CN112793585A - Automatic driving trajectory tracking control method - Google Patents
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Abstract
The invention discloses a track tracking control method for automatic driving, which periodically executes the following steps in the process that a vehicle runs along a navigation path: selecting a first pre-aiming point and a second pre-aiming point in front of a vehicle running track, wherein the distance between the second pre-aiming point and an origin of a coordinate system is greater than the distance between the first pre-aiming point and the origin of the coordinate system; proportional error control and differential error control in discrete PID control are executed according to the angle error and the coordinate error of the first preview point, and vehicle output is controlled to execute automatic driving track tracking control; then, position error control in discrete PID control is executed according to the coordinate error of the second preview point so as to eliminate the accumulated error of track tracking control of automatic driving of the vehicle; wherein, the origin of the coordinate system is the middle of the front face of the vehicle. The method can improve the track tracking precision at the turning place, so that the unmanned vehicle can safely and reliably run on the road with lower structured degree in the garden.
Description
Technical Field
The invention relates to the field of unmanned driving, in particular to a trajectory tracking control method for automatic driving.
Background
At present, more and more unmanned vehicles and equipment systems are applied to garden environments such as high-grade districts, parks, industrial parks and the like, and various garden scenes such as garden epidemic prevention, garden security, garden sweeping, garden express delivery and delivery, garden selling, garden ferrying and the like are given. Through the investment of park unmanned equipment such as unmanned epidemic prevention disinfection cleaning equipment, unmanned selling equipment, unmanned security equipment, unmanned ferry vehicles and the like, the park epidemic prevention cleaning and security strength can be increased, direct contact of personnel is reduced, the infection risk is effectively reduced, and the superiority of the unmanned equipment is embodied.
Compared with an unmanned vehicle system on an open road, the park unmanned system has different characteristics, which are mainly expressed in the following aspects:
(1) the speed is relatively low, the running speed is below 40KM/h, and unlike the automatic driving system on an open road, the speed may be required to be above 60 KM/h.
(2) The road range is limited, the road is relatively fixed, the road length is limited, and the driving map can be deployed in advance and stored in the automatic driving controller.
(3) The road structure degree is lower, traffic signs such as traffic lines and traffic lights which are obviously standard may not exist, and the difference of road characteristics such as road width, uphill and downhill, turning and the like may be larger.
Therefore, the problem of motion trajectory control of unmanned vehicles needs to be solved for roads with low structuralization degree in the garden.
In the prior art, vehicle trajectory tracking control can be described by a preview following theory, and the tracking control of the unmanned driving system on a driving path is to simulate a driver to continuously preview a road ahead, control a steering wheel according to the curvature of the road and the road condition ahead, and then gradually approach to an expected path. The preview following theory can be simplified into a system in which a preview and a follower are connected in series, as shown in fig. 1. The transfer characteristics of the preamera and the follower in the system are respectively expressed by P(s) and F(s), and an ideal preamera following system should conform to the expression P(s) and F(s) approximately equal to 1.
The preview follow-up theory describes a characteristic of follow-up control according to road condition information of a future road as shown in fig. 2, while for an excellent driver, the driving behavior of the excellent driver mostly meets the 'preview follow-up' theory as shown in fig. 3. The driver can always make the vehicle travel on the expected path as much as possible by inputting the proper turning angle no matter how the driving condition changes.
The path tracking algorithm developed according to the preview following theory can be divided into two parts, namely the design of a preview and the design of a follower. The selection of the preview point of the preview device is mainly positively correlated with the vehicle speed of the vehicle, and is a difficult point of algorithm design. The tracking control of the vehicle follows the "ackermann" geometric relationship when the vehicle is running at a relatively low speed and the front wheel steering angle is relatively small, that is, the curvature ρ of the path on which the vehicle runs is in direct proportion to the steering wheel steering angle δ', that is:
where λ is the gear ratio of the steering system, L is the wheelbase, and ρ is the curvature of the desired path of the vehicle.
