CN110320917B - Unmanned vehicle curve tracking control method - Google Patents
Unmanned vehicle curve tracking control method Download PDFInfo
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Abstract
The invention provides a method for controlling curve tracking of an unmanned vehicle, which comprises the following steps: when the vehicle turns at a first turning angle, acquiring a road point on a following road, which is closest to the midpoint of a first axle of the vehicle, and taking the road point, which is closest to the midpoint of the first axle of the vehicle, as a first target point; calculating the distance between the first target point and the midpoint of the first axle to obtain first deviation data; calculating a second corner according to the first deviation data and the current speed of the vehicle; a target steering angle at which the vehicle turns is determined based on the first steering angle and the second steering angle. Therefore, the inside and outside cutting characteristics caused by insufficient steering or over steering can be effectively improved in the running process of the vehicle on the curve, the running pose of the vehicle is improved, the passable width of the unmanned vehicle on the curve can be reduced, the tracking precision is improved, and the riding comfort of the vehicle running on the curve is improved.
Description
Technical Field
The invention relates to the technical field of automatic driving, in particular to a curve tracking control method for an unmanned vehicle.
Background
Vehicle tracking, also known as path tracking. The development of the unmanned technology has higher and higher requirements on the path tracking deviation, and the quality of the path tracking deviation directly influences the quality of the autonomous tracking performance of the vehicle. When the unmanned vehicle runs on a curve, if the tracking deviation is large, the vehicle can be internally tangent or externally tangent, and the passable width of the vehicle can be even increased. In order to improve the tracking performance of the vehicle curve and improve the driving pose of the vehicle at the curve, a curve tracking control method of the unmanned vehicle, which considers the inside and outside characteristics and the tracking precision of the vehicle, is necessary.
In the prior art, the unmanned trajectory tracking method adopts a pure tracking (pure pursuit) algorithm, and the method needs to be assisted by a bicycle model and an Ackerman geometric corner. The vehicle running pose of the existing preview algorithm under three curve working conditions is given as follows:
fig. 1 is a schematic view of a driving pose of a vehicle in a right-angle curve, as shown in fig. 1, in the right-angle curve (the vehicle completes the curve tracking process from small to large according to the number of the vehicle), at the midpoint of the front axle of the vehicle: 1-externally cutting; 2, returning to the original position; 3, externally cutting; 4-aligning; vehicle rear axle midpoint: 1-internally cutting; 2, returning to the original position; 3-internally cutting; 4-return to the positive.
FIG. 2 is a schematic view of a driving pose of a vehicle on a U-shaped curve, wherein the driving pose is as follows: 1, internally cutting; 2, externally cutting; 3, returning to the original position; 4, externally cutting; 5, aligning; at the vehicle rear axle midpoint: 1-externally cutting; 2, internally cutting; 3, returning to the original position; 4, internally cutting; 5-return to the right.
Fig. 3 is a schematic view of the driving pose of the vehicle in the S-shaped curve, which is similar to the right-angle curve and the U-shaped curve summarized above.
Through the analysis of the tracking poses of the vehicles under the three working conditions, the defects of the preview algorithm in the prior art can be obviously seen, on one hand, the defects cause the inward and outward tangency of the middle points of the front and rear axles of the vehicles, the passable width of the road is increased, and on the other hand, the tracking precision of the vehicles is seriously influenced.
Disclosure of Invention
The embodiment of the invention aims to provide a method for controlling the tracking of a curve of an unmanned vehicle, which aims to solve the problems that the traversable width of a road is increased and the tracking precision of the vehicle is influenced in the prior art.
In order to solve the above problem, in a first aspect, the present invention provides a method for controlling curve tracking of an unmanned vehicle, the method comprising:
when the vehicle turns at a first turning angle, acquiring a road point which is closest to the midpoint of a first axle of the vehicle on a following road, and taking the road point which is closest to the midpoint of the first axle of the vehicle as a first target point;
calculating the distance between the first target point and the midpoint of the first axle to obtain first deviation data;
calculating a second corner according to the first deviation data and the current speed of the vehicle;
and determining a target steering angle when the vehicle turns according to the first steering angle and the second steering angle.
