CN107436603B - Agricultural vehicle curve path automatic driving method and system - Google Patents
Agricultural vehicle curve path automatic driving method and system Download PDFInfo
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Abstract
The invention provides an automatic driving method and system for a curve path of an agricultural vehicle, wherein the method comprises the following steps: acquiring vehicle position information in the vehicle running process in real time; judging whether the angle deviation value of the vehicle and a preset curve operation track exceeds a preset maximum angle deviation value or not based on the acquired vehicle position information, if not, and if not, the vehicle does not run to a target position, and performing machine learning to acquire the curve path automatic driving following experience through enhanced signal feedback; and automatically driving the vehicle to follow the experience according to the curve path to control the vehicle to automatically travel along the curve path. The invention can enable the agricultural vehicle to autonomously learn the curve path following strategy in successful and failed experiences, the phenomenon of back-and-forth swing of operation due to improper angle adjustment is avoided, and the automatic driving track of the curve path of the agricultural vehicle has higher stability and reliability.
Description
Technical Field
The invention belongs to the technical field of agriculture, and particularly relates to an automatic driving method and system for a curve path of an agricultural vehicle.
Background
In the operation process of the agricultural tractor (such as cultivation, plowing and the like), the operation is often required to be carried out according to a set route track, in the curve path driving process, a driver is required to have high driving capability, otherwise, deviation of a driving path occurs, the operation effect is influenced, the curve path driving is realized by automatic driving, and the task of the driver can be released. The existing automatic driving scheme adopts an artificial setting mode, namely a mode of adjusting the running angle of the tractor through errors, the phenomenon of back-and-forth swing of operation due to improper angle adjustment can occur in the mode, and the stability and the reliability of the operation process of the tractor are not high.
Disclosure of Invention
The invention aims to provide an automatic driving method and system for a curve path of an agricultural vehicle.
The invention provides an automatic driving method for a curve path of an agricultural vehicle, which comprises the following steps:
acquiring vehicle position information in the vehicle running process in real time;
judging whether the angle deviation value of the vehicle and a preset curve operation track exceeds a preset maximum angle deviation value or not based on the acquired vehicle position information, if not, and if not, the vehicle does not run to a target position, and performing machine learning to acquire the curve path automatic driving following experience through enhanced signal feedback;
and automatically driving the vehicle to follow the experience according to the curve path to control the vehicle to automatically travel along the curve path.
Further, the method further comprises: and if the angle deviation value exceeds the preset maximum angle deviation value, storing the driving record, and recording the driving record as failed driving experience.
Further, the method further comprises: and if the angle deviation value does not exceed the preset maximum angle deviation value and the vehicle runs to the target position, storing the running record and recording the running record as a successful running experience.
Further, the obtaining of the curve path automatic driving following experience through the machine learning by enhancing the signal feedback specifically comprises:
judging whether the wheel corner adjustment strategy is good or bad through the enhanced signal feedback, and determining the wheel corner adjustment strategy as a wrong behavior strategy and learning when the enhanced signal feedback value is small; when the angle deviation value is not equal to zero, presetting the enhancement signal feedback value to be reduced along with the increase of the angle deviation value;
when the enhanced signal feedback value is larger, determining the wheel corner adjustment strategy as an effective behavior strategy, and recording; and when the angle deviation value is not equal to zero, presetting the enhancement signal feedback value to increase along with the reduction of the angle deviation value.
Further, the obtaining of the curve path automatic driving following experience through the machine learning by enhancing the signal feedback further comprises: and outputting a wheel rotation angle adjusting value according to the enhanced signal feedback value for the vehicle to adjust the wheel rotation angle.
Further, the obtaining of the automatic driving path following experience through the machine learning by enhancing the signal feedback further comprises: and when the angle deviation value is zero, judging that the vehicle running position is consistent with the preset curve operation track, and recording.
The invention also provides an agricultural vehicle curve path automatic driving system which comprises a vehicle controller and a memory, wherein the memory stores curve path automatic driving following experience data, and the vehicle controller is connected with the memory and is used for controlling the vehicle to automatically drive along the curve path according to the curve path automatic driving following experience data.
