Figure Description
[0042] The embedded-based vehicle assisted driving control method of this application will be further described in detail below with reference to the accompanying drawings.
[0043] Figure 1A flowchart illustrating the steps of an embedded vehicle assisted driving control method provided in an embodiment of this application;
[0044] Figure 2 A flowchart illustrating the steps of the method for obtaining the adjusted proportional parameters provided in this application embodiment. Detailed Implementation
[0045] To make the objectives, technical solutions, and advantages of this application clearer, the embedded vehicle assisted driving control method and system proposed in this application will be further described in detail below with reference to the accompanying drawings and implementation examples. It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit the scope of this application.
[0046] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.
[0047] Please see Figure 1 The diagram illustrates a flowchart of an embedded vehicle assisted driving control method according to an embodiment of this application, the method comprising the following steps:
[0048] Step 1: Obtain the vertical vibration data, horizontal vibration data, travel speed, and direction angle of the crop harvester at each moment in each monitoring cycle.
[0049] In the development of smart agriculture, agricultural machinery assisted driving technology is an indispensable and important part. It can significantly improve operational efficiency and quality, reduce labor intensity and input costs by achieving automatic driving in straight-line driving, and provide basic support for large-scale agricultural operations.
[0050] Based on the above analysis, taking a ginger harvester as an example, a two-dimensional accelerometer, a speed sensor, and an angle sensor are deployed on the harvester. When the harvester is harvesting ginger, the two-dimensional accelerometer collects the vertical vibration data and horizontal vibration data of the harvester at each moment in each monitoring cycle in real time, the speed sensor collects the driving speed of the harvester at each moment in each monitoring cycle in real time, and the angle sensor collects the direction angle of the harvester at each moment in each monitoring cycle in real time.
[0051] The collected data is then normalized. In this embodiment, the minimum normalization method is used for normalization. The minimum normalization method is a well-known technique and will not be described in detail here.
[0052] In this embodiment, the sensor's acquisition frequency is 100Hz and the monitoring period is 2s. As for other implementation methods, the implementer can set them according to the actual situation.
[0053] Thus, the vertical vibration data, horizontal vibration data, driving speed, and direction angle at each moment within each monitoring cycle are obtained.
[0054] Step 2: Analyze the fluctuations of vertical and horizontal vibration data in each monitoring period, assess the impact of unevenness of the field road surface on driving obstacles and deviation from driving direction, calculate the vertical and lateral disturbance values for each monitoring period, and obtain the vertical and lateral evaluation values for each control period based on the relative changes in vertical and lateral disturbance values between each control period and the reference period.
[0055] Furthermore, the uneven furrows formed by ridge planting of ginger cause continuous vibrations from the harvester wheels. During the ginger harvest period, the soil becomes loose and prone to collapse due to tuber enlargement, making it easy for the wheels to sink into or encounter hard clods of soil. This can result in sudden and severe lateral slippage or sinking, creating an instantaneous impact on the steering wheel and causing the harvester to deviate from its straight-line direction. Therefore, by analyzing the changes in vertical vibration data within the monitoring period, a vertical evaluation value is calculated, specifically:
[0056] When the harvester switches from manual driving mode to straight-line assisted driving mode, the last monitoring cycle before the switch is recorded as the reference cycle, and each monitoring cycle after the switch is recorded as the control cycle.
[0057] The peak of the absolute value of the vertical vibration data at all times within each monitoring cycle is recorded as the vertical peak.
[0058] In this embodiment, the AMPD (Automatic Multiscale-based Peak Detection) algorithm is used to obtain the peak. The AMPD algorithm is a well-known technology and will not be described in detail here.
[0059] The median of the absolute values of vertical vibration data at all times within each monitoring cycle is obtained and denoted as the vertical threshold.
[0060] The degree of dispersion of the absolute value of vertical vibration data at all times within each monitoring cycle is calculated and denoted as vertical dispersion.
[0061] In this embodiment, the degree of dispersion is measured by calculating the coefficient of variation of the absolute value of the vertical vibration data at all times within each monitoring cycle. The coefficient of variation is a well-known technique and will not be described in detail here. As other implementation methods, implementers can set it according to the actual situation.
[0062] The product of the mean of the peak values of all vertical peaks greater than the vertical threshold within each monitoring period and the vertical dispersion is used as the vertical disturbance value for each monitoring period.
[0063] After the harvester switches to the straight-line assisted driving mode, the ratio of the vertical disturbance value of each control cycle to the reference cycle is used as the vertical evaluation value of each control cycle.
