CN111399533B - Heterogeneous multi-unmanned aerial vehicle cooperative task allocation and path optimization method - Google Patents
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Abstract
The invention provides a heterogeneous multi-unmanned aerial vehicle cooperative task allocation and path optimization method, and particularly relates to the technical field of unmanned aerial vehicles, wherein the method comprises the following steps: the method comprises the steps of firstly determining relevant information of an unmanned aerial vehicle, relevant information of unmanned aerial vehicle sites and relevant information of a target object which needs to be obtained by the unmanned aerial vehicle in a target area, then calculating Euclidean distances from the unmanned aerial vehicle sites to all the target objects and Euclidean distances among all the target objects, then establishing a heterogeneous unmanned aerial vehicle variable benefit task allocation problem HU-TAP-VP model, obtaining an initial task allocation scheme set for executing a cooperative task, and finally optimizing by using a hybrid genetic simulation annealing algorithm HGSA introduced into an adaptive switching mechanism, so that a safe flyable path of each unmanned aerial vehicle is obtained. Based on the method provided by the embodiment of the invention, a high-quality task allocation scheme can be quickly obtained in a complex dangerous scene, and the access path of each unmanned aerial vehicle to the target object is optimized.
Description
Technical Field
The invention relates to the technical field of unmanned aerial vehicles, in particular to a heterogeneous multi-unmanned aerial vehicle cooperative task allocation and path optimization method.
Background
At present, unmanned aerial vehicles have been widely used in complex dangerous scenes such as military target reconnaissance and post-earthquake reconnaissance, because the time sensitivity of these tasks is higher, and have certain requirement to the completion quality of task moreover, so single unmanned aerial vehicle often can't accomplish, needs many heterogeneous unmanned aerial vehicles to accomplish above-mentioned tasks in coordination. The unmanned aerial vehicle can carry different types of sensors to acquire images of a target object, such as: the visible light radar and the synthetic aperture radar are used for photographing military targets or buildings in earthquake-stricken areas. The image acquired by different types of sensors is synthesized, so that the reliability of the acquired information can be greatly improved, and the information acquired by the unmanned aerial vehicle is only useful when the reliability of the information is higher than the minimum reliability required by a task.
In the existing scheme, targets are allocated to multiple drones under the condition of considering task time constraints, but dynamics constraints of the drones are not considered, so that a flyable path is not planned for each drone. Therefore, the path of each drone cannot be optimized, and useful information acquired by all drones cannot be maximized.
Disclosure of Invention
Technical problem to be solved
Aiming at the defects of the prior art, the invention provides a heterogeneous multi-unmanned aerial vehicle cooperative task allocation and path optimization method which can optimize the access path of each unmanned aerial vehicle to a target object under a complex dangerous scene so as to maximize the useful information acquired by the unmanned aerial vehicles.
(II) technical scheme
In order to achieve the purpose, the invention is realized by the following technical scheme:
the invention provides a heterogeneous multi-unmanned aerial vehicle cooperative task allocation and path optimization method, which comprises the following steps:
determining the coordinates and the importance degree of a target object in a target area, which needs to be acquired by the unmanned aerial vehicle;
determining the task execution time length for executing and accessing the target object;
acquiring the number of heterogeneous multi-unmanned aerial vehicles accessing the target object and relevant parameters of each unmanned aerial vehicle; the relevant parameters include: number, type of sensor carried, speed of flight and/or minimum turning radius;
determining the information fusion rate of various sensors for executing the task and the minimum reliability required by the task;
acquiring site coordinates of a site of the unmanned aerial vehicle;
calculating Euclidean distances from the station of the unmanned aerial vehicle to all the target objects and the Euclidean distances between all the target objects, storing the Euclidean distances by using a two-dimensional matrix, and recording the Euclidean distances as an Euclidean distance matrix;
calculating the flight time of each unmanned aerial vehicle from the station to each target object and the flight time of each unmanned aerial vehicle among all the target objects according to the flight speed, storing by using a three-dimensional matrix, and recording as a flight time matrix;
establishing a HU-TAP-VP model of the heterogeneous unmanned aerial vehicle variable-yield task allocation problem;
acquiring an initial task allocation scheme set of the heterogeneous multi-unmanned aerial vehicle for executing the cooperative task according to the coordinates of each target object, the importance degree of each target object and the task execution duration by adopting the HU-TAP-VP model;
optimizing the initial task allocation scheme set by adopting a hybrid genetic simulated annealing algorithm HGSA (hybrid genetic simulated annealing) introduced with a self-adaptive switching mechanism to obtain an optimal task allocation scheme of each unmanned aerial vehicle for accessing any one or more target objects;
and performing path optimization according to the access sequence of each unmanned aerial vehicle to the target object and the minimum turning radius of the unmanned aerial vehicle in the optimal task allocation scheme to obtain the safe flyable path of each unmanned aerial vehicle.
