CN113581201A - Potential field model-based collision avoidance control method and system for unmanned automobile - Google Patents

Potential field model-based collision avoidance control method and system for unmanned automobile Download PDF

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CN113581201A
CN113581201A CN202110829535.XA CN202110829535A CN113581201A CN 113581201 A CN113581201 A CN 113581201A CN 202110829535 A CN202110829535 A CN 202110829535A CN 113581201 A CN113581201 A CN 113581201A
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collision avoidance
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CN113581201B (en
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李海青
李思骏
罗久飞
夏傅豪
李靖
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Chongqing Zhiche Qinxing Technology Co ltd
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Chongqing University of Post and Telecommunications
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W60/00—Drive control systems specially adapted for autonomous road vehicles
    • B60W60/001—Planning or execution of driving tasks
    • B60W60/0011—Planning or execution of driving tasks involving control alternatives for a single driving scenario, e.g. planning several paths to avoid obstacles
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units, or advanced driver assistance systems for ensuring comfort, stability and safety or drive control systems for propelling or retarding the vehicle
    • B60W30/08—Active safety systems predicting or avoiding probable or impending collision or attempting to minimise its consequences
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B60W2050/0001—Details of the control system
    • B60W2050/0019—Control system elements or transfer functions
    • B60W2050/0028—Mathematical models, e.g. for simulation
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/10—Internal combustion engine [ICE] based vehicles
    • Y02T10/40—Engine management systems

Abstract

The invention relates to the field of automobile collision avoidance control, in particular to a potential field model-based unmanned automobile collision avoidance control method and system; the system comprises a potential field model building module, an MPC collision avoidance path planning module, an MPC control module and a vehicle model module; the potential field model is used for calculating collision avoidance path discrete points, the MPC collision avoidance path planning module is used for optimizing the collision avoidance path discrete points, the MPC control module is used for optimizing and solving the front wheel turning angle of the automobile according to the lateral displacement, the yaw angle and the actual state quantity of the automobile and inputting the front wheel turning angle into the vehicle model module, and the vehicle model module is used for outputting the actual state quantity of the vehicle. The invention adds a path planning on the basis of the traditional tracking control, can optimize the potential field minimum discrete point, plan the path, and then track the path through the tracking control, thereby realizing the obstacle avoidance control under the dynamic traffic environment and improving the smoothness and the same-row efficiency of the path in the obstacle avoidance process.

Description

Potential field model-based collision avoidance control method and system for unmanned automobile
Technical Field
The invention relates to the field of automobile collision avoidance control, in particular to a potential field model-based unmanned automobile collision avoidance control method and system.
Background
With the continuous increase of automobile holding capacity, the problems of urban congestion, energy consumption, traffic accidents and the like are increasingly prominent, and the automatic driving of automobiles becomes the development trend of the automobile industry. The collision avoidance planning is one of the intelligent automobile active collision avoidance core technologies, so that the burden of a driver is reduced, the commuting efficiency can be effectively improved, and the energy consumption is reduced.
The students at home and abroad have a lot of research results in the aspects of path planning and control methods of autonomous vehicles, and common trajectory planning algorithms comprise a random search method based on trajectory tracking, a trajectory planning method based on a specific function, a model prediction method based on an optimized trajectory, an artificial potential field method and the like. The above-described method is mainly applied to motion planning of autonomous cars and modeling of driver behavior in specific traffic scenarios, such as car following models. The main problems of the existing model are that the consideration of risk factors is lacked, the planning and modeling are not carried out on complex road conditions and vehicle-road interaction, and the existing evasion model is difficult to adapt to interaction and dynamic change of traffic environment and vehicle state.
Disclosure of Invention
The invention provides a potential field model-based unmanned automobile collision avoidance control method and system, aiming at the problems that the existing obstacle avoidance method does not consider the influence of complex and variable environmental factors and the self state of a vehicle on obstacle avoidance of an automatic driving vehicle, and is difficult to adapt to the interaction and dynamic change of a traffic environment and the state of the vehicle. The method comprises the steps of firstly obtaining obstacle avoidance path discrete points through calculation by using a dynamic potential field model, inputting the obstacle avoidance path discrete points into an MPC controller for optimization, then providing a tire rigidity prediction method based on obstacle avoidance path information, realizing prediction and linearization of tire force in the prediction field by using predicted tire state rigidity, and finally realizing vehicle obstacle avoidance control through the designed MPC obstacle avoidance controller.