In one possible implementation manner, the calculating the second turning angle according to the first deviation data and the current vehicle speed of the vehicle specifically includes:
using formulas
Calculating a second rotation angle;
where θ is the second steering angle, k1 is the first constant, error1 is the first deviation data, and v is the current vehicle speed of the vehicle.
In a possible implementation manner, the method further includes:
planning a path according to the position information of the target to be tracked, the current position information of the vehicle and the environment perception data to obtain a following road;
and when the vehicle tracks the target to be tracked along the following road, calculating a first corner at the turning position.
In a possible implementation manner, when the vehicle tracks the target to be tracked along the following road, calculating a first corner at a turn specifically includes:
when the vehicle turns, acquiring a road point which is closest to the midpoint of a second axle of the vehicle on a following road, and taking the road point which is closest to the midpoint of the second axle of the vehicle as a second target point;
obtaining second deviation data according to the distance between the second target point and the midpoint of the second axle;
determining the pre-aiming distance of the vehicle according to the second deviation data and the current speed of the vehicle;
determining a pre-aiming point according to the pre-aiming distance;
and calculating a first rotation angle of the vehicle according to the pre-aiming distance, the distance between the midpoint of the first axle and the midpoint of the second axle and the included angle of the tangent line of the circular arc from the midpoint of the second axle to the pre-aiming point.
In a possible implementation manner, the determining the pre-view distance of the vehicle according to the second deviation data and the current vehicle speed of the vehicle specifically includes:
using the formula l d ＝k2*v+x |error2| Calculating a pre-aiming distance;
wherein l d K2 is a second constant for the preview distance, v is the current vehicle speed of the vehicle, and error2 is second deviation data.
In a possible implementation manner, the calculating a first rotation angle of the vehicle according to the preview distance, the distance between the first axle midpoint and the second axle midpoint, and an included angle between the second axle midpoint and a tangent of an arc of the preview point specifically includes:
and the alpha is an included angle between the middle point of the second axle and the tangent line of the circular arc of the preview point.
In one possible implementation manner, the determining the target steering angle when the vehicle turns according to the first steering angle and the second steering angle specifically includes:
adding the first and second rotational angles;
the addition result is taken as the target rotation angle.
In a second aspect, the present invention provides an unmanned vehicle curve tracking control apparatus, comprising:
the vehicle driving device comprises an acquisition unit, a control unit and a control unit, wherein the acquisition unit is used for acquiring a road point which is closest to the midpoint of a first axle of a vehicle on a following road when the vehicle turns at a first turning angle, and taking the road point which is closest to the midpoint of the first axle of the vehicle as a first target point;
the calculating unit is used for calculating the distance between the first target point and the midpoint of the first axle to obtain first deviation data;
the calculating unit is further used for calculating a second corner according to the first deviation data and the current speed of the vehicle;
a determination unit configured to determine a target rudder angle at which the vehicle turns, based on the first rudder angle and the second rudder angle.
In a third aspect, the invention provides an apparatus comprising a memory for storing a program and a processor for performing the method of any of the first aspects.
In a fourth aspect, the present invention provides a computer program product comprising instructions which, when run on a computer, cause the computer to perform the method according to any one of the first aspect.
In a fifth aspect, the invention provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the method of any of the first aspects.
By applying the method and the device for controlling the curve tracking of the unmanned vehicle, provided by the embodiment of the invention, the inside and outside cutting characteristics caused by understeer and oversteer can be effectively improved in the curve driving process of the vehicle, and the driving pose of the vehicle is improved. And the passable width of the unmanned vehicle at the curve can be reduced, and the tracking precision is improved, so that the riding comfort of the vehicle running at the curve is improved.
