CN111338339A - Trajectory planning method and device, electronic equipment and computer readable medium - Google Patents

Trajectory planning method and device, electronic equipment and computer readable medium Download PDF

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CN111338339A
CN111338339A CN202010106957.XA CN202010106957A CN111338339A CN 111338339 A CN111338339 A CN 111338339A CN 202010106957 A CN202010106957 A CN 202010106957A CN 111338339 A CN111338339 A CN 111338339A
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李柏
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Beijing Jingdong Qianshi Technology Co Ltd
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Abstract

The embodiment of the disclosure provides a trajectory planning method, a trajectory planning device, electronic equipment and a computer readable medium, wherein the method comprises the following steps: acquiring a first track of a target object; constructing a first optimal control problem of the target object according to the first track, and solving the first optimal control problem to obtain a second track of the target object; determining a target linear constraint condition of the target object according to the obstacle information and the second track; and constructing a second optimal control problem comprising a target linear constraint condition according to the second track, and solving the second optimal control problem to obtain the target track of the target object. The trajectory planning method, the trajectory planning device, the electronic device and the computer readable medium provided by the embodiment of the disclosure construct the second optimal control problem based on the target linear constraint condition, and can avoid the solution obstacle of the nonlinear constraint condition, thereby greatly reducing the solution scale and difficulty of the optimal control problem and improving the solution efficiency.

Description

Trajectory planning method and device, electronic equipment and computer readable medium
Technical Field
The present disclosure relates to the field of automatic driving technologies, and in particular, to a trajectory planning method and apparatus, an electronic device, and a computer-readable medium.
Background
Trajectory planning is an important on-board module for unmanned vehicles. Compared with the low-speed condition in an unstructured scene, the running speed of vehicles on a road is high, so that the solution is required to have real-time performance, and a complete decision planning period (including time consumption for processing perception information) is usually required to be not more than 100 ms.
However, in the modeling solution process of the related art, the part causing the problem model solution difficulty is mainly focused on the collision avoidance constraint condition, which often represents larger scale and stronger (non-convex) nonlinearity, and seriously restricts the online solution efficiency.
Therefore, a new trajectory planning method, apparatus, electronic device and computer readable medium are needed.
The above information disclosed in this background section is only for enhancement of understanding of the background of the disclosure and therefore it may contain information that does not constitute prior art that is already known to a person of ordinary skill in the art.
Disclosure of Invention
In view of this, embodiments of the present disclosure provide a trajectory planning method, an apparatus, an electronic device, and a computer-readable medium, so as to avoid, at least to a certain extent, the defects of large solution scale, difficulty in solution, and low solution efficiency caused by non-linearity of a collision avoidance constraint condition.
Additional features and advantages of the disclosure will be set forth in the detailed description which follows, or in part will be obvious from the description, or may be learned by practice of the disclosure.
According to a first aspect of an embodiment of the present disclosure, a trajectory planning method is provided, which includes: acquiring a first track of a target object; constructing a first optimal control problem of the target object according to the first track, and solving the first optimal control problem to obtain a second track of the target object; determining a target linear constraint condition of the target object according to the obstacle information and the second track; and constructing a second optimal control problem comprising the target linear constraint condition according to the second track, and solving the second optimal control problem to obtain the target track of the target object.
In an exemplary embodiment of the present disclosure, determining the target linear constraint condition of the target object according to the obstacle information and the second trajectory includes: if the second track is overlapped with the obstacle information, adjusting the second track based on preset parameters; determining a first dimension neighborhood, a second dimension neighborhood and a third dimension neighborhood which are not collided with the obstacle by the adjusted second track at each sampling moment according to the obstacle information; and determining a target linear constraint condition of the target object according to the first dimension neighborhood, the second dimension neighborhood and the third dimension neighborhood.
In an exemplary embodiment of the present disclosure, determining a target linear constraint condition of the target object according to the first dimension neighborhood, the second dimension neighborhood and the third dimension neighborhood includes: respectively determining a first dimension random number, a second dimension random number and a third dimension random number of the first dimension neighborhood, the second dimension neighborhood and the third dimension neighborhood under uniform distribution; determining a plurality of combinations to be selected and volume information of each combination to be selected according to the first dimension random number, the second dimension random number and the third dimension random number, and removing the combinations to be selected overlapped with the barrier information; determining the selected combination with the largest volume information in the plurality of selected combinations after being removed as a target combination; determining the target linear constraint condition of the target object according to the target combination.
In an exemplary embodiment of the present disclosure, determining the target linear constraint of the target object according to the target combination comprises: determining target straight line information according to the first dimension, the second dimension and the third dimension in the target combination; and determining a target linear constraint condition of the target object according to the target straight line information.
In an exemplary embodiment of the present disclosure, determining a plurality of combinations to be selected and volume information of each combination to be selected according to the first-dimension random number, the second-dimension random number, and the third-dimension random number includes: and processing numerical value intervals formed by the first dimension random number, the second dimension random number and the third dimension random number by a Monte Carlo method to obtain a plurality of combinations to be selected and volume information thereof.
In an exemplary embodiment of the present disclosure, constructing a first optimal control problem according to the first trajectory includes: constructing a first target function according to a first preset formula and the first track; determining an initial moment constraint condition of the first track and a system dynamic equation constraint condition at each sampling moment; and constructing the first optimal control problem according to the first objective function, the initial moment constraint condition and the system dynamic equation constraint condition.
