CN110109451B - Novel geometric path tracking algorithm considering path curvature - Google Patents
Novel geometric path tracking algorithm considering path curvature Download PDFInfo
- Publication number
- CN110109451B CN110109451B CN201910285814.7A CN201910285814A CN110109451B CN 110109451 B CN110109451 B CN 110109451B CN 201910285814 A CN201910285814 A CN 201910285814A CN 110109451 B CN110109451 B CN 110109451B
- Authority
- CN
- China
- Prior art keywords
- path
- curvature
- point
- front wheel
- angle
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000004422 calculation algorithm Methods 0.000 title claims abstract description 26
- 238000000034 method Methods 0.000 claims abstract description 21
- 238000004088 simulation Methods 0.000 claims abstract description 8
- 238000012937 correction Methods 0.000 claims description 6
- 239000011159 matrix material Substances 0.000 claims description 5
- 238000004364 calculation method Methods 0.000 claims description 4
- 238000006243 chemical reaction Methods 0.000 claims description 3
- 230000003247 decreasing effect Effects 0.000 claims description 3
- 238000013461 design Methods 0.000 claims description 2
- 229910001316 Ag alloy Inorganic materials 0.000 claims 1
- BQCADISMDOOEFD-UHFFFAOYSA-N Silver Chemical compound [Ag] BQCADISMDOOEFD-UHFFFAOYSA-N 0.000 claims 1
- 238000002474 experimental method Methods 0.000 abstract description 2
- 230000000694 effects Effects 0.000 description 4
- 230000007423 decrease Effects 0.000 description 2
- 230000003278 mimic effect Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000011217 control strategy Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000002401 inhibitory effect Effects 0.000 description 1
- 230000000670 limiting effect Effects 0.000 description 1
- 230000010355 oscillation Effects 0.000 description 1
- 230000000452 restraining effect Effects 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0221—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory involving a learning process
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0223—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory involving speed control of the vehicle
Abstract
The invention discloses a novel geometric path tracking algorithm considering path curvature, which comprises the following steps: s1, acquiring path discrete coordinate points at a fixed frequency by using a GPS to obtain a reference path point set P; s2, searching a path point p closest to the current automobile positioni(piBelongs to P); s3, calculating a path point p by using a least square methodiAt the corresponding path curvature ρi(ii) a S4, calculating rho according to the Ackerman steering geometric modeliCorresponding front wheel angle thetaρiAnd front wheel steering angle
S5, calculating the current position and the path point p of the automobileiA course deviation yawrerr, a lateral deviation latErr; s6, according to the direction angle of the front wheel
A course deviation yawErr and a transverse deviation Laterr are designed to a path tracking controller, and a desired direction angle of the front wheel is calculated
Further, a desired rotation angle theta of the front wheel is obtainedt. The method is simple in algorithm, not only suitable for the technical field of simulation, but also capable of being realized on an embedded controller of a real vehicle, and has a good application prospect. Experiments prove that the method provided by the invention has small tracking error and stronger robustness.
Description
Technical Field
The invention relates to the technical field of unmanned vehicle path tracking, in particular to a novel geometric path tracking algorithm considering path curvature.
Background
The lateral control of the automobile is one of the key technologies for realizing the autonomous driving of the unmanned automobile. The path tracking controls the vehicle to travel along an expected path all the time through autonomous steering, meanwhile, the riding comfort and the traveling safety of the vehicle are guaranteed, the intelligent vehicle is a bridge for connecting upper-layer software and bottom-layer hardware of the intelligent vehicle, and the intelligent vehicle is a necessary link for achieving unmanned driving. The curvature-considered path tracking algorithm is that the front wheel rotation angle is estimated according to the curvature of the path to simulate the path change trend, then the negative feedback is used for eliminating the transverse deviation, and the heading deviation negative feedback is used for restraining the front wheel rotation angle to prevent overshoot.