[0064] It should be noted that the greater the vertical dispersion, the more severe the vibration of the harvester during operation. The greater the mean value, the more severe the harvester's up-and-down jolting. The greater the vertical disturbance value, the more severe the road surface of the ginger field, and the poorer its smoothness. The greater the vertical evaluation value, the more severe the unevenness of the road surface during this control cycle is compared to the road surface before the mode switch. This results in greater vibration in the vertical direction, which has a greater impact on the harvester's driving resistance and leads to a decrease in vehicle speed.
[0065] Secondly, the changes in horizontal vibration data during the monitoring period were analyzed, and the lateral evaluation value was calculated, specifically:
[0066] The peak of the absolute value of the horizontal vibration data at all times within each monitoring cycle is recorded as the transverse peak.
[0067] In this embodiment, the AMPD algorithm is used to obtain the peak. The AMPD algorithm is a well-known technology and will not be described in detail here.
[0068] The median of the absolute values of horizontal vibration data at all times within each monitoring cycle is obtained and denoted as the horizontal threshold.
[0069] The degree of dispersion of the absolute values of horizontal vibration data at all times within each monitoring cycle is calculated and denoted as the lateral dispersion.
[0070] In this embodiment, the degree of dispersion is measured by calculating the coefficient of variation of the absolute values of the horizontal vibration data at all times within each monitoring cycle. The coefficient of variation is a well-known technique and will not be elaborated here. As other implementation methods, implementers can set it according to their actual situation.
[0071] The product of the mean of the peak values of all horizontal peaks greater than the horizontal threshold within each monitoring period and the horizontal dispersion is used as the horizontal disturbance value for each monitoring period.
[0072] After the harvester switches to the straight-line assisted driving mode, the ratio of the lateral disturbance value of each control cycle to the reference cycle is used as the lateral evaluation value of each control cycle.
[0073] It should be noted that the greater the lateral dispersion, the more sudden and random the lateral swaying of the harvester is during operation, rather than a regular swaying, indicating complex road conditions. The greater the average value of the lateral peaks, the more severe the harvester's lateral swaying or impacts it has experienced. The greater the lateral disturbance value, the stronger the lateral force exerted by the unevenness of the ginger field ridges on the harvester, interfering with its straight-line travel and causing a strong tendency for the vehicle to skid or deviate from its straight-line trajectory. The greater the lateral evaluation value, the stronger the interference force causing the vehicle's lateral deviation within this control cycle is compared to the interference force before the mode switch, reflecting the greater impact of the unevenness of the field ridges on the harvester's deviation from its travel direction.
[0074] Thus, the vertical and lateral evaluation values for each control cycle are obtained.
[0075] Step 3: Analyze the changes in driving speed between each control cycle and the reference cycle. Combine the vertical evaluation value to assess the impact of crops on driving resistance and obtain the crop resistance coefficient for each control cycle. By analyzing the difference between the direction angle at different times in each control cycle and the direction angle at the last time of the reference cycle, combined with the lateral evaluation value and the crop resistance coefficient, assess the impact of crops on deviation from the driving direction and determine the crop disturbance degree for each control cycle.
[0076] Furthermore, when harvesting crops, the harvester needs to squeeze, pull, and cut the crop stems and fruits. The denser the crop growth and the thicker the stems, the more resistance the vehicle will encounter during travel. Therefore, the unevenness of the field ridges leading to travel resistance, as well as the obstructive force of the crops on the harvester, will affect the harvester's travel speed. By analyzing the changes in travel speed and the vertical evaluation values, the crop resistance coefficient is calculated, specifically:
[0077] Calculate the ratio between the average driving speed at all times within each control cycle and the average driving speed at all times within the reference cycle, denoted as the relative ratio, and perform a negative mapping on the relative ratio;
[0078] In this embodiment, the specific process of negative mapping is as follows: negative mapping is performed using an exponential function, assuming the relative ratio is denoted as... ,but The result is used as the result of the negative mapping, where, It is an exponential function with the natural constant as the base.
[0079] The normalized result of the ratio of the negative mapping result to the vertical evaluation value is used as the crop resistance coefficient for each control cycle.
[0080] In this embodiment, the arctangent normalization function is used for normalization, so that the crop resistance coefficient ranges from 0 to 1. The arctangent normalization function is a well-known technique and will not be described in detail here.
[0081] It should be noted that the relative comparison reflects the overall resistance experienced by the harvester, including ridge resistance and crop resistance. The smaller the relative comparison, the larger the negative mapping result, indicating that the harvester's travel speed is reduced and the resistance experienced by the harvester is greater. The smaller the vertical evaluation value, the smaller the impact of ridge unevenness on the harvester's travel resistance. By removing the resistance caused by ridge unevenness from the overall resistance, the crop resistance coefficient is calculated. The larger the value, the higher the contribution of crops to the harvester's travel resistance in the overall resistance, reflecting the higher the relative influence of crops on the travel resistance.