Drawings
FIG. 1 is a schematic view of a vehicle driving pose at a right-angle bend;
FIG. 2 is a schematic view of a driving pose of a vehicle at a U-shaped curve;
FIG. 3 is a schematic view of a driving pose of a vehicle at an S-shaped curve;
FIG. 4 is a flowchart of a method for controlling tracking of an unmanned aerial vehicle along a curve according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of the deviation relationship between the road point and the midpoint of the front axle;
FIG. 6 is a schematic view of the closest approach point of the vehicle;
FIG. 7 is a preview follow-up schematic;
FIG. 8 is a graph showing an ideal tracking effect of a vehicle on a curve;
fig. 9 is a schematic structural diagram of a curve tracking control device of an unmanned vehicle according to a second embodiment of the present invention.
Detailed Description
The present application will be described in further detail with reference to the following drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the relevant invention and not restrictive of the invention. It should be further noted that, for the convenience of description, only the portions related to the related invention are shown in the drawings.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
Fig. 4 is a flowchart of a method for controlling curve tracking of an unmanned Vehicle according to an embodiment of the present invention, where the method is applied to an unmanned Vehicle, and an execution subject of the method is a processor of the unmanned Vehicle, where the processor may be referred to as an Automated Vehicle Control Unit (AVCU), and the AVCU is equivalent to a "brain" of the unmanned Vehicle. As shown in fig. 4, the method comprises the steps of:
The first axle is a front axle of the vehicle, and the second axle is a rear axle of the vehicle. The following path is a path for the vehicle to follow the target to be followed, the following path is composed of a plurality of waypoints, each waypoint on the following path has waypoint attributes, and the waypoint attributes comprise an x coordinate, a y coordinate and a course angle of the waypoint.
Specifically, during the travel of the vehicle along the following path, the front-rear axis midpoint is constantly kept in the optimal tracking state when the vehicle is on the following path, but in actual travel, when the front-rear axis midpoint of the vehicle is on the left side of the following path, the deviation of the vehicle from the following path is negative, and conversely, the deviation is positive. Under the working condition of left-right turning, the deviation value when the vehicle is externally tangent is different in positive and negative.
Referring to FIG. 5, the deviation relationship between the waypoint and the front axle midpoint is shown in FIG. 5. Taking a left-side turn as an example, during the running process of the vehicle on a curve, the time deviation of the front axle midpoint during the interior contact is negative, the time deviation during the exterior contact is positive, the distance d1 between the front axle midpoint F and the waypoint 1 when the vehicle is in the exterior contact is positive, and the distance between the front axle midpoint F and the waypoint 2 when the vehicle is in the interior contact is negative.
During the driving process of the vehicle, a point closest to the middle point of the front axle of the vehicle exists in the road points, and as shown in fig. 6, a point C is the closest road point corresponding to the center a of the front axle.
The method for searching the road point closest to the front axle of the vehicle is as follows:
let A point coordinate (x) 0 ，y 0 ) B point coordinate (x) 1 ，x 1 ) C point coordinate (x) 2 ，y 2 ) D point coordinate (x) 3 ，y 3 ). The distance from the point BCD on the following path to the center of the front axle of the vehicle is as follows:
and the waypoint closest to the front axle is d min The corresponding waypoints.
Before step 401, the method further includes:
planning a path according to the position information of the target to be tracked, the current position information of the vehicle and the environment perception data to obtain a following road;
when the vehicle tracks the target to be tracked along the following road, a first corner at the corner is calculated.
When the vehicle tracks the target to be tracked along the following road, calculating a first corner at the turn, specifically comprising:
firstly, when the vehicle turns, a following road is obtained, and a road point which is closest to the midpoint of a second axle of the vehicle is obtained and taken as a second target point;
then, second deviation data is obtained according to the distance between the second target point and the midpoint of the second axle;
then, determining the pre-aiming distance of the vehicle according to the second deviation data and the current vehicle speed of the vehicle;
then, according to the pre-aiming distance, determining a pre-aiming point;
and finally, calculating a first corner of the vehicle according to the pre-aiming distance, the distance between the midpoint of the first axle and the midpoint of the second axle and the included angle of the tangent line of the circular arc from the midpoint of the second axle to the pre-aiming point.