As can be seen from the above formula, the steering angle control of the steering wheel and the curvature of the vehicle satisfy a proportional relationship. If the driver can grasp the mapping relationship between the steering wheel angle and the curvature of the path on the basis of a lot of practices, he can naturally determine the steering wheel angle when he observes a specific curvature. The automatic driving system essentially establishes the corresponding relation established by the above formula, so that a proper steering wheel angle can be judged according to the environmental information and the driving state.
The path tracking algorithm based on the preview tracking principle and the ackermann steering principle is also called as a geometry-based method, is relatively simpler than another model prediction control algorithm (MPC) based on a model, is more convenient to track and debug codes, has smaller arithmetic operation amount, but has the defect that the precision of track tracking at a turning place is not enough.
Disclosure of Invention
In view of the above-mentioned drawbacks (shortcomings) of the prior art, it is an object of the present invention to provide an automatic trajectory tracking control method that improves the accuracy of trajectory tracking at a turning place and enables an unmanned vehicle to safely and reliably travel on a road with a low degree of structurization of a campus.
In order to achieve the above object, the present invention provides an automatic driving trajectory tracking control method, which periodically performs, during a vehicle traveling along a navigation path:
selecting a first pre-aiming point and a second pre-aiming point in front of a vehicle running track, wherein the distance between the second pre-aiming point and an origin of a coordinate system is greater than the distance between the first pre-aiming point and the origin of the coordinate system;
proportional error control and differential error control in discrete PID control are executed according to the angle error and the coordinate error of the first preview point, and vehicle output is controlled to execute automatic driving track tracking control;
then, position error control in discrete PID control is executed according to the coordinate error of the second preview point so as to eliminate the accumulated error of track tracking control of automatic driving of the vehicle;
the origin of the coordinate system is the middle of the front face of the vehicle, the positive direction of the x axis faces to the right, the positive direction of the y axis faces forwards, the coordinate position of the pre-aiming point in the coordinate system is marked as (x, y), the coordinate error of the pre-aiming point is x, and the angle error of the pre-aiming point is theta (arctan/x).
The technical effects are as follows:
PID closed-loop control is carried out through the first pre-aiming point, accumulated errors are eliminated through the second pre-aiming point, errors of the system can be calibrated, and control accuracy and robustness of the system are improved.
Further, the distance between the first preview point and the origin of the coordinate system is: the minimum value of the preset first pre-aiming distance and the pre-aiming distance calculated according to the vehicle speed.
The technical effects are as follows:
at low speed, higher tracking accuracy can be obtained through a closer preview point.
Further, the distance between the second preview point and the origin of the coordinate system is a set fixed value.
Further, the discrete PID control specifically includes:
calculating the proportional error control term E _ P0_ P of the first preview point:
E_P0_P＝Kang1*θ+Kpos1*x
wherein, Kang1 and Kpos1 are PID control parameters;
calculating a differential error control term E _ P0_ D for the first preview point:
E_P0_D＝Dang*Δθ+Dpos*Δx
wherein, delta theta and delta x are respectively an angle difference value and a position difference value between the current time T and the last time T-1; dang and Dpos are PID control parameters;
calculating a position error control term E _ Pz of the second preview point:
E_Pz＝Kpos2*x+Ipos*∑x
wherein Kpos2 and Ipos are PID control parameters;
and finally, obtaining a current control output value u:
u＝E_P0_P+E_P0_D+E_Pz。
the technical effects are as follows:
a specific implementation method for carrying out PID closed-loop control through a first preview point and carrying out accumulated error elimination through a second preview point is provided.
Furthermore, the parameter values of the PID control parameters are combined with the driving map data to be labeled to form a configuration file, the configuration file is read according to the current position of the vehicle in the driving process of the vehicle, and the PID control parameters are applied to the automatic driving track tracking control of the current position of the vehicle.
The technical effects are as follows:
the PID control parameters are combined with the driving map data, and the optimal vehicle control effect can be obtained in the automatic driving process by reading the PID control parameters matched with the road attributes.