At present, many researchers at home and abroad aim at the path tracking control of the unmanned automobile, mostly use transverse deviation and course deviation as the input of a controller, and use a feedback control method to realize the path tracking of the unmanned automobile. One of the most common path tracking methods is the geometry controller, which uses the geometric relationship of the path to the vehicle and to the vehicle itself to provide a control solution to the path tracking problem. The most representative geometry controller is a pure tracking algorithm, which is widely used due to its simple model, small calculation amount and good tracking effect.
The key to the pure tracking algorithm is the reasonable choice of look-ahead distance. The forward looking distance is too large, so that the transverse error of tracking is slowly eliminated, and an obvious road-reading phenomenon appears on a turning road section; the forward looking distance is too small, so that a good tracking effect is achieved at an extremely low speed, but an extremely serious oscillation phenomenon occurs at a high speed, so that the tracking effect is deteriorated. Therefore, many domestic and foreign scholars propose many improved algorithms based on the pure tracking algorithm to extend the stability of the pure tracking algorithm. Anibal Ollero proposes a tracking algorithm for adjusting the look-ahead distance based on fuzzy logic; the MIT team proposes a tracking algorithm that adaptively adjusts the forward apparent distance reference point based on vehicle speed. Since the control strategy does not consider the curvature of the path, the phenomenon of copying the near path occurs when the curvature of the path changes continuously, especially when the curvature is large.
Disclosure of Invention
In order to solve the problems, the invention discloses a novel geometric path tracking algorithm considering path curvature, which is characterized in that the corresponding automobile front wheel corner is calculated according to the curvature of a reference path to simulate the change trend of the reference path, the transverse deviation in the simulation process is eliminated by negative feedback, the course deviation is utilized to inhibit overshoot, the path tracking is finally realized, and the tracking precision and the tracking stability can be improved.
In order to achieve the purpose, the technical scheme of the invention is as follows:
a novel geometric path tracking algorithm that takes into account path curvature, comprising the steps of:
s1, acquiring path discrete coordinate points at a fixed frequency by using a GPS to obtain a reference path point set P;
the GPS positioning precision is centimeter level, the reference path point set P is represented by plane right angle and is obtained by converting coordinates of longitude and latitude output by GPS into UTM coordinates (universal transverse axis mercator coordinates), so as to facilitate calculation of path curvature and positioning in a global coordinate system Xglobal-YglobalIs converted into a moving vehicle body coordinate system X _ v-X _ v.
S2, searching a path point p closest to the current automobile positioni(pi∈P)；
The data in the reference path point set are arranged in sequence, only the nearest path point needs to be searched for the first time in a global search mode, and only local search is needed to be performed later, so that the advantages of saving computing resources and improving the search speed are achieved.
S3, calculating a path point p by using a least square methodiAt the corresponding path curvature ρi；
Respectively select points pi(xi,yi) Front and back 3 path points, 7 points pi(xi-3,yi-3)，pi(xi-2,yi-2)，pi(xi-1,yi-1)，pi(xi,yi)，pi(xi+1,yi+1)，pi(xi+2,yi+2)，pi(xi+3,yi+3). Wherein i>3
Using least square method to make cubic curve y ═ ai+bi·x+ci·x2+di·x3Fitting, i.e. solving the matrix b from a.x ═ b, since the above-mentioned overdetermined system of equations is not solved, it is possible to determine the matrix b from the matrix bApproximate solution using least square principle
By
To obtain
Wherein:
obtaining a cubic curve y ═ ai0+bi0·x+ci0·x2+di0·x3
Reuse of curvature formula
Calculating the point pi(xi,yi) Curvature ρ of a pointi。
It should be noted that, the curve fitting method determines a curve fitting direction (along the x axis/y axis) by determining whether the x coordinate of the ordered path point changes monotonically (increases/decreases), specifically:
if x is satisfiedi-3＞xi-2＞xi-1＞xi＞xi+1＞xi+2＞xi+3The x coordinate of the ordered path point is monotonically decreased, if x is satisfiedi-3＜xi-2＜xi-1＜xi＜xi+1＜xi+2＜xi+3If the x coordinate of the ordered path point is monotonously increased, in the two cases, y is carried out along the x-axis direction as ai+bi·x+ci·x2+di·x3Fitting a curve;
otherwise, the x coordinate of the ordered path point is not monotonous, and in this case, x is carried out along the y-axis direction as ai+bi·y+ci·y2+di·y3And (6) fitting a curve.