In a first aspect of the present invention, the present invention provides a potential field model-based collision avoidance control method for an unmanned vehicle, the method comprising:
establishing a potential field model according to an object vector in a road and the road condition, and calculating collision avoidance path discrete points according to the potential field model;
optimizing the discrete points of the avoidance path, and calculating the minimum point of a risk field formed by combining the potential energy of each vehicle in the road condition from the discrete points of the avoidance path;
constructing a point quality model for obstacle avoidance path planning based on vehicle dynamics constraint according to the motion vector and the position vector of the vehicle;
constructing a first cost objective function by using the point quality model and taking a system stable point as a control target;
calculating the first generation price target function through an optimization algorithm to obtain an optimal control sequence of the system in any time domain corresponding to each moment;
adding the minimum point of the risk field into a point quality model corresponding to the first cost objective function, and calculating the optimal control variable corresponding to the lateral displacement and the yaw angle of the avoidance path under the target of minimizing the sum of the deviation of the output state quantity, the control variable and the potential energy of the minimum point of the risk field;
designing a linearized tire model, establishing a monorail vehicle kinematic model, and calculating a state quantity in a prediction time sequence and a control quantity of prediction output from the monorail vehicle kinematic model through a current state quantity of a system and a control increment in a control time domain;
taking the two norms of the deviation between the predicted output state quantity and the actual state quantity as performance indexes of speed control, taking the two norms of control increment as control input smooth indexes, and constructing a second cost objective function;
calculating the second cost objective function by adopting an Active-Set algorithm to obtain the optimal open-loop control increment corresponding to the front wheel corner and the yaw moment of the vehicle;
and inputting the optimal control variable and the first element of the optimal open-loop control increment serving as actual control variables and control increments into a vehicle model to complete path tracking control of the vehicle.
In a second aspect of the present invention, the present invention further provides a collision avoidance control system for an unmanned vehicle based on a potential field model, the system comprising:
the potential field model building module is used for building a potential field model according to object vectors in a road and road conditions and calculating discrete points of a collision avoidance path according to the potential field model;
the MPC collision avoidance path planning module is used for optimizing the collision avoidance path discrete points and calculating the minimum point of the risk field formed by combining the potential energy of each vehicle in the road condition from the collision avoidance path discrete points; constructing a point quality model for obstacle avoidance path planning based on vehicle dynamics constraint according to the motion vector and the position vector of the vehicle; constructing a first cost objective function by using the point quality model and taking a system stable point as a control target; calculating the first generation price target function through an optimization algorithm to obtain an optimal control sequence of the system in any time domain corresponding to each moment; adding the minimum point of the risk field into a point quality model corresponding to the first cost objective function, and calculating the optimal control variable corresponding to the lateral displacement and the yaw angle of the avoidance path under the target of minimizing the sum of the deviation of the output state quantity, the control variable and the potential energy of the minimum point of the risk field;
the MPC control module is used for designing a linearized tire model, establishing a single-track vehicle kinematic model, and calculating a state quantity in a prediction time sequence and a control quantity of prediction output from the single-track vehicle kinematic model through a current state quantity of a system and a control increment in a control time domain; taking the two norms of the deviation between the predicted output state quantity and the actual state quantity as performance indexes of speed control, taking the two norms of control increment as control input smooth indexes, and constructing a second cost objective function; calculating the second cost objective function by adopting an Active-Set algorithm to obtain the optimal open-loop control increment corresponding to the front wheel corner and the yaw moment of the vehicle;
and the vehicle model module is used for inputting the optimal control variable and the first element of the optimal open-loop control increment as actual control variables and control increments to a vehicle model to complete path tracking control of the vehicle.
The invention has the beneficial effects that:
according to the invention, the collision avoidance path points under the dynamic working condition can be predicted by establishing the comprehensive potential field model considering the road environment and the vehicle characteristics, the vehicle autonomous collision avoidance under the dynamic working condition is realized, the collision avoidance problem under the condition of considering the road environment and the autonomous vehicle characteristics can be effectively solved, the collision avoidance control effect under the dynamic working condition can be obviously improved through the MPC controller and the corresponding control method, and the obstacle avoidance efficiency and stability are improved.
Drawings
FIG. 1 is a flow chart of a collision avoidance control method for an unmanned vehicle according to an embodiment of the present invention;
FIG. 2 is a dynamic potential field map and a static potential field map in an embodiment of the present invention;
FIG. 3 is a schematic view of a kinematic model of a vehicle in an embodiment of the invention;
FIG. 4 is a schematic structural diagram of a collision avoidance control system of an unmanned vehicle according to an embodiment of the present invention;
fig. 5 is a schematic diagram of an obstacle avoidance planning path under a dynamic condition in an embodiment of the present invention;
FIG. 6 is a diagram illustrating a response of an obstacle avoidance control yaw angle under a dynamic condition in an embodiment of the present invention;
fig. 7 is a response diagram of the obstacle avoidance control sideslip angle under the dynamic condition in the embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 1 is a flowchart of a collision avoidance control method for an unmanned vehicle in an embodiment of the present invention, and as shown in fig. 1, the control method includes:
101. establishing a potential field model according to an object vector in a road and the road condition, and calculating collision avoidance path discrete points according to the potential field model;
in the embodiment of the present invention, a potential field model is first established according to the vectors of objects in the road and the condition of the road itself.