Optionally, the euclidean distances from the drone station to all the targets are calculated by the following formula:
wherein d is 0i Representing unmanned aerial vehicle sites to targetsThe Euclidean distance of the object i; x is a radical of a fluorine atom 0 An abscissa representing the drone site; x is a radical of a fluorine atom i Represents the abscissa of the object i; y is 0 Representing the unmanned plane station ordinate; y is i Represents the ordinate of the object i;
the Euclidean distances between all the targets are calculated by the following formula:
wherein d is ij Denotes the Euclidean distance, x, from the object i to the object j i Represents the abscissa of the object i; x is a radical of a fluorine atom j Represents the abscissa of the object j; y is i Represents the ordinate of the object i; y is j Represents the ordinate of the object j;
in the euclidean distance matrix, the first row is the distance from the unmanned aerial vehicle to the target object, the second row to the (N + 1) th row is the distance from the target object to the target object, and the columns of the euclidean distance matrix are the distances from the target object to the unmanned aerial vehicle station and from the target object to the target object.
Optionally, the objective function of the HU-TAP-VP model is expressed by equation (3):
wherein i is the number of the target object, w i Is the importance of the object i, y i And N is the number of the objects, and Max is a maximum function, wherein the decision variables are used for representing the credibility of the information in the object i obtained by the unmanned aerial vehicle sensor.
Optionally, the constraints of the HU-TAP-VP model are expressed by equations (4) to (8):
wherein, the first and the second end of the pipe are connected with each other,
a decision variable from a station to a target object i for the kth unmanned aerial vehicle, wherein 0 represents the station of the unmanned aerial vehicle, and U is a set of unmanned aerial vehicles;
for the decision variables of the k-th drone from object h to object i,
a decision variable from a target i to a target j for the kth unmanned aerial vehicle, wherein T is a set of the targets; k is the number of unmanned aerial vehicles;
time of flight, T, from object i to object j for the kth drone task Executing the task for a duration;
equation (6) is a binary decision variable
Is taken from the value of
When 1, it means that the k-th drone selects a path from the object i to the object j, and when 1, it is determined that the k-th drone is not on the path from the object i to the object j
A value of 0 indicates that the kth drone has not selected this path;
equation (4) is a binary decision variable y i When y takes a value of i When the value is 1, the credibility of the information in the target object i acquired by the unmanned aerial vehicle sensor is greater than or equal to the lowest credibility required by the task, and when y is i When the value is 0, the information credibility acquired by the sensor does not reach the lowest credibility required by the task, namely that the unmanned aerial vehicle does not access the target object i; gamma is the information fusion rate of various types of sensors executing the task, and f is the minimum reliability required by the task.
Optionally, the initial task allocation scheme set includes a plurality of task allocation schemes, and the task allocation schemes include a drone number and a task execution sequence of each of the heterogeneous multiple drones;
wherein the task execution sequence comprises: starting point of unmanned aerial vehicle, the target object number that passes through in proper order.
Optionally, the optimizing the initial task allocation scheme set by using a hybrid genetic simulated annealing algorithm HGSA that introduces an adaptive switching mechanism to obtain an optimal task allocation scheme for each drone that accesses any one or more of the targets includes:
performing iterative optimization on the initial task scheme set by adopting a genetic algorithm to obtain a task allocation scheme which is optimal compared with the initial path planning scheme;
when the optimization capability of the genetic algorithm presents a descending trend, introducing a self-adaptive switching mechanism, taking the optimal solution obtained by the genetic algorithm as the initial solution of the simulated annealing algorithm, and continuously optimizing by adopting the simulated annealing algorithm;
disturbing the initial path planning scheme by adopting a simulated annealing algorithm to obtain a plurality of adjacent domain schemes of the initial path planning scheme;
and optimizing the multiple field schemes through multiple rounds of iteration to obtain an optimal task allocation scheme of each unmanned aerial vehicle for accessing any one or more targets.
Optionally, performing path optimization according to the access sequence of each drone to the target object and the minimum turning radius of the drone in the optimal task allocation scheme to obtain a safe flyable path of each drone, including:
determining a course angle of each unmanned aerial vehicle when each unmanned aerial vehicle accesses each target object according to the access sequence of each unmanned aerial vehicle to the target object in the optimal task allocation scheme;
and optimizing the path of the unmanned aerial vehicle according to the minimum turning radius of the unmanned aerial vehicle to obtain the safe flyable path of each unmanned aerial vehicle.