In an exemplary embodiment of the disclosure, constructing a second optimal control problem including the target linear constraint from the second trajectory includes: constructing a second objective function according to a second preset formula and the second track; determining an initial moment constraint condition of the second track and a system dynamic equation constraint condition at each sampling moment; and constructing the second optimal control problem according to the second objective function, the initial moment constraint condition, the system dynamic equation constraint condition and the target linear constraint condition.
According to a second aspect of the embodiments of the present disclosure, a trajectory planning apparatus is provided, the apparatus including: the first track generation module is configured to acquire a first track of a target object; the second track generation module is configured to construct a first optimal control problem according to the first track, and solve the first optimal control problem to obtain a second track of the target object; a linear constraint condition module configured to determine a target linear constraint condition of the target object according to the obstacle information and the second trajectory; and the target trajectory planning module is configured to construct a second optimal control problem comprising the target linear constraint condition according to the second trajectory, and solve the second optimal control problem to obtain the target trajectory of the target object.
According to a third aspect of the embodiments of the present disclosure, an electronic device is provided, which includes: one or more processors; storage means for storing one or more programs; when executed by the one or more processors, cause the one or more processors to implement the trajectory planning method of any of the above.
According to a fourth aspect of embodiments of the present disclosure, a computer-readable medium is proposed, on which a computer program is stored, which when executed by a processor, implements the trajectory planning method according to any one of the above-mentioned embodiments.
According to the trajectory planning method, the trajectory planning device, the electronic device and the computer-readable medium provided by some embodiments of the present disclosure, the second optimal control problem of the target object is constructed based on the first trajectory, and the second trajectory for restoring the kinematic feasibility can be obtained. And determining a target linear constraint condition according to the obstacle information and the second track, and constructing a second optimal control problem based on the target linear constraint condition, so that the solving obstacle of the nonlinear constraint condition can be avoided, the solving scale and difficulty of the optimal control problem can be greatly reduced, and the solving efficiency can be improved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
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The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and together with the description, serve to explain the principles of the disclosure. The drawings described below are merely some embodiments of the present disclosure, and other drawings may be derived from those drawings by those of ordinary skill in the art without inventive effort.
FIG. 1 is a system block diagram illustrating a trajectory planning method and apparatus according to an exemplary embodiment;
FIG. 2 is a flow diagram illustrating a trajectory planning method according to an exemplary embodiment;
FIG. 3 is a flow chart illustrating a trajectory planning method according to another exemplary embodiment;
FIG. 4 is a flow chart illustrating a trajectory planning method according to yet another exemplary embodiment;
FIG. 5 is a flow chart illustrating a trajectory planning method according to yet another exemplary embodiment;
FIG. 6 is a block diagram illustrating a trajectory planner according to an exemplary embodiment;
FIG. 7 is a block diagram illustrating an electronic device in accordance with an exemplary embodiment;
FIG. 8 is a schematic diagram illustrating a computer-readable storage medium according to an example embodiment.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. The same reference numerals denote the same or similar parts in the drawings, and thus, a repetitive description thereof will be omitted.
The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to provide a thorough understanding of embodiments of the invention. One skilled in the relevant art will recognize, however, that the invention may be practiced without one or more of the specific details, or with other methods, components, devices, steps, and so forth. In other instances, well-known methods, devices, implementations or operations have not been shown or described in detail to avoid obscuring aspects of the invention.
The drawings are merely schematic illustrations of the present invention, in which the same reference numerals denote the same or similar parts, and thus, a repetitive description thereof will be omitted. Some of the block diagrams shown in the figures do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor devices and/or microcontroller devices.
The flow charts shown in the drawings are merely illustrative and do not necessarily include all of the contents and steps, nor do they necessarily have to be performed in the order described. For example, some steps may be decomposed, and some steps may be combined or partially combined, so that the actual execution sequence may be changed according to the actual situation.
In the related art, an optimal control problem related to a trajectory planning task is established in a Cartesian coordinate system, scattered points are adopted to determine obstacles in a scene, and motion trajectory information of surrounding movable obstacles is supplemented, so that a vehicle conforming to the constraint of a kinematic equation moves from a set starting point to a certain terminal point. However, such modeling mode includes a large number of complex collision avoidance constraints, and the online solving efficiency is severely restricted.
Therefore, a new trajectory planning method, apparatus, electronic device and computer readable medium are needed.
The following detailed description of exemplary embodiments of the invention refers to the accompanying drawings.
Fig. 1 is a system block diagram illustrating a trajectory planning method and apparatus according to an exemplary embodiment.
The server 105 may be a server providing various services, such as a background management server (for example only) providing support for a trajectory planning system operated by a user with the terminal devices 101, 102, 103. The backend management server may analyze and perform other processing on the received data such as the trajectory planning request, and feed back a processing result (e.g., the target trajectory — just an example) to the terminal device.
The server 105 may, for example, obtain a first trajectory of the target object; the server 105 may, for example, construct a first optimal control problem for the target object according to the first trajectory, and solve the first optimal control problem to obtain a second trajectory of the target object; the server 105 may determine a target linear constraint for the target object, for example, based on the obstacle information and the second trajectory. The server 105 may, for example, construct a second optimal control problem including the target linear constraint from the second trajectory, and solve the second optimal control problem to obtain the target trajectory of the target object.