Further, the method for setting the parameter value of the PID control parameter in the configuration file includes the following steps:
starting automatic driving, initializing a proportional parameter Kp, a derivative parameter Kd and an integral parameter Ki, and then entering a path to follow: reading CTE periodically, firstly adjusting a proportional parameter Kp, adjusting to enable the vehicle to advance towards the route direction, reducing the numerical value of Kp if the vehicle deviates from the route and cannot be adjusted back, then adjusting a differential parameter Kd according to the amplitude of each adjustment of the vehicle to enable the system to be in a damping state in a set constraint interval, and finally adjusting an integral parameter Ki according to the condition of accumulated error, wherein the CTE is the deviation of the vehicle and the driving track.
The technical effects are as follows:
the method for acquiring the PID control parameters required by automatic driving is simple and effective.
Further, the driving map is a laser point cloud map or a vector map.
Further, the driving map is a vector map, the vector map includes position coordinate information, road topology information and road attribute information, and the configuration file includes a corresponding relationship between a parameter value of the PID control parameter and the road attribute information, and a highest allowable speed;
reading a vector map file and acquiring the current position of the vehicle when the vehicle runs;
and reading the configuration file according to the road attribute of the current position of the vehicle, and acquiring the initial value of the PID control parameter of the current position of the vehicle.
The technical effects are as follows:
an implementation example of PID control parameters combined with a vector-based travel map is given.
The invention realizes the following technical effects:
the automatic driving track tracking control method provided by the invention can improve the track tracking precision at the turning place, so that the unmanned vehicle can safely and reliably run on the road with lower structuralization degree in the garden.
Drawings
FIG. 1 is a schematic diagram of a preview follower configuration of the prior art;
FIG. 2 is a flow chart of a prior art preview follow algorithm;
FIG. 3 is a prior art preview follow up schematic of a vehicle;
FIG. 4 is a flow chart of the trajectory tracking control method of autonomous driving of the present invention;
FIG. 5 is a flow chart of the present invention for finding a preview point;
FIG. 6 is a schematic illustration of the calculation of the vehicle preview error of the present invention;
FIG. 7 is a flow chart of a PID control parameter tuning method of the invention;
FIG. 8 is a control parameter annotation optimization method based on a campus map according to the present invention.
Detailed Description
To further illustrate the various embodiments, the invention provides the accompanying drawings. The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate embodiments of the invention and, together with the description, serve to explain the principles of the embodiments. Those skilled in the art will appreciate still other possible embodiments and advantages of the present invention with reference to these figures. Elements in the figures are not drawn to scale and like reference numerals are generally used to indicate like elements.
The invention will now be further described with reference to the accompanying drawings and detailed description.
The invention discloses a trajectory tracking control method for automatic driving, which is based on a preview following theory and is used for improving the precision of path tracking, and the method mainly comprises the following steps with reference to fig. 4:
during the vehicle driving along the navigation path, periodically executing:
selecting a first pre-aiming point and a second pre-aiming point in front of a vehicle running track, wherein the distance between the second pre-aiming point and an origin of a coordinate system is greater than the distance between the first pre-aiming point and the origin of the coordinate system;
proportional error control and differential error control in discrete PID control are executed according to the angle error and the coordinate error of the first preview point, and vehicle output is controlled to execute automatic driving track tracking control;
then, position error control in discrete PID control is executed according to the coordinate error of the second preview point so as to eliminate the accumulated error of track tracking control of automatic driving of the vehicle;
the origin of the coordinate system is the middle of the front face of the vehicle, the positive direction of the x axis faces to the right, the positive direction of the y axis faces forwards, the coordinate position of the pre-aiming point in the coordinate system is marked as (x, y), the coordinate error of the pre-aiming point is x, and the angle error of the pre-aiming point is theta (arctan/x).
(1) Preview point selection
In the present embodiment, the first preview point is labeled as P0, the selection of the first preview point P0 is made according to the vehicle speed, and the preview distance L of the first preview point (the distance between the first preview point P0 and the origin of the coordinate system) is obtained by the following formula:
L＝min(K*V,distance0)
wherein K is a coefficient; v is the vehicle speed; distance0 is a preset first preview distance.