The pre-aiming distance needs to be determined in order to enable the vehicle to follow the target to be followed well and avoid the phenomenon of overshoot during tracking when the vehicle is far away from the following path. Determining the pre-aiming distance of the vehicle according to the second deviation data and the current vehicle speed of the vehicle specifically comprises:
using the formula l d ＝k2*v+x |error2| Calculating a pre-aiming distance;
wherein l d K2 is a second constant for the preview distance, v is the current vehicle speed of the vehicle, and error2 is second deviation data. The second deviation data may be a distance between the coordinates of the second target point and the coordinates of the second axle midpoint.
The method includes the following steps that a first corner of a vehicle is calculated according to a pre-aiming distance, the distance between a first axle midpoint and a second axle midpoint and an included angle between a second axle midpoint and a tangent line of an arc of a pre-aiming point, and specifically includes the following steps:
referring to fig. 7, the first rotation angle of the vehicle may be calculated using the following equation:
wherein, δ is a first corner, L is a distance between a midpoint of the first axle and a midpoint of the second axle, and α is an included angle of a tangent line of an arc from the midpoint of the second axle to the preview point.
In fig. 7, the four-wheeled vehicle can be simplified into a two-wheeled bicycle model, and a pre-aiming point on the path is selected according to an appropriate pre-aiming distance based on the middle point of the rear axle of the vehicle. The trajectory tracking can be approximated as the arc of a circle traversed by the vehicle from the midpoint of the rear axle to the pre-aim point. The middle point of the rear axle of the vehicle is the tangent point of the circular arc, and the (g) on the path x ，g y ) The arc of the vehicle from the middle point of the rear axle to the pre-aiming point is the pure tracking path. R is the radius of the circular arc.
If only the first corner is considered, although the vehicle can be driven according to the set following path, if the selection of the pre-aiming distance is not proper, the driving pose of the vehicle is seriously influenced, and the vehicle is subjected to external tangent or internal tangent. Therefore, the second rotation angle needs to be calculated to compensate for the first rotation angle, so as to reduce the error.
The coordinate of the first target point and the coordinate of the midpoint of the first axle may be subtracted, and the absolute value is taken to obtain the first deviation data.
After the absolute value is taken, amplitude limiting processing can be performed to obtain first deviation data.
The inward turning or outward turning characteristics of the front wheels of the vehicle in the curve tracking process respectively correspond to the over-turning and under-turning of the front wheels, the over-turning and under-turning of the front wheels in the curve tracking process can be determined through the positive and negative of the calculated first deviation data, and the inward and outward turning characteristics are improved through a second turning angle obtained through the conversion of the first deviation data.
And step 403, calculating a second steering angle according to the first deviation data and the current vehicle speed of the vehicle.
In particular, using formulae
Calculating a second rotation angle;
where θ is the second steering angle, k1 is the first constant, error1 is the first deviation data, and v is the current vehicle speed of the vehicle.
In step 404, a target steering angle for turning the vehicle is determined according to the first steering angle and the second steering angle.
Specifically, the first corner and the second corner can be added to obtain a target corner, and the inside and outside characteristics caused by insufficient steering and excessive steering can be effectively relieved when the vehicle subjected to corner compensation tracks on the curve, so that the passable width and the tracking error of the road are reduced.
FIG. 8 is a graph of the ideal tracking effect of the vehicle in a curve. Under the ideal condition, all curve working conditions can be defined as circles with different curvature radiuses, when a vehicle tracks along a circular road, the optimal tracking state is that the midpoint of the front axle and the rear axle is kept on the circles at any time, and the deviation of the vehicle and the passable width of the road can be maintained at a small value at the moment. The method and the device have the advantages that the unmanned vehicle is enabled to be changed from the position posture shown in the figure 5 to the position posture of the figure 8 when tracking on the curve, and therefore the vehicle can well follow the path.