The server 105 may be a server of one entity, and may also be composed of a plurality of servers, for example, a part of the server 105 may be, for example, used as a trajectory planning task submitting system in the present disclosure, and is used to obtain a task to be executed by a trajectory planning command; and a portion of the server 105 may also be used, for example, as a trajectory planning system in the present disclosure, for obtaining a first trajectory of a target object; constructing a first optimal control problem of the target object according to the first track, and solving the first optimal control problem to obtain a second track of the target object; determining a target linear constraint condition of the target object according to the obstacle information and the second track; and constructing a second optimal control problem comprising the target linear constraint condition according to the second track, and solving the second optimal control problem to obtain the target track of the target object.
According to the trajectory planning method and device provided by the embodiment of the disclosure, the second optimal control problem is constructed based on the target linear constraint condition, so that the solution obstacle of the nonlinear constraint condition can be avoided, the solution scale and difficulty of the optimal control problem are greatly reduced, and the solution efficiency is improved.
FIG. 2 is a flow chart illustrating a trajectory planning method according to an exemplary embodiment. The trajectory planning method provided by the embodiments of the present disclosure may be executed by any electronic device with computing processing capability, such as the terminal devices 101, 102, and 103 and/or the server 105, and in the following embodiments, the server executes the method as an example for illustration, but the present disclosure is not limited thereto. The trajectory planning method 20 provided by the embodiment of the present disclosure may include steps S202 to S208.
As shown in fig. 2, in step S202, a first trajectory of the target object is acquired.
In the disclosed embodiments, the target object may be a vehicle, such as an unmanned vehicle or the like. The first trajectory may be a trajectory obtained from an upstream trajectory decision environment. The first trajectory may be a trajectory obtained by making a decision based on a start point and an end point. The first trajectory obtained by the upstream decision environment may not satisfy the kinematic feasibility and the upstream decision does not take into account the obstacle information.
In step S204, a first optimal control problem of the target object is constructed according to the first trajectory, and the first optimal control problem is solved to obtain a second trajectory of the target object.
In an exemplary embodiment, a first trajectory may be sampled and a first optimal control problem for the first trajectory at each sampling instant may be constructed. For example, the course of motion of the first trajectory corresponds to the time [0, t ]rough]，troughReal numbers greater than 0. The first track may be spaced in time
Figure BDA0002388317310000071
(wherein, N isfeAn integer greater than or equal to 1) and every time t after sampling is equal to k · Δ tsamplePosition of the lower first track record
Figure BDA0002388317310000072
The states are recorded in sequence (k ═ 0, 1.., N)fe). Wherein the first optimal control problem is used for tracking said waypoints
Figure BDA0002388317310000073
(k＝0,1,...,Nfe) A trajectory. The first optimal control problem may include a first objective function and a first constraint. The first objective function can be constructed based on the kinematic feasibility and the stability of the transverse and longitudinal motion of the motion trail; the first constraints may include system dynamic equation constraints. The system dynamics equation constraints may be as follows:
Figure BDA0002388317310000074
wherein (x)i(t),yi(t)) is the rear axle end point coordinates of the target object i (e.g., vehicle i). v. ofi(t) and ai(t) is the speed and acceleration along the longitudinal axis direction of the vehicle body, which can make the advancing direction of the vehicle be a positive direction; phi is ai(t) is the front wheel deflection angle of the target object i, which can be the left-turning direction as the positive direction; omegai(t) is the front wheel yaw angular velocity of the target object i; thetai(t) representing the attitude angle of the target object i in the coordinate system, namely the positive direction of the X axis of the coordinate system and four geometric dimension related parameters of the target object i; l iswRepresenting the front and rear wheel base.
The first constraints may further include an initial time constraint, the initial time constraint being a pair
Figure BDA0002388317310000075
A set point constraint.
The first objective function may be represented by the following equation:
Figure BDA0002388317310000076
wherein, w1、w2、w3More than or equal to 0 is the corresponding weight coefficient of each item.
In an exemplary embodiment, a first objective function may be constructed according to a first preset formula and the first trajectory; determining an initial moment constraint condition of the first track and a system dynamic equation constraint condition at each sampling moment; and constructing the first optimal control problem according to the first objective function, the initial moment constraint condition and the system dynamic equation constraint condition. The first objective function may be shown in formula (2). The system dynamics equation constraints may be shown in equation (1). The first optimal control problem may be:
Figure BDA0002388317310000081
the second trajectory of the target object obtained by solving the first optimal control problem can be expressed as χsmooth。
In step S206, a target linear constraint condition of the target object is determined according to the obstacle information and the second trajectory.
In the embodiment of the present disclosure, the second trajectory χ may besmoothCorresponding time interval t ∈ [0, trough]N of interval divisionfeOf the time points, the k-th time point is
Figure BDA0002388317310000082
(k＝0,1,...,Nfe). Suppose vehicle i is at t0The moment is in a collision-free state, and will be for the rest of NfeA time tk(k＝1,...,Nfe) A target linear constraint of the target object is determined. The target linear constraint condition may be a linear constraint condition for each of x (t), y (t), θ (t), and x (t), y (t), and θ (t). Wherein, the target linear constraint condition can be as follows:
Figure BDA0002388317310000083
wherein, ak，bk，
Figure BDA0002388317310000084
pk，qk，
Figure BDA0002388317310000085
Are constant coefficients.
