CN115542925A - Accurate deviation estimation method for transverse control of unmanned vehicle - Google Patents
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Abstract
The invention discloses a deviation accurate estimation method for transverse control of an unmanned vehicle, which is realized by a transverse deviation accurate estimation module and a course deviation accurate estimation module. According to the relative relation between an expected track and the current vehicle position, judging and generating an optimal estimation point according to three different categories, namely a first point, a last point and a path point, and calculating the lateral deviation estimation and the course deviation estimation through the optimal estimation point; and taking the calculated transverse deviation estimation and course deviation estimation as feedback control quantities to be brought into the track tracking control rate, thereby realizing the accurate tracking control of the unmanned vehicle. The invention can effectively estimate the lateral deviation and the course deviation of vehicle control, well solves the problem of overlarge change of the lateral position and the course between adjacent points on an expected track, and is particularly obviously promoted in tasks with large curvature, such as turning around or sharp turning, and the like, thereby effectively improving the tracking precision of vehicle control.
Description
Technical Field
The invention belongs to the field of unmanned vehicle motion control, and particularly relates to an accurate deviation estimation method for unmanned vehicle transverse control.
Background
The accurate track tracking technology is one of important components for realizing unmanned driving. The conventional track tracking technology mainly comprises two tracking methods, namely a pre-aiming point tracking method and a nearest point tracking method, aiming at the road conditions of large-curvature road conditions such as turning around or sharp turning, the track tracking technology of the pre-aiming point can generate the phenomena of turning starting in advance and turning ending in advance, and finally the tracking precision is poor or the traffic cannot be realized; the method comprises the steps that a closest point tracking method is applied to achieve accurate tracking of the road condition with large curvature, the most important feedback input of the method is deviation, the existing algorithm is that the closest point is traversed on an expected track through a current GPS position point, the transverse distance between the closest point and the current point is transverse deviation, and the calculated transverse deviation is insufficient in accuracy; the heading difference between the nearest point and the current point is taken as the heading deviation, and the heading change between the points on the expected track is large in the road condition with large curvature, so that the heading deviation calculated by the method has poor accuracy. Therefore, the accuracy of estimation of the lateral deviation and the heading deviation directly influences the tracking effect of the unmanned vehicle.
Disclosure of Invention
The invention provides an accurate deviation estimation method for transverse control of an unmanned vehicle, aiming at overcoming the defects in the prior art. The transverse deviation and the course deviation in the running process of the unmanned vehicle are accurately estimated, the deviation is utilized to realize the feedback control of the vehicle, and finally the accurate control of the unmanned vehicle is realized.
In order to achieve the purpose of the invention, the technical scheme adopted by the invention is as follows:
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 an expected track, the output is transverse deviation estimation, the expected track comprises longitude, latitude and course information of a point where the vehicle is expected to run, an optimal estimation point is generated by comparing a spatial relationship between a point C which is the minimum distance from an origin O of the current position of the vehicle in the expected 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 transverse deviation estimation lateralror is obtained through calculation;
the input of the course deviation accurate estimation module is an expected track, the output is course deviation estimation, and course estimation at an optimal estimation point and the current course of the vehicle are obtained through calculation
Comparing to obtain course deviation estimation
(ii) a Further, 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 an origin O (xo, yo) and the course of the current vehicle 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;
and step 3: judging whether the point C is the first point on the expected track, if so, taking the point F equal to the point C, namely (xf, yf) = (xc, yc), and min _0 equal to the 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 equal to 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 behind the point C on the expected track; namely:
and 4, step 4: if the point C is not the first point or the last point on the expected track, judging whether cosine values of < BCO and < FCO are greater than 0; if COs-FCO is greater than 0, the COs-FCO is an acute angle, which indicates that a point N which is closer to a distance | CO | between the point C and the point O exists ON a connecting line FC between the point F and the point C, the value | ON | of the point N is equal to the distance between the point O and the point FC, and the value is used as an estimated value of min _ 0; if COs-FCO is less than 0, the COs-FCO is an obtuse angle, a point which is closer than the distance | CO | between the point C and the 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 method for preparing the composite material,
and 5: comparing the magnitudes of min _0 and min _1 obtained in step 3 or step 4, and taking the smaller value as the absolute value of the lateral deviation estimation error:
|lateral_error| = min( min_0, min_1);
and 6: and judging whether yc is positive or negative in the current vehicle coordinate system at the point C, if yc is less than 0, estimating the lateral deviation as negative, and if yc is more than 0, estimating the lateral deviation as positive.