In some embodiments, the potential field model may be represented as follows:
Figure BDA0003174978110000041
wherein, UK_qA potential field model representing an object q; a. theqRepresenting uncertainty constants, for correction of the model, gamma1For calibration constants greater than 1, corrected for different vehicle performance, gamma2A correction factor greater than 0, determined from historical traffic accident data for the road segment, for calibrating the effect of speed on driving risk; mqRepresenting the equivalent mass, W, of an object q in the roadqA road condition factor representing the location of object q; r isq＝(x-xq，y-yq) Representing the positions (x, y) around the object q to the position (x) where the object q is locatedq，yq) A distance vector of (d); v. ofqIndicating the speed, theta, of a moving object qqRepresenting the direction of the speed of the object q and rqThe clockwise direction of the formed included angle is positive; t iscRepresents the road curvature factor, ± represents the vehicle turning direction, + represents the vehicle turning direction as left, -represents the vehicle turning direction as right.
In further embodiments, the potential field model may also be represented as follows:
Figure BDA0003174978110000051
wherein, UK_qA potential field model representing an object q; a. theqRepresenting an uncertainty constant for correcting the model; mqRepresenting the equivalent mass, W, of an object q in the roadqA road condition factor representing the location of object q; r isq＝(x-xq，y-yq) Representing q cycles of the objectAround each position (x, y) to the position (x) of the object qq，yq) A distance vector of (d); k is a radical of1And k2A gain factor being a potential energy field; v. ofqIndicating the speed, theta, of a moving object qqRepresenting the direction of the speed of the object q and rqThe clockwise direction of the formed included angle is positive; t iscRepresents the road curvature factor, ± represents the vehicle turning direction, + represents the vehicle turning direction as left, -represents the vehicle turning direction as right.
For the above model, the road condition factor WqCan be expressed as:
Figure BDA0003174978110000052
where κ is the visibility coefficient. μ is the adhesion coefficient. I is the road gradient coefficient. In addition, eta1And η2And correcting the coefficient according to accident data of the road section. Kappa*As a standard value of the visibility coefficient, μ*Is a standard value of road adhesion coefficient. These two parameters are determined according to local road conditions, typically with a value of 1.
Equivalent mass MqCan be expressed as:
Mq＝[16.435·ln(Δvq)-35.472]mqGqHq
wherein m isqIs the mass of the object q, GqIs the type of object HqIs the structural shape of the object, Δ vqIs the standard deviation of the vehicle speed from the current road average speed.
Fig. 2 is a dynamic potential field diagram in the embodiment of the present invention, and as shown in fig. 2, the center of the potential field is the centroid of the moving object, and the closer to the front of the vehicle, the more the kinetic energy field potential energy is, the more the risk characteristics of actual vehicle driving are met. Through the potential field model, discrete points of the collision avoidance path can be determined, wherein the discrete points are expressed as min (U)K_q) (ii) a The collision avoidance path discrete points are a series of object discrete points, and the collision avoidance path can be obtained after the discrete points are fitted.
102. Optimizing the discrete points of the avoidance path, and calculating the minimum point of a risk field formed by combining the potential energy of each vehicle in the road condition from the discrete points of the avoidance path;
because the path formed by the discrete points of the avoidance path calculated in the step 101 is not smooth enough and is not suitable for vehicle control, the method needs to optimize the path and obtains the minimum point of the risk field formed by combining the potential energy of each vehicle in the road condition by calculation from the discrete points of the avoidance path;
calculating to obtain a minimum point of a risk field formed by combining potential energy of each vehicle in the road condition, wherein the expression is as follows:
Figure BDA0003174978110000061
wherein E is the driving risk field U of the position of the vehicle qobs,qI.e. by the smallest n vehicle potential energy fields UK_qAnd (4) forming.
103. Constructing a point quality model for obstacle avoidance path planning based on vehicle dynamics constraint according to the motion vector and the position vector of the vehicle;
in consideration of vehicle dynamics constraints, a point mass model is employed, whose expression is as follows:
Figure BDA0003174978110000062
wherein xi ═ vy,vx,φ,X,Y]The total number of the 5 discrete state quantities is 5, and the state quantities are the vehicle speed, the heading angle and the abscissa and the ordinate of the vehicle body in the geodetic coordinate system in turn in the y direction and the x direction of the vehicle. ξ (t) represents the state quantity corresponding to the time t, i.e. in the control input sequence UtA state quantity track acting under the system; u (t) represents a control input amount at time t, which is a control amount corresponding to a state quantity trajectory composed of a lateral displacement and a yaw angle of the avoidance path. Mu is road adhesion coefficient, g is gravitational acceleration. Superscript denotes derivation.
104. Constructing a first cost objective function by using the point quality model and taking a system stable point as a control target;
in the embodiment of the invention, the system stability point is f (0,0) ═ 0, the point is taken as a system control target, and for any time domain N, the optimization target is the first generation price target function JNExpressed as:
Figure BDA0003174978110000071
wherein, Ut＝[ut,...,ut+N-1]Is a control input sequence in the time domain N, u (t) represents the control input quantity at time t; ξ (t) represents the state quantity corresponding to the time t, i.e. in the control input sequence UtA state quantity track acting under the system; l (-) represents the traceability of the trace of the state quantity of the expected output, and P (-) represents the interruption constraint.
