CN116560408A - Sequence convex optimization method and system for time optimal track of unmanned aerial vehicle - Google Patents
Sequence convex optimization method and system for time optimal track of unmanned aerial vehicle Download PDFInfo
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Abstract
The invention discloses a sequence convex optimization method and a system for an unmanned aerial vehicle time optimal track, which relate to the technical field of unmanned aerial vehicle path planning and comprise the following steps: acquiring unmanned plane parameter information, path constraint information, task environment information and algorithm parameter information; obtaining barrier information in an actual environment, constructing a grid chart of the actual environment, and uniformly dividing the map into grids with equal proportion; obtaining an unmanned aerial vehicle track optimization configuration space according to the real environment grid diagram; searching in the unmanned aerial vehicle track optimization configuration space by adopting a path planning algorithm based on A to obtain a feasible unmanned aerial vehicle navigation path; and obtaining a safe flight track with optimal time based on the SFC-SCP algorithm. The invention has the advantages of short navigation time, strong obstacle avoidance capability and short algorithm time consumption.
Description
Technical Field
The invention relates to the technical field of unmanned aerial vehicle path planning, in particular to a sequence convex optimization method and system for an unmanned aerial vehicle time optimal track.
Background
With rapid development of technology, unmanned aerial vehicles are applied to various fields such as exploration, reconnaissance and search and rescue. However, due to the complexity of a real environment and the limitation of the unmanned aerial vehicle, the safety and success rate of the unmanned aerial vehicle are difficult to ensure by the traditional track planning; in order for the drone to reach the target point quickly and safely to perform tasks, a feasible path that is collision-free and that optimizes specific performance metrics needs to be planned.
Aiming at the optimal track optimization problem of unmanned aerial vehicle time, in the prior art, after the non-convex problem is converted into the convex problem, a sequence convex optimization method is applied to realize quick solution. However, it is difficult to quickly obtain a feasible solution in a dense complex environment with many obstacles, and the sequence convex optimization is sensitive to an initial value, and may be trapped in a local minimum value in the complex environment, so that trace iteration cannot converge; therefore, it is urgently needed to design a new method for optimizing the time optimal trajectory of the unmanned aerial vehicle, so as to solve the above-mentioned existing technical problems.
Disclosure of Invention
In order to solve the problems, the invention provides a sequence convex optimization method for a time optimal track of an unmanned aerial vehicle, which comprises the following steps:
obtaining barrier information of the unmanned aerial vehicle in an actual environment, constructing a real environment grid chart, and carrying out path planning through an A-type algorithm to generate a path planning configuration space of the unmanned aerial vehicle;
and (3) adopting a track sequence convex optimization algorithm based on SFC-SCP, taking the flight time among track points as an optimization variable, and searching in a path planning configuration space to obtain a safe flight track with optimal time.
Preferably, in response to the construction process of the real environment raster image, an environment map aiming at the optimal track optimization problem of the unmanned aerial vehicle time is established by acquiring the initial state and the terminal state of the unmanned aerial vehicle, the state and control constraint boundary, threat position information, path discrete points, safe flight corridor boundary constraint, initial trust domain radius and convergence threshold value, and an obstacle is randomly generated in the environment map;
based on the environment map, a real environment raster image is constructed according to the obstacle information, and the map is uniformly divided into equal-proportion-sized grids.
Preferably, in response to a path planning process through an a-algorithm, a shortest path from a start point to an end point is obtained through the a-algorithm based on an environment map, and an initial reference track of the unmanned aerial vehicle is generated, wherein the initial reference track is expressed as:
wherein X is 0 Represents the reference track X in the subsequent iteration by using the obstacle avoidance path of the A-X algorithm as the reference track q The result of the previous iteration is solved; q represents the q-th iteration in sequence convex optimization, X q Representing the solution result of the q-th sequence iteration;
and constructing a path planning configuration space according to the initial reference track and the obstacle information.
Preferably, in response to a construction process of the path planning configuration space, a half-plane constraint set corresponding to each path point is obtained based on an initial reference track and combined with surrounding obstacles, and is allocated to each track point, wherein the half-plane constraint set is expressed as:
l(i，m)＝-H(i，m)×J(i，m)，i＝1，2，...，N；m＝1，2，...，M
wherein H (i, m) is a half-plane unit normal vector generated by the ith path point and the mth cylindrical obstacle, J (i, m) is a point coordinate nearest to the ith path point on the mth circular obstacle,and->Representing the position and the radius of the center point of the circle center of the mth cylindrical horizontal plane;
the half-plane constraint set of all path points on the planned path generated by the A-algorithm forms a convex domain set, and a path planning configuration space is constructed.