When the curve fit is along the y-axis, the curvature solution and the waypoint heading solution are also adjusted accordingly.
It is emphasized that in a local path, the y coordinate of an ordered path point is necessarily monotonic when its x coordinate is not monotonic.
S4, calculating rho according to the Ackerman steering geometric modeliCorresponding front wheel angle thetaρiAnd front wheel steering angle
According to the ackermann steering principle, the geometrical relationship between the front wheel turning angle and the turning curvature can be expressed as:
tan(θρi)＝L·ρi
thus, it is obtained:
θρi＝arctan(L·ρi)
further, the direction angle of the front wheel is obtained
Wherein, thetaρiIs the steering angle of the front wheel, L is the wheel base, rhoiIs a path point piThe curvature of the (c) is such that,
is the front wheel steering angle, yawcurrentThe current car heading angle.
S5, calculating the current position and the path point p of the automobileiA course deviation yawrerr, a lateral deviation latErr;
the first derivative of the cubic curve fitted in step S3 can be used to obtain the heading angle yaw of the path reference pointiNamely that
yawi＝y′(xi)
Then, obtaining the course deviation:
yawErr＝yawi-yawvehicle
wherein yawErr is the course deviation, yawiFor path referencePoint course angle, yawvehicleIs the vehicle heading angle.
For determining the lateral deviation, the coordinates (x) of the path reference point in the global coordinate system are determinedR,yR) Coordinate (x) converted to vehicle body coordinate systemr,yr) The conversion formula can be expressed as:
wherein (x)vehicle,yvehicle) Is the position coordinate of the automobile under the UTM coordinate.
Then the lateral deviation is obtained:
S6, according to the direction angle of the front wheel
A course deviation yawErr and a transverse deviation Laterr are designed to a path tracking controller, and a desired direction angle of the front wheel is calculated
Further, the expected rotation angle theta of the front wheel is obtainedt。
The path tracking controller may be represented as:
wherein
The desired front wheel heading angle (the angle of the front wheel to true north), to mimic the trend of path changes,
k1a lateral deviation correction coefficient for eliminating the lateral deviation,
k2is a course deviation correction systemAnd a number of the front wheel turning angle suppressing means for suppressing the overshoot.
Finally, the expected rotation angle of the front wheel is obtained
The invention has the beneficial effects that:
a novel geometric path tracking algorithm considering curvature is provided for an unmanned automobile, the path curvature is used for calculating a front wheel corner to simulate the path change trend, the transverse deviation is adjusted by negative feedback, and the transverse deviation exceeding is restrained by the transverse deviation negative feedback. Experiments prove that the path tracking algorithm provided by the invention has better applicability to curve paths. The method has the advantages of simple algorithm principle and low calculation complexity, is suitable for the technical field of simulation, can be realized on an embedded controller of a real vehicle, and has good application prospect.
Drawings
FIG. 1 is a flow chart of the algorithm of the present invention.
Fig. 2 is a schematic view of an ackermann steering model.
Fig. 3, 4 and 5 show simulation results of the path tracking controller according to the present invention.
Detailed Description
The present invention will be further illustrated with reference to the accompanying drawings and specific embodiments, which are to be understood as merely illustrative of the invention and not as limiting the scope of the invention.
The general design idea of the invention is as follows: and calculating the corresponding automobile front wheel corner according to the curvature of the reference path to simulate the change trend of the reference path, eliminating the transverse deviation in the simulation process by using negative feedback, and inhibiting overshoot by using course deviation to finally realize path tracking.