(III) advantageous effects
The invention provides a heterogeneous multi-unmanned aerial vehicle cooperative task allocation and path optimization method. Compared with the prior art, the method has the following beneficial effects:
1. under a complex dangerous scene, distributing proper targets for a plurality of unmanned aerial vehicles which cooperatively execute tasks, determining the access sequence of each unmanned aerial vehicle to the distributed targets, and finally optimizing a flyable path according to the access sequence of each unmanned aerial vehicle, so that all unmanned aerial vehicles obtain the most useful information within the given task time;
2. by the aid of the optimization method of loop iteration, task allocation and path optimization of multiple unmanned aerial vehicles are achieved for complex tasks needing to be completed by multiple heterogeneous unmanned aerial vehicles in a coordinated mode, task allocation time is shortened, and safe flyable paths are planned for each unmanned aerial vehicle.
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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 used in the embodiments or the prior art descriptions will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a schematic flow chart of a heterogeneous multi-drone cooperative task allocation and path optimization method according to an embodiment of the present application;
fig. 2 is a schematic diagram of an overall architecture of a cooperative task oriented to heterogeneous multiple drones according to an embodiment of the present application;
fig. 3 is a schematic diagram of a heterogeneous multi-drone cooperative task scenario according to an embodiment of the present application;
fig. 4 is a schematic diagram of an optimal task allocation scheme for multiple drones according to an embodiment of the present application;
fig. 5 is a schematic diagram of the flyable path of the unmanned aerial vehicle optimized on the basis of the optimal task allocation scheme shown in fig. 4.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention are clearly and completely described, and it is obvious that the described embodiments are a part of the embodiments of the present invention, but not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The embodiment of the application provides a heterogeneous multi-unmanned aerial vehicle cooperative task allocation and path optimization method.
In order to solve the technical problems, the general idea of the embodiment of the present application is as follows:
the method comprises the steps of determining relevant information of an unmanned aerial vehicle in a target area, relevant information of unmanned aerial vehicle sites and relevant information of a target object which needs to be obtained by the unmanned aerial vehicle, calculating Euclidean distances from the unmanned aerial vehicle sites to all the target objects and the Euclidean distances between all the target objects, establishing a heterogeneous unmanned aerial vehicle variable benefit task allocation problem HU-TAP-VP (heterogeneous UAV task allocation with variable task) model, obtaining an initial task allocation scheme set for executing a cooperative task, and finally introducing a hybrid genetic simulation annealing algorithm HGSA (hybrid genetic simulation annealing) of an adaptive switching mechanism for optimization, so that a safe flyable path of each unmanned aerial vehicle is obtained.
In order to better understand the technical solution, the technical solution will be described in detail with reference to the drawings and the specific embodiments.
Fig. 1 is a schematic flow chart of a method for distributing cooperative tasks and optimizing paths of heterogeneous multi-unmanned aerial vehicles according to an embodiment of the present application, and as can be seen from fig. 1, the method for distributing cooperative tasks and optimizing paths of heterogeneous multi-unmanned aerial vehicles according to the embodiment may include:
step S101, determining the coordinates and the importance degree of a target object in a target area, wherein the target object needs to be acquired by an unmanned aerial vehicle;
step S102, determining the task execution time length for executing the access target object;
step S103, acquiring the number of heterogeneous multi-unmanned aerial vehicles accessing a target object and relevant parameters of each unmanned aerial vehicle; the relevant parameters include: number, type of sensor carried, speed of flight and/or minimum turning radius;
step S104, determining the information fusion rate of various sensors for executing the task and the minimum reliability required by the task;
step S105, acquiring station coordinates of stations of the unmanned aerial vehicle;
step S106, calculating Euclidean distances from the station of the unmanned aerial vehicle to all the targets and Euclidean distances between all the targets, storing the Euclidean distances by using a two-dimensional matrix, and recording the Euclidean distances as an Euclidean distance matrix;
step S107, calculating the flight time of each unmanned aerial vehicle from the station to each target object and the flight time of each unmanned aerial vehicle among all the target objects according to the flight speed, storing by using a three-dimensional matrix, and recording as a flight time matrix;
step S108, establishing a heterogeneous unmanned aerial vehicle variable benefit task allocation problem HU-TAP-VP model;
step S109, acquiring an initial task allocation scheme set of the heterogeneous multi-unmanned aerial vehicle for executing the cooperative task according to the coordinate and the importance degree of each target object and the task execution duration by adopting an HU-TAP-VP model;
step S110, optimizing an initial task allocation scheme set by adopting a hybrid genetic simulated annealing algorithm HGSA (hybrid genetic simulated annealing) introducing a self-adaptive switching mechanism to obtain an optimal task allocation scheme of each unmanned aerial vehicle for accessing any one or more targets;
and S111, performing path optimization according to the access sequence of each unmanned aerial vehicle to the target object and the minimum turning radius of the unmanned aerial vehicle in the optimal task allocation scheme to obtain the safe flyable path of each unmanned aerial vehicle.
