CN119536301A - Trajectory planning method, device, aircraft and computer-readable storage medium - Google Patents
Trajectory planning method, device, aircraft and computer-readable storage medium Download PDFInfo
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- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
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- G05D1/40—Control within particular dimensions
- G05D1/46—Control of position or course in three dimensions
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- G05D1/247—Arrangements for determining position or orientation using signals provided by artificial sources external to the vehicle, e.g. navigation beacons
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- G—PHYSICS
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- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
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- G05D1/60—Intended control result
- G05D1/617—Safety or protection, e.g. defining protection zones around obstacles or avoiding hazards
- G05D1/622—Obstacle avoidance
- G05D1/633—Dynamic obstacles
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- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
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Abstract
The application discloses a track planning method, a track planning device, an aircraft and a computer readable storage medium, wherein the method comprises the steps of generating a key path point set based on a static obstacle corresponding to the aircraft, a local track of the last frame and a global navigation path; the method comprises the steps of determining a control point set of a cubic B spline curve based on a user set speed, a critical path point set and a cubic B spline basis function corresponding to the aircraft, determining a dynamic obstacle avoidance penalty function based on a dynamic obstacle and the control point set, determining a static obstacle avoidance penalty function based on a detour path point set, determining an optimization model based on the dynamic obstacle avoidance penalty function and the static obstacle avoidance penalty function, and generating a target track corresponding to the aircraft based on the optimization model and current flight parameters of the aircraft. The application realizes the goal of avoiding static threat and dynamic threat, improves the accuracy of the flight track, and improves the stability, comfort and safety of the aircraft.
Description
Technical Field
The present application relates to the technical field of aircrafts, and in particular, to a trajectory planning method, a trajectory planning device, an aircraft, and a computer readable storage medium.
Background
At present, in the field of multi-rotor aircraft, the application scene of non-cooperative static and dynamic obstacles brings great challenges to autonomous safe flight of a flying automobile, and the limited maneuverability of the aircraft makes the avoidance of the obstacles more difficult, so that the track of the aircraft needs to be accurately planned.
In the related art, trajectory planning can be classified into sampling-based, search-based, optimization-based, and data-based methods. The sampling-based method is to randomly scatter points in a configuration space, and search a path with optimal cost after constructing a route map or a route tree by connecting line segments, but the sampling and node updating strategies influence time efficiency and path quality and are difficult to treat dynamic obstacles; the method based on the search is that a state space is discretized into a three-dimensional grid map according to a certain resolution, a heuristic search algorithm is adopted to obtain a feasible path or an optimal path, but the resolution and the heuristic search algorithm influence search efficiency, continuous space is difficult to process, space and time complexity is high, the method based on the optimization is that a multi-constraint optimization model is built, a gradient descent method is adopted to solve the multi-constraint optimization model to obtain an optimal track, the target and constraint are well defined, applicability is wide, but the non-convex optimization problem consumes more time, the solution quality is greatly influenced by an initial value, the method based on the data is that a deep neural network large model is built by a deep learning and reinforcement learning method, a large amount of data is utilized to train, and then the state and environment information of an aircraft are input into the large model to generate the track, so that adaptability and generalization capability are strong, but the track quality is greatly influenced by the data quality and the model interpretation is poor.
Therefore, how to improve the accuracy of the flight path of the aircraft is a problem that needs to be solved at present.
Disclosure of Invention
The application mainly aims to provide a track planning method, a track planning device, an aircraft and a computer readable storage medium, and aims to solve the technical problem of how to improve the accuracy of the flight track of the aircraft.
In order to achieve the above object, the present application provides a trajectory planning method, including:
Generating a critical path point set based on a static obstacle corresponding to an aircraft, a local track of the last frame and a global navigation path, wherein the critical path point set comprises an obstacle detouring path point set corresponding to the static obstacle;
Determining a control point set of a cubic B-spline curve based on the user set speed, the critical path point set and the cubic B-spline basis function corresponding to the aircraft;
Determining a dynamic obstacle avoidance penalty function based on the dynamic obstacle and the control point set, determining a static obstacle avoidance penalty function based on the obstacle detouring path point set, and determining an optimization model based on the dynamic obstacle avoidance penalty function and the static obstacle avoidance penalty function;
And generating a target track corresponding to the aircraft based on the optimization model and the current flight parameters of the aircraft.
In addition, to achieve the above object, the present application also provides an aircraft including:
The first generation module is used for generating a critical path point set based on a static obstacle corresponding to the aircraft, a local track of the last frame and a global navigation path, wherein the critical path point set comprises an obstacle detouring path point set corresponding to the static obstacle;
The first determining module is used for determining a control point set of a cubic B spline curve based on the corresponding user set speed of the aircraft, the critical path point set and the cubic B spline basis function;
The second determining module is used for determining a dynamic obstacle avoidance penalty function based on the dynamic obstacle and the control point set, determining a static obstacle avoidance penalty function based on the obstacle detouring path point set, and determining an optimization model based on the dynamic obstacle avoidance penalty function and the static obstacle avoidance penalty function;
And the second generation module is used for generating a target track corresponding to the aircraft based on the optimization model and the current flight parameters of the aircraft.
In addition, in order to achieve the above purpose, the application also provides a track planning device, which comprises a memory, a processor and a track planning program stored in the memory and capable of running on the processor, wherein the track planning program is executed by the processor to realize the steps of the track planning method.
In addition, in order to achieve the above object, the present application also provides a computer-readable storage medium having stored thereon a trajectory planning program which, when executed by a processor, implements the steps of the aforementioned trajectory planning method.