At low speed, the preview distance L is proportional to the vehicle speed V, and at high speed, the preview distance L is defined as a first preview distance 0.
Meanwhile, in order to prevent drift of the preview point, a double-point preview strategy is adopted, a second preview point is selected at a distance of 6 meters in front of a vehicle running track, the point is marked as Pz, errors of the system are calibrated by tracking the fixed Pz point, and the control precision and robustness of the system are improved.
The pre-aiming distance of the second pre-aiming point is determined by a large number of data test verifications, and 6 meters is a preferred parameter in the embodiment.
After the pre-aiming distance L is obtained, a pre-aiming point needs to be found and marked by a certain method. In this embodiment, a process of finding a preview point is given, as shown in fig. 5, including the following steps:
step S11: reading a local path array line [ ];
step S12: initializing a pre-aiming distance S as 0;
step S13: s ═ S + Line [ i ];
step S14: judging that S > is L, and returning to the step S13 when S > is L and S < L;
step S15: setting Line [ i ] as a preview point, and determining a coordinate error x and an angle error theta of the preview point according to coordinates (x, y) of the Line [ i ], wherein the theta is arctan (y/x).
The local path array is an array of track points of vehicle front driving calculated by the autopilot planning decision module, data of each element of the array comprises longitude and latitude and course angle information of the track points on a front driving route, the distance between two adjacent points of the array is 0.1 meter, and the number of the array is generally 500, so that track line information of 50 meters in front of the vehicle can be represented.
(2) Discrete PID-based closed loop control
The PID control, i.e. proportional, integral and differential control, abbreviated as PID control, is one of the main techniques of system control due to its simple structure, good stability, reliable operation and convenient adjustment. When the structure and parameters of a controlled object cannot be completely known, the system parameters cannot be obtained through effective measurement means, or an accurate mathematical model cannot be obtained, other technologies of control theory such as model-based implementation are difficult to adopt, the structure and parameters of a system controller must be determined by experience and field debugging, and the application of the PID control technology is most convenient.
For a computer program, a discretized PID formula is implemented as follows:
wherein k is the current time, k-1 is the last time, and u () is the control output; e () is an error; . Kpe (k) is a proportional error control term for PID control;
an integral error control term for PID control; kd(e (k) -e (k-1)) is a differential error control term for PID control.
As described above, to increase the control accuracy, 2 preview points, P0 and Pz, are used.
The calculation of the vehicle preview error is schematically shown in fig. 6, the origin of the calculated coordinate system is the middle of the front face of the vehicle, x faces to the right, y faces forwards, x in fig. 6 represents the transverse distance error between the current preview point and the vehicle position, and θ represents the angle error between the current preview point and the vehicle position.
In this embodiment, the PID closed-loop control using the dual preview points for discretization specifically includes:
firstly, a PD control strategy (namely proportional control) is adopted for error calculation of a first pre-aiming point P0, PD control can prevent oscillation divergence caused by overshoot of control, the system operation is more stable, an error term comprises an angle error theta and a coordinate error x, the angle error theta can be obtained by averaging the angle average values of a single point of the pre-aiming point or n points before and after the pre-aiming point, and in the embodiment, the angle average values of 7 points before and after the pre-aiming point are obtained by averaging.
Calculating a proportional error control term of the first preview point P0:
E_P0_P＝Kang1*θ+Kpos1*x
wherein Kang1 and Kpos1 are adaptive PID control parameters;
the differential error control term for the first preview point P0 is calculated:
E_P0_D＝Dang*Δθ+Dpos*Δx
wherein, Δ θ and Δ x are the angular difference and the position difference between the current time T and the previous time T-1, respectively, and Dang and Dpos are PID control parameters. Representing the differential amount of the system control, the relevant position error value is obtained in the actual system by reading the position sensor information of the vehicle, such as the lateral distance error and the angle error are calculated by the current position information output by the high-precision positioning device. The Dang, Dpos, etc. parameters represent the parameters of system control, and are the arrays we need to adapt.
