CN210011734U - Unmanned vehicle path tracking control device based on multi-point tracking - Google Patents
Unmanned vehicle path tracking control device based on multi-point tracking Download PDFInfo
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- CN210011734U CN210011734U CN201920476278.4U CN201920476278U CN210011734U CN 210011734 U CN210011734 U CN 210011734U CN 201920476278 U CN201920476278 U CN 201920476278U CN 210011734 U CN210011734 U CN 210011734U
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Abstract
The utility model relates to an unmanned vehicle path tracking control device based on multiple spot tracking, controlling means comprises GPS data acquisition module, vehicle speed acquisition module, data preprocessing module, calculation module and drive-by-wire actuating system. The control method comprises the steps of collecting and acquiring GPS position and attitude information of a vehicle in real time, and collecting GPS coordinates of points on a tracking track path; preprocessing the track information, and smoothing the tracking track by using a Gaussian filtering method; determining a pre-aiming window, a pre-aiming interval and a pre-aiming point of a tracking path reference point, calculating course angle deviation and position deviation of a current vehicle coordinate point and a selected pre-aiming point, calculating a front wheel rotating angle corresponding to each pre-aiming coordinate point according to the position deviation and the course angle deviation, determining a front wheel rotating angle corresponding to each pre-aiming coordinate point according to road condition information and vehicle state information, and summing and outputting the front wheel rotating angles according to equal weight to achieve the purpose of path tracking.
Description
Technical Field
The utility model relates to an unmanned vehicle path tracking control device, in particular to unmanned vehicle path tracking control device based on multiple spot is trailed.
Background
The path tracking is an essential link for executing a planning decision layer instruction in the unmanned automobile, and the driving safety of the unmanned automobile is directly influenced by the quality of the path tracking capability. For the path tracking method, whether the selection of the tracking reference point is good or not has great influence on the path tracking capability, which influences the path tracking precision and even influences the safety of the unmanned automobile.
Different from the traditional path tracking method adopting a single-point preview method, the dynamically adjusted multi-point preview path tracking method not only can adjust the positions of preview points according to the change of the vehicle state, but also can adjust the number of the preview points and the corresponding output weight of each preview point according to the vehicle running information. With the gradual development of the unmanned technology, the path tracking research aiming at multi-point preview is gradually deepened, so that the path tracking precision is improved, and the comfort and the safety of the unmanned vehicle are improved.
Chinese patent CN201810409217.6 discloses a pure tracking model based path tracking method for industrial robot, which uses the average value of the angular deviation and the position deviation of all points in the target point set according to the pure tracking method, and it is also single-point preview essentially, and it is not possible to dynamically adjust the position of the target point set according to the vehicle driving state and road parameters, and it is likely not to reach the ideal tracking accuracy.
Chinese patent cn201711183868.x discloses a feedforward control method for unmanned vehicle path tracking, in which a path curvature is added as an interference input item in a traditional vehicle path tracking system model in the control method for path tracking. In addition, in this patent, a feedforward control method is designed with only the lateral position deviation going to zero as an objective function. Tracking errors caused by course angle deviation are not considered, and the phenomenon that vehicles are drawn like a dragon under certain road conditions is caused.
Chinese patent CN200910160909.2 discloses an intelligent vehicle path tracking front wheel steering angle compensation control method, which uses an improved Pure Pursuit algorithm, wherein the forward looking distance in a path tracking controller is obtained by a longitudinal vehicle speed fuzzy controller and the path curvature information in an online self-adaptive manner, and the forward looking distance is essentially single-point preview and cannot comprehensively consider the simultaneous influence of the remote point track and the near point track of the preview on path tracking in the driving process of the vehicle.
Disclosure of Invention
The utility model aims at providing an unmanned vehicle path tracking controlling means based on multiple spot tracking just lies in to above-mentioned prior art not enough, follows the angle that improves path tracking precision, adopts dynamic adjustment's multiple spot preview method, improves the path tracking precision and the security of vehicle.
The utility model aims at realizing through the following technical scheme:
a multi-point tracking-based unmanned vehicle path tracking control device is composed of a GPS data acquisition module 1, a vehicle speed acquisition module 2, a data preprocessing module 3, a calculation module 4 and a line control execution system 5. The GPS data acquisition module 1 and the vehicle speed acquisition module 2 are respectively connected with the line control execution system 5 through a data preprocessing module 3 and a calculation module 4.
The GPS data acquisition module 1 and the vehicle speed acquisition module 2 provide original data for the data preprocessing module 3, the data preprocessing module 3 transmits processed information to the calculation module 4, and the output of the calculation module is connected with the line control execution system 5.
