CN115542925B - Deviation accurate estimation method for unmanned vehicle transverse control - Google Patents
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Abstract
The invention discloses a deviation accurate estimation method for unmanned vehicle transverse control, which is realized through a transverse deviation accurate estimation module and a course deviation accurate estimation module. According to the method, according to the relative relation between an expected track and the current vehicle position, an optimal estimation point is generated according to three different categories of a first point, a last point and a path point, and the lateral deviation estimation and the course deviation estimation are calculated through the optimal estimation point; and taking the calculated lateral deviation estimation and the course deviation estimation as feedback control quantities to bring the tracking control rate into the tracking control rate, so that the precise tracking control of the unmanned vehicle is realized. The method can effectively estimate the transverse deviation and the course deviation of the vehicle control, well solves the problem of overlarge transverse position and course change between adjacent points on the expected track, and improves remarkably in the tasks with large curvature such as turning around or turning sharply, thereby effectively improving the tracking precision of the vehicle control.
Description
Technical Field
The invention belongs to the field of unmanned vehicle motion control, and particularly relates to a deviation accurate estimation method for unmanned vehicle transverse control.
Background
The accurate track tracking technology is one of important components for realizing unmanned driving. The current track tracking technology mainly comprises two tracking methods based on a pre-aiming point and a nearest point, and aiming at the road conditions of large curvature road conditions such as turning around or turning sharply, the track tracking technology applying the pre-aiming point can generate the phenomena of turning in advance and turning in advance, and finally causes poor tracking precision or incapability of passing; the accurate tracking of the large-curvature road condition is realized by applying a nearest point tracking method, the most important feedback input is deviation, the current algorithm traverses the nearest point on the expected track through the current GPS position point, the transverse distance between the nearest point and the current point is transverse deviation, and the calculated transverse deviation is not accurate enough; the course difference between the closest point and the current point is taken as the course deviation, and the course change between the points on the expected track is larger in the large-curvature road condition, so that the accuracy of the course deviation calculated by the method is poor. The accuracy of the lateral deviation and heading deviation estimates is thus a direct impact on the unmanned vehicle tracking effect.
Disclosure of Invention
The invention provides a deviation accurate estimation method for unmanned vehicle transverse control, which aims to overcome the defects in the prior art. The transverse deviation and the course deviation in the running process of the unmanned vehicle are accurately estimated, and the feedback control of the vehicle is realized by utilizing the deviation, so that the accurate control of the unmanned vehicle is finally realized.
In order to achieve the aim of the invention, the invention adopts the following technical scheme:
a deviation accurate estimation method for unmanned vehicle transverse control realizes accurate estimation of deviation through a transverse deviation accurate estimation module and a course deviation accurate estimation module;
the input of the transverse deviation accurate estimation module is a desired track, the output is transverse deviation estimation, the desired track comprises longitude, latitude and heading information of a point through which the vehicle is expected to travel, an optimal estimation point is generated by comparing the spatial relationship between a point C with the minimum distance from an origin O of the current position of the vehicle in the desired track and the origin O of the current position of the vehicle according to three different categories of a first point, a last point and a path point, the position deviation between the optimal estimation point and the origin O of the current position of the vehicle is obtained, and the transverse deviation estimation laser_error is obtained by calculation;
the input of the course deviation accurate estimation module is a desired track, the output is course deviation estimation, and the course estimation at the optimal estimation point and the current course of the vehicle are obtained through calculation
Comparing to obtain course deviation estimation
The method comprises the steps of carrying out a first treatment on the surface of the Further, the method for calculating the lateral deviation estimate is as follows:
step 1: converting each point on the expected track into an expected path point under a vehicle coordinate system, wherein the vehicle coordinate system is a right-hand coordinate system taking the longitude and latitude of the current vehicle as an origin O (xo, yo) and the current vehicle heading as an X axis;