Preferably, in response to the acquisition process of the obstacle, cylindrical threat and prismatic threat are adopted as the obstacle, and the height is set to be infinitely high, the obstacle is expressed as:
wherein I 2 Is a 2-norm number of the sample,and->Representing cylindrical threats and prismatic threats, p xy Representing the current position of the drone->And->Represents the position and radius of the center point of the circle center of the mth cylindrical horizontal plane,/->And b m，i Half space coefficient representing the ith edge of the mth diamond threat, +.>Representing the edge number of the mth diamond threat.
Preferably, in response to the construction process of the convex domain set, a convex polygon area is constructed by combining cube trust domain constraint and half-plane constraint, and is used as a constraint area of a track point, and the convex domain set is constructed according to the constraint area, wherein the constraint area is expressed as:
wherein B (p) i ) Representing a fixed size square trust zone constraint;representing a path point p i Threat to cylinder shape>Is a half space of (2); />Representing a path point p i Threat p with prismatic form m Is a half space of (2);
the set of convex domains is expressed as: Γ= { SFC 1 ∪SFC 2 ∪...∪SFC N }。
Preferably, in response to the process of acquiring the safe flight trajectory, a trapezoidal integral method is used for discretizing the unmanned aerial vehicle kinematic model, linearizing the right end item of the discretized unmanned aerial vehicle kinematic model according to the reference state trajectory to obtain linear constraints of position and speed respectively, and acquiring the safe flight trajectory through a trajectory sequence convex optimization algorithm based on an SFC-SCP, wherein the linear constraints are expressed as:
wherein p= [ p ] x ，p y ，p z ]Is a three-dimensional space position, v= [ v ] x ，v y ，v z ]For speed, a= [ a ] x ，a y ，a z ]For acceleration, Δt is the time interval from the kth point to the kth+1th point.
Preferably, in response to constructing an SFC-SCP-based track sequence convex optimization algorithm, the problems of initial value sensitivity and result non-convergence when the SCP solves the track are relieved through front-end construction of the SFC, and the SFC-SCP-based track sequence convex optimization algorithm is generated.
Preferably, in response to a process of acquiring a safe flight trajectory through an SFC-SCP-based trajectory sequence convex optimization algorithm, judging whether the acquisition process meets a convergence condition, and if so, outputting a current trajectory planning result; if the reference track is not satisfied, updating the reference track, and continuing to carry out iterative solution, wherein the convergence condition is as follows: all constraint conditions are met, and the track results of two successive iterations are identical within a certain error range, expressed as:
wherein q represents the q-th iteration, X in the sequence convex optimization q The result of the solution of the q-th iteration is represented.
The invention discloses a sequence convex optimization system for an unmanned aerial vehicle time optimal track, which comprises the following steps:
the data acquisition module is used for acquiring barrier information of the unmanned aerial vehicle in an actual environment;
the data processing module is used for constructing a real environment raster image according to the obstacle information, carrying out path planning through an A-algorithm, and generating a path planning configuration space of the unmanned aerial vehicle;
and the flight track planning module is used for searching in a path planning configuration space by adopting a track sequence convex optimization algorithm based on the SFC-SCP and taking the flight time among track points as an optimization variable to obtain a safe flight track with optimal time.
The invention discloses the following technical effects:
according to the unmanned aerial vehicle navigation method and system, according to the task requirement that the unmanned aerial vehicle reaches a given target point, the unmanned aerial vehicle feasible path meeting the requirement can be generated by utilizing the strong robustness and convergence of the safety flight corridor, and the unmanned aerial vehicle navigation method and system have the advantages of short navigation time, strong obstacle avoidance capability and short algorithm time consumption.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of an optimization method of an unmanned aerial vehicle time optimal track provided by an embodiment of the invention;
FIG. 2 is a schematic view of a cylindrical threat semi-plane provided by an embodiment of the invention;
FIG. 3 is a schematic diagram of a prismatic threat half-plan provided by an embodiment of the invention;
FIG. 4 is a schematic illustration of a multi-planar safe flight corridor with single track points provided in an embodiment of the present invention;
FIG. 5 is a schematic view of a multi-planar safe flight corridor with a set of trajectory points according to an embodiment of the present invention;
fig. 6 is a schematic illustration of l=7 discrete point corridor constraints provided by an embodiment of the present invention;
FIG. 7 is a graph of a comparison of a scenario-algorithm planning result provided by an embodiment of the present invention;
FIG. 8 is a three-dimensional graph of a scenario-algorithm planning result provided by an embodiment of the present invention;
FIG. 9 is a diagram showing a comparison of two-dimensional results of a scene two planning provided by an embodiment of the present invention;
FIG. 10 is a schematic diagram of a comparison of a scenario two planning result and a three-dimensional diagram according to an embodiment of the present invention;
FIG. 11 is a trace time chart of a 100-time random scene algorithm provided by an embodiment of the invention;
fig. 12 is a time-consuming diagram of 100 times of solving a random scene algorithm according to an embodiment of the present invention.