By applying the curve tracking control method of the unmanned vehicle, provided by the embodiment of the invention, the internal and external characteristics caused by understeer and oversteer of the vehicle can be effectively improved in the curve driving process, and the driving pose of the vehicle is improved. And the passable width of the unmanned vehicle at the curve can be reduced, and the tracking precision is improved, so that the riding comfort of the vehicle running at the curve is improved.
Fig. 9 is a schematic structural diagram of a curve tracking control device of an unmanned vehicle according to a second embodiment of the present invention. As shown in fig. 9, the unmanned aerial vehicle curve tracking control apparatus 900 includes: an acquisition unit 901, a calculation unit 902 and a determination unit 903.
The acquiring unit 901 is configured to acquire a waypoint that is closest to a midpoint of a first axle of the vehicle on a following road when the vehicle turns at the first turning angle, and take the waypoint that is closest to the midpoint of the first axle of the vehicle as a first target point;
the calculating unit 902 is configured to calculate a distance between the first target point and a midpoint of the first axle to obtain first deviation data;
the calculating unit 902 is further configured to calculate a second turning angle according to the first deviation data and the current vehicle speed of the vehicle;
the determination unit 903 is configured to determine a target steering angle at which the vehicle turns, based on the first steering angle and the second steering angle.
By applying the curve tracking control device of the unmanned vehicle provided by the embodiment of the invention, the internal and external characteristics caused by understeer and oversteer of the vehicle can be effectively improved in the curve driving process, and the driving pose of the vehicle is improved. And the passable width of the unmanned vehicle at the curve can be reduced, and the tracking precision is improved, so that the riding comfort of the vehicle running at the curve is improved.
The third embodiment of the invention provides equipment, which comprises a memory and a processor, wherein the memory is used for storing programs, and the memory can be connected with the processor through a bus. The memory may be a non-volatile memory, such as a hard disk drive and a flash memory, in which a software program and a device driver are stored. The software program is capable of performing various functions of the above-described methods provided by embodiments of the present invention; the device drivers may be network and interface drivers. The processor is used for executing a software program, and the software program can realize the method provided by the first embodiment of the invention when being executed.
A fourth embodiment of the present invention provides a computer program product including instructions, which, when the computer program product runs on a computer, causes the computer to execute the method provided in the first embodiment of the present invention.
The fifth embodiment of the present invention provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the method provided in the first embodiment of the present invention is implemented.
Those of skill would further appreciate that the various illustrative components and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative components and steps have been described above generally in terms of their functionality in order to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied in hardware, a software module executed by a processor, or a combination of the two. A software module may reside in Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
The above embodiments are provided to further explain the objects, technical solutions and advantages of the present invention in detail, it should be understood that the above embodiments are merely exemplary embodiments of the present invention and are not intended to limit the scope of the present invention, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (4)
1. A method for controlling curve tracking of an unmanned vehicle, the method comprising:
when the vehicle turns at a first turning angle, acquiring a road point which is closest to the midpoint of a first axle of the vehicle on a following road, and taking the road point which is closest to the midpoint of the first axle of the vehicle as a first target point;
calculating the distance between the first target point and the midpoint of the first vehicle axle to obtain first deviation data;
calculating a second corner according to the first deviation data and the current speed of the vehicle;
determining a target steering angle when the vehicle turns according to the first steering angle and the second steering angle;
wherein, calculate first corner, specifically include:
when the vehicle turns, acquiring a road point which is closest to the midpoint of a second axle of the vehicle on a following road, and taking the road point which is closest to the midpoint of the second axle of the vehicle as a second target point;
obtaining second deviation data according to the distance between the second target point and the midpoint of the second axle;
determining the pre-aiming distance of the vehicle according to the second deviation data and the current speed of the vehicle;
determining a pre-aiming point according to the pre-aiming distance;
calculating a first corner of the vehicle according to the pre-aiming distance, the distance between the midpoint of the first axle and the midpoint of the second axle and the included angle between the midpoint of the second axle and the tangent line of the circular arc of the pre-aiming point;
wherein, the determining the preview distance of the vehicle according to the second deviation data and the current vehicle speed of the vehicle specifically comprises:
using the formula l d ＝k2*v+x |error2| Calculating a pre-aiming distance;
wherein l d K2 is a second constant, v is the current speed of the vehicle, and error2 is second deviation data;
the calculating a first rotation angle of the vehicle according to the pre-aiming distance, the distance between the midpoint of the first axle and the midpoint of the second axle, and the included angle between the midpoint of the second axle and the tangent line of the arc of the pre-aiming point specifically includes:
wherein δ is a first corner, L is a distance between a midpoint of the first axle and a midpoint of the second axle, and α is an included angle between the midpoint of the second axle and a tangent of an arc of the preview point;
wherein, calculating a second corner according to the first deviation data and the current speed of the vehicle specifically comprises:
using formulas
Calculating a second rotation angle;
where θ is the second rotation angle, k1 is the first constant, and error1 is the first deviation data.