The obstacle information describes t ═ tkAnd at the moment, the position and the posture of the dynamic/static obstacle in the scene. For example, the situation that the obstacle occupies the road surface can be recorded in the form of a grid map or in the form of geometrical polygon vertex coordinates.
In step S208, a second optimal control problem including the target linear constraint condition is constructed according to the second trajectory, and the second optimal control problem is solved to obtain the target trajectory of the target object.
In an embodiment of the present disclosure, the second optimal control problem may include a second objective function and a second constraint. The second objective function may include a first sub-objective function J1A second sub-targeting function J2And a third sub-targeting function J3Fourth sub-targeting function J4And a fifth sub-targeting function J5. Wherein, J1For describing the tracking of a first trajectory, J2For describing the tracking of the second trajectory, J3For describing avoidance of obstacle information, J4And J5The method is used for describing the transverse and longitudinal smoothness of the motion trail of the vehicle. First sub-targeting function J1Can be represented, for example, by the following formula:
Figure BDA0002388317310000091
second sub-targeting function J2Can be represented, for example, by the following formula:
Figure BDA0002388317310000092
wherein
Figure BDA0002388317310000093
For the previous period track at a specific time t in the current period time intervalkIn the position of (a) in the first,
Figure BDA0002388317310000094
the maximum application range of the planned track representing the previous period in the current period is satisfied
Figure BDA0002388317310000095
Third sub-targeting function J3Can be represented, for example, by the following formula:
Figure BDA0002388317310000096
wherein,
Figure BDA0002388317310000097
describing the geometric center (x) of the target object and the obstacle j or road blind area jGj,yGj) Euclidean distance of, Kj0 characterizes the distancing requirement for the jth obstacle (relatively large K)jThe value means to a lesser extent away from the obstacle j).
Fourth sub-targeting function J4Can be represented by the following formula:
Figure BDA0002388317310000098
fifth sub-targeting function J5Can be represented by the following formula:
Figure BDA0002388317310000099
by weighting and summarizing the performance cost polynomials, a second objective function can be obtained which is shaped as follows:
J＝w1·J1+w2·J2+w3·J3+w4·J4+w5·J5, (6)
wherein w1,w2,w3,w4,w5More than or equal to 0 is the corresponding weight coefficient of each item. To this end, the second optimal control problem may be expressed as follows:
Figure BDA0002388317310000101
in an exemplary embodiment, a second objective function may be constructed according to a second preset formula and the second trajectory; determining an initial moment constraint condition of the second track and a system dynamic equation constraint condition at each sampling moment; and constructing the second optimal control problem according to the second objective function, the initial moment constraint condition, the system dynamic equation constraint condition and the target linear constraint condition. The initial time constraint condition of the second objective function and the construction mode of the system dynamic equation constraint condition may be similar to the initial time constraint condition of the first objective function and the system dynamic equation constraint condition, and are not repeated here.
According to the trajectory planning method provided by the embodiment of the disclosure, the second optimal control problem of the target object is constructed based on the first trajectory, and the second trajectory for recovering the kinematic feasibility can be obtained. And determining a target linear constraint condition according to the obstacle information and the second track, and constructing a second optimal control problem based on the target linear constraint condition, so that the solving obstacle of the nonlinear constraint condition can be avoided, the solving scale and difficulty of the optimal control problem can be greatly reduced, and the solving efficiency can be improved.
FIG. 3 is a flow chart illustrating a trajectory planning method according to another exemplary embodiment. The trajectory planning method 30 provided by the embodiment of the present disclosure may include steps S302 to S306.
As shown in fig. 3, in step S302, if the second trajectory overlaps with the obstacle information, the second trajectory is adjusted based on the preset parameter.
In the disclosed embodiment, the obstacle information may be in the form of a mesh graph or in the form of geometric polygon vertex coordinates. When the second trajectory overlaps with the obstacle information, it is considered that the target object collides with the obstacle when moving along the second trajectory. The preset parameter may be a preset step size. The specific adjustment may be the pseudo code of module D as follows:
module D, position posture legalization processing algorithm
Figure BDA0002388317310000111
Wherein dx and dy are preset parameters. The input of module D is (x)basic,ybasic,θbasic) The output is the fine-tuned pose
Figure BDA0002388317310000112
The module D is used for fixing the attitude angle theta of the vehiclebasicOn the premise of (x), the study is carried outbasic,ybasic) Whether a legal position exists in the tiny neighborhood is judged until a certain pose which does not collide with the barrier is found
Figure BDA0002388317310000113
Thus, the adjusted second trajectory is obtained.
In step S304, a first dimension neighborhood, a second dimension neighborhood, and a third dimension neighborhood where the adjusted second trajectory does not collide with the obstacle at each sampling time are determined according to the obstacle information.