The method comprises the steps of generating a critical path point set based on a static obstacle corresponding to an aircraft, a local track of a previous frame and a global navigation path, then determining a control point set of a cubic B spline curve based on a user set speed, the critical path point set and the cubic B spline basis function corresponding to the aircraft, then determining a dynamic obstacle avoidance penalty function based on the obstacle detouring path point set, determining an optimization model based on the dynamic obstacle avoidance penalty function and the static obstacle avoidance penalty function, then generating a target track corresponding to the aircraft based on the optimization model and the current flight parameters of the aircraft, generating a safe topological path by adopting an obstacle detouring path mode aiming at static threats to establish the static obstacle avoidance penalty function, establishing a local track capable of reducing influence of downwash interference by utilizing data of the dynamic obstacle aiming at the dynamic threat, and constructing a multi-constraint optimization model by the static obstacle avoidance requirement to generate a safe executed local track, thereby realizing the static threat and the dynamic threat, improving the accuracy of the static threat and the flying track, and improving the flying comfort and the safety of the aircraft.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description, serve to explain the principles of the application.
In order to more clearly illustrate the embodiments of the application or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described, and it will be obvious to a person skilled in the art that other drawings can be obtained from these drawings without inventive effort.
FIG. 1 is a schematic flow chart of a track planning method according to an embodiment of the present application;
FIG. 2 is a schematic diagram of a scenario of a backup intersection in an embodiment of a trajectory planning method according to the present application;
FIG. 3 is a schematic diagram of a scenario in which a set of critical path points is implemented in an embodiment of a trajectory planning method according to the present application;
FIG. 4 is a schematic view of a scenario provided in a further embodiment of the trajectory planning method of the present application;
FIG. 5 is a schematic view of a scenario provided in another embodiment of a trajectory planning method according to the present application;
FIG. 6 is a schematic diagram of a scenario provided in a further embodiment of the trajectory planning method of the present application;
FIG. 7 is a schematic block diagram of an aircraft according to the application;
Fig. 8 is a schematic block diagram of a track planning apparatus according to an embodiment of the application.
The achievement of the objects, functional features and advantages of the present application will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
For a better understanding of the technical solution of the present application, the following detailed description will be given with reference to the drawings and the specific embodiments.
The main solution of the application is that a critical path point set is generated based on a static obstacle corresponding to an aircraft, a local track of a previous frame and a global navigation path, wherein the critical path point set comprises a obstacle detouring path point set corresponding to the static obstacle, a control point set of a cubic B spline curve is determined based on a user set speed corresponding to the aircraft, the critical path point set and a cubic B spline basis function, a dynamic obstacle detouring penalty function is determined based on a dynamic obstacle and the control point set, a static obstacle detouring penalty function is determined based on the obstacle detouring path point set, an optimization model is determined based on the dynamic obstacle detouring penalty function and the static obstacle detouring penalty function, and a target track corresponding to the aircraft is generated based on the optimization model and current flight parameters of the aircraft.
At present, in the field of multi-rotor aircraft, the application scene of non-cooperative static and dynamic obstacles brings great challenges to autonomous safe flight of a flying automobile, and the limited maneuverability of the aircraft makes the avoidance of the obstacles more difficult, so that the track of the aircraft needs to be accurately planned.
In the related art, trajectory planning can be classified into sampling-based, search-based, optimization-based, and data-based methods. The sampling-based method is to randomly scatter points in a configuration space, and search a path with optimal cost after constructing a route map or a route tree by connecting line segments, but the sampling and node updating strategies influence time efficiency and path quality and are difficult to treat dynamic obstacles; the method based on the search is that a state space is discretized into a three-dimensional grid map according to a certain resolution, a heuristic search algorithm is adopted to obtain a feasible path or an optimal path, but the resolution and the heuristic search algorithm influence search efficiency, continuous space is difficult to process, space and time complexity is high, the method based on the optimization is that a multi-constraint optimization model is built, a gradient descent method is adopted to solve the multi-constraint optimization model to obtain an optimal track, the target and constraint are well defined, applicability is wide, but the non-convex optimization problem consumes more time, the solution quality is greatly influenced by an initial value, the method based on the data is that a deep neural network large model is built by a deep learning and reinforcement learning method, a large amount of data is utilized to train, and then the state and environment information of an aircraft are input into the large model to generate the track, so that adaptability and generalization capability are strong, but the track quality is greatly influenced by the data quality and the model interpretation is poor. Therefore, how to improve the accuracy of the flight path of the aircraft is a problem that needs to be solved at present.
According to the application, a safe topological path is generated by adopting a barrier-bypassing path mode aiming at the static threat to establish a static barrier-avoiding punishment function, the dynamic barrier-avoiding punishment function capable of reducing the influence of the washdown interference is established by utilizing the data of the dynamic barrier aiming at the dynamic threat, and a multi-constraint optimization model is established by the static and dynamic barrier-avoiding requirements to generate a safe execution local track, so that the aims of avoiding the static threat and the dynamic threat are fulfilled, the accuracy of the flight track is improved, and the flight stability, comfort and safety of the aircraft are improved.
It should be noted that, the execution body of the present embodiment may be a track planning apparatus, or may be a computing service device with functions of data processing, network communication and program running, such as a tablet computer, a personal computer, a mobile phone, or a track planning apparatus capable of implementing the above functions, which is not limited in this embodiment. The present embodiment and the following embodiments will be described below using a trajectory planning device as an execution subject.