The calculation method of the course deviation estimation comprises the following steps:
step 1: if the minimum lateral deviation estimation point is a point on the expected track, namely the point C, the heading of the point C is
The current course is
And obtaining course deviation estimation according to the following formula:
step 2: if the minimum lateral deviation estimation point is not a point on the expected track, obtaining an optimal estimation point N according to a lateral deviation accurate estimation module, and if the point N is a point between the point C and the 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 by calculating according to the following formula:
according to the physical space continuity and smoothness of the vehicle operation, a track between a point F and a point C is regarded as a section of circular arc, the center of the circular arc is a point S, and the course of the intersection point M of the SN extension line and the circular arc is calculated
As heading estimate for point N
The course bias estimate is derived by:
in the formula (I), the compound is shown in the specification,
for the course estimate of the point N,
the course of the point M is the course,
the course of the point F is the course,
the course of the point C is the course,
the current heading of the vehicle is the current heading,
is a heading bias estimate.
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 calculated by the following equations:
wherein, | CO | is the distance between the point C and the point O, | ON | is the distance between the point O and the point N;
according to the physical space continuity and smoothness of the vehicle running, a 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
As heading estimate for point N
The course bias estimate is derived by:
in the formula (I), the compound is shown in the specification,
for the course estimate of the point N,
the course of the point M is the course,
the heading of the point B is the direction of the point B,
the course of the point C is the course,
the current heading of the vehicle is the current heading,
is a heading bias estimate.
Furthermore, the lateral deviation estimation and the course deviation estimation are used as feedback control quantity to be brought into a track tracking control rate, so that accurate tracking control of the unmanned vehicle is achieved.
Has the advantages that:
the invention can accurately estimate the transverse deviation and the course deviation of the current vehicle relative to the expected track, reduce the error input of the system and has particularly obvious improvement in the tasks with large curvature such as turning around or sharp turning; by the scheme, the transverse deviation and the course deviation of vehicle control can be effectively estimated, and the problem of overlarge transverse position and course change between adjacent points on an expected track is well solved, so that the tracking precision of vehicle control is effectively improved. The estimation method provided by the invention is simple in calculation, high in estimation precision and convenient to use.
Drawings
FIG. 1 is an analysis diagram of the lateral deviation accurate estimation of the deviation accurate estimation method for the lateral control of the unmanned vehicle according to the present invention;
FIG. 2 is a flow chart of the lateral deviation accurate estimation of the deviation accurate estimation method for the lateral control of the unmanned vehicle of the present invention;
FIG. 3 is a first analysis chart of course deviation accurate estimation of the deviation accurate estimation method for the unmanned vehicle lateral control of the present invention.
FIG. 4 is a second analysis diagram of the course deviation accurate estimation of the deviation accurate estimation method for the lateral control of the unmanned vehicle of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. In addition, the technical features involved in the embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
In this embodiment, as shown in fig. 1, 2, and 3, the method for accurately estimating the lateral deviation for the lateral control of the unmanned aerial vehicle according to the present invention is performed as follows:
step one, obtaining an expected track:
a GPS/IMU combined navigation system is applied in advance to collect a section of expected track, and the expected track comprises real-time longitude, latitude and heading information of a vehicle. In an actual application process, the expected trajectory may also be generated by way of path planning.
Step two, acquiring GPS position and course information of the vehicle at the current moment through a GPS/IMU integrated navigation system, and converting points of an expected track acquired in advance into expected path points under a vehicle coordinate system with the current moment pose as an origin:
in the formula, R is the earth radius, PI represents the circumference ratio, lat is the latitude of the expected waypoint, lng is the longitude of the expected 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 are the vertical projection of the expected track point on the coordinate plane of the current vehicle, az is the heading of the vehicle at the current time, and X, Y are the coordinates of the expected waypoint in the coordinate system of the vehicle.
Step three, traversing a point C (xc, yc) with the minimum 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:
in the formula, x and y are coordinates of the expected road point in the vehicle coordinate system, and dis is the distance between the expected road point and the current vehicle.