step 2: traversing the expected track, and selecting a point C (xc, yc) with the smallest distance from an origin O (xo, yo) of the current position of the vehicle in the expected track;
step 3: judging whether the point C is the first point on the expected track, if so, taking the point F to be equal to the point C, namely (xf, yf) = (xc, yc), wherein the min_0 is equal to a distance value |FO| between the point F and the point O; if not, taking the point F as the previous point of the point C on the expected track; judging whether the point C is the last point on the expected track, if so, taking the point B as the point C, namely (xb, yb) = (xc, yc), wherein min_1 is equal to the distance value |BO| between the point B and the point O, and if not, taking the point B as the point after the point C on the expected track; namely:
step 4: if the point C is not the first point or the last point on the expected track, judging whether the cosine values of the angle BCO and the angle FCO are larger than 0; if COs & lt FCO is greater than 0, the angle FCO is an acute angle, which indicates that a point N which is closer to a connecting line FC between a point F and a point C than a distance |CO| between the point C and the point O exists, the value |ON| is equal to the distance between the point O and the FC, and the value is taken as an estimated value of min_0; if COs < FCO is smaller than 0, the COs < FCO is an obtuse angle, which means that a point which is closer to a distance |CO| between a point F and a point O does not exist on a connecting line FC between the point F and the point C, and the |CO| is used as an estimated value of min_0; namely:
in the same way, the processing method comprises the steps of,
step 5: comparing the sizes of the min_0 and the min_1 obtained in the step 3 or the step 4, and taking the smaller value as the absolute value of the lateral deviation estimation laser_error:
|lateral_error| = min( min_0, min_1);
step 6: if yc <0, the lateral deviation is estimated to be negative, and if yc >0, the lateral deviation is estimated to be positive.
The course deviation estimation calculation method comprises the following steps:
step 1: if the minimum lateral deviation estimated point is the point on the expected track, namely the point C, the heading of the point C is
The current heading is +.>
The heading bias estimate is derived according to the following formula:
step 2: if the minimum lateral deviation estimation point is not a point on the expected track, an optimal estimation point N is obtained according to the lateral deviation accurate estimation module, and if the point N is a point between a point C and a point F, a distance |CN| between the point C and the point N and a distance |CF| between the point C and the point F are obtained through the following formula:
according to the physical space continuity and smoothness of the running of the vehicle, the track between the point F and the point C is regarded as a section of circular arc, the circle center of the circular arc is the point S, and the course of the intersection point M of the SN extension line and the circular arc is calculated
Heading estimation as Point N +.>
The heading bias estimate is derived by:
in the method, in the process of the invention,
estimated heading for Point N, +.>
For heading of point M, +.>
For heading of point F, +.>
Heading for point C, ++>
For the current vehicle heading,/->
And estimating the heading deviation.
If the point N is a point between the point C and the point B, the distance |BN| between the point B and the point N and the distance |CB| between the point C and the point B are obtained through the following formula:
wherein, |CO| is the distance between point C and point O, and|ON| is the distance between point O and point N;
according to the physical space continuity and smoothness of the running of the vehicle, the track between the point B and the point C is regarded as a section of circular arc, the circle center of the circular arc is the point S, and the course of the intersection point M of the SN extension line and the circular arc is calculated
Heading estimation as Point N +.>
The heading bias estimate is derived by:
in the method, in the process of the invention,
estimated heading for Point N, +.>
For heading of point M, +.>
For heading of Point B, +.>
Heading for point C, ++>
For the current vehicle heading,/->
And estimating the heading deviation.
Further, the lateral deviation estimation and the course deviation estimation are used as feedback control quantities to be brought into a track tracking control rate, so that accurate tracking control of the unmanned vehicle is achieved.
The beneficial effects are that:
the invention can accurately estimate the transverse deviation and the course deviation of the current vehicle relative to the expected track, reduces the input of systematic errors, and is particularly remarkable in the tasks of turning around or turning in sharp turns and other large curvatures; by the scheme, the transverse deviation and the course deviation of the vehicle control can be effectively estimated, the problem of overlarge transverse position and course change between adjacent points on the expected track is well solved, and therefore the tracking precision of the vehicle control is effectively improved. The estimation method provided by the invention has the advantages of simple calculation, high estimation precision and convenient use.