Further, the curve path automatic driving following experience data is obtained by the following method:
the method comprises the steps of acquiring vehicle position information in the vehicle driving process in real time;
and judging whether the angle deviation of the vehicle and the preset curve operation track exceeds a preset maximum angle deviation value or not based on the acquired vehicle position information, if not, judging that the vehicle does not run to the target position, and performing machine learning by enhancing signal feedback.
Further, the machine learning by enhancing signal feedback specifically includes:
judging whether the wheel corner adjustment strategy is good or bad through the enhanced signal feedback, and determining the wheel corner adjustment strategy as a wrong behavior strategy and learning when the enhanced signal feedback value is small; when the angle deviation value is not equal to zero, presetting the enhancement signal feedback value to be reduced along with the increase of the angle deviation value;
when the enhanced signal feedback value is larger, determining the wheel corner adjustment strategy as an effective behavior strategy, and recording; when the angle deviation value is not equal to zero, presetting the enhanced signal feedback value to be increased along with the reduction of the angle deviation value;
and outputting a wheel rotation angle adjusting value according to the enhanced signal feedback value.
Compared with the prior art, the invention has the beneficial effects that: by enhancing signal setting, the tractor is enabled to autonomously learn the path following strategy by utilizing a machine learning principle. Compared with the traditional mode of manually setting and adjusting the running angle of the tractor by error, the invention ensures that the tractor autonomously learns the path following strategy in successful and failed experiences, avoids the phenomenon of back-and-forth swinging of operation caused by improper angle adjustment, and has better stability and reliability in the curve path automatic driving track of the tractor.
Drawings
FIG. 1 is a schematic diagram illustrating the operation of a tractor according to an embodiment of the present invention;
FIG. 2 is a flow chart of an embodiment of the method for automatically driving a curve path of an agricultural vehicle according to the present invention.
Detailed Description
The present invention is described in detail with reference to the embodiments shown in the drawings, but it should be understood that these embodiments are not intended to limit the present invention, and those skilled in the art should understand that functional, methodological, or structural equivalents or substitutions made by these embodiments are within the scope of the present invention.
The embodiment provides an automatic driving method for a curve path of an agricultural vehicle, which comprises the following steps:
acquiring vehicle position information in the vehicle running process in real time;
judging whether the angle deviation value of the vehicle and a preset curve operation track exceeds a preset maximum angle deviation value or not based on the acquired vehicle position information, if not, and if not, the vehicle does not run to a target position, and performing machine learning to acquire the curve path automatic driving following experience through enhanced signal feedback;
and automatically driving the vehicle to follow the experience according to the curve path to control the vehicle to automatically travel along the curve path.
In this embodiment, the method further includes: and if the angle deviation value exceeds the preset maximum angle deviation value, storing the driving record, and recording the driving record as failed driving experience.
In this embodiment, the method further includes: and if the angle deviation value does not exceed the preset maximum angle deviation value and the vehicle runs to the target position, storing the running record and recording the running record as a successful running experience.
In this embodiment, the obtaining of the curve path automatic driving following experience through the machine learning by enhancing the signal feedback specifically includes:
judging whether the wheel corner adjustment strategy is good or bad through the enhanced signal feedback, and determining the wheel corner adjustment strategy as a wrong behavior strategy and learning when the enhanced signal feedback value is small; when the angle deviation value is not equal to zero, presetting the enhancement signal feedback value to be reduced along with the increase of the angle deviation value;
when the enhanced signal feedback value is larger, determining the wheel corner adjustment strategy as an effective behavior strategy, and recording; and when the angle deviation value is not equal to zero, presetting the enhancement signal feedback value to increase along with the reduction of the angle deviation value.
In this embodiment, the obtaining of the curve path automatic driving following experience through the machine learning by enhancing the signal feedback further includes: and outputting a wheel rotation angle adjusting value according to the enhanced signal feedback value for the vehicle to adjust the wheel rotation angle.
In this embodiment, the obtaining of the curve path automatic driving following experience through the machine learning by enhancing the signal feedback further includes: and when the angle deviation value is zero, judging that the vehicle running position is consistent with the preset curve operation track, and recording.