105. Calculating the first generation price target function through an optimization algorithm to obtain an optimal control sequence of the system in any time domain corresponding to each moment;
by optimizing and solving the first generation price target function, the optimal control sequence U of the system at the t moment can be obtainedt,t＝[ut,t,...ut+N-1,t]At this time, the first element of the optimal control sequence may be used as the actual input of the controlled object, i.e., u (ξ (t)) ═ ut,t。
The optimization algorithm may be any existing algorithm, for example, the obstacle avoidance principle based on convex approximation and the unmanned vehicle path planning model prediction algorithm published in the automated chemistry report in 1 month 2020 by korea and the like may be adopted. 106. Adding the minimum point of the risk field into a point quality model corresponding to the first cost objective function, and calculating the optimal control variable corresponding to the lateral displacement and the yaw angle of the avoidance path under the target of minimizing the sum of the deviation of the output state quantity, the control variable and the potential energy of the minimum point of the risk field;
taking the point quality model established in the step 104 as a prediction model of an obstacle avoidance path planning module, and adding the potential field minimum point into the point quality model to plan a collision avoidance path, wherein the expression is as follows:
Figure BDA0003174978110000072
s.tUmin≤Ut≤Umax
wherein, UtRepresenting the corresponding optimal control sequence of the system at the time t; n is a radical ofpIs a prediction time domain; e is a driving safety field formed by n obstacles, lambda represents a track point output by the system, and lambdarefRepresenting the track point of the reference output, wherein (t + i | t) is the prediction of the current time t to the time after i steps; q and R are respectively a weight matrix for adjusting the tracking capability of the reference track point and an adjusting control sequence UiA weight matrix of (a); first item
Figure BDA0003174978110000081
Indicating the predicted time domain NpDeviation of the internal system output from a reference output; second item
Figure BDA0003174978110000082
The requirement of the system on the magnitude of the control variable is indicated, the third item E indicates the requirement on the magnitude of the field intensity value of the driving safety field, namely, in the optimization process, the driving risk is ensured to be minimum, and the deviation with the reference track is considered; u shapeminAnd UmaxRespectively representing the minimum value of the control sequence and the maximum value set of the control sequence in the system.
107. Designing a linearized tire model, establishing a monorail vehicle kinematic model, and calculating a state quantity in a prediction time sequence and a control quantity of prediction output from the monorail vehicle kinematic model through a current state quantity of a system and a control increment in a control time domain;
designing a linear tire model based on the vehicle kinematics model, represented as follows:
1) the state of stiffness of the tire is defined,
defining the tyre state rigidity C as the lateral force F under each slip angle alphayThe ratio of the slip angle α is expressed as follows:
Figure BDA0003174978110000083
wherein the slip angles alpha of the front and rear tiresfAnd alpharAre respectively defined as follows:
Figure BDA0003174978110000084
wherein alpha isf，αrIs the side slip angle, v, of the front and rear tiresxIs the longitudinal speed, v, of the vehicleyIs the lateral speed of the vehicle, omega is the yaw rate of the vehicle, lf，lrRespectively, the wheelbase and the wheelbase, and delta is the steering angle.
The slip rate s of the tire on the ground can be calculated by the following formula:
Figure BDA0003174978110000085
wherein, ω isTIs the angular velocity of the wheel, v is the wheel center velocity, and r is the wheel rolling radius.
2) And (3) carrying out linear equation design on the linear tire model, substituting the tire lateral force and the tire slip angle in the nonlinear tire model into the defined tire state rigidity C to obtain the tire rigidity of each tire, wherein based on the obtained tire rigidity, the lateral force of the front tire and the rear tire can be represented in a linear mode as follows:
Fx,i＝Cxiαxi
Fy,i＝Ciαi
wherein, subscript i ═ f, r refers to front and rear tires, respectively, and xy refers to x direction and y direction, respectively; i.e. Fx,iRepresents the lateral force in the x direction received by the front and rear tires i; fy,jIndicating the front and rear tires i receivedIs measured in the y direction.
After the nonlinear tire model and the linear tire model are established, a single-track vehicle kinematic model is established, and the expression of the single-track vehicle kinematic model is as follows:
Figure BDA0003174978110000091
where m is the vehicle mass, vx，vyω is the longitudinal speed, lateral speed and yaw rate of the vehicle, axAnd ayAcceleration of the mass center in x-direction and y-direction in the coordinate game of the vehicle, IzFor the moment of inertia of the vehicle about the z-axis, Fxf，Fxr，∑MzLongitudinal, transverse and yaw forces respectively, FyfAnd FyrLateral forces respectively applied to the front and rear wheels, /)f，lrRespectively the fore-aft wheelbase. X and Y are positions in a geodetic coordinate system.