Based on this, the present application proposes a track planning method of the first embodiment, referring to fig. 1, the track planning method includes steps S101 to S104:
step S101, generating a critical path point set based on a static obstacle corresponding to an aircraft, a local track of a previous frame and a global navigation path, wherein the critical path point set comprises an obstacle detouring path point set corresponding to the static obstacle;
In the application, when track planning is carried out, static obstacle information of a static obstacle is obtained based on a perception module of an aircraft, a last frame of local track and a global navigation path corresponding to the aircraft are obtained, a key path point set is generated based on the obtained parameters, specifically, a current position and a current speed corresponding to the aircraft are obtained, a global navigation path and a corresponding last frame of local track of the aircraft are obtained, a standby path (Fallback path) is determined based on the current position, the current speed, the last frame of local track and the global navigation path, the static obstacle information of the static obstacle corresponding to the aircraft is obtained, a obstacle detouring path point set (a set formed by points in the obstacle detouring path) corresponding to the static obstacle is determined according to the static obstacle information and the standby path, and a key path point set is generated based on an initial path point set corresponding to the obstacle detouring path point set and the standby path, and the initial path point set formed by the path points in the standby path is determined.
Step S102, determining a control point set of a cubic B spline curve based on a user set speed, a critical path point set and a cubic B spline basis function corresponding to the aircraft;
after the critical path point set is obtained, a user set speed and a cubic B-spline basis function are obtained, a control point set of the cubic B-spline curve is determined based on the user set speed, the critical path point set and the cubic B-spline basis function, specifically, a target limiting speed and a critical path point moment corresponding to each critical path point in the critical path point set are determined according to the user set speed, and then the control point set of the cubic B-spline curve is determined based on the cubic B-spline basis function, the target limiting speed corresponding to each critical path point and the critical path point moment.
Step S103, determining a dynamic obstacle avoidance penalty function based on the dynamic obstacle and the control point set, determining a static obstacle avoidance penalty function based on the obstacle detouring path point set, and determining an optimization model based on the dynamic obstacle avoidance penalty function and the static obstacle avoidance penalty function;
After the control point set of the cubic B spline curve is obtained, a dynamic obstacle avoidance penalty function is determined based on the dynamic obstacle and the control point set, specifically, the minimum moment and the maximum moment corresponding to each dynamic obstacle are determined based on the predicted track of each dynamic obstacle and the track of the aircraft corresponding to the critical path point set, and the dynamic obstacle avoidance penalty function is determined based on the current position of the aircraft, the dynamic obstacle, the minimum moment and the maximum moment.
Meanwhile, the static obstacle avoidance penalty function is determined based on the obstacle detouring path point set, and in a feasible implementation manner, step S103 may further include steps S1031 to S1033:
Step S1031, determining a first static parameter corresponding to each critical path point based on the obstacle surface points and the movement direction in the obstacle detouring path point set, and each critical path point and the safety distance;
step S1032, determining second static parameters corresponding to each critical path point based on the first static parameters, each critical path point and the corresponding motion direction;
Step S1033, determining a static obstacle avoidance penalty function based on the second static parameter.
And when the static obstacle avoidance penalty function is determined, acquiring the obstacle surface points and the movement directions in the obstacle detouring path point set, and determining a first static parameter based on the obstacle surface points and the movement directions in the obstacle detouring path point set, the key path points and the safety distances.
Acquiring each critical path point and a corresponding movement direction, determining a second static parameter based on the first static parameter, each critical path point and the corresponding movement direction, and determining a static obstacle avoidance penalty function based on the second static parameter corresponding to each static obstacle information, wherein the specific formula is as follows:
dso＝(Pi-Psurf,i)TVsurf,i)-dsafe
Wherein J so is a static obstacle avoidance penalty function, P i is an i-th critical path point, Q j is a J-th control point in a control point set, N j,3 (the term) is a cubic B-spline basis function, d so is a first static parameter, P surf,i is an obstacle surface point, i.e., an i-th critical path point, V surf,i is a movement direction corresponding to a critical path point P surf,i, d safe is a safety distance, and G i is a second static parameter.
After the dynamic obstacle avoidance penalty function and the static obstacle avoidance penalty function are obtained, an optimization model is determined based on the dynamic obstacle avoidance penalty function and the static obstacle avoidance penalty function, and further, in a feasible implementation manner, step S103 may further include steps S1031 to S1032:
step S1031, determining a track smoothness penalty function based on the critical path point set, and determining a dynamic feasibility penalty function based on the control point set of the cubic B spline curve;
Step S1032, determining an optimization model based on the dynamic obstacle avoidance penalty function, the static obstacle avoidance penalty function, the track smoothness penalty function, and the dynamic feasibility penalty function.
After the dynamic obstacle avoidance penalty function and the static obstacle avoidance penalty function are obtained, the aircraft determines a trajectory smoothness penalty function based on the set of critical path points, and specifically calculates the trajectory smoothness penalty function according to the jerk of each critical path point, further, in a feasible implementation, step S1031 may further include step a:
and a step a, determining a track smoothness penalty function based on jerk corresponding to each critical path point in the critical path point set.
Specifically, jerk corresponding to each critical path point in the critical path point set is obtained, and a track smoothness penalty function is calculated based on each jerk, wherein the track smoothness penalty function has the formula:
Wherein J s is a trajectory smoothness penalty function, J i is jerk corresponding to the ith critical path point, n is the number of control points in the control point set, and n-3=l is the number of critical path points.
Meanwhile, determining a dynamic feasibility punishment function based on a control point set of a cubic B spline curve, specifically acquiring three-dimensional speed, Z-axis speed, three-dimensional acceleration, three-dimensional and speed upper limit, Z-axis maximum speed and three-axis combined acceleration maximum value corresponding to each control point in the control point set, and calculating the dynamic feasibility punishment function based on the acquired parameters, wherein in a feasible implementation, the step S1031 can further comprise the steps of B1-B2:
step b1, obtaining three-dimensional speed, Z-axis speed, three-dimensional acceleration, three-dimensional and speed upper limit, Z-axis maximum speed and three-axis combined acceleration maximum values corresponding to all control points in a control point set;
step b2, determining a first penalty function based on the three-dimensional speed and the three-dimensional sum speed upper limit, determining a second penalty function based on the Z-axis speed and the Z-axis maximum speed, and determining a third penalty function based on the three-dimensional acceleration and the three-axis sum acceleration maximum value;
And b3, determining a dynamic feasibility punishment function based on the first punishment function, the second punishment function and the third punishment function.