The embodiment of the invention provides a method for distributing cooperative tasks and optimizing paths of heterogeneous multi-unmanned aerial vehicles. Based on the method provided by the embodiment of the invention, a high-quality task allocation scheme can be quickly obtained in a complex dangerous scene, and the access path of each unmanned aerial vehicle to the target object is optimized.
Fig. 2 is a schematic diagram of a heterogeneous multi-drone cooperative task architecture according to an embodiment of the present application, and as can be seen from fig. 2, a command center may obtain multiple targets (for example, military targets) in a task area that need heterogeneous multi-drone cooperative reconnaissance, determine heterogeneous multi-drone cooperative tasks to determine useful information of each target, and generate a multi-drone task allocation and path planning scheme by calling an intelligent planning module, further, the multi-drone task allocation and path planning scheme may be issued to a multi-drone management system, so that each drone performs a scheme execution, and the acquired information is returned to the multi-drone management system, and then returned to the command center by the multi-drone management system. The method for distributing the heterogeneous multi-unmanned aerial vehicle cooperative tasks and optimizing the paths for executing the steps S101-111 can be executed by an intelligent planning module or a command center.
The following describes the steps S101 to 111 in detail.
Referring to step S101, first, coordinates of a target object and a degree of importance of the target object in the target area, which require the unmanned aerial vehicle to acquire information, are determined. In the case of performing military object reconnaissance, the main purpose of multiple drones performing military object (i.e., the above-mentioned objects) reconnaissance is to obtain information about the objects as accurately as possible during the designated mission time, so as to take follow-up action with pertinence, so that the drones do not need to access all the objects in the target area, but selectively access some of the objects, such as: important military targets (such as missiles and radar sites) should be the target of priority. Therefore, the importance levels of different objects in the object region are different from each other, and in this embodiment, the importance levels of the objects can be expressed by weights, and the larger the weight value is, the more important the object is. Fig. 3 shows a schematic diagram of a multi-unmanned aerial vehicle collaborative task scene according to an embodiment of the present invention, where 1,2, 3, and 4 in fig. 3 respectively represent four collaborative tasks, and the lighter the color of the task (in this embodiment, the starting point and the ending point of the unmanned aerial vehicle) is, the smaller the weight is, that is, the smaller the importance degree is, and the weights of the target objects shown in fig. 3 are 4, 3, 2, and 1 in order from large to small.
Fig. 4 is an optimal task allocation scheme. Fig. 5 is a flyable path of the unmanned aerial vehicle obtained by optimization based on the optimal task allocation scheme shown in fig. 4, a solid line and a dotted line in fig. 4 respectively represent the task allocation schemes of two heterogeneous unmanned aerial vehicles, and a solid line and a dotted line in fig. 5 respectively represent the flyable paths of the two heterogeneous unmanned aerial vehicles.
In this embodiment, each target in the target area may be set according to different attributes of the target, and the weight of the target may be w i The object coordinates of each object may be obtained by GPS or other methods, but the present invention is not limited thereto. Of course, the target area in practical application can also beThe invention is not limited by the areas with other attributes and needing the unmanned aerial vehicle to perform reconnaissance.
Referring to the above step S102, the execution duration of the task of executing the access target object is determined.
In practical application, the mission of reconnaissance on military targets needs to be completed cooperatively by unmanned aerial vehicles carrying different types of sensors, such as: some unmanned aerial vehicles carry on the visible light radar, and some unmanned aerial vehicles carry on infrared radar, synthesize the image that different unmanned aerial vehicles acquireed and can effectively promote the credibility of information, and then promote the completion quality of cooperative task. The task execution duration in this embodiment is specific to all the drones, that is, the completion quality of the task is determined by how many targets all the drones complete the cooperative reconnaissance task within a specified time (without considering the return duration). The duration of the task execution is long, the duration of the unmanned aerial vehicle can be not considered, and in practical application, the duration of the unmanned aerial vehicle is generally far longer than the duration of the task execution, so that the time for the unmanned aerial vehicle to return to a station is not considered during task allocation and path planning.