Figure BDA0003174978110000092
Is the yaw angle, δ is the steering angle; superscript denotes derivation.
By substituting the linearized representation of the lateral forces of the front and rear tires into the above-described single-track vehicle kinematics model, a predictive model for the MPC controller can be derived as:
Figure BDA0003174978110000101
obtaining a vehicle linear kinetic equation by linearizing the predictive model about the operating point of the vehicle:
Figure BDA0003174978110000102
wherein A (t), B (t) are Jacobian matrixes of the state equation expression f relative to the state quantity xi and the controlled quantity u respectively, and are selected
Figure BDA0003174978110000103
Solving the deflection angle of the front wheel for output quantity according to the slip angle and the transverse displacement; then a discrete linear time-varying equation of state can be obtained as:
Figure BDA0003174978110000104
wherein the content of the first and second substances,
Ak,t＝I+TA(t)，Bk,t＝TB(t)，
Figure BDA0003174978110000105
i is the identity matrix and T is the sampling time. For the Jacobian matrix solution there are:
Figure BDA0003174978110000106
Figure BDA0003174978110000107
suppose that
Figure BDA0003174978110000111
Converting the control quantity u (t) of the discrete linear time-varying state equation into a control increment delta u (t), and performing corresponding conversion on the system state equation to obtain a new space state expression as follows:
Figure BDA0003174978110000112
wherein each matrix is defined as follows:
Figure BDA0003174978110000113
Figure BDA0003174978110000114
Δu(k|t)＝u(k|t)-u(k-1|t)
wherein n is a state quantity dimension, and m is a control quantity dimension. If the state quantity and the control increment at the time t are acquired from the system, the output quantity of the system at the time t +1 can be predicted by using the formula, and the output quantity of the system at the time k can be acquired through iterative calculation.
Calculating the prediction output, and taking the prediction time domain as N according to the model prediction control theorypControl time domain as NcThe state quantity and prediction output in the prediction time domain can be obtained as follows:
Figure BDA0003174978110000115
the output of the system at the future time is expressed in a matrix mode:
Figure BDA0003174978110000116
in the formula (I), the compound is shown in the specification,
Figure BDA0003174978110000121
Figure BDA0003174978110000122
Figure BDA0003174978110000123
therefore, the state quantity and the output quantity in the prediction time domain can be calculated by the current state quantity of the system and the control increment in the control time domain.
108. Taking the two norms of the deviation between the predicted output state quantity and the actual state quantity as performance indexes of speed control, taking the two norms of control increment as control input smooth indexes, and constructing a second cost objective function;
in order to construct the second generation price target function, firstly some constraints need to be designed:
1) in order to make the tracking process more stable, the invention sets the vehicle limit front wheel deflection angle and the front wheel deflection angle increment as follows:
-25°≤δ≤25°
-0.5°≤Δδ≤0.5°
2) according to the previous researches, on a good dry asphalt pavement, the centroid deviation angle limit of the vehicle for stable running can reach +/-12 degrees, on an ice and snow road, the limit value can reach +/-2 degrees, so the centroid deviation angle constraint condition is set as follows:
beta is more than or equal to 12 degrees and less than or equal to 12 degrees (good road surface)
Beta is more than or equal to-2 degrees and less than or equal to 2 degrees (ice and snow road surface)
3) The longitudinal acceleration and the lateral acceleration are limited by the ground adhesion coefficient, and the following relationship exists:
Figure BDA0003174978110000131
if the longitudinal speed is not changed, further obtaining:
|ay|≤μg
4) if the road attachment condition is good, the constraint is loose, but the stability of the vehicle body is affected by the overlarge lateral acceleration, the optimal solution is not generated in the solving process due to the undersize, and therefore the constraint is set as a soft constraint condition, namely, each control period controller dynamically adjusts the constraint to the solving condition, and the constraint is as follows:
Figure BDA0003174978110000132
wherein a isy.minAnd ay.maxRespectively, the minimum and maximum of the lateral acceleration, epsilon being the relaxation factor.
After the constraint condition is set, the embodiment of the present invention continues to use the two-norm of the deviation between the expected trajectory and the actual trajectory as the performance index of the speed control, and use the two-norm of the control input increment as the control input smoothing index, and obtain the following second-generation price target function:
Figure BDA0003174978110000133
wherein J represents a second cost objective function; n is a radical ofpIs a prediction time domain, NcIs the control time domain; λ (k + i) represents the trace point output by the system at time k + i, λref(k) Representing the trace point of the reference output of the system at the moment k; q and R are weighting matrices; Δ u (k + i-1) represents the optimal open-loop control increment corresponding to the front wheel angle and yaw moment of the vehicle at time k + i-1.