And when the dynamic feasibility punishment function is determined, acquiring the three-dimensional speed, the Z-axis speed, the three-dimensional acceleration, the three-dimensional and speed upper limit, the Z-axis maximum speed and the three-axis total acceleration maximum value corresponding to each control point in the control point set.
Then, a first punishment function is determined based on the three-dimensional speed and the three-dimensional and speed upper limit, a second punishment function is determined based on the Z-axis speed and the Z-axis maximum speed, a third punishment function is determined based on the three-dimensional acceleration and the three-axis combined acceleration maximum value, a dynamic feasibility punishment function is determined based on the first punishment function, the second punishment function and the third punishment function, and the three-axis speed and the Z-axis speed punishment function are established by taking the Z-axis tracking capacity limit of the aircraft into consideration in the speed decision, so that the aircraft can track the optimized track of the steep rise and fall of the altitude is ensured, wherein the specific formula is as follows:
Jd＝Jυ+Jυ,z+Ja;
Wherein J d is a dynamic feasibility penalty function, J v is a first penalty function, J v,z is a second penalty function, J a is a third penalty function, v i is a three-dimensional speed of an ith control point in the control point set, v upper,i is a three-dimensional acceleration of an ith control point in the control point set, v z,i is a Z-axis speed of an ith control point in the control point set, v max,z is a Z-axis maximum speed, a i is a three-dimensional acceleration of an ith control point in the control point set, and a max is a tri-axial acceleration maximum.
After the dynamic feasibility punishment function is acquired, an optimization model is determined based on the dynamic obstacle avoidance punishment function, the static obstacle avoidance punishment function, the track smoothness punishment function and the dynamic feasibility punishment function, the path smoothness and the dynamic feasibility punishment function are established based on the non-uniform B-spline control points, and the static and dynamic obstacle avoidance punishment items are combined to construct a multi-constraint optimization model so as to generate a smooth, safe and executable local track, so that the stability and the comfort of flight are improved. The specific formula of the optimization model is as follows:
J=wsJs+wdJd+wdoJdo+wsoJso;
Wherein, J is an optimization model, J s is a trajectory smoothness penalty function, J d is a dynamic feasibility penalty function, J do is a dynamic obstacle avoidance penalty function, J so is a static obstacle avoidance penalty function, and w s、wd、wdo、wso is a weight coefficient corresponding to the trajectory smoothness penalty function, the dynamic feasibility penalty function, the dynamic obstacle avoidance penalty function, and the static obstacle avoidance penalty function, respectively.
Step S104, generating a target track corresponding to the aircraft based on the optimization model and the current flight parameters of the aircraft.
After the optimization model is obtained, current flight parameters of the aircraft are obtained, a target track corresponding to the aircraft is generated based on the current flight parameters and the optimization model, specifically, the current flight parameters are calculated in a general calculation mode in the related technology, related parameters of the optimization model are obtained, and the target track corresponding to the aircraft is generated based on the optimization model through the related parameters.
After the target track is generated, determining the speed and the acceleration of the target track corresponding to the optimal control point, determining whether the speed and the acceleration of the optimal control point meet the corresponding constraint conditions, if so, outputting the target track, sampling the target track to obtain a plurality of local track sampling points, determining whether each local track sampling point meets the safety distance constraint with the static obstacle and the dynamic obstacle in time and space, if so, controlling the aircraft based on the target track, and if the local track sampling points do not meet the safety distance constraint with the static obstacle or the safety distance constraint with the dynamic obstacle, generating a stopping track and sending the stopping track to a control module of the aircraft for execution. If the speed and acceleration of the optimal control point do not meet the corresponding constraint conditions, a reassignment time interval is obtained, and the time node vector U of the control point set Q is adjusted based on the reassignment time interval, for example, if the reassignment time interval is Δt, the adjusted u= [ t 0+Δt,t1+Δt,…,tm-1+Δt,tm +Δt ], and the step S102 is executed again to obtain a new target track.
A critical path point set is generated based on a static obstacle corresponding to an aircraft, a local track of a previous frame and a global navigation path, then a control point set of a cubic B spline curve is determined based on a user set speed, the critical path point set and a cubic B spline basis function corresponding to the aircraft, then a dynamic obstacle avoidance penalty function is determined based on the dynamic obstacle and the control point set, a static obstacle avoidance penalty function is determined based on the obstacle detouring path point set, an optimization model is determined based on the dynamic obstacle avoidance penalty function and the static obstacle avoidance penalty function, then a target track corresponding to the aircraft is generated based on the optimization model and the current flight parameters of the aircraft, a safe topological path is generated by adopting a obstacle detouring path mode aiming at static threats to establish the static obstacle avoidance penalty function, a dynamic obstacle avoidance function capable of reducing the influence of downwash interference is established by utilizing data of the dynamic threats, a multi-constraint optimization model is established by static and dynamic obstacle avoidance requirements to generate a safe executed local track, the accuracy of the static threat and the static threat is achieved, the flying stability of the aircraft is improved, the flying comfort and the safety of the aircraft are improved.
In a possible implementation manner, the step S101 may further include steps S201 to S202:
Step S201, determining a standby path based on a current position, a current speed, a local track of a previous frame and a global navigation path corresponding to the aircraft;
Step S202, generating a critical path point set based on the static obstacle corresponding to the aircraft and the initial path point set corresponding to the standby path.