Referring to step S103, the number of heterogeneous multiple drones accessing the target object and the relevant parameters of each drone are obtained.
With the military target reconnaissance area mentioned in the above embodiments, the unmanned aerial vehicle can rapidly enter a reconnaissance disaster area and rapidly capture image and video information through the mounted sensor, so that the unmanned aerial vehicle has been widely applied to military reconnaissance operations. In practical applications, unmanned aerial vehicles performing cooperative tasks may be heterogeneous, and the sensors mounted on the unmanned aerial vehicles may have different flight speeds and turning radii. Therefore, in the above step S103, not only the number of heterogeneous multiple drones, but also relevant parameters of each drone, which may include drone number, type of sensor mounted, flight speed, and/or minimum turning radius, need to be acquired. Through the acquisition of relevant parameters of all unmanned aerial vehicles in the heterogeneous unmanned aerial vehicle, the unmanned aerial vehicle can be set individually and the route can be optimized in the subsequent route optimization of the unmanned aerial vehicle, so that the use efficiency of each unmanned aerial vehicle is improved. Wherein, unmanned aerial vehicle's serial number is the only serial number that can carry out the one-to-one with unmanned aerial vehicle.
Referring to step S104 described above, the information fusion rate of the various types of sensors performing the task, and the minimum confidence level required for the task are determined.
After the unmanned aerial vehicle enters a target area, the image and video data are rapidly captured through the mounted sensor. When the unmanned aerial vehicle acquires information, the trust degree of the system to the sensor measurement at a certain moment is called as the trust degree of the sensor at the moment.
The information fusion rate of various sensors for executing the task and the lowest reliability required by the task are determined, comparison can be carried out, and if the reliability of the information acquired by the sensors is lower than the lowest reliability required by the task, the reliability of the information is improved by synthesizing images acquired by different types of sensors based on a quantitative model so as to be matched with the reliability required by the task.
Referring to step S105 described above, the station coordinates of the station of the drone are acquired. In the embodiment of the invention, the actual earthquake post-disaster investigation of the station of the unmanned aerial vehicle is equivalent to a command center. In the embodiment of the invention, the station of the unmanned aerial vehicle can be simultaneously used as a starting point for executing the access task for the unmanned aerial vehicle to the target object. And the station coordinates can be used as the starting point coordinates and the end point coordinates of the unmanned aerial vehicle at the same time. Specifically, when the site coordinates are obtained, the site coordinates can be obtained by using a GPS or other methods, which is not limited in the present invention.
Referring to the step S106, euclidean distances from the station of the unmanned aerial vehicle to all the targets and between all the targets are calculated, and are stored by using a two-dimensional matrix and are recorded as a euclidean distance matrix.
Euclidean distance is a commonly used definition of distance, referring to the true distance between two points in m-dimensional space, or the natural length of a vector (i.e., the distance of the point from the origin). The euclidean distance in two and three dimensions is the actual distance between two points. In mathematics, a distance matrix is a matrix (i.e., a two-dimensional array) that contains the distances between a set of points. Thus, given N points in euclidean space, the distance matrix is an N × N symmetric matrix with non-negative real numbers as elements.
The Euclidean distance from the station of the unmanned aerial vehicle to all the targets is calculated by the following formula:
wherein x is 0 An abscissa representing the drone site; x is the number of i Represents the abscissa of the object i; y is 0 Representing the unmanned plane station ordinate; y is i Represents the ordinate of the object i;
the euclidean distances between all targets are calculated by the following formula:
wherein x is i An abscissa representing an object i; x is the number of j Represents the abscissa of the object j; y is i Represents the ordinate of the object i; y is j Representing the ordinate of the object j.
In the euclidean distance matrix, the first row is the distance from the drone to the target, the second row to the N +1 th row is the distance from the target to the target, and the columns of the euclidean distance matrix are the distances from the target to the drone station and from the target to the target, as shown in table 1.
TABLE 1
In table 1, 0 denotes the drone station; t is a unit of 1 Denotes the object number 1, T 2 Denotes the object 2 number, T N Represents the target N number; d is a radical of 0T1 Representing slave dronesEuclidean distance of station to target 1, d 0T2 Representing the Euclidean distance, d, from the drone station to the target 2 0TN Representing the euclidean distance from the drone station to the target N; d T1T2 Denotes the Euclidean distance, d, from the object 1 to the object 2 T1TN Represents the euclidean distance from the target 1 to the target N; inf represents infinity (this patent does not allow the drone to stay at the same target, so sets the distance from itself to infinity).