Thus, the tracking control problem can be described as:
Figure BDA0003174978110000134
-25°≤δ≤25°
-0.5°≤Δδ≤0.5°
beta is more than or equal to 12 degrees and less than or equal to 12 degrees (good road surface)
Beta is more than or equal to-2 degrees and less than or equal to 2 degrees (ice and snow road surface)
Figure BDA0003174978110000141
|ay|≤μg
Figure BDA0003174978110000142
109. Calculating the second cost objective function by adopting an Active-Set algorithm to obtain the optimal open-loop control increment corresponding to the front wheel corner and the yaw moment of the vehicle;
in the embodiment of the invention, the optimal open-loop control increment corresponding to the front wheel corner and the yaw moment of the vehicle can be obtained by solving the tracking control problem model corresponding to the second cost objective function;
the expression of the optimal open-loop control sequence delta u is as follows:
Figure BDA0003174978110000143
110. and inputting the optimal control variable and the first element of the optimal open-loop control increment serving as actual control variables and control increments into a vehicle model to complete path tracking control of the vehicle.
In the embodiment of the invention, the first element in the control system, namely the first element comprising the optimal control variable and the first element of the optimal open-loop control increment are used as the actual control output increment and input into a Carsim vehicle model to realize the automobile path tracking control. Fig. 4 is a schematic structural diagram of a collision avoidance control system of an unmanned vehicle in an embodiment of the present invention, and as shown in fig. 4, the system structure includes: the potential field model building module is used for building a potential field model according to object vectors in a road and road conditions and calculating discrete points of a collision avoidance path according to the potential field model;
the MPC collision avoidance path planning module is used for optimizing the collision avoidance path discrete points and calculating the minimum point of the risk field formed by combining the potential energy of each vehicle in the road condition from the collision avoidance path discrete points; constructing a point quality model for obstacle avoidance path planning based on vehicle dynamics constraint according to the motion vector and the position vector of the vehicle; constructing a first cost objective function by using the point quality model and taking a system stable point as a control target; calculating the first generation price target function through an optimization algorithm to obtain an optimal control sequence of the system in any time domain corresponding to each moment; adding the minimum point of the risk field into a point quality model corresponding to the first cost objective function, and calculating the optimal control variable corresponding to the lateral displacement and the yaw angle of the avoidance path under the target of minimizing the sum of the deviation of the output state quantity, the control variable and the potential energy of the minimum point of the risk field;
the MPC control module is used for designing a nonlinear tire model and a linearized tire model, establishing a monorail vehicle kinematic model, and calculating a state quantity in a prediction time sequence and a control quantity of prediction output from the monorail vehicle kinematic model through a current state quantity of a system and a control increment in a control time domain; taking the two norms of the deviation between the predicted output state quantity and the actual state quantity as performance indexes of speed control, taking the two norms of control increment as control input smooth indexes, and constructing a second cost objective function; calculating the cost target by adopting an Active-Set algorithm to obtain the optimal open-loop control increment corresponding to the front wheel corner and the yaw moment of the vehicle;
and the vehicle model module is used for inputting the optimal control variable and the first element of the optimal open-loop control increment as actual control variables and control increments to a vehicle model to complete path tracking control of the vehicle. In an embodiment of the invention, the potential field model construction module is adapted to apply the desired lateral displacement Yref(t) and expectation
Figure BDA0003174978110000151
Inputting the data into an MPC collision avoidance path planning module; the MPC collision avoidance path planning module expects the planned result to be the transverse displacement Yref_local(t) and the desired longitudinal displacement Xref_local(t) input to the MPC control module, which outputs the steering angle δ of the front tire to the vehicle model modulefAt this time, the vehicle model module may feed back the state quantity ξ in real time to the MPC control module.
In an embodiment, this embodiment specifically describes the method of the present invention with a certain vehicle model and Simulink of Carsim automobile simulation software as a platform, and the main parameters are shown in table 1:
TABLE 1 potential field model parameters and controller parameters
Figure BDA0003174978110000152
Based on the potential field model-based unmanned automobile collision avoidance control method and system provided by the invention, simulation result graphs shown in fig. 5-7 can be obtained by calculating the potential field model parameters and the controller parameters, fig. 5 shows an obstacle avoidance planning path graph of the invention, fig. 6 shows a change graph of a tracked vehicle yaw angle along with tracking time, and fig. 7 shows a change graph of a tracked vehicle sideslip angle along with tracking time.
In the description of the present invention, it is to be understood that the terms "coaxial", "bottom", "one end", "top", "middle", "other end", "upper", "one side", "top", "inner", "outer", "front", "center", "both ends", and the like, indicate orientations or positional relationships based on those shown in the drawings, and are only for convenience of description and simplicity of description, and do not indicate or imply that the devices or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, are not to be construed as limiting the present invention.
In the present invention, unless otherwise expressly stated or limited, the terms "mounted," "disposed," "connected," "fixed," "rotated," and the like are to be construed broadly, e.g., as meaning fixedly connected, detachably connected, or integrally formed; can be mechanically or electrically connected; the terms may be directly connected or indirectly connected through an intermediate, and may be communication between two elements or interaction relationship between two elements, unless otherwise specifically limited, and the specific meaning of the terms in the present invention will be understood by those skilled in the art according to specific situations.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.