Detailed Description
For the purposes of making the objects, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is apparent that the described embodiments are only some embodiments of the present application, but not all embodiments. The components of the embodiments of the present application, which are generally described and illustrated in the figures herein, may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present application, as provided in the accompanying drawings, is not intended to limit the scope of the application, as claimed, but is merely representative of selected embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present application without making any inventive effort, are intended to be within the scope of the present application.
As shown in fig. 1-12, the invention provides a method for optimizing a time optimal track sequence of an unmanned aerial vehicle constrained by a safety flight corridor, which comprises the following steps:
acquiring an initial state and a terminal state of the unmanned aerial vehicle, a state and control constraint boundary, threat position information, a path discrete point, a safe flight corridor boundary constraint, an initial trust zone radius and a convergence threshold;
obtaining barrier information in an actual environment, constructing a grid chart of the actual environment, and uniformly dividing the map into grids with equal proportion;
the unmanned aerial vehicle parameter information comprises the size of the unmanned aerial vehicle, the path constraint information comprises the starting point position and the target point position of the unmanned aerial vehicle, and the task environment information comprises the map size, the number of obstacles and the obstacle position.
The method for constructing the unmanned aerial vehicle time optimal track sequence convex optimization according to the real environment raster image specifically comprises the following steps:
in a real environment grid diagram, a shortest safety path from a starting point to an end point is obtained through an A-class path planning algorithm;
constructing convex polyhedron constraint of track points for threat areas and path points in a real environment raster image;
and constructing an unmanned aerial vehicle track optimization model constrained by a safety flight corridor, taking the flight time among track points as an optimization variable, and solving by using a sequence convex optimization method.
Optionally, a path planning algorithm based on an a-algorithm is adopted to search in the space of the convex optimization configuration of the unmanned aerial vehicle time optimal track sequence, so that a feasible unmanned aerial vehicle flight path is obtained.
The threat is divided into a cylindrical threat and a prismatic threat, a semi-plane set is obtained for the two threats respectively, and a planning convex domain of each path point is obtained. The cylindrical threat half-plane may be expressed as:
l(i,m)＝-Η(i,m)×J(i,m),i＝1,2,...,N；m＝1,2,...,M (3)
wherein H (i, m) is a half-plane unit normal vector generated by the ith path point and the mth cylindrical obstacle, J (i, m) is a point coordinate closest to the ith path point on the mth circular obstacle, l (i, m) is a distance constraint between the ith path point and the mth cylindrical obstacle, and the half-plane space of the cylindrical obstacle is described by using formula (4):
Η(i,m)×p i ≥J(i,m) (4)
the prismatic threat half-plane firstly obtains a prismatic threat horizontal center point, forms a plurality of rays according to the point and each vertex, judges which two ray intermediate areas the path point is positioned in, as shown in a formula (8), and uses the edge where the two vertexes are positioned as the half-plane, as shown in a formula (5).
Wherein,,b m，j the spatial coefficients of the half-planes formed by the jth vertex and the center point of the mth prismatic threat are respectively.
Wherein a_p m And b_p m The space coefficients of the half planes on the sides of the path points are respectively usedRepresenting a path point p i Threat p with prismatic form m Is a half space of (c).
In order to increase the calculation efficiency of the flight corridor, when a half plane is obtained, only the half plane of the obstacle in a certain range of the current path point is taken, namely, a square trust zone constraint with a fixed size is added to each path point, and the square trust zone constraint can be expressed as a formula (7), wherein eta is half of the side length of a square.
Combining cube trust domain constraint and half-plane constraint can construct a convex polygonal area, wherein the area is a constraint area of a track point in an optimization algorithm, and can be expressed as:
wherein B (p) i ) Representing a fixed size square trust zone constraint;representing a path point p i Threat to cylinder shape>Is a half space of (2); />Representing a path point p i Threat p with prismatic form m Is a half space of (c).