Referring to the step S107, the flight time of each unmanned aerial vehicle from the station to each target object and the flight time of each unmanned aerial vehicle between all target objects are calculated according to the flight speed, and are stored by using the three-dimensional matrix and are recorded as the flight time matrix.
Calculating the flight time of each unmanned aerial vehicle in the flight process based on different flight speeds of different unmanned aerial vehicles, wherein the flight time from a station to each target object and the flight time between the target objects are included, and the flight time between different targets is stored by using a three-dimensional matrix which can be recorded as a flight time matrix, wherein the 1 st line of the flight time matrix is the serial number of the station of the unmanned aerial vehicle, and the 2 nd line is the serial number of the (N + 1) th line of the flight time matrix; the columns of the matrix are the numbers of the targets; the pages of the matrix are the numbers of the unmanned aerial vehicles.
Referring to the step S108, a heterogeneous unmanned aerial vehicle variable benefit task allocation problem HU-TAP-VP model is established.
In this embodiment, the objective function of the HU-TAP-VP model is expressed by equation (3):
wherein i is the number of the target object, w i Is the importance of the object i, y i And N is the number of the objects, and Max is a maximum function, wherein the decision variables are used for representing the information credibility of the unmanned aerial vehicle sensor in the object i.
Further, the constraints of the HU-TAP-VP model are expressed by equations (4) to (8):
wherein the content of the first and second substances,
for decision variables from the station to the target object i of the kth unmanned aerial vehicle, 0 represents the station of the unmanned aerial vehicle, and U is a set of the unmanned aerial vehicles;
for the decision variable of the k-th drone from target h to target i,
a decision variable from a target i to a target j for the kth unmanned aerial vehicle, wherein T is a set of targets; k is the number of unmanned aerial vehicles;
for the flight time, T, from object i to object j of the kth unmanned aerial vehicle task Executing the task for a duration;
equation (6) is a binary decision variable
Is taken from the value of
When 1, it means that the k-th drone selects a path from the object i to the object j, and when 1, it indicates that
A value of 0 indicates that the kth drone has not selected this path;
equation (4) is a binary decision variable y i When y takes a value of i When the value is 1, the credibility of the information in the target object i acquired by the unmanned aerial vehicle sensor is greater than or equal to the minimum credibility required by the task, and when y is greater than or equal to the minimum credibility required by the task i When the value is 0, the information credibility acquired by the sensor does not reach the lowest credibility required by the task, which is equivalent to that the unmanned aerial vehicle does not access the target object i. Gamma is the information fusion rate of various types of sensors executing the task, and f is the minimum reliability required by the task.
Referring to step S109, after the HU-TAP-VP model is established, the HU-TAP-VP model may be used to obtain an initial task allocation scheme set for the heterogeneous multi-drone to execute the cooperative task according to the coordinates of each target object, the importance degree of each target object, and the task execution duration.
Optionally, the initial task allocation scheme set mentioned in this embodiment includes a plurality of task allocation schemes; each task allocation scheme can comprise the unmanned aerial vehicle number and the task execution sequence of each heterogeneous multi-unmanned aerial vehicle; wherein the task execution sequence comprises: starting point of unmanned aerial vehicle, the target object number that passes through in proper order. It should be noted that the task execution path of the unmanned aerial vehicle provided by this embodiment does not need to return to the end point, and therefore, the task execution sequence does not include the end point.
Referring to the step S110, the hybrid genetic simulated annealing algorithm HGSA introduced with the adaptive switching mechanism is used to optimize the initial task allocation plan set to obtain an optimal task allocation plan for each frame of the unmanned aerial vehicle to access any one or more of the targets.
Hybrid Genetic simulation Annealing Algorithm HGSA (hybrid Genetic modeling and Annealing) is a new process-based hybrid sequencing Genetic Algorithm which is provided by using the advantages of Genetic Algorithm (GA) and Simulated Annealing Algorithm (SA). The HGSA algorithm can obtain a feasible solution with high quality of the HU-TAP-VP model in a short time, and can meet the actual requirement of rapidly planning the task path of each unmanned aerial vehicle.