Claims (10)

1. A collision avoidance control method of an unmanned automobile based on a potential field model is characterized by comprising the following steps:
establishing a potential field model according to an object vector in a road and the road condition, and calculating collision avoidance path discrete points according to the potential field model;
optimizing the discrete points of the avoidance path, and calculating the minimum point of a risk field formed by combining the potential energy of each vehicle in the road condition from the discrete points of the avoidance path;
constructing a point quality model for obstacle avoidance path planning based on vehicle dynamics constraint according to the motion vector and the position vector of the vehicle;
constructing a first cost objective function by using the point quality model and taking a system stable point as a control target;
calculating the first generation price target function through an optimization algorithm to obtain an optimal control sequence of the system in any time domain corresponding to each moment;
adding the minimum point of the risk field into a point quality model corresponding to the first cost objective function, and calculating the optimal control variable corresponding to the lateral displacement and the yaw angle of the avoidance path under the target of minimizing the sum of the deviation of the output state quantity, the control variable and the potential energy of the minimum point of the risk field;
designing a linearized tire model, establishing a monorail vehicle kinematic model, and calculating a state quantity in a prediction time sequence and a control quantity of prediction output from the monorail vehicle kinematic model through a current state quantity of a system and a control increment in a control time domain;
taking the two norms of the deviation between the predicted output state quantity and the actual state quantity as performance indexes of speed control, taking the two norms of control increment as control input smooth indexes, and constructing a second cost objective function;
calculating the second cost objective function by adopting an Active-Set algorithm to obtain the optimal open-loop control increment corresponding to the front wheel corner and the yaw moment of the vehicle;
and inputting the optimal control variable and the first element of the optimal open-loop control increment serving as actual control variables and control increments into a vehicle model to complete path tracking control of the vehicle.
2. The potential field model-based collision avoidance control method for the unmanned automobile, according to claim 1, wherein the potential field model comprises:
Figure FDA0003174978100000021
wherein, UK_qRepresenting a potential field model; a. theqRepresenting uncertainty constants, for correction of the model, gamma1For calibration constants greater than 1, corrected for different vehicle performance, gamma2A correction factor greater than 0, determined from historical traffic accident data for the road segment, for calibrating the effect of speed on driving risk; mqRepresenting the equivalent mass, W, of an object q in the roadqA road condition factor representing the location of object q; r isq＝(x-xq，y-yq) Representing the positions (x, y) around the object q to the position (x) where the object q is locatedq，yq) A distance vector of (d); v. ofqIndicating the speed, theta, of a moving object qqRepresenting the direction of the speed of the object q and rqThe clockwise direction of the formed included angle is positive; t isqRepresenting a road curvature factor; + -represents the vehicle turning direction, + represents the vehicle turning direction as left, -represents the vehicle turning direction as right.
3. The potential field model-based collision avoidance control method for the unmanned vehicle according to claim 1, wherein the potential field model further comprises:
Figure FDA0003174978100000022
wherein, UK_qRepresenting a potential field model; a. theqRepresenting an uncertainty constant for correcting the model; mqRepresenting the equivalent mass, W, of an object q in the roadqA road condition factor representing the location of object q; r isq＝(x-xq，y-yq) Representing the positions (x, y) around the object q to the location of the object qPosition (x)q，yq) A distance vector of (d); k is a radical of1And k2A gain factor expressed as a potential energy field; v. ofqIndicating the speed, theta, of a moving object qqRepresenting the direction of the speed of the object q and rqThe clockwise direction of the formed included angle is positive; t isqRepresents the road curvature factor, ± represents the vehicle turning direction, + represents the vehicle turning direction as left, -represents the vehicle turning direction as right.
4. The potential field model-based collision avoidance control method for the unmanned vehicle according to claim 1, wherein the first cost objective function is expressed as:
Figure FDA0003174978100000023
wherein, JNRepresenting a first cost objective function; u shapetRepresenting a control input sequence, U, in an arbitrary time domain, Nt＝[u(t),...,u(t+N-1)]U (t) represents a control input amount at time t; ξ (t) represents the state quantity corresponding to the time t, i.e. in the control input sequence UtA state quantity track acting under the system; l (-) represents the traceability of the trace of the state quantity of the expected output, and P (-) represents the interruption constraint.