Drawings
Fig. 1 is an analysis chart of a lateral deviation accurate estimation of a deviation accurate estimation method for unmanned vehicle lateral control of the present invention;
FIG. 2 is a flow chart of a lateral deviation accurate estimation of the deviation accurate estimation method for unmanned vehicle lateral control of the present invention;
fig. 3 is a first analysis chart of the heading deviation accurate estimation of the deviation accurate estimation method for unmanned vehicle transverse direction control of the present invention.
Fig. 4 is a second analysis chart of the heading deviation accurate estimation of the deviation accurate estimation method for unmanned vehicle transverse direction control of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention. In addition, the technical features of the embodiments of the present invention described below may be combined with each other as long as they do not collide with each other.
In this embodiment, as shown in fig. 1, 2 and 3, the method for accurately estimating lateral deviation for controlling the lateral direction of an unmanned vehicle according to the present invention comprises the following steps:
step one, acquiring an expected track:
a GPS/IMU integrated navigation system is pre-applied to collect a desired track, wherein the desired track comprises longitude, latitude and heading information of a vehicle in real time. In the actual application process, the expected track can also be generated by a path planning mode.
Acquiring GPS position and heading information of a vehicle at the current moment through a GPS/IMU integrated navigation system, and converting a point of a pre-acquired expected track into an expected path point under a vehicle coordinate system taking the pose at the current moment as an origin:
where R is the earth radius, PI is the circumference ratio, lat is the latitude of the desired waypoint, lng is the longitude of the desired waypoint, lat0 is the latitude of the vehicle at the current time, lng0 is the longitude of the vehicle at the current time, X, Y is the vertical projection of the desired waypoint on the current vehicle coordinate plane, az is the heading of the vehicle at the current time, and X, Y are the coordinates of the desired waypoint in the vehicle coordinate system.
Step three, traversing a point C (xc, yc) with the smallest distance from the current position O (xo, yo) of the vehicle in the expected track, and calculating the distance dis between the expected road point and the current vehicle:
where x, y are coordinates of the desired waypoint in the vehicle coordinate system and dis is the distance between the desired waypoint and the current vehicle.
Judging whether the point C is the first point on the expected track, if so, taking the point F as the point C, (xf, yf) = (xc, yc), wherein min_0 is equal to a distance value |FO| between the point F and the point O, and if not, taking the point F as the previous point of the point C on the expected track; judging whether the point C is the last point on the expected track, if so, taking the point B as a point after the point C on the expected track, wherein (xb, yb) = (xc, yc), and min_1 as a distance |BO| between the point B and the point O;
fifthly, if the point C is not the first point or the last point on the expected track, judging whether the cosine values of the BCO and the FCO are larger than 0; if COs & lt FCO is greater than 0, the angle FCO is an acute angle, which indicates that a point N which is closer to a connecting line FC between a point F and a point C than a distance |CO| between the point C and the point O exists, the value |ON| is equal to the distance between the point O and the FC, and the value is taken as an estimated value of min_0; if COs < FCO is smaller than 0, the COs < FCO is an obtuse angle, which means that a point which is closer to a distance |CO| between a point F and a point O does not exist on a connecting line FC between the point F and the point C, and the |CO| is used as an estimated value of min_0; namely:
in the same way, the processing method comprises the steps of,
step six, comparing the sizes of the min_0 and the min_1, and taking the smaller value as the absolute value of the lateral deviation estimation:
|lateral_error| = min( min_0, min_1);
and step seven, judging whether the point C is positive or negative in the current vehicle coordinate system, if yc <0, estimating the lateral deviation to be negative, and if yc >0, estimating the lateral deviation to be positive.
Step eight, estimating the minimum transverse deviation and estimating the minimum transverse deviation point according to the minimum transverse deviation obtained in the step five and the step six, if the minimum transverse deviation estimated point is a point on the expected track, namely the point C, and the heading of the point C is that of the point C
The current heading is +.>
The heading bias estimate may be derived according to the following formula:
step nine: if the minimum lateral deviation estimation point is not a point on the expected track, obtaining an optimal estimation point N according to the lateral deviation accurate estimation module, and if the point N is a point between a point C and a point F, obtaining a distance |CN| between the point C and the point N and a distance |CF| between the point C and the point F through the following formula as shown in FIG. 3:
where |co| is the distance between point C and point O, and |on| is the distance between point O to point N.