[0082] Secondly, the lush foliage and tangled root systems of ginger plants, along with their haphazard lodging, exert lateral forces on the vehicle, causing the harvester to deviate from its intended direction. For example, if the ginger crop lodges to one side, the lodged crop will obstruct the harvester, causing it to veer off course to avoid the obstacle. Therefore, both uneven field ridges and lodging of crops can lead to deviations in the vehicle's direction of travel. The deviation of the azimuth angle within the control period is analyzed, and combined with the lateral assessment value and crop resistance coefficient, the crop disturbance degree is calculated, specifically:
[0083] The direction angle at the last moment within the reference period is denoted as the straight-ahead direction angle;
[0084] The difference between the direction angle and the straight-ahead direction angle at each moment within each control cycle is taken as the deviation angle at each moment within each control cycle.
[0085] Calculate the absolute value of the mean of the deviation angles at all times within each control cycle and the ratio of the preset maximum angle, which is denoted as the deviation amount.
[0086] In this embodiment, when the harvester is traveling in a straight line, the deviation of the direction angle of travel is taken as the reference, and the range of possible deviations is as follows: Therefore, the preset maximum angle value is .
[0087] The ratio of the deviation to the lateral evaluation value is used as the crop deviation for each control period;
[0088] The product of the crop deviation and the crop resistance coefficient is used as the crop disturbance degree for each control cycle.
[0089] It should be noted that the larger the deviation, the more significant the deviation in the harvester's travel direction. This deviation is caused by the combined effects of uneven field ridges and lodging of crops. The lateral assessment value reflects the degree of influence of field ridge unevenness on the deviation in the vehicle's travel direction. By eliminating the deviation caused by field ridge unevenness, the crop deviation degree is calculated. The larger the value, the greater the influence of crops on the deviation in the vehicle's travel direction. Combined with the crop resistance coefficient, which reflects the degree of obstruction of the vehicle's forward movement by crops, the greater the obtained crop interference degree, the more serious the interference of crops on the harvester's straight-line travel, and the higher the impact on the deviation in the harvester's travel direction. By combining the degree of deviation in the vehicle's travel direction caused by crops and the degree of obstruction, the risk of deviation in the harvester's travel direction caused by crops is comprehensively reflected.
[0090] Thus, the crop disturbance degree for each control cycle is obtained.
[0091] Step 4: Based on the differences in lateral disturbance values and crop disturbance levels between each control cycle and its adjacent control cycles, calculate the adjustment coefficient for each control cycle, adjust the proportional parameters of the PID algorithm for the next control cycle, and control the straight-line driving direction of the harvester in combination with the PID algorithm.
[0092] Furthermore, based on crop disturbance degree and cross-sectional evaluation values, the dominant factors were determined, specifically:
[0093] The normalized result of the ratio of the horizontal assessment value to the crop disturbance degree in each control period was used as the dominant factor in each control period.
[0094] In this embodiment, the sigmoid function is used for normalization. The sigmoid function is a well-known technique and will not be described in detail here. As other implementation methods, implementers may use other methods of the prior art, such as the tanh function. This embodiment does not impose any special restrictions on this.
[0095] It should be noted that, when calculating the ratio, to avoid the denominator being 0, a preset value greater than 0 is added to the denominator. The range of values for this preset value greater than 0 is [range missing]. In this embodiment, the preset value greater than 0 is 1. In other implementation methods, the implementer can set it according to the actual situation. The larger the dominant factor, the more the unevenness of the field road surface is the dominant factor affecting the deviation of the vehicle's driving direction. This reflects that the field road surface has a greater impact on the deviation of the harvester's driving direction. In order to ensure that the harvester can maintain straight driving, it is necessary to increase the proportional parameter of the PID (proportion integration differentiation) algorithm to restore the straight driving direction of the harvester in time.
[0096] Secondly, PID algorithms with fixed proportional parameters struggle to adapt to directional deviations caused by various factors. When unevenness in the field ridges causes deviation, insufficient response may lead to lag in correction, and insufficient corrective torque may fail to promptly counteract sudden lateral slippage, resulting in significant directional deviation. Conversely, when changes in the forces exerted by agricultural products cause deviation, an overreaction may cause over-adjustment. Therefore, road disturbances are sudden and severe, leading to rapid and irregular changes in vehicle direction, requiring stronger control to correct deviations. This necessitates increasing the proportional parameter of the PID algorithm to improve control sensitivity and response speed. However, crop disturbances are persistent and stable. When crop disturbances significantly impact the harvester's directional deviation, a gradual correction is needed to avoid over-adjustment. Therefore, reducing the proportional parameter of the PID algorithm is necessary to ensure stable control performance even under the influence of crop lodging.