According to the above method, oneEach path point p i Can generate a convex domain SFC i Traversing the a-planned path points can obtain a convex domain set, which can be expressed as formula (9):
Γ＝{SFC 1 ∪SFC 2 ∪...∪SFC N } (9)
based on obstacle avoidance constraint of a safety corridor, after discretization and linearization of an unmanned plane motion equation, as shown in the formulas (10) and (11), taking the flight time between track points as an optimization variable, and sequentially solving by using a sequence convex optimization method to obtain a safe flight track with optimal time.
Wherein p= [ p ] x ,p y ,p z ]Is a three-dimensional space position, v= [ v ] x ,v y ,v z ]For speed, a= [ a ] x ,a y ,a z ]For acceleration, Δt is the time interval from the kth point to the kth+1th point.
After the dispersion and linearization, the unmanned aerial vehicle time optimal track planning modeling is as follows:
the invention provides an unmanned aerial vehicle time optimal track sequence convex optimization method based on safe flight corridor constraint, which comprises the following steps: acquiring unmanned plane parameter information, path constraint information, task environment information and algorithm parameter information; obtaining barrier information in an actual environment, constructing a grid chart of the actual environment, and uniformly dividing the map into grids with equal proportion; obtaining an unmanned aerial vehicle track optimization configuration space according to a real environment raster image; searching in an unmanned aerial vehicle track optimization configuration space by adopting a path planning algorithm based on A to obtain a feasible unmanned aerial vehicle navigation path; and obtaining a safe flight track with optimal time based on the SFC-SCP algorithm.
Example 1: the implementation steps of the unmanned aerial vehicle time optimal track sequence convex optimization method based on the safety flight corridor constraint provided by the embodiment are as follows:
step one: the problem information input comprises an unmanned aerial vehicle initial state and terminal state, a state and control constraint boundary, threat position information, a path discrete point, a safety flight corridor boundary constraint, an initial trust zone radius and a convergence threshold.
Step two: according to the parameter input of the specific example, an environment map aiming at the optimal track optimization problem of the unmanned aerial vehicle time is established. The environmental map scale is 100 x 20, the rate of each is 0.1m, and the map comprises 50 barriers, including 30 cylindrical barriers (diameter is between 3 and 6 m), 20 polygonal barriers, and the height of the barriers is infinitely high. In order to embody the planning efficiency of the algorithm in a complex environment, the barriers are randomly generated.
Step three: and obtaining a shortest path from the starting point to the end point by adopting an A-algorithm, and obtaining an initial reference track according to the following formula.
Wherein X is 0 Represents the reference track X in the subsequent iteration by using the obstacle avoidance path of the A-X algorithm as the reference track q The result of the previous iteration is solved; q represents the q-th iteration in sequence convex optimization, X q Representing the result of the solution for the q-th rank iteration.
Step four: solving a half-plane constraint set corresponding to each path point by combining surrounding obstacles, and distributing the half-plane constraint set to each track point, wherein the half-plane constraint set is shown in a formula (4); the set of half-plane constraints for all path points on the planned path generated by the a-algorithm constitutes a convex domain set, which can be represented as equation (10).
FIG. 2 is a schematic view of a cylindrical threat semi-plane depicting the semi-planar space of a cylindrical barrier using equation (5);
FIG. 3 is a schematic diagram showing a half plane of a prismatic threat, first, a horizontal center point of the prismatic threat is obtained, a plurality of rays are formed according to the point and each vertex, and a middle area of two rays in which a path point is located is determined, as shown in formula (6), and an edge in which the two vertices are located is used as a half plane, as shown in formula (7);
combining cube trust zone constraint and half-plane constraint, a convex polygonal area can be constructed, wherein the area is a constraint area of a track point in an optimization algorithm, as shown in fig. 4, a green area is cube boundary constraint, a red area is a constructed safety flight corridor and can be expressed as a formula (9), and the red point is a path point;
a path point p i Can generate a convex domain SFC i Traversing the a-planned path points results in a convex set of domains, which can be represented by equation (10), as shown in the red region of fig. 5.
Step five: and constructing a convex optimization model of the time optimal track optimization problem according to the reference track.
Step six: and solving by using a convex optimization algorithm to obtain the optimal control sequence of the current iteration.