Secondly, for PD control, the accumulative error of the system is brought, so the position error control item E _ Pz of the second fixed preview point Pz is introduced to eliminate the accumulative error, and the formula is as follows:
E_Pz＝Kpos2*x+Ipos*∑x
wherein, Kpos2 and Ipos are PID control parameters.
And finally, obtaining a current control output value u:
u＝E_P0_P+E_P0_D+E_Pz
u represents the output control amount of the steering wheel angle at the current position.
Thirdly, the vehicle may have a fixed transverse system control error beta, a numerical value is obtained through calibration, the calibration mode is that the vehicle is driven to run linearly under the condition that a load planning path is not provided, the control output value at the moment is beta, the numerical value of the beta is equal to 0 under an ideal condition, and the finally obtained control output is (u-beta).
Fourthly, the PID control parameters are obtained through path following simulation or test, and referring to fig. 7, the adjusting method of the PID control parameters is approximately as follows: starting automatic driving, initializing a proportional parameter Kp, a derivative parameter Kd and an integral parameter Ki, and then entering a path to follow: reading CTE periodically, firstly adjusting the proportional parameter Kp to make the vehicle advance towards the route direction, if the vehicle deviates from the route, reducing the value of Kp, then adjusting the derivative parameter Kd according to the adjustment amplitude of the vehicle to make the system in a better damping state, and finally adjusting the integral parameter Ki according to the accumulated error condition. We define the deviation of the vehicle from the driving trajectory as cte (cross track error). In fig. 7, the proportional parameter Kp, the derivative parameter Kd, and the integral parameter Ki are denoted as KPO, KDO, and KIO, respectively. And instantly storing the adjusted proportional parameter Kp, differential parameter Kd and integral parameter Ki into a configuration file.
(3) PID control parameter marking optimization based on park map
For the unmanned vehicle running in the park, the running route of the vehicle is relatively fixed, the PID control parameters of the vehicle can be optimized in advance for each road section of the running route of the vehicle, and the implementation mode is as follows: and marking the PID control parameters to be tested and verified by combining with the map data, so that the PID control parameters are associated with the road attribute information. The driving map is divided into a laser point cloud map and a vector map. The point cloud map contains the environmental information of the route, and can carry out positioning based on point cloud matching without depending on the common satellite positioning. The vector map contains position coordinate information, road topology information, road attribute information and maximum allowable speed.
In the embodiment, a technical scheme based on a vector map is provided, and the corresponding relation between the road attribute information, the maximum allowable speed and the control parameter is increased.
First, information of each location point on the vector map is defined as shown in table 1.
TABLE 1 vector map file
Among them, RTK (Real-time kinematic) is a Real-time dynamic carrier-phase differential technique.
Next, a profile defining the correspondence between the road attributes and the PID control parameters is shown in table 2.
TABLE 2 PID control parameter Profile
PID control parameters in the automatic running process of the vehicle are obtained:
in the process of automatic driving of the vehicle, the PID control parameters are obtained according to the current position matching, as shown in fig. 8, the method comprises the following steps:
reading a vector map file and a PID control parameter configuration file;
according to the road attribute of the current position of the vehicle, obtaining the highest allowable speed and PID control parameters of the vehicle on the current road section;
and calculating the output control quantity according to the current speed and the PID control parameter. The control quantity can reduce the transverse position error and the angle error of the system, so that the vehicle can run along the front track line (which can be a curve or a straight road).
The invention provides a track tracking control method in combination with the characteristics of a park low-speed running environment, compared with various algorithms based on model predictive control, the method has the characteristics of small calculation operand and simple parameter deployment and adjustment, and meanwhile, the method of adopting double-point pre-aiming and map marking optimization ensures the control precision, thereby being a simple and feasible park low-speed running control scheme.