An embodiment of the invention comprises the following steps:
s1, acquiring path discrete coordinate points at fixed frequency by using a GPS to obtain a reference path point set P;
the GPS positioning precision is centimeter level, the reference path point set P is represented by a plane right angle, and coordinates are converted into UTM coordinates through longitude and latitude output by the GPS(Universal Cross-axis mercator coordinates) to facilitate the computation of path curvature and to be located in the global coordinate System Xglobal-YglobalIs converted into a moving vehicle body coordinate system X _ v-X _ v.
S2, searching a path point p closest to the current automobile positioni(pi∈P)；
The data in the reference path point set are arranged in sequence, only the nearest path point needs to be searched for the first time in a global search mode, and only local search is needed to be performed later, so that the advantages of saving computing resources and improving the search speed are achieved.
S3, calculating a path point p by using a least square methodiAt the corresponding path curvature ρi；
Respectively select points pi(xi,yi) Front and back 3 path points, 7 points pi(xi-3,yi-3)，pi(xi-2,yi-2)，pi(xi-1,yi-1)，pi(xi,yi)，pi(xi+1,yi+1)，pi(xi+2,yi+2)，pi(xi+3,yi+3). Wherein i>3。
Using least square method to make cubic curve y ═ ai+bi·x+ci·x2+di·x3Fitting, i.e. obtaining the matrix b from a.x ═ b, and since the system of overdetermined equations is not solved, the approximate solution is obtained using the principle of least squares
By
To obtain
Wherein:
obtaining a cubic curve of y ═ ai0+bi0·x+ci0·x2+di0·x3
Reuse of curvature formula
Calculating the point pi(xi,yi) Curvature ρ of a pointi。
It should be noted that, the curve fitting method determines a curve fitting direction (along the x axis/y axis) by determining whether the x coordinate of the ordered path point changes monotonically (increases/decreases), specifically:
if x is satisfiedi-3＞xi-2＞xi-1＞xi＞xi+1＞xi+2＞xi+3The x coordinate of the ordered path point is monotonically decreased, if x is satisfiedi-3＜xi-2＜xi-1＜xi＜xi+1＜xi+2＜xi+3If the x coordinate of the ordered path point is monotonously increased, in the two cases, y is carried out along the x-axis direction as ai+bi·x+ci·x2+di·x3Fitting a curve;
otherwise, the x coordinate of the ordered path point is not monotonous, and in this case, x is carried out along the y-axis direction as ai+bi·y+ci·y2+di·y3And (6) fitting a curve.
When the curve fit is along the y-axis, the curvature solution and the waypoint heading solution are also adjusted accordingly.
It is emphasized that in a local path, the y coordinate of an ordered path point is necessarily monotonic when its x coordinate is not monotonic.
S4, calculating rho according to the Ackerman steering geometric modeliCorresponding front wheel angle thetaρiAnd front wheel steering angle
According to the ackerman steering principle, the geometrical relationship between the front wheel turning angle and the turning curvature can be expressed as:
tan(θρi)＝L·ρi
thus, it is obtained:
θρi＝arctan(L·ρi)
further, the direction angle of the front wheel is obtained
Wherein, thetaρiIs the steering angle of the front wheel, L is the wheel base, rhoiIs a path point piThe curvature of the (c) is such that,
is the front wheel steering angle, yawcurrentThe current car heading angle.
S5, calculating the current position and the path point p of the automobileiA course deviation yawrerr, a lateral deviation latErr;
the first derivative of the cubic curve fitted in step S3 can be used to obtain the heading angle yaw of the path reference pointiNamely, it is
yawi＝y′(xi)
Then, obtaining the course deviation:
yawErr＝yawi-yawvehicle
wherein yawErr is the course deviation, yawiIs the path reference point course angle, yawvehicleIs the vehicle heading angle.