The periods of data acquisition and data acquisition of the GPS data acquisition module 1 and the vehicle speed acquisition module 2 are both T1The signal output period of the computing module 4 is T2(ii) a The GPS data acquisition module 1 acquires and acquires the position and attitude information of the vehicle in real time and acquires GPS coordinate points on a path to be tracked, and the vehicle speed acquisition module 2 acquires vehicle speed information.
The data preprocessing module 3 preprocesses the coordinate points on the tracking path, and takes the preprocessed GPS coordinate path points as reference points for path tracking.
The calculation module 4 calculates a preview selection window of the tracking path reference point, namely a preview farthest distance and a preview nearest distance; determining a preview interval, and filtering and selecting a plurality of preview points; calculating course angle deviation and position deviation of the selected interference aiming point of the current vehicle coordinate point; calculating a front wheel rotation angle corresponding to each pre-aiming coordinate point; and determining the weight of the front wheel rotation angle corresponding to each preview coordinate point according to the road condition information and the vehicle state information, and finally summing and outputting the front wheel rotation angles according to the previous weights.
Compared with the prior art, the beneficial effects of the utility model reside in that: the utility model discloses the upper and lower limits of window are aimed in advance to the information dynamic adjustment according to the running state of vehicle and road environment to dynamic adjustment has aimed in advance in the window and has aimed the interval, makes the definite more reasonable of point of aiming in advance, all has great adaptability to different running state and different road conditions. The invention further uses the combined action of the course deviation and the position deviation to enable the information considered during the calculation of the front wheel corner to be more comprehensive, and finally calculates the final output steering wheel corner according to equal weight through each pre-aiming point.
Drawings
FIG. 1 is a schematic flow chart of a method for controlling the path tracking of an unmanned vehicle based on multipoint tracking;
FIG. 2 is a schematic diagram of a path tracking control device of an unmanned vehicle based on multi-point tracking;
FIG. 3 is a schematic view of a heading angle deviation and a lateral position deviation.
In the figure, the system comprises a GPS data acquisition module 1, a vehicle speed acquisition module 3, a data preprocessing module 4, a calculation module 5 and a line control execution system.
Detailed Description
The present invention is further described in connection with the following examples, which are intended to illustrate the invention and not to limit the scope of the invention, which, after reading the present invention, are intended to cover modifications of the invention in its various equivalent forms by those skilled in the art, which fall within the scope of the appended claims.
As shown in fig. 1 and 2, the unmanned vehicle path tracking control device based on multipoint tracking is composed of a GPS data acquisition module 1, a vehicle speed acquisition module 2, a data preprocessing module 3, a calculation module 4 and a line control execution system 5. The GPS data acquisition module 1 and the vehicle speed acquisition module 2 provide original data for the data preprocessing module 3, the data preprocessing module 3 transmits processed information to the calculation module 4, and the output of the calculation module is connected with the line control execution system 5. Referring to fig. 3, a is a preview point in the preview window, b is a preprocessed ideal reference track, c is a transverse deviation between a center point of a center line of a rear axle of the vehicle and the preview point, d is the vehicle, e is an included angle between a longitudinal axis of the vehicle and a connecting line between the center point of the center line of the rear axle of the vehicle and a boundary reference point of the preview window, and an angle formed by rotating the longitudinal axis of the vehicle to the connecting line between the center point of the center line of the rear axle of the vehicle and the boundary reference point of the preview window in a counterclockwise direction by taking the longitudinal axis of the vehicle as a reference line is positive.
The periods of data acquisition and data acquisition of the GPS data acquisition module 1 and the vehicle speed acquisition module 2 are both T1In this embodiment, 0.002 second is taken, and the signal output period of the calculating module 4 is T2In this example, 0.005 second is taken.