According to the agricultural vehicle curve path automatic driving method provided by the embodiment, the curve path following strategy is autonomously learned by the tractor through signal enhancement setting and a machine learning principle, so that the tractor autonomously learns the curve path following strategy in successful and failed experiences, the phenomenon of back-and-forth swing of operation due to improper angle adjustment is avoided, and the curve path following driving of the tractor is more stable and reliable.
The invention also provides an agricultural vehicle curve path automatic driving system which comprises a vehicle controller and a memory, wherein the memory stores the automatic driving path following experience data, and the vehicle controller is connected with the memory and is used for controlling the vehicle to automatically drive on a straight path according to the automatic driving path following experience data.
In the present embodiment, the curve path automatic driving following empirical data is obtained by the following method:
the method comprises the steps of acquiring vehicle position information in the vehicle driving process in real time;
and judging whether the angle deviation value of the vehicle and the preset curve operation track exceeds a preset maximum angle deviation value or not based on the acquired vehicle position information, if not, judging that the vehicle does not run to the target position, and performing machine learning by enhancing signal feedback.
In this embodiment, the machine learning by enhancing signal feedback specifically includes:
judging whether the wheel corner adjustment strategy is good or bad through the enhanced signal feedback, and determining the wheel corner adjustment strategy as a wrong behavior strategy and learning when the enhanced signal feedback value is small; when the angle deviation value is not equal to zero, presetting the enhancement signal feedback value to be reduced along with the increase of the angle deviation value;
when the enhanced signal feedback value is larger, determining the wheel corner adjustment strategy as an effective behavior strategy, and recording; when the angle deviation value is not equal to zero, presetting the enhanced signal feedback value to be increased along with the reduction of the angle deviation value;
and outputting a wheel rotation angle adjusting value according to the enhanced signal feedback value.
The agricultural vehicle curve path automatic driving system provided by the embodiment enables the tractor to autonomously learn the curve path following strategy by enhancing signal setting and utilizing the machine learning principle, so that the tractor autonomously learns the path following strategy in successful and failed experiences, the phenomenon of back-and-forth swing of operation due to improper angle adjustment can not occur, and the tractor curve path following driving has higher stability and reliability.
The present invention will be described in further detail below with reference to a tractor as an example.
Referring to fig. 1, in the embodiment, an artificial intelligence machine learning manner is adopted, and an automatic driving path following process is realized by enabling a tractor to autonomously learn a path following process, and a specific implementation scheme is as follows.
(1) The mathematical expression of the tractor running process.
Designing a preset curve track, dispersing the curve into a set formed by a plurality of point coordinates, installing a GPS positioning system on a tractor (point A), and reading the position information of the tractor and the coordinates (x) of the point A in real time by a GPS in the driving processA,yA) On the preset curve set track, selecting point B closest to point A with coordinate (x)B,yB) Selecting the next adjacent point C of the B point with the coordinate of (x)C,yC) The angular deviation theta is calculated at A, B, C points, and the deviation angle theta formed by point A on the left side of point B is positive, the deviation angle theta formed by point A on the right side of point B is negative, and the allowable maximum angular deviation is
(2) The tractor automatically drives and follows the way study expression.
A curve track is arranged on a computer, 1000 independent tests are designed, and the tractor is tested each time and runs from the starting point to the end point of the preset track. During driving, the tractor automatically adjusts the wheel rotation angle, and the automatic driving path following is realized by utilizing the machine learning principle. Specifically, during driving, the tractor judges the 'good' and 'bad' of the corner adjusting strategy through enhancing signal feedback. When the enhancement signal value is small, the system will recognize and learn (be punished) the wrong behavior strategy and try to avoid the error from happening again in the later decision making process; when the enhancement signal is large, the effective decision strategy will be remembered by the system in the form of a reward (rewarded), which will be prioritized later in the decision process.
(3) And (3) loading the tractor by an automatic driving method.
After 1000 independent tests on the computer, the tractor's computing system learned that if the tractor was driven, a curved path autopilot would be achieved. The learning experience is stored and loaded on the tractor real vehicle, so that the automatic driving of the real vehicle is realized.