While the invention has been particularly shown and described with reference to a preferred embodiment, it will be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (8)
1. An automatic driving trajectory tracking control method is characterized in that: during the vehicle driving along the navigation path, periodically executing:
selecting a first pre-aiming point and a second pre-aiming point in front of a vehicle running track, wherein the distance between the second pre-aiming point and an origin of a coordinate system is greater than the distance between the first pre-aiming point and the origin of the coordinate system;
proportional error control and differential error control in discrete PID control are executed according to the angle error and the coordinate error of the first preview point, and vehicle output is controlled to execute automatic driving track tracking control;
then, position error control in discrete PID control is executed according to the coordinate error of the second preview point so as to eliminate the accumulated error of track tracking control of automatic driving of the vehicle;
the origin of the coordinate system is the middle of the front face of the vehicle, the positive direction of the x axis faces to the right, the positive direction of the y axis faces forwards, the coordinate position of the pre-aiming point in the coordinate system is marked as (x, y), the coordinate error of the pre-aiming point is x, and the angle error of the pre-aiming point is theta (arctan/x).
2. The trajectory tracking control method for autonomous driving according to claim 1, characterized in that: the distance between the first preview point and the origin of the coordinate system is as follows: the minimum value of the preset first pre-aiming distance and the pre-aiming distance calculated according to the vehicle speed.
3. The trajectory tracking control method for autonomous driving according to claim 1, characterized in that: and the distance between the second preview point and the origin of the coordinate system is a set fixed value.
4. The trajectory tracking control method for autonomous driving according to claim 1, characterized in that:
the discrete PID control specifically includes:
calculating the proportional error control term E _ P0_ P of the first preview point:
E_P0_P＝Kang1*θ+Kpos1*x
wherein, Kang1 and Kpos1 are PID control parameters;
calculating a differential error control term E _ P0_ D for the first preview point:
E_P0_D＝Dang*Δθ+Dpos*Δx
wherein, delta theta and delta x are respectively an angle difference value and a position difference value between the current time T and the last time T-1; dang and Dpos are PID control parameters;
calculating a position error control term E _ Pz of the second preview point:
E_Pz＝Kpos2*x+Ipos*∑x
wherein Kpos2 and Ipos are PID control parameters;
and finally, obtaining a current control output value u:
u＝E_P0_P+E_P0_D+E_Pz。
5. the trajectory tracking control method for autonomous driving according to claim 4, characterized in that:
and marking the parameter values of the PID control parameters in combination with the driving map data to form a configuration file, reading the configuration file according to the current position of the vehicle in the driving process of the vehicle, and applying the PID control parameters to the automatic driving track tracking control of the current position of the vehicle.
6. The trajectory tracking control method for autonomous driving according to claim 5, characterized in that: the method for setting the parameter values of the PID control parameters in the configuration file comprises the following steps:
starting automatic driving, initializing a proportional parameter Kp, a derivative parameter Kd and an integral parameter Ki, and then entering a path to follow: reading CTE periodically, firstly adjusting a proportional parameter Kp, adjusting to enable the vehicle to advance towards the route direction, reducing the numerical value of Kp if the vehicle deviates from the route and cannot be adjusted back, then adjusting a differential parameter Kd according to the amplitude of each adjustment of the vehicle to enable the system to be in a damping state in a set constraint interval, and finally adjusting an integral parameter Ki according to the condition of accumulated error, wherein the CTE is the deviation of the vehicle and the driving track.
7. The trajectory tracking control method for autonomous driving according to claim 5, characterized in that:
the driving map is a laser point cloud map or a vector map.
8. The trajectory tracking control method for autonomous driving according to claim 7, characterized in that: the driving map is a vector map, the vector map comprises position coordinate information, road topology information and road attribute information, and the configuration file comprises the corresponding relation between the parameter value of the PID control parameter and the road attribute information as well as the highest allowable speed;
reading a vector map file and acquiring the current position of the vehicle when the vehicle runs;
and reading the configuration file according to the road attribute of the current position of the vehicle, and acquiring the parameter value of the PID control parameter of the current position of the vehicle.
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