When track planning is performed, a current position and a current speed corresponding to the aircraft are obtained, a global navigation path and a local track corresponding to a previous frame of the aircraft are obtained at the same time, and a standby path (Fallback path) is determined based on the current position, the current speed, the local track of the previous frame and the global navigation path, specifically, in a feasible embodiment, step S201 may include steps S2011-S2013:
Step 2011, determining a first projection point in a local track of a previous frame based on the current position and the current speed of the aircraft;
step S2012, determining the last local track point in the local track of the previous frame and a corresponding second projection point in the global navigation path of the aircraft;
step S2013, determining the backup path based on the first projection point, the second projection point, the local track of the previous frame, and the global navigation path.
After the current position, the current speed and the local track of the previous frame are obtained, the current position is projected onto the local track of the previous frame according to the current position and the current speed, and a first projection point corresponding to the current aircraft is determined in the local track of the previous frame.
And simultaneously, acquiring the last local track point in the local track of the previous frame, and determining a second projection point corresponding to the last local track point in the global navigation path, wherein the second projection point can be the intersection point between the local track of the previous frame and the global navigation path.
After the first projection point and the second projection point are obtained, a standby path is determined based on the first projection point, the second projection point, the local track of the previous frame and the global navigation path, as shown in fig. 2, the issuing curve is the global navigation path, the middle yellow curve is the local track of the previous frame, and the path corresponding to the purple discrete point set is the standby path. Wherein, when generating the backup path, the path length limitation of the aircraft is considered, i.e. the backup path is smaller than or equal to the path length limitation.
After the backup path is acquired, static obstacle information of the static obstacle corresponding to the aircraft is acquired, a detour path point set (a set formed by points in the detour path) corresponding to the static obstacle is determined according to the static obstacle information and the backup path, a critical path point set is generated according to an initial path point set corresponding to the detour path point set and the backup path, and the initial path point set is a set formed by path points in the backup path, specifically, in a possible implementation, step S202 may include steps S2021 to S2024:
Step S2021, sampling the standby path according to the key path point distance to obtain an initial path point set;
step S2022, determining whether there is a collision segment in the initial set of path points based on the static grid map corresponding to the static obstacle information;
step S2023, determining a set of obstacle detouring path points based on the collision section, wherein the set of obstacle detouring path points comprises obstacle surface points and corresponding directions of movement;
step S2024 generates the critical path point set based on the obstacle detouring path point set and the initial path point set.
After the standby path is acquired, acquiring the corresponding critical path point distance, sampling the standby path according to the critical path point distance to obtain an initial path point set, namely generating discrete initial path points to obtain the initial path point set.
After the initial path point set is obtained, static barrier information of a static barrier corresponding to the aircraft is obtained, a static grid map is built according to the static barrier information, whether collision sections exist in the initial path point set or not is determined according to the static grid map, specifically, whether all initial path points in the initial path point set are in the static grid map corresponding to the static barrier or not is checked one by one, if the initial path points in the static grid map exist in the initial path point set, the initial path points in the static grid map are connected to form collision sections, a barrier-surrounding path point set is determined based on the collision sections, the barrier-surrounding path point set comprises barrier surface points and corresponding movement directions, as shown in fig. 3, a broken line is a barrier-surrounding path formed by the barrier-surrounding path point set, a red solid point in the broken line is a barrier-surrounding path point corresponding to each initial path point in the collision section, a green curve is a standby path, and an arrow corresponding to the red solid point is a movement direction corresponding to the barrier-surrounding path point.
Next, a critical path point set is generated based on the obstacle detouring path point set and the initial path point set, the critical path point set including obstacle detouring path points in the obstacle detouring path point set and path points in the initial path point set other than the initial path points included in the collision zone.
By detecting the collision section and searching the safe obstacle detouring path corresponding to the collision section in the static occupation grid map, time consumption is reduced, and a static obstacle detouring penalty function is conveniently established through a point set (obstacle detouring path point set) of 'obstacle surface points-movement direction', so that the goal of avoiding static threat is realized.
The track planning method provided by the embodiment determines the standby path based on the current position, the current speed, the local track of the last frame and the global navigation path corresponding to the aircraft, then generates a critical path point set based on the static obstacle corresponding to the aircraft and the initial path point set corresponding to the standby path, and obtains the critical path point set by detecting the collision section and searching the safe obstacle detouring path corresponding to the collision section in the static occupied grid map so as to improve the accuracy of the flight track and further improve the flight stability, comfort and safety of the aircraft.
In one possible implementation, step S102 may include steps S301 to S302:
Step S301, determining a target limiting speed and a critical path point moment corresponding to each critical path point in the critical path point set based on the user set speed;
Step S302, a control point set of the cubic B spline curve is determined based on the cubic B spline basis function, the target limiting speed corresponding to each critical path point and the critical path point moment.
After acquiring the critical path point set, acquiring a user set speed, and determining a target limiting speed corresponding to each critical path point in the critical path point set and a critical path point time based on the user set speed, wherein the target limiting speed is a speed upper limit corresponding to each critical path point, and the critical path point time is a time when the aircraft obtained according to the target limiting speed reaches each critical path point, specifically, in a feasible implementation, step S301 may include steps S3011 to S3014:
Step S3011, determining a first speed upper limit corresponding to each critical path point based on the user-set speed and the steep rise and fall speed upper limit;
Step S3012, determining the upper speed limit of the current critical path point based on the upper speed limit of the previous critical path point in the first upper speed limit and the corresponding upper acceleration limit, and obtaining the second upper speed limit corresponding to each critical path point;
step S3013, determining the upper speed limit of the current critical path point based on the upper speed limit of the next critical path point in the second upper speed limit and the corresponding upper acceleration limit, and obtaining the target limiting speed corresponding to each critical path point;
Step S3014, determining a time set based on the distances of the critical path points and the target limiting speed, where the time set includes the critical path point moments corresponding to the critical path points.