5. The potential field model-based collision avoidance control method for the unmanned vehicle, as claimed in claim 1, wherein the calculating of the optimal control variables corresponding to the lateral displacement and the yaw angle of the avoidance path, i.e. the calculating of the optimized path, comprises:
Figure FDA0003174978100000031
s.t Umin≤Ut≤Umax
wherein, UtRepresenting the corresponding optimal control sequence of the system at the time t; n is a radical ofpIs a prediction time domain; driving safety formed by E n barriersFull field, λ represents the trace point of the system output, λrefRepresenting the track point of the reference output, wherein (t + i | t) is the prediction of the current time t to the time after i steps; u shapeiThe control variable corresponding to the i moment in the prediction time domain; q and R are respectively a weight matrix for adjusting the tracking capability of the reference track point and an adjusting control sequence UiA weight matrix of (a); first item
Figure FDA0003174978100000032
Indicating the predicted time domain NpDeviation of the internal system output from a reference output; second item
Figure FDA0003174978100000033
The requirement of the system on the magnitude of the control variable is shown, the third item | E | represents the requirement on the magnitude of the field intensity value of the driving safety field, namely, in the optimization process, the driving risk is ensured to be minimum, and the deviation from the reference track is considered; u shapeminAnd UmaxRespectively representing the minimum value of the control sequence and the maximum value set of the control sequence in the system.
6. The potential field model-based unmanned automobile collision avoidance control method according to claim 5, wherein the minimum point of the risk field is a driving safety field formed by n obstacles, and is represented as
Figure FDA0003174978100000034
Wherein E is the risk field U of the location of the vehicle qobs,qI.e. by the smallest n vehicle potential fields UK_qAnd (4) forming.
7. The potential field model-based unmanned automobile collision avoidance control method of claim 1, wherein the single-track vehicle kinematic model comprises tire state stiffness defined by a ratio of a lateral force of a tire to the slip angle; designing a linear tire model based on the tire state stiffness; obtaining a single-track vehicle kinematic model from the non-linear tire model and the linearized tire model, represented as:
Figure FDA0003174978100000041
where m is the vehicle mass, vx，vyω is the longitudinal speed, lateral speed and yaw rate of the vehicle, respectively; a isxAnd ayThe centroid acceleration in the x direction and the y direction in the vehicle coordinate system; i iszFor the moment of inertia of the vehicle about the z-axis, Fxf，Fxr，∑MzThe longitudinal force, the transverse force and the yaw moment borne by the vehicle are respectively; fyfAnd FyrThe lateral forces applied to the front wheel and the rear wheel are respectively; lf，lrRespectively the front and rear wheelbases; x and Y are the positions of the vehicles in the geodetic coordinate system;
Figure FDA0003174978100000042
is a yaw angle; delta is a steering angle; superscript denotes derivation.
8. The potential field model-based collision avoidance control method for the unmanned vehicle according to claim 1, wherein the second cost objective function is expressed as:
Figure FDA0003174978100000043
wherein J represents a second cost objective function; n is a radical ofpIs a prediction time domain, NcIs the control time domain; λ (k + i) represents the trace point output by the system at time k + i, λref(k) Representing the trace point of the reference output of the system at the moment k; q1And R1Respectively adjusting a weighting matrix of the tracking capability of the reference track point and a weighting matrix of the control increment; Δ u (k + i-1) represents the optimal open-loop control increment corresponding to the front wheel angle and yaw moment of the vehicle at time k + i-1.
9. The potential field model-based unmanned automobile collision avoidance control method according to claim 8, wherein the optimal open-loop control increment corresponding to the front wheel turning angle and the yaw moment of the vehicle is represented as:
Figure FDA0003174978100000044
where Δ u represents an optimal open-loop control increment corresponding to the front wheel angle and yaw moment of the vehicle.
10. An unmanned vehicle collision avoidance control system based on a potential field model, the system comprising:
the potential field model building module is used for building a potential field model according to object vectors in a road and road conditions and calculating discrete points of a collision avoidance path according to the potential field model;
the MPC collision avoidance path planning module is used for optimizing the collision avoidance path discrete points and calculating the minimum point of the risk field formed by combining the potential energy of each vehicle in the road condition from the collision avoidance path discrete points; constructing a point quality model for obstacle avoidance path planning based on vehicle dynamics constraint according to the motion vector and the position vector of the vehicle; constructing a first cost objective function by using the point quality model and taking a system stable point as a control target; calculating the first generation price target function through an optimization algorithm to obtain an optimal control sequence of the system in any time domain corresponding to each moment; adding the minimum point of the risk field into a point quality model corresponding to the first cost objective function, and calculating the optimal control variable corresponding to the lateral displacement and the yaw angle of the avoidance path under the target of minimizing the sum of the deviation of the output state quantity, the control variable and the potential energy of the minimum point of the risk field;
the MPC control module is used for designing a linearized tire model, establishing a single-track vehicle kinematic model, and calculating a state quantity in a prediction time sequence and a control quantity of prediction output from the single-track vehicle kinematic model through a current state quantity of a system and a control increment in a control time domain; taking the two norms of the deviation between the predicted output state quantity and the actual state quantity as performance indexes of speed control, taking the two norms of control increment as control input smooth indexes, and constructing a second cost objective function; calculating the second cost objective function by adopting an Active-Set algorithm to obtain the optimal open-loop control increment corresponding to the front wheel corner and the yaw moment of the vehicle;
and the vehicle model module is used for inputting the optimal control variable and the first element of the optimal open-loop control increment as actual control variables and control increments to a vehicle model to complete path tracking control of the vehicle.
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