Step four, 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 the 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 to be equal to the point C, (xb, yb) = (xc, yc), and if not, taking the point B to be the point behind the point C on the expected track, wherein min _1 is equal to the distance | BO | between the point B and the point O;
step five, if the point C is not the first point or the last point on the expected track, judging whether cosine values of the & lt BCO and the & lt FCO are larger than 0; if COs & lt FCO is greater than 0, then & lt FCO is an acute angle, which indicates that a point N which is closer than a distance | CO | between the point C and the point O exists ON a connecting line FC between the point F and the point C, the value | ON | of the point N is equal to the distance from the point O to the point FC, and the value is used as an estimated value of min _ 0; if COs & lt FCO is less than 0, then & lt FCO is an obtuse angle, a point which is closer than the distance | CO | between the point C and the point O does not exist on a connecting line FC between the point F and the point C, and | CO | is used as an estimated value of min _ 0; namely:
in the same way, the method for preparing the composite material,
step six, comparing the magnitudes of min _0 and min _1, and taking the smaller value as the absolute value of the lateral deviation estimation:
|lateral_error| = min( min_0, min_1);
and seventhly, judging whether yc is positive or negative in the current vehicle coordinate system of the point C, if yc is less than 0, estimating that the transverse deviation is negative, and if yc is more than 0, estimating that the transverse deviation is positive.
Step eight, estimating a point according to the minimum transverse deviation and the minimum transverse deviation obtained in the step five and the step six, wherein if the estimated point of the minimum transverse deviation is a point on the expected track, the point is a point C, and the heading of the point C is
The current course is
The course deviation estimate can be derived from the following equation:
step nine: if the estimated point with the minimum lateral deviation is not a point on the expected track, obtaining an optimal estimated point N according to the accurate lateral deviation estimation module, and if the point N is a point between the point C and the point F, as shown in fig. 3, obtaining a distance | CN | between the point C and the point N and a distance | CF | between the point C and the point F by the following formula:
where | 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 vehicle operation, a track between a point F and a point C is regarded as a section of circular arc, the center of the circular arc is a point S, and the course of the intersection point M of the SN extension line and the circular arc is calculated
As heading estimate for point N
The course bias estimate is derived by:
in the formula (I), the compound is shown in the specification,
for the course estimate of the point N,
the course of the point M is the course,
the course of the point F is the course,
is the heading of the point C and,
is the current heading of the vehicle and is,
is a heading bias estimate.
If point N is a point between point C and point B, as shown in fig. 4, the distance | BN | between point B and point N and the distance | CB | between point C and point B are calculated by the following equations:
according to the physical space continuity and smoothness of the vehicle running, a 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
As heading estimate for point N
The course bias estimate is derived by:
in the formula (I), the compound is shown in the specification,
for the course estimate of the point N,
the course of the point M is the course,
the heading of the point B is the direction of the point B,
the course of the point C is the course,
the current heading of the vehicle is the current heading,
is a heading bias estimate.
Step ten: and solving to obtain the expected turning curvature of the vehicle according to the obtained lateral deviation estimation and course deviation estimation by applying a track tracking controller of the following formula:
q0=tan(－1*(heading_error*PI/180)+arctan(k*lateral_error/(speed))/L
in the formula, the heading _ error is course deviation estimation, the lateral _ error is transverse deviation estimation, k is a proportional parameter required to be adjusted for track tracking, speed is the current speed of the unmanned vehicle, L is the wheelbase of the unmanned vehicle, and q0 is the expected steering curvature.
Step eleven: the desired steering angle is solved for based on the vehicle characteristics. Taking a certain type of chevroleraceae park car as an example, the expected steering angle is obtained according to the relationship between the steering size of the steering wheel of the car and the steering curvature of the car:
turn=－4857*q0*q0*q0+117.4*q0*q0+2753*q0+3266
where q0 is the vehicle desired steering curvature and turn is the vehicle desired steering angle.