Specifically, the step S110 may further include: firstly, iterative optimization is carried out on an initial task scheme set by adopting a genetic algorithm, and a task allocation scheme which is optimal compared with the initial path planning scheme is obtained; secondly, when the optimization capability of the genetic algorithm presents a descending trend, introducing a self-adaptive switching mechanism, taking the optimal solution obtained by the genetic algorithm as the initial solution of the simulated annealing algorithm, and continuously optimizing by adopting the simulated annealing algorithm; thirdly, disturbing the initial path planning scheme by adopting a simulated annealing algorithm to obtain a plurality of adjacent domain schemes of the initial path planning scheme; and finally, optimizing a plurality of field schemes through a plurality of rounds of iteration to obtain an optimal task allocation scheme of each unmanned aerial vehicle for accessing any one or more targets.
In the HGSA algorithm, for the two-stage GA and SA hybrid algorithm, the switching point between the two algorithms is crucial, which is formally expressed as the number of iterations. If the switch is too early, the quality of the solution is degraded, and if the switch is too late, the efficiency of the algorithm is affected. In the adaptive switching mechanism, regarding the selection of the switching point, the optimal solution is output to the SA after GA iteration is performed for a fixed number of times, where the number of iterations mostly comes from experience and reference of the user, and the present invention is not limited.
And finally, after the optimal task allocation scheme is obtained, executing step S111, and performing path optimization according to the access sequence of each unmanned aerial vehicle to the target object and the minimum turning radius of the unmanned aerial vehicle in the optimal task allocation scheme to obtain the safe flyable path of each unmanned aerial vehicle. Optionally, a course angle of each unmanned aerial vehicle when accessing each target object may be determined according to an access sequence of each unmanned aerial vehicle to the target object in the optimal task allocation scheme; and optimizing the path of the unmanned aerial vehicle according to the minimum turning radius of the unmanned aerial vehicle to obtain the safe flyable path of each unmanned aerial vehicle.
In this embodiment, the heading angle of the unmanned aerial vehicle when accessing each target object may be preferentially one or more of 0 °,45 °,90 °,135 °,180 °,225 °,270 °, and 315 °, and of course, the practical application may also include heading angles of other angles, which is not limited in the present invention.
In summary, compared with the prior art, the method has the following beneficial effects:
1. under a complex dangerous scene, distributing proper targets for a plurality of unmanned aerial vehicles which cooperatively execute tasks, determining the access sequence of each unmanned aerial vehicle to the distributed targets, and finally optimizing a flyable path according to the access sequence of each unmanned aerial vehicle, so that all unmanned aerial vehicles obtain the most useful information within the given task time;
2. the information reliability can be improved by combining images acquired by different types of sensors, and a quantitative model is provided;
3. a high-quality task allocation scheme can be quickly obtained by introducing a hybrid genetic simulated annealing algorithm of a self-adaptive switching mechanism, and a safe flyable path of each unmanned aerial vehicle is planned on the basis.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (5)
1. A heterogeneous multi-unmanned aerial vehicle cooperative task allocation and path optimization method is characterized by comprising the following steps:
determining the coordinates and the importance degree of a target object in a target area, which needs to be acquired by the unmanned aerial vehicle;
determining the task execution time length for accessing the target object;
acquiring the number of heterogeneous multi-unmanned aerial vehicles accessing the target object and relevant parameters of each unmanned aerial vehicle; the relevant parameters include: number, type of sensor carried, flight speed and/or minimum turning radius;
determining the information fusion rate of various sensors for executing the task and the minimum reliability required by the task;
acquiring site coordinates of a site of the unmanned aerial vehicle;
calculating Euclidean distances from the station of the unmanned aerial vehicle to all the target objects and Euclidean distances between all the target objects, storing the Euclidean distances by using a two-dimensional matrix, and recording the Euclidean distances as an Euclidean distance matrix;
calculating the flight time of each unmanned aerial vehicle from the station to each target object and the flight time of each unmanned aerial vehicle among all the target objects according to the flight speed, storing by using a three-dimensional matrix, and recording as a flight time matrix;
establishing a HU-TAP-VP model of the variable-income task allocation problem of the heterogeneous unmanned aerial vehicle;
acquiring an initial task allocation scheme set of the heterogeneous multi-unmanned aerial vehicle for executing the cooperative task according to the coordinate of each target object, the importance degree of each target object and the task execution time by using the HU-TAP-VP model;
optimizing the initial task allocation scheme set by adopting a hybrid genetic simulated annealing algorithm HGSA (hybrid genetic simulated annealing) introducing a self-adaptive switching mechanism to obtain an optimal task allocation scheme of each unmanned aerial vehicle for accessing any one or more targets;