According to the convex set attribute, any two points in one convex set are connected, all points on a straight line are in the convex set, namely, two path points are in one convex set, and the planned result meets the path constraint requirement. However, when two path points are respectively located in different convex sets, the two path points satisfy the constraint condition, but the points on the connecting line between the two points do not necessarily satisfy the convex set requirement, as shown by the black straight line in fig. 6. Therefore, the locus between two path points is described by using l difference points, and as shown by green points in the figure, the green diamond points are constrained at the intersection of two convex sets, so that the whole locus meets the obstacle avoidance constraint. In the figure, a red triangular point is a path point, a blue filled polygon is a safety flight corridor, a green point is a difference track point, and after the difference point is added, the red track can meet corridor constraint.
Step six: judging whether convergence conditions are met, and if so, outputting a current track planning result; if not, updating the reference track, and returning to the step four. Convergence conditions: all constraint conditions are met, and the track results of two successive iterations are identical within a certain error range, which can be expressed as:
wherein q represents the q-th iteration, X in the sequence convex optimization q The result of the solution of the q-th iteration is represented.
For the above specific example, the comparison between the path result obtained by the unmanned aerial vehicle time optimal track sequence convex optimization method based on the safety flight corridor constraint and other algorithms is shown in the paths of fig. 7-10.
The path results obtained using the a-algorithm or the SCP algorithm are shown in the paths of fig. 7-10. Although the path result obtained by the traditional method can avoid the obstacle and guide the robot to lead to reach the target position, the planned path needs to bypass or cling to the obstacle to realize obstacle avoidance, and the path planning method provided by the patent can better realize obstacle avoidance through convex optimization of the track sequence and can reach the target point in optimal time.
As can be seen from the track time and the solving time of the 100-time random scene algorithm in fig. 11 and 12, the comparison results of the three algorithms show that the problems of initial value sensitivity and result non-convergence when the SCP solves the track are relieved by front-end construction of the SFC. On the basis of 10.48% of flight time loss, the success rate is improved by 10%, the algorithm solving time is reduced by 56%, and the SFC-SCP method has better robustness and calculation efficiency.
According to the unmanned aerial vehicle time optimal track optimization simulation result and analysis, the unmanned aerial vehicle time optimal track sequence convex optimization method based on the safety flight corridor constraint can provide a feasible path meeting actual complex constraint for an unmanned aerial vehicle, and compared with a traditional method, the path result has the advantages of time optimization and strong obstacle avoidance capability, so that the unmanned aerial vehicle time optimal track sequence convex optimization method has engineering practicability and can achieve the expected aim of the invention.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In the description of the present invention, it should be understood that the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more such feature. In the description of the present invention, the meaning of "a plurality" is two or more, unless explicitly defined otherwise.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (10)
1. The sequence convex optimization method for the unmanned aerial vehicle time optimal track is characterized by comprising the following steps of:
obtaining barrier information of an unmanned aerial vehicle in an actual environment, constructing a real environment grid diagram, and carrying out path planning through an A-type algorithm to generate a path planning configuration space of the unmanned aerial vehicle;
and (3) adopting a track sequence convex optimization algorithm based on SFC-SCP, taking the flight time among track points as an optimization variable, and searching in the path planning configuration space to obtain a safe flight track with optimal time.
2. The sequence convex optimization method for the time optimal trajectory of the unmanned aerial vehicle according to claim 1, wherein:
responding to a construction process of a real environment raster image, establishing an environment map aiming at the optimal track optimization problem of the unmanned aerial vehicle time by acquiring an initial state and a terminal state of the unmanned aerial vehicle, a state and control constraint boundary, threat position information, a path discrete point, a safety flight corridor boundary constraint, an initial trust domain radius and a convergence threshold value, and randomly generating an obstacle in the environment map;
based on the environment map, the real environment grid map is constructed according to the obstacle information, and the map is uniformly divided into grids with equal proportion sizes.
3. The sequence convex optimization method for the time optimal trajectory of the unmanned aerial vehicle according to claim 2, wherein:
and responding to a path planning process through an A-algorithm, obtaining a shortest path from a starting point to an end point through the A-algorithm based on the environment map, and generating an initial reference track of the unmanned aerial vehicle. Wherein the initial reference trajectory is expressed as:
wherein X is 0 Represents the reference track X in the subsequent iteration by using the obstacle avoidance path of the A-X algorithm as the reference track q The result of the previous iteration is solved; q represents the q-th iteration in sequence convex optimization, X q Representing the solution result of the q-th sequence iteration;
and constructing the path planning configuration space according to the initial reference track and the obstacle information.