According to the physical space continuity and smoothness of the running of the vehicle, the track between the point F and the point C is regarded as a section of circular arc, the circle center of the circular arc is the point S, and the course of the intersection point M of the SN extension line and the circular arc is calculated
Heading estimation as Point N +.>
The heading bias estimate is derived by: />
In the method, in the process of the invention,
estimated heading for Point N, +.>
For heading of point M, +.>
For heading of point F, +.>
Heading for point C, ++>
For the current vehicle heading,/->
And estimating the heading deviation.
If the point N is a point between the point C and the point B, as shown in fig. 4, a distance |bn| between the point B and the point N, and a distance |cb| between the point C and the point B are calculated by the following formula:
according to the physical space continuity and smoothness of the running of the vehicle, the track between the point B and the point C is regarded as a section of circular arc, the circle center of the circular arc is the point S, and the course of the intersection point M of the SN extension line and the circular arc is calculated
Heading estimation as Point N +.>
The heading bias estimate is derived by:
in the method, in the process of the invention,
estimated heading for Point N, +.>
For heading of point M, +.>
For heading of Point B, +.>
Heading for point C, ++>
For the current vehicle heading,/->
And estimating the heading deviation.
Step ten: and (3) obtaining the expected steering curvature of the vehicle according to the obtained transverse deviation estimation and heading deviation estimation solution by using a track tracking controller as follows:
q0=tan(－1*(heading_error*PI/180)+arctan(k*lateral_error/(speed))/L
in the formula, head_error is heading deviation estimation, lateral_error is transverse deviation estimation, k is a proportional parameter required to be adjusted for track tracking realization, speed is the current speed of the unmanned vehicle, L is the wheelbase of the unmanned vehicle, and q0 is the desired steering curvature.
Step eleven: the desired steering angle is solved according to the vehicle characteristics. Taking a certain type of bergamot paqi vehicle as an example, the expected steering angle is obtained according to the relation between the steering size of the steering wheel of the vehicle and the steering curvature of the vehicle:
turn=－4857*q0*q0*q0+117.4*q0*q0+2753*q0+3266
where q0 is the desired steering curvature of the vehicle and turn is the desired steering angle of the vehicle.
It will be readily appreciated by those skilled in the art that the foregoing description is merely a preferred embodiment of the invention and is not intended to limit the invention, but any modifications, equivalents, improvements or alternatives falling within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (3)
1. A deviation accurate estimation method for unmanned vehicle transverse control is characterized in that accurate estimation of deviation is achieved through a transverse deviation accurate estimation module and a course deviation accurate estimation module;
the input of the transverse deviation accurate estimation module is a desired track, the output is transverse deviation estimation, the desired track comprises longitude, latitude and heading information of a point through which the vehicle is expected to travel, an optimal estimation point is generated by comparing the spatial relationship between a point C with the minimum distance from an origin O of the current position of the vehicle in the desired track and the origin O of the current position of the vehicle according to three different categories of a first point, a last point and a path point, the position deviation between the optimal estimation point and the origin O of the current position of the vehicle is obtained, and the transverse deviation estimation laser_error is obtained by calculation;
the input of the course deviation accurate estimation module is a desired track, the output is course deviation estimation, and the course estimation at the optimal estimation point and the current course of the vehicle are obtained through calculation
Comparing to obtain course deviation estimation
；
The calculation method of the lateral deviation estimation is as follows:
step 1: converting each point on the expected track into an expected path point under a vehicle coordinate system, wherein the vehicle coordinate system is a right-hand coordinate system taking the longitude and latitude of the current vehicle as the origin point O (xo, yo) of the current position of the vehicle and the current vehicle heading as an X axis;
step 2: traversing the expected track, and selecting a point C (xc, yc) with the smallest distance from an origin O (xo, yo) of the current position of the vehicle in the expected track;