After the user set speed is obtained, the steep rise and fall speed upper limit is obtained, and the first speed upper limit corresponding to each critical path point is determined based on the user set speed and the steep rise and fall speed upper limit, as shown in fig. 4, the steep rise and fall calculated speed upper limit in fig. 4 is the steep rise and fall speed upper limit, the yellow curve is the user set speed, for each critical path point, the first speed upper limit of the critical path point is smaller than or equal to the steep rise and fall speed upper limit, and the first speed upper limit of the critical path point is smaller than or equal to the user set speed.
And then determining the upper speed limit of the next critical path point based on the upper speed limit of the previous critical path point in the first upper speed limit and the corresponding upper acceleration limit, and obtaining a second upper speed limit corresponding to each critical path point, specifically, for each critical path point in the critical path points, obtaining the upper speed limit of the previous critical path point in the first upper speed limit, and calculating the upper speed limit of the current critical path point based on the upper speed limit of the previous critical path point and the upper acceleration limit of the aircraft, so as to obtain the second upper speed limit, as shown in fig. 5.
After the second speed upper limit corresponding to each critical path point is obtained, determining the speed upper limit of the current critical path point based on the speed upper limit of the next critical path point in the second speed upper limit and the corresponding acceleration upper limit, and obtaining the target limiting speed corresponding to each critical path point, specifically, for each of the critical path points, the upper speed limit of the next critical path point is obtained in the second upper speed limit, and the upper speed limit of the current critical path point is calculated based on the upper speed limit of the next critical path point and the upper acceleration limit of the aircraft, so that the target limiting speed is obtained, as shown in fig. 6.
After the target limiting speed is obtained, a time set is determined based on the critical path point distance and the target limiting speed, specifically, for each critical path point in the critical path points, the critical path point moment corresponding to the critical path point is calculated according to the speed limit of the previous critical path point in the target limiting speed, the speed limit of the critical path point and the critical path point distance, and then the time set is obtained through the critical path point moment corresponding to each critical path point.
After obtaining the target limiting speed and the critical path point time corresponding to each critical path point, obtaining a cubic B-spline basis function, and determining a control point set of the cubic B-spline curve based on the cubic B-spline basis function, the target limiting speed and the critical path point time corresponding to each critical path point, specifically, in a feasible embodiment, step S302 may include steps S3021 to S3022:
Step S3021, obtaining a first speed and a first acceleration of a first critical path point in the set of critical path points and a second speed and a second acceleration of a last critical path point in the set of critical path points based on the target limiting speed;
In step S3022, a set of control points for the cubic B-spline curve is determined based on the set of critical path points, the first velocity, the first acceleration, the second velocity, and the second acceleration at the moment of the critical path points.
After the target limiting speed is obtained, a first speed V 0 of a first one of the set of critical path points and a second speed V l of a last one of the set of critical path points are obtained based on the target limiting speed, and a first acceleration a 0 of the first one of the set of critical path points and a second acceleration a l of the last one of the set of critical path points are obtained.
Next, a control point set Q of a cubic B spline curve is determined based on the critical path point set, the critical path point time first velocity V 0, the first acceleration a 0, the second velocity V l, and the second acceleration a l, wherein the cubic B spline basis function N i,3 (t) is:
The control point set of the cubic B spline curve is q= [ Q 1,Q2,…,Qn-1,Qn ], the time node vector of the control point set Q is u= [ T 0,t1,…,tm-1,tm ], the critical path point set is p= [ P 1,P2,…,Pl-1,Pl ], and the time set is t= [ T 1,T2,…,Tl-1,Tl ], wherein n=l+2, m=n+4, T i＝ti+4-ti+3, then:
and then the non-uniform B-spline parameterized equation can be obtained as follows:
The above formula can be written as running with bq=p, the matrix B can be obtained by solving the formulas of Pi, V 0、A0、Vl and a l, and then the control point set Q of the cubic B spline curve can be obtained by solving the formula by QR decomposition.
The track planning method provided by the embodiment determines the target limiting speed and the critical path point moment corresponding to each critical path point in the critical path point set based on the user setting speed, then determines the control point set of the cubic B spline curve based on the cubic B spline basis function, the target limiting speed and the critical path point moment corresponding to each critical path point, and improves the solving speed of the optimization problem and the quality of the generated track by taking the generated high-quality control point set as an initial solution through the non-uniform B spline parameterization theory, thereby improving the accuracy of the flight track and further improving the stability, comfort and safety of the aircraft flight.
In a possible implementation, step S103 may include steps S401 to S403:
step S401, determining the minimum moment and the maximum moment corresponding to each dynamic obstacle respectively based on the first starting moment and the first track duration corresponding to the predicted track of each dynamic obstacle and the second starting moment and the second track duration corresponding to the aircraft track of the critical path point set;
Step S402, determining collision cost corresponding to each dynamic obstacle based on the current position, the minimum moment, the maximum moment, the bounding boxes corresponding to each dynamic obstacle and the safe distance of the aircraft;
step S403, determining the dynamic obstacle avoidance penalty function based on the collision cost corresponding to each dynamic obstacle.