performing path optimization according to the access sequence of each unmanned aerial vehicle to the target object and the minimum turning radius of the unmanned aerial vehicle in the optimal task allocation scheme to obtain a safe flyable path of each unmanned aerial vehicle;
the constraints of the HU-TAP-VP model are expressed by equations (4) to (8):
wherein, the first and the second end of the pipe are connected with each other,
for decision variables from the station to the target object i of the kth unmanned aerial vehicle, 0 represents the station of the unmanned aerial vehicle, and U is a set of the unmanned aerial vehicles;
for the decision variable of the k-th drone from target h to target i,
a decision variable from a target i to a target j for the kth unmanned aerial vehicle, wherein T is a set of the targets; k is the number of unmanned aerial vehicles;
time of flight, T, from object i to object j for the kth drone task Executing the task for a duration;
equation (6) is a binary decision variable
Is taken from the value of
When 1, it means that the k-th drone selects a path from the object i to the object j, and when 1, it indicates that
When the number is 0, the k-th unmanned aerial vehicle does not select the path;
equation (4) is a binary decision variable y i When y takes a value of i When the value is 1, the credibility of the information in the target object i acquired by the unmanned aerial vehicle sensor is greater than or equal to the lowest credibility required by the task, and when y is i When the value is 0, the information credibility obtained by the sensor does not reach the lowest credibility required by the task, and the method is equivalent to the unmanned aerial vehicleNo access to the object i; gamma is the information fusion rate of various sensors executing the task, and f is the minimum reliability required by the task;
the optimizing the initial task allocation scheme set by adopting a hybrid genetic simulated annealing algorithm HGSA introducing a self-adaptive switching mechanism to obtain an optimal task allocation scheme of each unmanned aerial vehicle for accessing any one or more targets comprises the following steps:
performing iterative optimization on the initial task allocation scheme set by adopting a genetic algorithm to obtain a task allocation scheme which is optimal compared with the initial path planning scheme;
when the optimization capability of the genetic algorithm presents a descending trend, introducing a self-adaptive switching mechanism, taking the optimal solution obtained by the genetic algorithm as the initial solution of the simulated annealing algorithm, and continuously optimizing by adopting the simulated annealing algorithm;
disturbing the initial path planning scheme by adopting a simulated annealing algorithm to obtain a plurality of neighborhood schemes of the initial path planning scheme;
and optimizing the plurality of neighborhood schemes through multiple iterations to obtain an optimal task allocation scheme of each unmanned aerial vehicle for accessing any one or more targets.
2. The method of claim 1, wherein the euclidean distances of the drone station to all of the targets are calculated by:
wherein d is 0k Representing the Euclidean distance from the unmanned aerial vehicle station to a target k; x is the number of 0 An abscissa representing the unmanned aerial vehicle station; x is a radical of a fluorine atom k Represents the abscissa of the object k; y is 0 Representing the unmanned plane station ordinate; y is k Represents the ordinate of the target k;
the Euclidean distances between all the targets are calculated by the following formula:
wherein, d kj Representing the Euclidean distance, x, of target k to target j k Represents the abscissa of the object k; x is the number of j Represents the abscissa of the object j; y is k Represents the ordinate of the object k; y is j Represents the ordinate of the object j;
in the Euclidean distance matrix, the first row is the distance from the unmanned aerial vehicle to the target object, the second row to the (N + 1) th row is the distance from the target object to the target object, and the columns of the Euclidean distance matrix are the distances from the target object to the unmanned aerial vehicle station and from the target object to the target.
3. The method of claim 1, wherein the objective function of the HU-TAP-VP model is represented by equation (3):
wherein i is the number of the target object, w i Is the importance of the object i, y i And N is the number of the objects, and Max is a maximum function, wherein the decision variables are used for representing the information credibility of the unmanned aerial vehicle sensor in the object i.
4. The method of claim 1, wherein the initial set of task allocation plans includes a plurality of task allocation plans, the task allocation plans including a drone number, a task execution order, for each of the heterogeneous multiple drones;
wherein the task execution sequence comprises: starting point of unmanned aerial vehicle, the target object number that passes through in proper order.
5. The method of claim 1, wherein performing path optimization according to the sequence of the target object visited by each drone and the minimum turning radius of the drone in the optimal task allocation scheme to obtain the safe flyable path of each drone comprises:
determining a course angle of each unmanned aerial vehicle when each unmanned aerial vehicle accesses each target object according to the access sequence of each unmanned aerial vehicle to the target object in the optimal task allocation scheme;
and optimizing the path of the unmanned aerial vehicle according to the minimum turning radius of the unmanned aerial vehicle to obtain the safe flyable path of each unmanned aerial vehicle.
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