As shown in fig. 2, the implementation steps of the method for automatically driving the curve path of the tractor according to the embodiment include:
step 1: and initializing parameter design.
In this embodiment, an algorithm simulation of path tracking is first performed in a computer. Designing an operation curve track, and dispersing the curve into a set formed by a plurality of point coordinates, wherein the tractor starting point coordinate is (x)0,y0) The coordinate of the end point is (x)G,yG) The tractor runs at a constant speed, the number of tests is N, the initial number of tests N is 0, and the maximum number of tests N is 1000.
Step 2: and (5) judging the test times.
And judging whether the test is executed 1000 times, if so, namely N is greater than 1000, terminating the program, and entering the step 8, otherwise, executing the next step.
And step 3: and designing an initial angle.
At the starting position (x)0,y0) At an initial angle theta0As an adjustment value for the tractor travel angle.
And 4, step 4: and calculating the angle deviation.
According to the adjustment of the angle, the tractor drives to the next state A point with the coordinate of (x)A,yA) Selecting a point B which is closest to the point B and has the coordinate of (x)B,yB) The calculation method is to select all points on the curve, calculate the distance between the point A and all points, and take the point with the minimum value as the point B close to the point. Selecting a next adjacent point C of the point B with the coordinate of (x)C,yC) The angle deviation theta is calculated by utilizing the cosine theorem, and the calculation method is as follows:
wherein
And 5: and (5) judging the running state.
Judging whether the running state of the tractor is consistent with the curve or not by judging whether the theta is larger than the theta
If it is not
The tractor drives in the direction opposite to the curve, the driving experience is a failed driving experience, the driving data is stored, experience is provided for the subsequent tractor driving, and the step 3 is repeated. If it is not
Indicating that the tractor is still traveling within normal operating range, the next step.
Step 6: and judging whether the target is reached.
Judging whether the tractor runs to the target position of the track or not by the following judgment method if the tractor runs to the target position of the track
When the tractor runs to the target position of the track, the running process is a successful running process, successful experiences are stored, experiences are provided for the follow-up running of the tractor, and the step 2 is repeated, wherein N is N + 1; otherwise, it indicates that the tractor is not driven to the target position, and the next step.
And 7: and (4) machine learning.
Designing an enhanced signal of a machine learning system according to the position offset of the tractor, wherein the design method is that r-theta, when theta is 0, the tractor driving position is consistent with a preset track, and the machine learning system obtains the maximum reward value r-0; when θ ≠ 0, the value of the enhancement signal will decrease as d increases, indicating that the machine learning system is getting a corresponding penalty. The values of the enhanced signals represent 'good' and 'bad' executed by the machine learning system, and provide judgment basis for the machine learning system. The machine learning system is toAnd making a corresponding angle adjustment decision according to the value of the enhanced signal, and outputting an angle adjustment theta. The actual angle is adjusted to be positive or negative in consideration of the positional deviation
The adjustment of the angle will bring the tractor to the next state and repeat step 4.
And 8: and (6) loading for actual measurement.
The automatic following experience learned by the tractor is stored and loaded on a real vehicle. The actual vehicle realizes better automatic driving according to the learned driving experience of the tractor.
The embodiment provides an automatic driving method for a curve path of a tractor, and has the following technical effects:
1) the automatic following of the agricultural tractor along the preset curve line is realized, the work tasks of the driver are liberated, and the phenomenon of deviation of the driving operation caused by insufficient judgment capability or improper operation of the driver is reduced.
2) The automatic driving is realized by utilizing a machine learning mode, so that the tractor autonomously learns a path following strategy in successful and failed experiences, the phenomenon of back-and-forth swing of operation due to improper angle adjustment is avoided, and the automatic driving of the curve path of the tractor has higher stability and reliability.
3) The automatic driving path following method of the tractor is also suitable for other agricultural vehicles needing curve driving operation.
The above-listed detailed description is only a specific description of a possible embodiment of the present invention, and they are not intended to limit the scope of the present invention, and equivalent embodiments or modifications made without departing from the technical spirit of the present invention should be included in the scope of the present invention.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein.