After the control point set of the cubic B spline curve is obtained, the size information and the predicted track of the dynamic obstacle are obtained through a perception module of the aircraft, a corresponding first starting time and first track duration are obtained based on the predicted track of each dynamic obstacle, for example, K dynamic obstacles exist currently, K epsilon [1, K ] are predicted tracks phi k corresponding to the K dynamic obstacle at a first starting time T k,start, the first track duration is T k, the track ending time is T k,end＝Tk-tk,start, the track corresponding to the critical path point set can be the track generated by the aircraft at a second starting time T o,start, namely the track generated by the aircraft at a second starting time T o,start is aircraft track phi o, the second track duration is T o, and the track ending time is T o,end＝To-to,start. Specifically, in one possible implementation, step S401 may include steps S4011 to S4012:
step S4011, determining minimum time corresponding to each dynamic obstacle based on the first start time and the second start time;
step S4012 determines, based on the first start time, the first track duration, the second start time, and the second track duration, maximum times corresponding to the dynamic obstacles are determined, respectively.
For the kth dynamic obstacle, there are the following three conditions, (1), t k,end≤tostart or t kstart＞toend between the kth dynamic obstacle and the aircraft, then no intersection exists between the kth dynamic obstacle and the aircraft, and no collision risk exists, (2), t o,start≤tk,start and t k,start＜to,end and t k,end≥to,end, or t o,start≤tk,start and t k,start＜to,end and t k,ennd＜to,end, then the minimum moment t min＝tk,start, the maximum moment t max＝min{to,end,tk,end};(3)、to,start≥tk,start and t o,start＜tk,ennd and t k,end≥to,end, or t o,start≥tk,start and t o,start＜tk,end and t k,end＜to,end, then the minimum moment t min＝to,start, and the maximum moment t max＝min{to,end,tk,end }.
Further, after the first start time, the second start time, and the second track duration are obtained, a minimum time t min corresponding to each dynamic obstacle is determined based on the first start time and the second start time, and a maximum time t max corresponding to each dynamic obstacle is determined based on the first start time, the first track duration, the second start time, and the second track duration, wherein t min＝max{to,start,tk,star},tmax＝min{to,start+To,tk,star+Tk.
After the minimum time and the maximum time corresponding to each dynamic obstacle are obtained, determining the collision cost corresponding to each dynamic obstacle based on the current position, the minimum time, the maximum time, the bounding box corresponding to each dynamic obstacle and the safety distance of the aircraft, specifically, in a feasible embodiment, step S402 may include steps S4021 to S4023:
step S4021, determining the nearest point between the aircraft and each dynamic obstacle based on the current position of the aircraft and the boundary frame corresponding to each dynamic obstacle, and determining three-dimensional relative position information between the nearest point and the current position of the aircraft;
step S4022, determining dynamic parameters based on the three-dimensional relative position information and the safety distance;
step S4023, determining collision costs corresponding to each dynamic obstacle based on the dynamic parameters, the minimum time and the maximum time of each dynamic obstacle.
The formula of the collision cost is:
tmin＝max{to,tk}
tmax＝min{to+To,tk+Tk}
wherein J do,k is the collision cost corresponding to the kth dynamic obstacle, For the three-dimensional relative position information of the current position of the aircraft and the closest point between the boundary boxes corresponding to the kth dynamic obstacle, Φ o (t) is the current position of the moment t k in the aircraft track, F (-) is a function of finding the closest point from the current position to the boundary box and calculating the three-dimensional relative position of a given point and the closest point, r is the radius of the corresponding sphere model of the aircraft, d safe is a safe distance, E represents the conversion of the Euclidean distance into an ellipsoid distance with a shorter Z axis to reduce the washout interference, d k (t) is a first dynamic parameter of the moment t k, G k (t) is a second dynamic parameter of the moment t k, t min is the minimum moment, and t max is the maximum moment.
After the collision cost is obtained, determining the dynamic obstacle avoidance penalty function based on the collision cost corresponding to each dynamic obstacle, wherein the dynamic obstacle avoidance penalty function has the formula:
wherein J do is a dynamic obstacle avoidance penalty function, and K is the number of dynamic obstacles.
The track planning method provided by the embodiment respectively determines the minimum moment and the maximum moment corresponding to each dynamic obstacle based on the first starting moment and the first track duration corresponding to the predicted track of each dynamic obstacle and the second starting moment and the second track duration corresponding to the track of the aircraft by using the critical path point set, then determines the collision cost corresponding to each dynamic obstacle based on the current position, the minimum moment, the maximum moment and the bounding box corresponding to each dynamic obstacle and the safety distance of the aircraft, then determines the dynamic obstacle avoidance penalty function based on the collision cost corresponding to each dynamic obstacle, and establishes the dynamic obstacle avoidance penalty function capable of reducing the influence of the downward-washing interference by using the predicted track, thereby realizing the goal of avoiding dynamic threat, improving the accuracy of the flight track and further improving the stability, the comfort and the safety of the aircraft.
An embodiment of the present application further provides an aircraft, referring to fig. 7, the aircraft includes:
A first generation module 10, configured to generate a critical path point set based on a static obstacle corresponding to an aircraft, a local track of a previous frame, and a global navigation path, where the critical path point set includes an obstacle detouring path point set corresponding to the static obstacle;
A first determining module 20, configured to determine a control point set of a cubic B-spline curve based on a user set speed, a critical path point set, and a cubic B-spline basis function corresponding to the aircraft;
a second determining module 30, configured to determine a dynamic obstacle avoidance penalty function based on the dynamic obstacle and the control point set, determine a static obstacle avoidance penalty function based on the obstacle detouring path point set, and determine an optimization model based on the dynamic obstacle avoidance penalty function and the static obstacle avoidance penalty function;
a second generating module 40, configured to generate a target track corresponding to the aircraft based on the optimization model and the current flight parameters of the aircraft.
The track planning device provided by the embodiment of the application can solve the technical problem of how to improve the accuracy of the flight track of the aircraft by adopting the track planning method in the embodiment. Compared with the prior art, the beneficial effects of the track planning device provided by the embodiment of the application are the same as those of the track planning method provided by the embodiment, and other technical features in the track planning device are the same as those disclosed by the method of the embodiment, so that the description is omitted herein.