The GPS data acquisition module 1 acquires and acquires the position and posture information of the vehicle in real time, and acquires GPS coordinate points on a path to be tracked, and the vehicle speed acquisition module 2 acquires vehicle speed information;
the data preprocessing module 3 preprocesses the coordinate points on the tracking path, and takes the preprocessed GPS coordinate path points as reference points for path tracking;
the calculation module 4 calculates a selection preview window of the tracking path reference point, namely the preview farthest distance and the preview nearest distance;
the calculation module 4 determines the preview interval, and filters and selects the preview points if the preview points are intervened;
the calculation module 4 calculates the course angle deviation and the position deviation of the interference aiming point selected by the current vehicle coordinate point;
the calculation module 4 calculates a front wheel rotation angle corresponding to each preview coordinate point;
the calculation module 4 determines the weight of the front wheel rotation angle corresponding to each pre-aiming coordinate point according to the road condition information and the vehicle state information, and finally, the front wheel rotation angles are summed and output according to the previous weights;
when the data preprocessing module 3 calculates, the method comprises the following steps:
converting GPS longitude and latitude coordinate information acquired by a GPS data acquisition module 1 into rectangular coordinates, neglecting factors on the altitude during conversion, assuming that GPS coordinate points acquired by a data acquisition processing unit are on the same horizontal height, selecting a first point on a reference path as a rectangular coordinate origin, and obtaining tracking path track coordinates under a rectangular coordinate system;
secondly, filtering the points on the tracking path obtained by conversion by a Gaussian filtering method to remove burr points on the path;
the calculation module 4 calculates a selected preview window of the tracking path reference point, comprising the steps of:
firstly, determining the upper bound distance of the preview window according to the following formula:
wherein k is1The value of the speed coefficient is the upper bound of the preview window, the value range is 0.1-3, in the example, 3, v is takencarThe value calculated in this example is the speed value in m/s, k, which is the actual speed of the vehicle2The angle coefficient of the upper bound of the preview window is 0-20, 10 is taken in the example, theta is the included angle of the connecting line of the central line of the longitudinal axis of the vehicle, the central point of the central line of the rear axle of the vehicle and the upper bound reference point of the preview window, the angle of the connecting line of the central line of the rear axle of the vehicle and the upper bound reference point of the preview window which rotates anticlockwise by taking the longitudinal axis of the vehicle as the reference line is positive, k is3The minimum preview distance of the upper bound of the preview window is 5-10, and 8 is taken in the example;
secondly, determining the lower bound distance of the preview window according to the following formula:
Llow＝i1×vcar+i2×|sinθ|2+i3，
wherein i1The value of the speed coefficient is the lower bound of the preview window, the value range is 0.1-3, in the example, 1, v is takencarIs the actual speed of the vehicle, the value speed value in calculation, i2The angle coefficient of the upper bound of the preview window is in a range of 0-20, in the example, the value is 10, theta is an included angle between a connecting line of a longitudinal axis of the vehicle and a central line midpoint of a rear shaft of the vehicle and an upper bound reference point of the preview window, an angle from a connecting line of the longitudinal axis of the vehicle serving as a reference line and rotating to the central line midpoint of the rear shaft of the vehicle and the upper bound reference point of the preview window in a counterclockwise direction is positive, and i3The minimum preview distance of the upper bound of the preview window is 5-10, in this example, 5 is taken;
the calculation module 4 calculates the determination of the preview interval and the preview point, and comprises the following steps:
step one, the determination of the interval between the preview points is calculated according to the following formula:
wherein L is the interval between adjacent preview points, LupIs the upper bound distance, L, of the preview windowlowTaking 1 as a preview interval coefficient in the example, and R is the maximum curvature radius of an arc in the preview window;
step two, determining a preview point in a preview window according to the following method:
if L is ≧ (L)up-Llow) Then only take Lup，LlowTwo pre-aiming points corresponding to the pre-aiming distances are determined by taking the middle point of the rear axle of the vehicle as the origin and taking the L as the Lup，LlowDrawing a circle for the radius, and taking an intersection point of the circle and the reference path in front of the vehicle as a pre-aiming point;
if L < (L)up-Llow) Then the pre-aiming distance corresponding to each pre-aiming point is Llow，Llow+l,Llow+2×l,…,LupThe center point of the rear axle of the vehicle is also taken as the circle center, and L is taken as the circle centerlow，Llow+l,Llow+2×l,…,LupDrawing a circle for the radius, and taking an intersection point of the circle and the reference path in front of the vehicle as a pre-aiming point;
the calculation module 4 calculates a front wheel rotation angle corresponding to each preview coordinate point, and the calculation formula is as follows:
where δ' is the front wheel steering angle, L is the distance between the front and rear axles of the vehicle, α is the sailing angle deviation, LdFor the pre-aiming distance, kk is the coefficient of lateral deviation, the value of this example is 0.05, derrIs a lateral position deviation;
the calculation module 4 determines the weight of the front wheel rotation angle corresponding to each preview coordinate point according to the road condition information and the vehicle state information, and finally, the front wheel rotation angles are summed and output according to equal weight, and the calculation formula is as follows:
wherein δ is an output front wheel steering angle δ'iAnd n is the number of the preview points.
The embodiment provides a method for controlling the path tracking of an unmanned vehicle based on multipoint tracking. According to the control method of the embodiment, the paths with different curvatures can be tracked, so that the steering safety and the path tracking accuracy of the unmanned vehicle are improved.