Book of JapaneseIn an embodiment, among other things, a Cartesian coordinate system may be established for three dimensions based on a lateral axis direction, a longitudinal axis direction, and a pose angle of a target object (e.g., a vehicle). The first dimensional neighborhood may, for example, be a local neighborhood of the X-axis in a cartesian coordinate system, the second dimensional neighborhood may, for example, be a local neighborhood of the Y-axis in a cartesian coordinate system, and the third dimensional neighborhood may, for example, be a local neighborhood of the Z-axis in a cartesian coordinate system. Let t be in the second trajectorykPose at time (x)basic,ybasic,θbasic) Is a pose where no collision occurs. In (x)basic，ybasic，θbasic) Find a simple-contoured local neighborhood ζ around, so that once (x)i(tk),yi(tk),θi(tk) Falls in ζ), no collision must occur. With (x)basic,ybasic) Is an origin O' in the range of thetabasicThe pointing direction constructs a new coordinate system X 'O' Y 'for the positive direction of the X' axis. Temporarily fixing thetai(tk)＝θbasicThe vehicle body will be translated in the positive X 'direction from O' until collision occurs, and the length of translation at that time is recorded
Figure BDA0002388317310000121
Similarly, the vehicle body is respectively translated along the negative direction of the X 'axis and the positive and negative directions of the Y' axis to obtain the translation length
Figure BDA0002388317310000122
In addition, x is temporarily fixedi(tk)＝xbasic、yi(tk)＝ybasicWill thetai(tk) Take a value of thetabasicThe two sides are respectively expanded until collision, and theta can be obtainedleft、θrightt. Due to (x)basic,ybasic,θbasic) In the absence of a collision, it can be determined that,
Figure BDA0002388317310000123
a local 6-dimensional space is formed. Wherein,
Figure BDA0002388317310000124
is a neighborhood of the first dimension and,
Figure BDA0002388317310000125
being neighbourhoods of second dimension, thetaleft+θrighIs a third dimension neighborhood.
In step S306, a target linear constraint condition of the target object is determined according to the first dimension neighborhood, the second dimension neighborhood and the third dimension neighborhood.
In the embodiment of the present disclosure, a cube with a maximum volume that can be constructed according to the first dimension neighborhood, the second dimension neighborhood, and the third dimension neighborhood may be used as a target cube, and a target linear constraint condition of the target object may be determined according to the size of the target cube. Wherein, a reasonable value can be further determined in the 6-dimensional space obtained in step S304, and a proper enclosure (x) can be further determinedbasic，ybasic，θbasic) A partial cube ζ of no dimension
Figure BDA0002388317310000126
Except for the extreme case, it is impossible to uniquely determine the size of ζ because the length-eliminating interval arrangement in the three dimensions of X '-Y' - θ corresponds to the size of a cube without collision, and the dimensions of the X ', Y' axes and the θ axes are different, which further aggravates the difficulty in selecting the size of the cube of ζ. For this purpose, a dimensional transformation weighting factor w may be introducedrad2mThe radian measure in the theta dimension (third dimension) is converted into a length measure > 0, then a scheme which can maximize the volume of zeta in X '-Y' -theta is selected, and a target linear constraint condition of the target object is determined based on the scheme.
Steps S302 to S306 of the embodiment of the present disclosure may be used as an alternative to step S206 in the embodiment of fig. 2.
FIG. 4 is a flow chart illustrating a trajectory planning method according to yet another exemplary embodiment. The trajectory planning method 40 provided by the embodiment of the present disclosure may include steps S402 to S408.
In step S402, a first dimension random number, a second dimension random number, and a third dimension random number of the first dimension neighborhood, the second dimension neighborhood, and the third dimension neighborhood, which are uniformly distributed, are respectively determined.
In the embodiments of the present disclosure, the data may be respectively in the intervals
Figure BDA0002388317310000131
[0,θleft]、[0,θright]Generating uniformly distributed random numbers and assigning the random numbers to the random numbers
Figure BDA0002388317310000132
And
Figure BDA0002388317310000133
wherein,
Figure BDA0002388317310000134
is a random number of a first dimension and is,
Figure BDA0002388317310000135
is a random number of a second dimension,
Figure BDA0002388317310000136
is a third dimension random number.
In step S404, a plurality of combinations to be selected and volume information of each combination to be selected are determined according to the first-dimension random number, the second-dimension random number, and the third-dimension random number, and combinations to be selected that overlap with the obstacle information are removed.
In the embodiment of the present disclosure, a cube ζ formed by each of the first-dimension random number, the second-dimension random number, and the third-dimension random number may be used as a candidate combination, and the volume of the cube ζ
Figure BDA0002388317310000137
And is the volume information of the candidate combination. Further, it is possible to detect whether or not the vehicle footprints corresponding to all the poses in the cube ζ overlap with the obstacle information, that is, whether or not a collision occurs. If no collision occurs, the candidate combination corresponding to the current cube ζ is combined with the volume of the candidate combination
Figure BDA0002388317310000138
Figure BDA0002388317310000139
Recording; and if collision occurs, giving up the collision, continuously generating a new cube setting scheme, and finally obtaining the eliminated combinations to be selected and the volume information of each combination to be selected.
In an exemplary embodiment, a numerical interval formed by the first-dimension random number, the second-dimension random number, and the third-dimension random number may be processed by a monte carlo method to obtain a plurality of combinations to be selected and volume information thereof. Wherein N can be randomly generatedMC(NMCAn integer greater than 0) candidate combinations. N is a radical ofMCMay be a predetermined specific value.