Claims (6)
1. An agricultural vehicle curve path automatic driving method is characterized by comprising the following steps:
acquiring vehicle position information in the vehicle running process in real time;
whether the angle deviation value of the vehicle and the preset curve operation track exceeds the preset maximum angle deviation value is judged based on the acquired vehicle position information, if not, the vehicle does not run to the target position, machine learning is carried out through enhanced signal feedback to acquire curve path automatic driving following experience, and the method specifically comprises the following steps of:
judging whether the wheel corner adjustment strategy is good or bad through the enhanced signal feedback, and determining the wheel corner adjustment strategy as a wrong behavior strategy and learning when the enhanced signal feedback value is small; when the angle deviation value is not equal to zero, presetting an enhanced signal feedback value to be reduced along with the increase of the angle deviation value;
when the enhanced signal feedback value is larger, determining the wheel corner adjustment strategy as an effective behavior strategy, and recording; when the angle deviation value is not equal to zero, presetting an enhanced signal feedback value to be increased along with the reduction of the angle deviation value;
and automatically driving the vehicle to follow the experience according to the curve path to control the vehicle to automatically travel along the curve path.
2. The method of claim 1, further comprising: and if the angle deviation value exceeds the preset maximum angle deviation value, storing the driving record, and recording the driving record as failed driving experience.
3. The method of claim 1, further comprising: and if the angle deviation value does not exceed the preset maximum angle deviation value and the vehicle runs to the target position, storing the running record and recording the running record as a successful running experience.
4. The method of claim 1, wherein the obtaining of curve path autopilot following experience by machine learning with enhanced signal feedback further comprises: and outputting a wheel rotation angle adjusting value according to the enhanced signal feedback value for the vehicle to adjust the wheel rotation angle.
5. The method of claim 1, wherein the obtaining of the automatic driving path following experience through the machine learning by enhancing the signal feedback further comprises: and when the angle deviation value is zero, judging that the running position of the vehicle is consistent with the preset curve operation track, and recording.
6. The curve path automatic driving system of the agricultural vehicle is characterized by comprising a vehicle controller and a memory, wherein curve path automatic driving following experience data are stored in the memory, and the vehicle controller is connected with the memory and is used for controlling the vehicle to automatically drive along a curve path according to the curve path automatic driving following experience data; the curve path automatic driving following experience data is obtained by the following method:
the method comprises the steps of acquiring vehicle position information in the vehicle driving process in real time;
judging whether the angle deviation of the vehicle and a preset curve operation track exceeds a preset maximum angle deviation value or not based on the acquired vehicle position information, if not, if the vehicle does not run to a target position, and performing machine learning by enhancing signal feedback; the machine learning by enhancing signal feedback specifically includes:
judging whether the wheel corner adjustment strategy is good or bad through the enhanced signal feedback, and determining the wheel corner adjustment strategy as a wrong behavior strategy and learning when the enhanced signal feedback value is small; when the angle deviation value is not equal to zero, presetting an enhanced signal feedback value to be reduced along with the increase of the angle deviation value;
when the enhanced signal feedback value is larger, determining the wheel corner adjustment strategy as an effective behavior strategy, and recording; when the angle deviation value is not equal to zero, presetting an enhanced signal feedback value to be increased along with the reduction of the angle deviation value;
and outputting a wheel rotation angle adjusting value according to the enhanced signal feedback value.
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| CN115855022A (en) | 2017-04-07 | 2023-03-28 | 辉达公司 | Performing autonomous path navigation using deep neural networks |
| US10705525B2 (en) | 2017-04-07 | 2020-07-07 | Nvidia Corporation | Performing autonomous path navigation using deep neural networks |
| CN108820042B (en) * | 2018-05-25 | 2020-04-10 | 东软集团股份有限公司 | Automatic driving method and device |
| CN113156934B (en) * | 2019-12-31 | 2022-02-25 | 上海司南卫星导航技术股份有限公司 | Automatic driving system and method of vehicle and non-transitory computer readable storage medium |
| CN116048103B (en) * | 2023-03-27 | 2023-11-03 | 惠民县农业技术推广中心(惠民县农业广播电视学校) | Method, device, equipment and storage medium for automatically adjusting handover line of agricultural machine |
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