Claims (2)
1. The utility model provides an unmanned vehicle path tracking control device based on multiple spot tracking which characterized in that: the system is composed of a GPS data acquisition module (1), a vehicle speed acquisition module (2), a data preprocessing module (3), a calculation module (4) and a line control execution system (5); the GPS data acquisition module (1) and the vehicle speed acquisition module (2) are respectively connected with the line control execution system (5) through the data preprocessing module (3) and the calculation module (4);
the GPS data acquisition module (1) is used for acquiring and acquiring the position and attitude information of the vehicle in real time and acquiring GPS coordinate points on a path to be tracked; the vehicle speed acquisition module (2) is used for acquiring vehicle speed information; the data preprocessing module (3) is used for transmitting the processed information to the computing module (4); the calculation module (4) is used for calculating a selection preview window of a tracking path reference point; determining a preview interval, and filtering and selecting a plurality of preview points; calculating course angle deviation and position deviation of the selected interference aiming point of the current vehicle coordinate point; calculating a front wheel rotation angle corresponding to each pre-aiming coordinate point; determining the weight of a front wheel corner corresponding to each preview coordinate point according to the road condition information and the vehicle state information; the output of the computing module (4) is connected with a line control execution system (5).
2. The unmanned vehicle path tracking control device based on multipoint tracking as claimed in claim 1, characterized in that: the GPS data acquisition module (1) and the vehicle speed acquisition module (2) have data acquisition and data acquisition periods of 0.002 seconds, and the signal output period of the calculation module (4) is 0.005 seconds.
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Cited By (9)
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| CN110001637A (en) * | 2019-04-10 | 2019-07-12 | 吉林大学 | A kind of pilotless automobile path following control device and control method based on multiple spot tracking |
| CN111619588A (en) * | 2020-06-08 | 2020-09-04 | 三一汽车制造有限公司 | Driving direction control system and method and unmanned device |
| CN111796521A (en) * | 2020-07-08 | 2020-10-20 | 中国第一汽车股份有限公司 | Foresight distance determining method, device, equipment and storage medium |
| CN112269388A (en) * | 2020-10-30 | 2021-01-26 | 广东杜尼智能机器人工程技术研究中心有限公司 | Automatic navigation control method and device for unmanned sweeper and storage medium |
| CN112572437A (en) * | 2020-12-18 | 2021-03-30 | 北京京东乾石科技有限公司 | Method, device and equipment for determining driving direction and storage medium |
| CN114291117A (en) * | 2022-01-25 | 2022-04-08 | 中山大学 | Four-wheel steering unmanned vehicle trajectory tracking control method |
| CN114355924A (en) * | 2021-12-28 | 2022-04-15 | 北京理工大学 | Improved pure tracking path tracking method |
| CN114995436A (en) * | 2022-06-10 | 2022-09-02 | 桂林电子科技大学 | Vehicle path tracking lateral deviation calculation method based on preview point |
| CN116749950A (en) * | 2023-07-25 | 2023-09-15 | 云创智行科技(苏州)有限公司 | Adaptive steering judging method and device |
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| CN110001637B (en) * | 2019-04-10 | 2023-09-29 | 吉林大学 | Unmanned vehicle path tracking control device and control method based on multipoint tracking |
| CN110001637A (en) * | 2019-04-10 | 2019-07-12 | 吉林大学 | A kind of pilotless automobile path following control device and control method based on multiple spot tracking |
| CN111619588A (en) * | 2020-06-08 | 2020-09-04 | 三一汽车制造有限公司 | Driving direction control system and method and unmanned device |
| CN111796521B (en) * | 2020-07-08 | 2022-06-10 | 中国第一汽车股份有限公司 | Foresight distance determining method, device, equipment and storage medium |
| CN111796521A (en) * | 2020-07-08 | 2020-10-20 | 中国第一汽车股份有限公司 | Foresight distance determining method, device, equipment and storage medium |
| CN112269388A (en) * | 2020-10-30 | 2021-01-26 | 广东杜尼智能机器人工程技术研究中心有限公司 | Automatic navigation control method and device for unmanned sweeper and storage medium |
| CN112572437A (en) * | 2020-12-18 | 2021-03-30 | 北京京东乾石科技有限公司 | Method, device and equipment for determining driving direction and storage medium |
| CN114355924A (en) * | 2021-12-28 | 2022-04-15 | 北京理工大学 | Improved pure tracking path tracking method |
| CN114355924B (en) * | 2021-12-28 | 2023-10-13 | 北京理工大学 | Improved pure tracking path tracking method |
| CN114291117B (en) * | 2022-01-25 | 2023-05-09 | 中山大学 | Four-wheel steering unmanned vehicle track tracking control method |
| CN114291117A (en) * | 2022-01-25 | 2022-04-08 | 中山大学 | Four-wheel steering unmanned vehicle trajectory tracking control method |
| CN114995436A (en) * | 2022-06-10 | 2022-09-02 | 桂林电子科技大学 | Vehicle path tracking lateral deviation calculation method based on preview point |
| CN114995436B (en) * | 2022-06-10 | 2024-05-21 | 桂林电子科技大学 | Vehicle path tracking lateral deviation calculation method based on pre-aiming point |
| CN116749950A (en) * | 2023-07-25 | 2023-09-15 | 云创智行科技(苏州)有限公司 | Adaptive steering judging method and device |
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