In step S406, the candidate combination with the largest volume information among the plurality of candidate combinations after being removed is determined to be the target combination.
In the embodiment of the present disclosure, the cube corresponding to the candidate combination with the largest volume information may be selected as the size of the cube ζ corresponding to the target combination, and may still be referred to as
Figure BDA00023883173100001310
In step S408, a target linear constraint condition of the target object is determined according to the target combination.
In the embodiment of the present disclosure, a plurality of straight lines may be determined according to the size of the cube ζ, and a target linearity constraint condition of the target object may be determined according to the plurality of straight lines.
Steps S402 to S408 of the embodiment of the present disclosure may be used as an alternative to step S306 in the embodiment of fig. 3.
FIG. 5 is a flowchart illustrating a trajectory planning method according to yet another exemplary embodiment. The trajectory planning method 50 provided by the embodiment of the present disclosure may include steps S502 to S504.
In step S502, target straight line information is determined according to the first dimension, the second dimension, and the third dimension of the target combination.
In the embodiment of the disclosure, the first dimension, the second dimension and the third dimension in the target combination may be respectively
Figure BDA0002388317310000141
Wherein the target straight Line information may include a first target straight Line1Second target straight Line2Third target straight Line3And a fourth target straight Line4. The following two coordinate points pass through a first target straight Line1：
Figure BDA0002388317310000142
Figure BDA0002388317310000143
Two point coordinates (x) are known1,y1)、(x2,y2) A straight line can be determined
(y2-y1)·x+(x1-x2)·y+(x2y1-x1y2)＝0. (C1)
According to the formula, the straight Line can be determined1And the rest 3 straight lines2～Line4The determination is similar. Due to Line1And Line2、Line3And Line4Parallel, therefore the four straight lines must be written in the following format:
Figure BDA0002388317310000144
in step S504, a target linear constraint condition of the target object is determined from the target straight line information.
In the embodiment of the disclosure, the target linear constraint condition of the target object may be determined according to a rectangular region surrounded by the target straight line information. Wherein, the side of the plane divided by a straight line can be represented by writing the equation of the straight line as an inequalityAnd (4) a region. Due to the point (x)basic,ybasic) And the point is determined to be in the enclosed rectangular area, so that the positive and negative signs of each inequality can be judged by respectively substituting the point into the left part of the equation of 4 straight lines in the target straight line information. In order to unify the formats, inequalities of the form ≦ 0 may be uniformly established, which requires that both sides of some 2 inequalities are simultaneously multiplied by-1, and the finally obtained target linear constraint condition is written as the following format:
Figure BDA0002388317310000151
furthermore, the angle θi(tk) May be limited to thetabasic-θleft≤θi(tk)≤θbasic+θright. (C3b)
To this end, a target linear constraint in the form of equation (C3) is obtained. Here, the formula (C3) may be rewritten into a form shown in the formula (4).
Steps S502 to S504 of the embodiment of the present disclosure may be used as an alternative to step S408 in the embodiment of fig. 4.
According to the trajectory planning method provided by the embodiment of the disclosure, when the second optimal control problem is constructed, the complex collision avoidance constraint condition can be modified into a linear target linear constraint condition, and after the trajectory planning problem formed by the collision avoidance constraint condition is converted into a nonlinear planning problem in numerical solution, all the collision avoidance constraint conditions in the trajectory planning problem have a linear form, so that the solution scale and difficulty are greatly reduced.
It should be clearly understood that this disclosure describes how to make and use particular examples, but the principles of this disclosure are not limited to any details of these examples. Rather, these principles can be applied to many other embodiments based on the teachings of the present disclosure.
Those skilled in the art will appreciate that all or part of the steps for implementing the above embodiments are implemented as a computer program executed by a Central Processing Unit (CPU). When executed by a central processing unit CPU, performs the above-described functions defined by the above-described methods provided by the present disclosure. The program may be stored in a computer readable storage medium, which may be a read-only memory, a magnetic or optical disk, or the like.
Furthermore, it should be noted that the above-mentioned figures are only schematic illustrations of the processes involved in the methods according to exemplary embodiments of the present disclosure, and are not intended to be limiting. It will be readily understood that the processes shown in the above figures are not intended to indicate or limit the chronological order of the processes. In addition, it is also readily understood that these processes may be performed synchronously or asynchronously, e.g., in multiple modules.
The following are embodiments of the disclosed apparatus that may be used to perform embodiments of the disclosed methods. For details not disclosed in the embodiments of the apparatus of the present disclosure, refer to the embodiments of the method of the present disclosure.
FIG. 6 is a block diagram illustrating a trajectory planner according to an exemplary embodiment. Referring to fig. 6, a trajectory planning apparatus 60 provided in an embodiment of the present disclosure may include: a first trajectory generation module 602, a second trajectory generation module 604, a linear constraint module 606, and a target trajectory planning module 608.
In the trajectory planner 60, a first trajectory generation module 602 may be configured to obtain a first trajectory of the target object.
The second trajectory generation module 604 may be configured to construct a first optimal control problem according to the first trajectory, and solve the first optimal control problem to obtain a second trajectory of the target object.