[0097] Based on the above analysis, the adjustment coefficient is calculated by considering the differences in the dominant factors between adjacent control cycles, as follows:
[0098] Calculate the difference between the dominant factor and the previous control cycle for each control cycle, and use it as the adjustment coefficient for each control cycle.
[0099] It should be noted that when the adjustment coefficient is greater than 0, it indicates that the impact of the unevenness of the field ridges on the deviation of the harvester's travel direction gradually increases relative to the impact of the crops. When the adjustment coefficient is equal to 0, it indicates that the impact of the unevenness of the field ridges on the deviation of the harvester's travel direction remains unchanged. When the adjustment coefficient is less than 0, it indicates that the impact of the unevenness of the field ridges on the deviation of the harvester's travel direction gradually decreases, while the impact of crop lodging gradually increases. Therefore, the larger the adjustment coefficient, the more the proportional parameter of the PID algorithm needs to be increased.
[0100] The first control cycle after the harvester is switched to the straight-line assisted driving mode is recorded as the first control cycle.
[0101] The proportional parameters of the PID algorithm corresponding to the first control cycle are tuned using the Ziegler-Nichols method to obtain the proportional parameters of the first control cycle.
[0102] It should be noted that the process of tuning PID parameters using the Ziegler-Nichols method is a well-known technique and will not be elaborated here.
[0103] Therefore, by using the adjustment coefficients and proportional parameters of each control cycle, combined with the proportional parameter of the first control cycle, the proportional parameter of the next control cycle is adjusted, specifically as follows:
[0104]
[0105] in, After switching to straight-line assisted driving mode, the first Each control cycle corresponds to the adjusted proportional parameter. For the first The proportional parameter for each control cycle This refers to the proportional parameters for the first control cycle after switching to straight-line assisted driving mode. For the first Adjustment coefficient for each control cycle;
[0106] It should be noted that the above When the value is larger and greater than 0, it indicates that the impact of the unevenness of the field ridges is gradually increasing, thus requiring an increase in the proportional parameter to enhance control. When the value is less than 0, it indicates that the impact of the unevenness of the field ridges is gradually decreasing, and the proportional parameter needs to be reduced to avoid over-adjustment. When the value is 0, it indicates that the impact of the unevenness of the field ridges has not changed, and the proportional parameter remains unchanged. The flowchart of the method for obtaining the adjusted proportional parameter provided in this application embodiment is shown below. Figure 2 As shown.
[0107] Based on the adjusted proportional parameters, a PID algorithm is used to control the direction of travel of the harvester;
[0108] In this embodiment, the specific control process of the PID algorithm is as follows: the mean value of the deviation angle at all times within each control cycle is taken as the error, and the rate of change of the error between each control cycle and the previous control cycle is taken as the error rate of change. Based on the adjusted proportional parameter, the error and the rate of change of the error are taken as the input of the PID algorithm, and the input voltage of the servo motor is controlled based on the output control quantity. The servo motor drives the steering wheel through the mechanical device to correct the driving direction of the harvester, thereby controlling the driving direction of the harvester to make it drive straight. The PID algorithm is a well-known technology and will not be described in detail here.
[0109] Based on the same inventive concept as the above methods, this application also provides an embedded vehicle assisted driving control system, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any one of the above-described embedded vehicle assisted driving control methods.
[0110] It should be understood that, although Figure 1The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order in which these steps are executed, and they can be performed in other orders. Furthermore, Figure 1 At least some of the steps in the process may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.
[0111] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.
[0112] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application. Therefore, any simple modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of this application, without departing from the content of the technical solution of this application, shall fall within the protection scope of the technical solution of this application.