For determining the lateral deviation, the coordinates (x) of the path reference point in the global coordinate system are determinedR,yR) Coordinate (x) converted into vehicle body coordinate systemr,yr) The conversion formula can be expressed as:
wherein (x)vehicle,yvehicle) The position coordinates of the automobile in the UTM coordinate system.
Then the lateral deviation is obtained:
S6, according to the direction angle of the front wheel
A course deviation yawErr and a transverse deviation Laterr are designed to a path tracking controller, and a desired direction angle of the front wheel is calculated
Further, a desired rotation angle theta of the front wheel is obtainedt。
The path tracking controller may be represented as:
wherein
The desired front wheel heading angle (the angle of the front wheel to true north), to mimic the trend of path changes,
k1a lateral deviation correction coefficient for eliminating the lateral deviation,
k2the correction coefficient is used to restrain the front wheel angle and prevent overshoot.
Finally, the expected rotation angle of the front wheel is obtained
The simulation results show that: when the initial transverse deviation is 1 meter, the tracking average transverse deviation is 11.19cm, and the course deviation is 2.86 degrees; when the initial transverse deviation is-1 meter, the tracking average transverse deviation is 11.13cm, and the course deviation is 2.88 degrees; when there is no initial lateral deviation, the tracking average lateral deviation is 2.21cm and the heading deviation is 0.85 °. Simulation test results show that the path tracking algorithm has a good tracking effect.
The technical means disclosed in the invention scheme is not limited to the technical means disclosed in the above embodiment, and also includes the technical scheme formed by any improvement of the above technical features.
Claims (6)
1. A novel geometric path tracking algorithm that takes into account path curvature, comprising the steps of:
s1, acquiring path discrete coordinate points at a fixed frequency by using a GPS to obtain a reference path point set P;
s2, searching a path point p closest to the current automobile positioni(pi∈P)；
S3, calculating a path point p by using a least square methodiAt the corresponding path curvature ρi；
S4, calculating rho according to the Ackerman steering geometric modeliCorresponding front wheel corner thetaρiAnd front wheel steering angle
S5, calculating the current position and the path point p of the automobileiA course deviation yawrerr, a lateral deviation latErr;
s6, according to the direction angle of the front wheel
Automobile current course angle yawvehicleCourse deviation yawar and lateral deviation latErr design path tracking controller:
wherein
For desired direction angle, k, of the front wheel1For the lateral deviation correction coefficient, k2Calculating the expected turning angle of the front wheel for the course deviation correction coefficient
2. A novel geometric path-tracking algorithm taking into account path curvature according to claim 1, characterized in that: in the step S1, the step of,
the GPS positioning accuracy is centimeter level, the reference path point set P is represented by plane right angle and obtained by converting coordinates into universal transverse axis mercator coordinates through longitude and latitude output by GPS, and the purpose is to calculate path curvature and position the path in a global coordinate system X convenientlyglobal-YglobalIs converted into a moving vehicle body coordinate system X _ v-X _ v.