In an exemplary embodiment, the second trajectory generation module 604 may include a first objective function unit, a first constraint unit, and a first optimal control problem construction unit. The first objective function unit may be configured to construct a first objective function according to a first preset formula and the first trajectory. The first constraint unit may be configured to determine initial time instant constraints for the first trajectory and system dynamic equation constraints at each sampling time instant. The first optimal control problem construction unit may be configured to construct the first optimal control problem according to the first objective function, the initial time constraint, and the system dynamic equation constraint.
Linear constraint module 606 may be configured to determine a target linear constraint for the target object based on the obstacle information and the second trajectory.
In an exemplary embodiment, the linear constraint module 606 may include a preset adjustment submodule, a neighborhood determination submodule, and a linear constraint submodule. The preset adjusting submodule may be configured to adjust the second trajectory based on a preset parameter if the second trajectory overlaps with the obstacle information. The neighborhood determination submodule may be configured to determine, according to the obstacle information, a first-dimension neighborhood, a second-dimension neighborhood, and a third-dimension neighborhood in which the adjusted second trajectory does not collide with the obstacle at each sampling time. A linear constraint sub-module may be configured to determine a target linear constraint for the target object based on the first dimension neighborhood, the second dimension neighborhood, and the third dimension neighborhood.
In an exemplary embodiment, the linear constraint condition submodule may include a random number generation unit, a candidate combination generation unit, a target combination generation unit, and a linear constraint condition unit. Wherein the random number generation unit may be configured to determine a first dimension random number, a second dimension random number, and a third dimension random number of the first dimension neighborhood, the second dimension neighborhood, and the third dimension neighborhood, respectively, under uniform distribution; the candidate combination generating unit may be configured to determine a plurality of candidate combinations and volume information of each candidate combination according to the first-dimension random number, the second-dimension random number, and the third-dimension random number, and eliminate candidate combinations overlapping with the obstacle information; the target combination generating unit may be configured to determine, as a target combination, a candidate combination with the largest volume information among the plurality of rejected candidate combinations; the linear constraint unit may be configured to determine the target linear constraint of the target object from the target combination.
In an exemplary embodiment, the linear constraint condition unit may include a target straight line generation subunit and a linear constraint condition subunit. Wherein, the target straight line information can be determined according to the first dimension size, the second dimension size and the third dimension size in the target combination; the linear constraint sub-unit may be configured to determine a target linear constraint of the target object from the target straight line information.
In an exemplary embodiment, the candidate combination generating unit may be configured to process, by using a monte carlo method, a value interval formed by each of the first-dimension random number, the second-dimension random number, and the third-dimension random number, and obtain a plurality of candidate combinations and volume information thereof.
The target trajectory planning module 608 may be configured to construct a second optimal control problem including the target linear constraint according to the second trajectory, and solve the second optimal control problem to obtain a target trajectory of the target object.
In an exemplary embodiment, the target trajectory planning module 608 may include a second objective function unit, a second constraint unit, and a second optimal control problem construction unit. The second objective function unit may be configured to construct a second objective function according to a second preset formula and the second trajectory. The second constraint unit may be configured to determine initial time instant constraints for the second trajectory and system dynamic equation constraints at each sampling time instant. The second optimal control problem construction unit may be configured to construct the second optimal control problem according to the second objective function, the initial time constraint, the system dynamic equation constraint, and the target linear constraint.
According to the trajectory planning device provided by the embodiment of the disclosure, the second optimal control problem of the target object is constructed based on the first trajectory, and the second trajectory for recovering the kinematic feasibility can be obtained. And determining a target linear constraint condition according to the obstacle information and the second track, and constructing a second optimal control problem based on the target linear constraint condition, so that the solving obstacle of the nonlinear constraint condition can be avoided, the solving scale and difficulty of the optimal control problem can be greatly reduced, and the solving efficiency can be improved.
FIG. 7 is a block diagram illustrating an electronic device in accordance with an example embodiment.
An electronic device 200 according to this embodiment of the present disclosure is described below with reference to fig. 7. The electronic device 200 shown in fig. 7 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.
As shown in fig. 7, the electronic device 200 is embodied in the form of a general purpose computing device. The components of the electronic device 200 may include, but are not limited to: at least one processing unit 210, at least one memory unit 220, a bus 230 connecting different system components (including the memory unit 220 and the processing unit 210), a display unit 240, and the like.
Wherein the storage unit stores program code executable by the processing unit 210 to cause the processing unit 210 to perform the steps according to various exemplary embodiments of the present disclosure described in the above-mentioned electronic prescription flow processing method section of the present specification. For example, the processing unit 210 may perform the steps as shown in fig. 2, 3, 4, 5.
The memory unit 220 may include readable media in the form of volatile memory units, such as a random access memory unit (RAM)2201 and/or a cache memory unit 2202, and may further include a read only memory unit (ROM) 2203.
The storage unit 220 may also include a program/utility 2204 having a set (at least one) of program modules 2205, such program modules 2205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may comprise an implementation of a network environment.
Bus 230 may be one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.
The electronic device 200 may also communicate with one or more external devices 300 (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that enable a user to interact with the electronic device 200, and/or with any devices (e.g., router, modem, etc.) that enable the electronic device 200 to communicate with one or more other computing devices. Such communication may occur via an input/output (I/O) interface 250. Also, the electronic device 200 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network such as the Internet) via the network adapter 260. The network adapter 260 may communicate with other modules of the electronic device 200 via the bus 230. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the electronic device 200, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which may be a personal computer, a server, or a network device, etc.) to execute the above method according to the embodiments of the present disclosure.