The application provides a track planning device which comprises at least one processor and a memory in communication connection with the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the track planning method in the first embodiment.
Referring now to fig. 8, a schematic diagram of a track planning apparatus suitable for use in implementing embodiments of the present application is shown. The trajectory planning device in the embodiment of the present application may include, but is not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (Personal DIGITAL ASSISTANT: personal digital assistants), PADs (Portable Application Description: tablet computers), PMPs (Portable MEDIA PLAYER: portable multimedia players), vehicle-mounted terminals (e.g., car navigation terminals), and the like, and fixed terminals such as digital TVs, desktop computers, and the like. The trajectory planning device shown in fig. 8 is only an example and should not be construed as limiting the functionality and scope of use of the embodiments of the application.
As shown in fig. 8, the trajectory planning device may include a processing device 1001 (e.g., a central processing unit, a graphics processor, etc.) that may perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 1002 or a program loaded from a storage device 1003 into a random access Memory (RAM: random Access Memory) 1004. In the RAM1004, various programs and data required for the operation of the trajectory planning device are also stored. The processing device 1001, the ROM1002, and the RAM1004 are connected to each other by a bus 1005. An input/output (I/O) interface 1006 is also connected to the bus. In general, a system including an input device 1007 such as a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc., an output device 1008 including a Liquid crystal display (LCD: liquid CRYSTAL DISPLAY), a speaker, a vibrator, etc., a storage device 1003 including a magnetic tape, a hard disk, etc., and a communication device 1009 may be connected to the I/O interface 1006. The communication means 1009 may allow the trajectory planning means to communicate wirelessly or by wire with other devices to exchange data. While a trajectory planning device having various systems is shown in the figures, it should be understood that not all of the illustrated systems are required to be implemented or provided. More or fewer systems may alternatively be implemented or provided.
In particular, according to embodiments of the present disclosure, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method shown in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through a communication device, or installed from the storage device 1003, or installed from the ROM 1002. The above-described functions defined in the method of the disclosed embodiment of the application are performed when the computer program is executed by the processing device 1001.
The track planning device provided by the application can solve the technical problem of how to improve the accuracy of the flight track of the aircraft by adopting the track planning method in the embodiment. Compared with the prior art, the beneficial effects of the track planning device provided by the application are the same as those of the track planning method provided by the embodiment, and other technical features of the track planning device are the same as those disclosed by the method of the previous embodiment, and are not repeated here.
It is to be understood that portions of the present disclosure may be implemented in hardware, software, firmware, or a combination thereof. In the description of the above embodiments, particular features, structures, materials, or characteristics may be combined in any suitable manner in any one or more embodiments or examples.
The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
The present application provides a computer readable storage medium having computer readable program instructions (i.e., a computer program) stored thereon for performing the trajectory planning method of the above embodiments.
The computer readable storage medium provided by the present application may be, for example, a U disk, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, or device, or a combination of any of the foregoing. More specific examples of a computer-readable storage medium may include, but are not limited to, an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access Memory (RAM: random Access Memory), a Read-Only Memory (ROM), an erasable programmable Read-Only Memory (EPROM: erasable Programmable Read Only Memory 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. In this embodiment, a computer-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, or device. Program code embodied on a computer readable storage medium may be transmitted using any appropriate medium, including but not limited to electrical wiring, fiber optic cable, RF (Radio Frequency) and the like, or any suitable combination of the foregoing.
The computer readable storage medium may be included in the track planning apparatus or may exist alone without being assembled into the track planning apparatus.
The computer readable storage medium carries one or more programs, when the one or more programs are executed by the track planning device, the track planning device generates a critical path point set based on a static obstacle corresponding to an aircraft, a local track of a previous frame and a global navigation path, wherein the critical path point set comprises an obstacle detouring path point set corresponding to the static obstacle, determines a control point set of a cubic B spline curve based on a user set speed corresponding to the aircraft, the critical path point set and the cubic B spline basis function, determines a dynamic obstacle detouring penalty function based on the dynamic obstacle and the control point set, determines a static obstacle detouring penalty function based on the obstacle detouring path point set, determines an optimization model based on the dynamic obstacle detouring penalty function and the static obstacle detouring penalty function, and generates a target track corresponding to the aircraft based on the optimization model and current flight parameters of the aircraft.
Computer program code for carrying out operations of the present application may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ 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 computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of remote computers, the remote computer may be connected to the user's computer through any kind of network, including a local area network (LAN: local Area Network) or a wide area network (WAN: wide Area Network), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The modules involved in the embodiments of the present application may be implemented in software or in hardware. Wherein the name of the module does not constitute a limitation of the unit itself in some cases.
The readable storage medium provided by the application is a computer readable storage medium, and the computer readable storage medium stores computer readable program instructions (namely computer programs) for executing the trajectory planning method, so that the technical problem of how to improve the accuracy of the flight trajectory of the aircraft can be solved. Compared with the prior art, the beneficial effects of the computer readable storage medium provided by the application are the same as those of the track planning method provided by the above embodiment, and are not described herein.
An embodiment of the application provides a computer program product comprising a computer program which, when executed by a processor, implements the steps of a trajectory planning method as described above.
The computer program product provided by the application can solve the technical problem of how to improve the accuracy of the flight trajectory of the aircraft. Compared with the prior art, the beneficial effects of the computer program product provided by the embodiment of the present application are the same as those of the track planning method provided by the above embodiment, and are not described herein.
The foregoing description is only of the preferred embodiments of the present application, and is not intended to limit the scope of the application, but rather is intended to cover any equivalents of the structures or equivalent processes disclosed herein or in the alternative, which may be employed directly or indirectly in other related arts.
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