3. A novel geometric path-tracking algorithm taking into account path curvature according to claim 1, characterized in that: in the step S3, in the above step,
calculation of Path points p Using the least squares methodi(piE P) corresponding path curvature PiThe specific process is as follows:
respectively select point pi(xi,yi) The simulation proves that the better fitting result is obtained when N is 3 for each of the front and back N path points, and the following description is given by taking N as 3, and the 7 (2N +1) path points are p respectivelyi(xi-3,yi-3)，pi(xi-2,yi-2)，pi(xi-1,yi-1)，pi(xi,yi)，pi(xi+1,yi+1)，pi(xi+2,yi+2)，pi(xi+3,yi+3) Wherein i>3；
Using least square method to make cubic curve y ═ ai+bi·x+ci·x2+di·x3Fitting, i.e. obtaining the matrix b from a.x ═ b, and since the system of overdetermined equations is not solved, the approximate solution is obtained using the principle of least squares
By
To obtain
Wherein:
obtaining a cubic curve of y ═ ai0+bi0·x+ci0·x2+di0·x3，
Reuse of curvature formula
Calculating the point pi(xi,yi) Curvature of the point, get ρi。
4. A novel geometric path-tracking algorithm taking into account path curvature according to claim 3, characterized in that: in the step S3, the curve fitting method adopts an ordered discrete point fitting method: determining the curve fitting direction along the x axis or the y axis by judging whether the x coordinate value of the ordered path point changes monotonously;
in particular to a method for preparing a high-performance nano-silver alloy,
if x is satisfiedi-3＞xi-2＞xi-1＞xi＞xi+1＞xi+2＞xi+3The x coordinate of the ordered path point is monotonically decreasing, if x is satisfiedi-3＜xi-2＜xi-1＜xi＜xi+1＜xi+2＜xi+3If the x coordinate of the ordered path point is monotonically increasing, in both cases, y is carried out along the x-axis direction as ai+bi·x+ci·x2+di·x3Fitting a curve;
otherwise, the x coordinate of the ordered path point does not change monotonically, and in this case, x is carried out along the y-axis direction as ai+bi·y+ci·y2+di·y3Fitting a curve;
when the curve fitting is along the y axis, the curvature solving and the path point course solving also need to be correspondingly adjusted;
when the x coordinate value of the ordered path point does not change monotonically in the local path, the y coordinate thereof changes monotonically without fail.
5. A novel geometric path-tracking algorithm taking into account path curvature according to claim 1, characterized in that: in the step S4, in the above step,
according to the ackermann steering geometry principle, the geometrical relationship between the front wheel turning angle and the turning curvature can be expressed as:
tan(θρi)＝L·ρi
thus, it is obtained:
θρi＝arctan(L·ρi)
further, obtain
Wherein, thetaρiIs the steering angle of the front wheel, L is the wheel base, rhoiIs a path point piThe curvature of the (c) is such that,
is the front wheel steering angle, yawcurrentThe current car heading angle.
6. A novel geometric path-tracking algorithm taking into account path curvature according to claim 1, characterized in that: in the step S5, in the above step,
the first derivative of the cubic curve fitted in step S3 can be used to obtain the heading angle yaw of the path reference pointiNamely, it is
yawi＝y′(xi)
Then, obtaining the course deviation:
yawErr＝yawi-yawvehicle
wherein yawErr is the course deviation, yawiIs the path reference point course angle, yawvehicleIs the angle of the course of the automobile,
coordinate (x) of path reference point in global coordinate systemR,yR) Coordinate (x) converted to vehicle body coordinate systemr,yr) The conversion formula can be expressed as:
wherein (x)vehicle,yvehicle) Is the position coordinate of the automobile under the mercator coordinate of the transverse axis,
then the lateral deviation is obtained:
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910285814.7A CN110109451B (en) | 2019-04-10 | 2019-04-10 | Novel geometric path tracking algorithm considering path curvature |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910285814.7A CN110109451B (en) | 2019-04-10 | 2019-04-10 | Novel geometric path tracking algorithm considering path curvature |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110109451A CN110109451A (en) | 2019-08-09 |