2. The method of claim 1, further comprising, prior to the method:
planning a path according to the position information of the target to be tracked, the current position information of the vehicle and the environment perception data to obtain a following road;
and when the vehicle tracks the target to be tracked along the following road, calculating a first corner at the turning position.
3. The method according to claim 1, wherein determining the target steering angle at which the vehicle is turning based on the first steering angle and the second steering angle specifically comprises:
adding the first and second rotational angles;
the addition result is taken as the target rotation angle.
4. An unmanned vehicle curve tracking control apparatus, the apparatus comprising:
the vehicle driving device comprises an acquisition unit, a control unit and a control unit, wherein the acquisition unit is used for acquiring a road point which is closest to the midpoint of a first axle of a vehicle on a following road when the vehicle turns at a first turning angle, and taking the road point which is closest to the midpoint of the first axle of the vehicle as a first target point;
the calculating unit is used for calculating the distance between the first target point and the midpoint of the first axle to obtain first deviation data;
the calculating unit is further used for calculating a second corner according to the first deviation data and the current speed of the vehicle;
a determination unit configured to determine a target rudder angle at the time of turning of the vehicle, based on the first rudder angle and the second rudder angle;
wherein the calculating unit specifically includes:
when the vehicle turns, acquiring a road point which is closest to the midpoint of a second axle of the vehicle on a following road, and taking the road point which is closest to the midpoint of the second axle of the vehicle as a second target point;
obtaining second deviation data according to the distance between the second target point and the midpoint of the second axle;
determining the pre-aiming distance of the vehicle according to the second deviation data and the current speed of the vehicle;
determining a pre-aiming point according to the pre-aiming distance;
calculating a first corner of the vehicle according to the pre-aiming distance, the distance between the midpoint of the first axle and the midpoint of the second axle and the included angle between the midpoint of the second axle and the tangent line of the circular arc of the pre-aiming point;
wherein, the determining the preview distance of the vehicle according to the second deviation data and the current vehicle speed of the vehicle specifically comprises:
using the formula l d ＝k2*v+x |error2| Calculating a pre-aiming distance;
wherein l d K2 is a second constant, v is the current speed of the vehicle, and error2 is second deviation data;
the calculating a first corner of the vehicle according to the pre-aiming distance, the distance between the midpoint of the first axle and the midpoint of the second axle, and the included angle between the midpoint of the second axle and the tangent line of the arc of the pre-aiming point specifically includes:
wherein δ is a first rotation angle, L is a distance between a midpoint of the first axle and a midpoint of the second axle, and α is an included angle between the midpoint of the second axle and a tangent of an arc of the preview point;
the calculating unit calculates a second corner according to the first deviation data and the current speed of the vehicle, and specifically includes:
using formulas
Calculating a second rotation angle;
where θ is the second rotation angle, k1 is the first constant, and error1 is the first deviation data.
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