4. A sequence convex optimization method for a time optimal trajectory of a drone according to claim 3, wherein:
responding to the construction process of the path planning configuration space, and solving a half-plane constraint set corresponding to each path point based on the initial reference track and combining surrounding obstacles, and distributing the half-plane constraint set to each path point, wherein the half-plane constraint set is expressed as:
l(i,m)＝-Η(i,m)×J(i,m),i＝1,2,...,N；m＝1,2,...,M
wherein H (i, m) is a half-plane unit normal vector generated by the ith path point and the mth cylindrical obstacle, J (i, m) is a point coordinate nearest to the ith path point on the mth circular obstacle,and->Representing the position and the radius of the center point of the circle center of the mth cylindrical horizontal plane;
and constructing a convex domain set by a half-plane constraint set of all path points on a planning path generated by an A-algorithm, and constructing the path planning configuration space.
5. The sequence convex optimization method for the time optimal trajectory of the unmanned aerial vehicle according to claim 4, wherein:
in response to an acquisition process of an obstacle, adopting a cylindrical threat and a prismatic threat as the obstacle, and setting a height to be infinitely high, the obstacle is expressed as:
wherein I 2 Is a 2-norm number of the sample,and->Representing cylindrical threats and prismatic threats, p xy Representing the current position of the drone->And->Represents the position and radius of the center point of the circle center of the mth cylindrical horizontal plane,/->And b m,i Half space coefficient representing the ith edge of the mth diamond threat, +.>Representing the edge number of the mth diamond threat.
6. The sequence convex optimization method for the time optimal trajectory of the unmanned aerial vehicle according to claim 5, wherein:
responding to a construction process of a convex domain set, constructing a convex polygon area by combining cube trust domain constraint and half-plane constraint, and constructing the convex domain set according to the constraint area serving as a constraint area of a track point, wherein the constraint area is expressed as:
B(p i ) Representing a fixed size square trust zone constraint;representing a path point p i Threat to cylinder shape>Is a half space of (2); />Representing a path point p i Threat p with prismatic form m Is a half space of (2);
the set of convex domains is represented as:
r＝{SFC 1 ∪SFC 2 ∪...∪SFC N }。
7. the sequence convex optimization method for the time optimal trajectory of the unmanned aerial vehicle according to claim 6, wherein:
in response to the acquisition process of the safe flight trajectory, performing discretization processing on the unmanned aerial vehicle kinematic model by using a trapezoidal integral method, linearizing the right end item of the discretized unmanned aerial vehicle kinematic model according to a reference state trajectory to respectively obtain linear constraints of position and speed, and acquiring the safe flight trajectory by using the trajectory sequence convex optimization algorithm based on an SFC-SCP, wherein the linear constraints are expressed as:
wherein p= [ p ] x ,p y ,p z ]Is a three-dimensional space position, v= [ v ] x ,v y ,v z ]For speed, a= [ a ] x ,a y ,a z ]For acceleration, Δt is the time interval from the kth point to the kth+1th point.
8. The sequence convex optimization method for the time optimal trajectory of the unmanned aerial vehicle according to claim 7, wherein:
and responding to the construction of an SFC-SCP-based track sequence convex optimization algorithm, and relieving the problem of initial value sensitivity and result non-convergence when the SCP solves the track by constructing an SFC through the front end, so as to generate the SFC-SCP-based track sequence convex optimization algorithm.
9. The sequence convex optimization method for the time optimal trajectory of the unmanned aerial vehicle according to claim 8, wherein:
responding to a process of acquiring a safe flight track through a track sequence convex optimization algorithm based on SFC-SCP, judging whether the acquisition process meets a convergence condition, and if so, outputting a current track planning result; if the reference track is not satisfied, updating the reference track, and continuing to carry out iterative solution, wherein the convergence condition is as follows: all constraint conditions are met, and the track results of two successive iterations are identical within a certain error range, expressed as:
wherein q represents the q-th iteration, X in the sequence convex optimization q The result of the solution of the q-th iteration is represented.
10. A sequential convex optimization system for a time optimal trajectory of an unmanned aerial vehicle, comprising:
the data acquisition module is used for acquiring barrier information of the unmanned aerial vehicle in an actual environment;
the data processing module is used for constructing a real environment raster image according to the obstacle information, carrying out path planning through an A-algorithm and generating a path planning configuration space of the unmanned aerial vehicle;
and the flight path planning module is used for searching in the path planning configuration space by adopting a track sequence convex optimization algorithm based on the SFC-SCP and taking the flight time among the track points as an optimization variable to obtain a safe flight path with optimal time.
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