Fig. 8 schematically illustrates a computer-readable storage medium in an exemplary embodiment of the disclosure.
Referring to fig. 8, a program product 400 for implementing the above method according to an embodiment of the present disclosure is described, which may employ a portable compact disc read only memory (CD-ROM) and include program code, and may be run on a terminal device, such as a personal computer. However, the program product of the present disclosure is not limited thereto, and in this document, a readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The computer readable storage medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable storage medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations for the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., through the internet using an internet service provider).
The computer readable medium carries one or more programs which, when executed by a device, cause the computer readable medium to perform the functions of: acquiring a first track of a target object; constructing a first optimal control problem of the target object according to the first track, and solving the first optimal control problem to obtain a second track of the target object; determining a target linear constraint condition of the target object according to the obstacle information and the second track; and constructing a second optimal control problem comprising the target linear constraint condition according to the second track, and solving the second optimal control problem to obtain the target track of the target object.
Those skilled in the art will appreciate that the modules described above may be distributed in the apparatus according to the description of the embodiments, or may be modified accordingly in one or more apparatuses unique from the embodiments. The modules of the above embodiments may be combined into one module, or further split into multiple sub-modules.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which may be a personal computer, a server, a mobile terminal, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.
Exemplary embodiments of the present disclosure are specifically illustrated and described above. It is to be understood that the present disclosure is not limited to the precise arrangements, instrumentalities, or instrumentalities described herein; on the contrary, the disclosure is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.

Claims (10)

1. A trajectory planning method, comprising:
acquiring a first track of a target object;
constructing a first optimal control problem of the target object according to the first track, and solving the first optimal control problem to obtain a second track of the target object;
determining a target linear constraint condition of the target object according to the obstacle information and the second track;
and constructing a second optimal control problem comprising the target linear constraint condition according to the second track, and solving the second optimal control problem to obtain the target track of the target object.
2. The method of claim 1, wherein determining a target linear constraint for the target object based on the obstacle information and the second trajectory comprises:
if the second track is overlapped with the obstacle information, adjusting the second track based on preset parameters;
determining a first dimension neighborhood, a second dimension neighborhood and a third dimension neighborhood which are not collided with the obstacle by the adjusted second track at each sampling moment according to the obstacle information;
and determining a target linear constraint condition of the target object according to the first dimension neighborhood, the second dimension neighborhood and the third dimension neighborhood.
3. The method of claim 2, wherein determining a target linear constraint for the target object based on the first dimension neighborhood, the second dimension neighborhood, and the third dimension neighborhood comprises:
respectively determining a first dimension random number, a second dimension random number and a third dimension random number of the first dimension neighborhood, the second dimension neighborhood and the third dimension neighborhood under uniform distribution;
determining a plurality of combinations to be selected and volume information of each combination to be selected according to the first dimension random number, the second dimension random number and the third dimension random number, and removing the combinations to be selected overlapped with the barrier information;
determining the selected combination with the largest volume information in the plurality of selected combinations after being removed as a target combination;
determining the target linear constraint condition of the target object according to the target combination.
4. The method of claim 3, wherein determining the target linear constraint for the target object based on the target combination comprises:
determining target straight line information according to the first dimension, the second dimension and the third dimension in the target combination;
and determining a target linear constraint condition of the target object according to the target straight line information.
5. The method of claim 3, wherein determining a plurality of candidate combinations and volume information for each candidate combination based on the first-dimension random number, the second-dimension random number, and the third-dimension random number comprises:
and processing numerical value intervals formed by the first dimension random number, the second dimension random number and the third dimension random number by a Monte Carlo method to obtain a plurality of combinations to be selected and volume information thereof.
6. The method of claim 1, wherein constructing a first optimal control problem from the first trajectory comprises:
constructing a first target function according to a first preset formula and the first track;
determining an initial moment constraint condition of the first track and a system dynamic equation constraint condition at each sampling moment;
and constructing the first optimal control problem according to the first objective function, the initial moment constraint condition and the system dynamic equation constraint condition.
7. The method of claim 1, wherein constructing a second optimal control problem including the target linear constraint from the second trajectory comprises:
constructing a second objective function according to a second preset formula and the second track;
determining an initial moment constraint condition of the second track and a system dynamic equation constraint condition at each sampling moment;
and constructing the second optimal control problem according to the second objective function, the initial moment constraint condition, the system dynamic equation constraint condition and the target linear constraint condition.
8. A trajectory planning apparatus, comprising:
the first track generation module is configured to acquire a first track of a target object;
the second track generation module is configured to construct a first optimal control problem according to the first track, and solve the first optimal control problem to obtain a second track of the target object;
a linear constraint condition module configured to determine a target linear constraint condition of the target object according to the obstacle information and the second trajectory;
and the target trajectory planning module is configured to construct a second optimal control problem comprising the target linear constraint condition according to the second trajectory, and solve the second optimal control problem to obtain the target trajectory of the target object.
9. An electronic device, comprising:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-7.
10. A computer-readable medium, on which a computer program is stored, which program, when being executed by a processor, is adapted to carry out the method of any one of claims 1-7.
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