It will be understood by those skilled in the art that the foregoing is only a preferred embodiment of the present invention, and is not intended to limit the invention, and that any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (3)
1. A deviation accurate estimation method for the transverse control of an unmanned vehicle is characterized in that the accurate estimation of the deviation is realized through a transverse deviation accurate estimation module and a course deviation accurate estimation module;
the input of the transverse deviation accurate estimation module is an expected track, the output is transverse deviation estimation, the expected track comprises longitude, latitude and course information of a point where the vehicle is expected to run, an optimal estimation point is generated by comparing a spatial relationship between a point C which is the minimum distance from an origin O of the current position of the vehicle in the expected 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 transverse deviation estimation lateralror is obtained through calculation;
the input of the course deviation accurate estimation module is an expected track, the output of the course deviation accurate estimation module is course deviation estimation, course estimation at an optimal estimation point is obtained through calculation, and the course estimation and the current course of the vehicle are obtained
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 current vehicle longitude and latitude as an original point O (xo, yo) of the current position of the vehicle and the current vehicle course 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;
and step 3: judging whether the point C is the first point on the expected track, if so, taking the point F equal to the point C, namely (xf, yf) = (xc, yc), and min _0 equal to the 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 to be equal to 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 to be the point behind the point C on the expected track; namely:
wherein, | CO | is the distance between the point C and the point O, | FO | is the distance between the point F and the point O;
and 4, step 4: if the point C is not the first point or the last point on the expected track, judging whether cosine values of the & lt BCO and the & lt FCO are more than 0; if COs-FCO is greater than 0, the COs-FCO is an acute angle, which indicates that a point N which is closer to a distance | CO | between the point C and the point O exists ON a connecting line FC between the point F and the point C, the value | ON | of the point N is equal to the distance between the point O and the point FC, and the value is used as an estimated value of min _ 0; if COs & lt FCO is less than 0, then & lt FCO is an obtuse angle, a point which is closer than the distance | CO | between the point C and the point O does not exist on a connecting line FC between the point F and the point C, and | CO | is used as an estimated value of min _ 0; namely:
in the same way, the method for preparing the composite material,
wherein, | FC | is the distance between point F and point C, | BC | is the distance between point B and point C;
and 5: comparing the magnitudes of min _0 and min _1 obtained in step 3 or step 4, and taking the smaller value as the absolute value of the lateral deviation estimation filter _ error:
|lateral_error| = min( min_0, min_1);
step 6: and judging whether yc is positive or negative in the current vehicle coordinate system at the point C, if yc is less than 0, estimating that the lateral deviation is negative, and if yc is more than 0, estimating that the lateral deviation is positive.
2. The method for accurately estimating the deviation for the lateral control of the unmanned vehicle as claimed in claim 1, wherein the calculation method of the heading deviation estimation is as follows:
step 1: if the estimated point of the minimum transverse deviation is a point on the expected track, namely the point C, the heading of the point C is
The current course is
And obtaining course deviation estimation according to the following formula:
and 2, step: if the minimum lateral deviation estimation point is not a point on the expected track, obtaining an optimal estimation point according to a lateral deviation accurate estimation module, and marking the optimal estimation point as a point N, and if the point N is a point between the point C and the point F, calculating a distance | CN | between the point C and the point N and a distance | CF | between the point C and the point F according to the following formula:
wherein, | CO | is the distance between the point C and the point O, | ON | is the distance between the point O and the point N;
according to the physical space continuity and smoothness of the vehicle operation, a track between a point F and a point C is regarded as a section of circular arc, the center of the circular arc is a 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 course bias estimate is derived by:
in the formula (I), the compound is shown in the specification,
for the course estimate of the point N,
the course of the point M is the course,
the course of the point F is the course,
is the heading of the point C and,
is the current heading of the vehicle and is,
estimating course deviation;
if point N is a point between point C and point B, the distance | BN | between point B and point N, and the distance | CB | between point C and point B are calculated by the following equations:
according to the physical space continuity and smoothness of the vehicle running, a 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
As heading estimate for point N
The course bias estimate is derived by:
in the formula (I), the compound is shown in the specification,
for the course estimate of the point N,
the course of the point M is the course,
the heading of the point B is the direction of the point B,
the course of the point C is the course,
the current heading of the vehicle is the current heading,
is a heading bias estimate.
3. The method as claimed in claim 2, wherein the lateral deviation estimate and the heading deviation estimate are used as feedback control quantities to be brought into a trajectory tracking control rate, thereby realizing accurate tracking control of the unmanned vehicle.
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