step 3: judging whether the point C is the first point on the expected track, if so, taking the point F to be equal to the point C, namely (xf, yf) = (xc, yc), wherein the min_0 is equal to a distance value |FO| between the point F and the point O; if not, taking the point F as the previous point of the point C on the expected track; judging whether the point C is the last point on the expected track, if so, taking the point B as the point C, namely (xb, yb) = (xc, yc), wherein min_1 is equal to the distance |BO| between the point B and the point O, and if not, taking the point B as the point after the point C on the expected track; namely:
wherein, |CO| is the distance between point C and point O, and |FO| is the distance between point F and point O;
step 4: if the point C is not the first point on the expected track and the point C is not the last point on the expected track, judging whether the cosine values of the angle BCO and the angle FCO are larger than 0; if COs & lt FCO is greater than 0, the angle FCO is an acute angle, which indicates that a point N which is closer to a connecting line FC between a point F and a point C than a distance |CO| between the point C and the point O exists, the value |ON| is equal to the distance between the point O and the FC, and the value is taken as an estimated value of min_0; if COs < FCO is smaller than 0, the COs < FCO is an obtuse angle, which means that a point which is closer to a distance |CO| between a point F and a point O does not exist on a connecting line FC between the point F and the point C, and the |CO| is used as an estimated value of min_0; namely:
in the same way, the processing method comprises the steps of,
wherein, FC is the distance between point F and point C, BC is the distance between point B and point C;
step 5: comparing the sizes of the min_0 and the min_1 obtained in the step 3 or the step 4, and taking the smaller value as the absolute value of the lateral deviation estimation laser_error:
|lateral_error| = min( min_0, min_1);
step 6: if yc <0, the lateral deviation is estimated to be negative, and if yc >0, the lateral deviation is estimated to be positive.
2. The accurate estimation method of deviation for unmanned vehicle lateral control according to claim 1, wherein the calculation method of heading deviation estimation is as follows:
step 1: if the minimum lateral deviation estimated point is the point on the expected track, namely the point C, the heading of the point C is
The current heading is +.>
The heading bias estimate is derived according to the following formula:
step 2: if the minimum lateral deviation estimation point is not the point on the expected track, obtaining an optimal estimation point according to a lateral deviation accurate estimation module, marking the optimal estimation point as a point N, and if the point N is a point between a point C and a point F, obtaining a distance |CN| between the point C and the point N and a distance |CF| between the point C and the point F through the following formula:
wherein, |CO| is the distance between point C and point O, and|ON| is the distance between point O and point N;
according to the physical space continuity and smoothness of the running of the vehicle, the track between the point F and the point C is regarded as a section of circular arc, the circle center of the circular arc is the point S, and the course of the intersection point M of the SN extension line and the circular arc is calculated
Heading estimation as point N
Is obtained by the following formulaEstimating course deviation:
in the method, in the process of the invention,
estimated heading for Point N, +.>
For heading of point M, +.>
For heading of point F, +.>
Heading for point C, ++>
For the current vehicle heading,/->
Estimating course deviation;
if the point N is a point between the point C and the point B, the distance |BN| between the point B and the point N and the distance |CB| between the point C and the point B are obtained through the following formula:
according to the physical space continuity and smoothness of the running of the vehicle, the track between the point B and the point C is regarded as a section of circular arc, the circle center of the circular arc is the point S, and the course of the intersection point M of the SN extension line and the circular arc is calculated
Heading estimation as point N
By the followingDeriving a heading bias estimate:
in the method, in the process of the invention,
estimated heading for Point N, +.>
For heading of point M, +.>
For heading of Point B, +.>
Heading for point C, ++>
For the current vehicle heading,/->
And estimating the heading deviation.
3. The deviation accurate estimation method for unmanned vehicle lateral control according to claim 2, wherein the lateral deviation estimation and the heading deviation estimation are taken as feedback control amounts into a tracking control rate, thereby realizing accurate tracking control of the unmanned vehicle.
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