| CN110109451B true CN110109451B (en) | 2022-07-12 |
Family
ID=67484116
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910285814.7A Active CN110109451B (en) | 2019-04-10 | 2019-04-10 | Novel geometric path tracking algorithm considering path curvature |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110109451B (en) |
Families Citing this family (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110823238A (en) * | 2019-11-05 | 2020-02-21 | 湖南大学 | Improved cubic spline interpolation curve path point fitting method |
| CN110806218B (en) * | 2019-11-29 | 2021-09-07 | 北京京东乾石科技有限公司 | Parking path planning method, device and system |
| CN112000090B (en) * | 2020-07-03 | 2023-06-27 | 中国北方车辆研究所 | Track queue management method for unstructured road |
| CN112099493B (en) * | 2020-08-31 | 2021-11-19 | 西安交通大学 | Autonomous mobile robot trajectory planning method, system and equipment |
| CN112527000B (en) * | 2020-12-23 | 2021-10-26 | 中南大学 | Local path planning method and system for mine underground intelligent driving |
| CN113050657B (en) * | 2021-03-29 | 2021-09-17 | 紫清智行科技(北京)有限公司 | Waypoint processing method and system for automatic driving tracking |
| CN113325849A (en) * | 2021-05-31 | 2021-08-31 | 安徽农业大学 | Motion control method for high-ground-clearance plant protection machine |
| CN113848953B (en) * | 2021-10-25 | 2023-12-08 | 济南大学 | Unmanned path tracking control method for large-scale hydraulic plant protection machine |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103197675A (en) * | 2013-03-13 | 2013-07-10 | 北京矿冶研究总院 | Autonomous driving and obstacle avoidance motion control and target path planning method for underground carry scraper |
| KR20140040983A (en) * | 2012-09-27 | 2014-04-04 | 한국전자통신연구원 | Lane tracking apparatus using camera direction control and method thereof |
| CN104571112A (en) * | 2015-01-14 | 2015-04-29 | 中国科学院合肥物质科学研究院 | Pilotless automobile lateral control method based on turning curvature estimation |
| CN106681335A (en) * | 2017-01-22 | 2017-05-17 | 无锡卡尔曼导航技术有限公司 | Obstacle-avoiding route planning and control method for unmanned agricultural machine driving |
| CN106909144A (en) * | 2017-01-22 | 2017-06-30 | 无锡卡尔曼导航技术有限公司 | For the unpiloted field obstacle-avoiding route planning of agricultural machinery and its control method |
| CN107963126A (en) * | 2016-10-19 | 2018-04-27 | 中车株洲电力机车研究所有限公司 | A kind of multi-shaft steering vehicle deep camber automatic Pilot rotating direction control method |
| CN108791491A (en) * | 2018-06-12 | 2018-11-13 | 中国人民解放军国防科技大学 | Vehicle lateral tracking control method based on self-evaluation learning |
| CN109032131A (en) * | 2018-07-05 | 2018-12-18 | 东南大学 | A kind of dynamic applied to pilotless automobile is overtaken other vehicles barrier-avoiding method |
| CN109508007A (en) * | 2018-12-11 | 2019-03-22 | 东南大学 | A kind of agricultural machinery track following, obstacle avoidance system and method based on Multi-source Information Fusion |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8825328B2 (en) * | 2011-04-19 | 2014-09-02 | Ford Global Technologies | Detection of and counter-measures for jackknife enabling conditions during trailer backup assist |
| CN105867377B (en) * | 2016-04-13 | 2019-01-08 | 浙江理工大学 | A kind of automatic steering control of farm mechanism control method |
| CN108710294B (en) * | 2018-04-13 | 2021-03-05 | 四川智动木牛智能科技有限公司 | Unmanned vehicle path tracking feedforward control method |
-
2019
- 2019-04-10 CN CN201910285814.7A patent/CN110109451B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20140040983A (en) * | 2012-09-27 | 2014-04-04 | 한국전자통신연구원 | Lane tracking apparatus using camera direction control and method thereof |
| CN103197675A (en) * | 2013-03-13 | 2013-07-10 | 北京矿冶研究总院 | Autonomous driving and obstacle avoidance motion control and target path planning method for underground carry scraper |