Claims
1. A vehicle-assisted driving control method based on embedded systems, characterized in that, The method includes the following steps: Acquire vertical vibration data, horizontal vibration data, travel speed, and azimuth angle of the crop harvester at each moment within each monitoring cycle; Analyze the fluctuations of vertical and horizontal vibration data in each monitoring period, assess the impact of unevenness of the field road surface on driving obstacles and deviation from driving direction, and calculate the vertical and lateral disturbance values for each monitoring period. After the harvester switches to the straight-line assisted driving mode, the last monitoring cycle before the switch is recorded as the reference cycle, and the monitoring cycle after the switch is recorded as the control cycle. Based on the relative changes in vertical and lateral disturbance values between each control cycle and the reference cycle, the vertical and lateral evaluation values for each control cycle are obtained respectively. The changes in driving speed between each control cycle and the reference cycle are analyzed. Combined with the vertical evaluation value, the impact of crops on driving resistance is evaluated, and the crop resistance coefficient for each control cycle is obtained. By comparing the differences between the azimuth angle at different times within each control cycle and the azimuth angle at the last time of the reference cycle, and combining the lateral assessment value and the crop resistance coefficient, the impact of crop deviation on the driving direction is assessed, and the crop disturbance degree of each control cycle is determined. Based on the differences in lateral disturbance values and crop disturbance levels between each control cycle and its adjacent control cycles, the adjustment coefficient for each control cycle is calculated, the proportional parameters of the PID algorithm for the next control cycle are adjusted, and the straight-line driving direction of the harvester is controlled in combination with the PID algorithm. The crop resistance coefficients obtained for each control cycle include: Calculate the ratio between the average driving speed at all times within each control cycle and the average driving speed at all times within the reference cycle, denoted as the relative ratio, and perform a negative mapping on the relative ratio; The crop resistance coefficient is the normalized result of the ratio of the negative mapping result to the vertical evaluation value; Determining the crop disturbance degree for each control cycle includes: The direction angle at the last moment within the reference cycle is recorded as the straight-ahead direction angle; the difference between the direction angle and the straight-ahead direction angle at each moment within each control cycle is recorded as the deviation angle at each moment within each control cycle. The absolute value of the mean of the deviation angles at all times within each control cycle is calculated as the ratio of the preset maximum angle, and recorded as the deviation amount; the ratio of the deviation amount to the lateral evaluation value is used as the crop deviation degree for each control cycle. The crop disturbance degree is the product of the crop deviation degree and the crop resistance coefficient; The calculation of the adjustment coefficients for each control cycle includes: The normalized result of the ratio of the horizontal assessment value to the crop disturbance degree in each control period was used as the dominant factor in each control period. The adjustment coefficient is the difference between the dominant factor in each control cycle and the previous control cycle; After switching to straight-line assisted driving mode, the first Each control cycle corresponds to the adjusted proportional parameter. The calculation formula is: ,in, For the first The proportional parameter for each control cycle This refers to the proportional parameters for the first control cycle after switching to straight-line assisted driving mode. For the first Adjustment coefficient for each control cycle.
2. The embedded-based vehicle assisted driving control method as described in claim 1, characterized in that, The calculation of the vertical disturbance value for each monitoring period includes: The peak of the absolute value of the vertical vibration data at all times within each monitoring cycle is recorded as the vertical peak. The median of the absolute values of vertical vibration data at all times within each monitoring period is obtained and denoted as the vertical threshold. The degree of dispersion of the absolute value of vertical vibration data at all times within each monitoring cycle is calculated and denoted as vertical dispersion. The vertical disturbance value is the product of the mean of the peak values of all vertical peaks greater than the vertical threshold within each monitoring period and the vertical dispersion.
3. The embedded-based vehicle assisted driving control method as described in claim 1, characterized in that, The calculation process for the lateral disturbance value is as follows: The peak of the absolute value of the horizontal vibration data at all times within each monitoring period is recorded as the transverse peak. The median of the absolute values of horizontal vibration data at all times within each monitoring period is obtained and denoted as the horizontal threshold. The degree of dispersion of the absolute value of horizontal vibration data at all times within each monitoring cycle is calculated and denoted as the lateral dispersion. The lateral disturbance value is the product of the mean of the peak values of all lateral peaks greater than the lateral threshold within each monitoring period and the lateral dispersion.
4. The embedded-based vehicle assisted driving control method as described in claim 1, characterized in that, The process of obtaining the vertical and lateral evaluation values for each control cycle includes: The vertical evaluation value is the ratio of the vertical disturbance value between each control cycle and the reference cycle; The lateral evaluation value is the ratio of the lateral disturbance value between each control period and the reference period.
5. The embedded-based vehicle assisted driving control method as described in claim 1, characterized in that, The process of obtaining the proportional parameter of the first control cycle is as follows: using the Ziegler-Nichols method, the proportional parameter of the PID algorithm corresponding to the first control cycle is tuned to obtain the proportional parameter of the first control cycle.
6. An embedded-based vehicle driver assistance control system, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the embedded vehicle assisted driving control method as described in any one of claims 1-5.