| CN104571112A (en) * | 2015-01-14 | 2015-04-29 | 中国科学院合肥物质科学研究院 | Pilotless automobile lateral control method based on turning curvature estimation |
| CN107963126A (en) * | 2016-10-19 | 2018-04-27 | 中车株洲电力机车研究所有限公司 | A kind of multi-shaft steering vehicle deep camber automatic Pilot rotating direction control method |
| CN106681335A (en) * | 2017-01-22 | 2017-05-17 | 无锡卡尔曼导航技术有限公司 | Obstacle-avoiding route planning and control method for unmanned agricultural machine driving |
| CN106909144A (en) * | 2017-01-22 | 2017-06-30 | 无锡卡尔曼导航技术有限公司 | For the unpiloted field obstacle-avoiding route planning of agricultural machinery and its control method |
| CN108791491A (en) * | 2018-06-12 | 2018-11-13 | 中国人民解放军国防科技大学 | Vehicle lateral tracking control method based on self-evaluation learning |
| CN109032131A (en) * | 2018-07-05 | 2018-12-18 | 东南大学 | A kind of dynamic applied to pilotless automobile is overtaken other vehicles barrier-avoiding method |
| CN109508007A (en) * | 2018-12-11 | 2019-03-22 | 东南大学 | A kind of agricultural machinery track following, obstacle avoidance system and method based on Multi-source Information Fusion |
Non-Patent Citations (4)
| Title |
|---|
| 基于SVD算法的车前影像轨迹提取方法研究;李耀南，等;《制造业自动化》;20181031;第40卷(第10期);31-36 * |
| 基于模型预测控制的路径跟踪控制方法研究;杨阳阳;《中国优秀硕士学位论文全文数据库 工程科技II辑》;20190215(第2期);C035-192 * |
| 智能驾驶车辆横向跟踪控制与实车试验;王俊;《上海汽车》;20180731(第7期);58-63 * |
| 适应地下停车场弯曲坡道工况的车辆自动驾驶技术研究;张旭培;《中国优秀硕士学位论文全文数据库工程科技II辑》;20190215(第2期);C035-22 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110109451A (en) | 2019-08-09 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110109451B (en) | Novel geometric path tracking algorithm considering path curvature | |
| CN107340772B (en) | Unmanned-oriented anthropomorphic reference trajectory planning method | |
| CN111806467B (en) | Variable speed dynamic track changing planning method based on vehicle driving rule | |
| CN109799828B (en) | Transverse and longitudinal coordination control method for automatic driving vehicle | |
| CN110171416B (en) | Vehicle running control method and device, computer readable medium and electronic equipment | |
| CN107544520B (en) | Control method for automatic driving of four-wheel carrier | |
| CN104571112B (en) | Pilotless automobile lateral control method based on turning curvature estimation | |
| CN108732921B (en) | Transverse extension preview switching control method for automatic driving automobile | |
| WO2019042295A1 (en) | Path planning method, system, and device for autonomous driving | |
| CN109388140B (en) | Improved pure tracking control method for ground vehicle path tracking | |
| Hernandez et al. | Steering control of automated vehicles using absolute positioning GPS and magnetic markers | |
| CN110502009A (en) | The automatic driving vehicle path tracking control method estimated based on course | |
| CN111923908A (en) | Stability-fused intelligent automobile path tracking control method | |
| CN113104037B (en) | Method and system for determining steering angle of vehicle steering wheel | |
| CN110006419A (en) | Vehicle track tracking point determination method based on preview | |
| CN110687797B (en) | Self-adaptive MPC parking transverse control method based on position and posture | |
| CN111086510B (en) | Front wheel steering vehicle lane keeping control method based on prediction function control | |
| CN113291323B (en) | Automatic driving path tracking control method and system for rhombic vehicle and vehicle | |
| CN111158379B (en) | Steering wheel zero-bias self-learning unmanned vehicle track tracking method | |
| CN115489594A (en) | Pure-tracking intelligent vehicle path following control method | |
| CN113311845B (en) | Pure tracking control error compensation method and device based on path curvature | |
| CN110362088B (en) | Tracking control system and method suitable for unmanned straddle carrier | |
| CN115542925B (en) | Deviation accurate estimation method for unmanned vehicle transverse control | |
| CN111674406A (en) | Method for controlling vehicle transverse direction of automatic driving system | |
| CN111221338A (en) | Path tracking method, device, equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |