CN116954256A - Unmanned aerial vehicle distributed task allocation method considering reachable domain constraint - Google Patents
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Abstract
The invention discloses an unmanned aerial vehicle distributed task allocation method considering reachability domain constraint, which comprises the following steps: establishing a multi-unmanned aerial vehicle task allocation model, a missile mass center motion model, a guided trajectory kinematics model and a three-dimensional proportional guided control rate model; initializing unmanned aerial vehicle parameter information and task environment information; according to the unmanned aerial vehicle parameter information, calculating the reachable domain of each unmanned aerial vehicle at the current moment; task allocation is carried out on each unmanned aerial vehicle through a distributed auction algorithm, and a matching list of each unmanned aerial vehicle and a target is obtained; calculating the reference striking trajectory and striking time of each unmanned aerial vehicle on an allocated target at the current moment according to the matching list of each unmanned aerial vehicle and the target; and when the striking time of all the unmanned aerial vehicles is smaller than the set value, obtaining a final matching list of the unmanned aerial vehicles and the targets. The invention can output the target task allocation and striking results of the multi-unmanned aerial vehicle with high efficiency.
Description
Technical Field
The invention relates to the technical field of unmanned aerial vehicles, in particular to an unmanned aerial vehicle distributed task allocation method considering reachability domain constraint.
Background
Unmanned aerial vehicles should have optimality, timeliness, and dynamic adjustment capabilities when task allocation hits targets. The task allocation generally takes global information into consideration to provide an optimal scheme, however, due to factors such as flight performance, the unmanned aerial vehicle can only hit targets within a certain range, and often cannot hit the global optimal targets in the dynamic adjustment process, so that the optimality of a task allocation algorithm is reduced. Therefore, the reachable domain constraint of the unmanned aerial vehicle needs to be considered in the task allocation process, which is the basis for maximizing the combat effectiveness and successfully completing the task.
The task allocation method mainly comprises a centralized method and a distributed method. The centralized method realizes task scheduling of the whole system through the central node. However, since the centralized task allocation architecture has a central computing node, the system is less resistant to being destroyed by relying on global communication. In addition, as the scale of the task allocation problem increases, the time consumption of the task allocation method under the centralized architecture increases in an ultra-linear manner, and the requirement of online real-time solution is difficult to meet. The distributed method does not depend on a central computing node, realizes efficient allocation of tasks through communication negotiation between unmanned aerial vehicles, and has better algorithm robustness and stability.
In order to meet the reachable domain constraint of the unmanned aerial vehicle, the reachable domain range needs to be calculated in real time according to the flight path of the unmanned aerial vehicle. However, the accurate reachable domain constraint model is a complex Gao Weijiang nonlinear problem, a large number of iterative computations are required, and a certain difficulty still exists for online real-time computation, so that timeliness of task allocation is difficult to ensure.
Disclosure of Invention
In view of the above, the invention provides an unmanned aerial vehicle distributed task allocation method considering the reachable domain constraint, which has the characteristics of high timeliness and strong optimality.
The invention discloses an unmanned aerial vehicle distributed task allocation method considering reachability domain constraint, which comprises the following steps:
step 1: taking the maximum unmanned aerial vehicle task execution profit sum as an optimization target, and taking the unmanned aerial vehicle reachable domain constraint into consideration to establish a multi-unmanned aerial vehicle task allocation model; taking the solving of the reachable domains of each unmanned aerial vehicle and the reference trajectory as targets, and taking the dynamic constraint of the unmanned aerial vehicle into consideration, establishing a missile mass center motion model, a guided trajectory motion model and a three-dimensional proportional guiding control rate model;
step 2: initializing unmanned aerial vehicle parameter information and task environment information; the unmanned aerial vehicle parameter information comprises an unmanned aerial vehicle number, an unmanned aerial vehicle flight speed, an unmanned aerial vehicle overload, an unmanned aerial vehicle pitch angle and an unmanned aerial vehicle yaw angle; the task environment information comprises an unmanned aerial vehicle starting point position, a target number, a target point position, an unmanned aerial vehicle communication topology diameter and an unmanned aerial vehicle communication connection relation;
step 3: according to the unmanned aerial vehicle parameter information, calculating the reachable domain of each unmanned aerial vehicle at the current moment;
step 4: task allocation is carried out on each unmanned aerial vehicle through a distributed auction algorithm, and a matching list of each unmanned aerial vehicle and a target is obtained;
step 5: calculating the reference striking trajectory and striking time of each unmanned aerial vehicle on an allocated target at the current moment according to the matching list of each unmanned aerial vehicle and the target;
step 6: judging whether the striking time of all unmanned aerial vehicles is smaller than a set value; if yes, the algorithm is ended; if not, then a new target appears at random in the scene, the position, the trajectory deflection angle and the trajectory inclination angle information of each unmanned aerial vehicle are calculated, and the steps 3 to 6 are re-executed until the striking time of all unmanned aerial vehicles is smaller than the set value.
Further, the establishing a multi-unmanned aerial vehicle task allocation model by taking the maximum unmanned aerial vehicle task execution profit sum as an optimization target and considering the unmanned aerial vehicle reachable domain constraint comprises the following steps:
consider that there are N drones initially, the drone set being denoted u= { U 1 ,U 2 ,…,U N Considering that there are M targets initially, the target set is denoted as T= { T 1 ，T 2 ，...，T M -N is less than or equal to M; between unmanned plane i and unmanned plane jThe communication link (i, j) is bi-directional, the communication topology graph G (t) = (V, epsilon (t)) between the drones is a dynamic undirected graph, where v= {1,..the n } represents the set of vertices of the drone sequence numbers, epsilon (t) = { (i, j) |i, j e V } represents the set of dynamic communication links between the drones;
taking the sum of the maximum unmanned aerial vehicle task execution benefits as an optimization target, and constructing the following multi-unmanned aerial vehicle task allocation model:
T α(i) ∈η(i)，i＝1，2，...，N
β i，j ＝O-[Dis i，j ]-p j
wherein alpha (i) represents the task assigned to by the unmanned aerial vehicle i,represents an integer set, beta i，j Represents the net benefit obtained by the unmanned aerial vehicle i in executing the task j, eta (i) represents the reachable domain of the unmanned aerial vehicle i, O is a very large positive integer, dis i，j Representing the Euclidean distance, p, between unmanned plane i and task j j For the price of task j []Representing an upward rounding.
Further, the method for establishing a missile centroid motion model, a guided trajectory motion model and a three-dimensional proportional guidance control rate model by taking the solving of the reachable domain and the reference trajectory of each unmanned aerial vehicle as targets and taking the mechanical constraint of the unmanned aerial vehicle into consideration comprises the following steps:
taking the motion law of the mass center of the unmanned aerial vehicle into consideration, assuming the unmanned aerial vehicle to fly at a constant speed, establishing a mass center kinematics and dynamics equation set of the unmanned aerial vehicle, and constructing an unmanned aerial vehicle mass center motion model;
taking the guiding trajectory kinematics rule into consideration, constructing a guiding trajectory kinematics model as follows;
and taking the relative motion relation between the unmanned aerial vehicle and the target into consideration, and striking by adopting a proportional guidance method to construct a three-dimensional proportional guidance control rate model.
Further, the unmanned aerial vehicle centroid motion model is:
wherein (x, y, z) represents the mass center coordinates of the unmanned aerial vehicle under the ground coordinate system, V represents the speed of the unmanned aerial vehicle, θ represents the ballistic dip angle, ψ V Represents the ballistic deflection angle, n y Represents a longitudinal overload, n z Representing a lateral overload.
Further, the guided ballistic kinematic model is:
wherein r represents the distance between the drone and the target, (q y ,q z ) Respectively represent the included angles between the unmanned plane-target connecting line and the longitudinal and lateral surfaces, V T Representative of the target speed, θ T Represents the inclination angle of the target trajectory, psi VT Representing the target ballistic deflection.
Further, the three-dimensional proportional navigational control rate model is:
where N represents the proportional pilot control rate coefficient.
Further, the step 3 includes:
step 31: acquiring unmanned aerial vehicle parameter information at the current moment, and presetting a lateral overload coefficient range of the unmanned aerial vehicle and a longitudinal overload coefficient range of the unmanned aerial vehicle;
step 32: the lateral overload coefficient of the unmanned aerial vehicle and the longitudinal overload coefficient of the unmanned aerial vehicle meet the equation constraint n y 2 +n z 2 =c, C represents a constant; the lateral overload coefficients of the unmanned aerial vehicle are respectively taken down from the boundary and the upper boundary, the corresponding longitudinal overload coefficients of the unmanned aerial vehicle are obtained, and two characteristic parameter points P about the reachable domain are calculated according to the mass center motion model and the guide trajectory kinematic model of the unmanned aerial vehicle 1 ,P 2 The method comprises the steps of carrying out a first treatment on the surface of the The longitudinal overload coefficient of the unmanned aerial vehicle respectively takes down the boundary and the upper boundary, the lateral overload coefficient of the unmanned aerial vehicle takes 0, and two characteristic parameter points P above and below the reachable domain are calculated according to the mass center motion model and the guide trajectory kinematic model of the unmanned aerial vehicle 3 ,P 4 ；
Step 33: adopting a sector-like representation unmanned aerial vehicle reachable domain, and taking left and right characteristic points P 1 ,P 2 Is taken as the center of the region according to the three characteristic points P on the left and the right 1 ,P 3 ,P 2 Determination of major axis alpha 2 And minor axis beta 2 Drawing a semi-ellipse epsilon 1 According to the three characteristic points P of left and right 1 ,P 4 ,P 2 Determining sector radius gamma 1 Forming a fan-like reachable domain; the reachable domains of each unmanned aerial vehicle are recorded, forming a set of reachable domains η (i), i=1, 2.
Further, the step 4 includes:
step 41: acquiring unmanned aerial vehicle parameter information and task environment information at the current moment, and acquiring a reachable domain of each unmanned aerial vehicle according to the number of the unmanned aerial vehicle from a reachable domain set eta (i), i=1, 2.
Step 42: the income of the unmanned aerial vehicle on the targets outside the reachable domain is reduced to be an extremely small integer D, the unmanned aerial vehicle is ensured to bid and bid for the targets in the reachable domain only, and an initial price set p of the targets is generated according to initial information init And initial winning bid set beta init Simulating an asynchronous communication process according to the communication link;
step 43: each unmanned aerial vehicle sequentially carries out bidding on a target with highest income in a reachable domain, the unmanned aerial vehicle with highest bidding obtains the target, and before a new round of bidding begins, information interaction between unmanned aerial vehicles is determined by communication link parameters;
step 44: by iterating until each drone is assigned to a corresponding destination and no production occursWhen allocation conflict occurs, the task allocation algorithm converges to obtain a final matching list alpha of the unmanned aerial vehicle and the target fin 。
Further, the following formula is adopted to reduce the benefit of the unmanned aerial vehicle to the target outside the reachable domain of the unmanned aerial vehicle to be a minimum integer D:
in the above step 43, auction information required for the unmanned aerial vehicle i is input with: target sequence number alpha (i) capable of obtaining maximum net benefit and allocated to unmanned plane i at current moment, and price set p of each target at current moment i,j The unmanned aerial vehicle i gives the unmanned aerial vehicle with the largest serial number among all unmanned aerial vehicles with the highest price to the target j in the price information received at the current moment, namely the bidding winner b i,j ，j＝1,2,...,M。
Further, the step 5 includes:
step 51: acquiring unmanned aerial vehicle parameter information and task environment information at the current moment, and determining a matching relationship between the unmanned aerial vehicle and the target according to a matching list of the unmanned aerial vehicle and the target at the current moment;
step 52: and inputting parameter information corresponding to the current moment of the unmanned aerial vehicle and the target according to the unmanned aerial vehicle mass center motion model, the guidance trajectory kinematics model and the three-dimensional proportional guidance control rate model, and acquiring the reference hit trajectory and hit time of the unmanned aerial vehicle on the allocated target.
Due to the adoption of the technical scheme, the invention has the following advantages:
1. the invention discloses an unmanned aerial vehicle distributed task allocation method considering reachable domain constraint, which aims at the problem of high time consumption of online accurate calculation of the reachable domain of an unmanned aerial vehicle, and establishes an unmanned aerial vehicle centroid motion model, a guide trajectory kinematic model, a three-dimensional proportional guide control rate model and a multi-unmanned aerial vehicle task allocation model. Based on a distributed auction algorithm, the task allocation method considering the reachable domain constraint based on the competition mechanism is provided, and the target task allocation and the hitting results of the multiple unmanned aerial vehicles are output efficiently.
2. According to the unmanned aerial vehicle distributed task allocation method considering the reachable domain constraint, the class sector is adopted to represent the reachable domain of the unmanned aerial vehicle, the reachable domain is rapidly expressed through the characteristic parameters of the reachable domain of the unmanned aerial vehicle, the regional constraint is provided for the allocation of the cooperative hit target task, the reachable domain constraint of the unmanned aerial vehicle is considered in the task ordering process of the distributed auction algorithm, and the target task allocation result of the unmanned aerial vehicle meets the actual kinematic performance of the unmanned aerial vehicle.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments described in the embodiments of the present invention, and other drawings may be obtained according to these drawings for those skilled in the art.
Fig. 1 is a flowchart of a method for distributing distributed tasks of an unmanned aerial vehicle in consideration of the reachability domain constraint in the embodiment of the present invention;
FIG. 2 is a flowchart of an internal algorithm of a unmanned aerial vehicle according to the distributed auction algorithm of the embodiment of the present invention;
FIGS. 3 (a) through 3 (d) are front views of three-dimensional distributed task allocation and strike planning results at different times, respectively, taking into account reachability domain constraints in accordance with embodiments of the present invention;
fig. 4 (a) to 4 (d) are top views of three-dimensional distributed task allocation and strike planning results at different moments in time, respectively, taking into account the reachable domain constraints according to embodiments of the present invention.
Detailed Description
The present invention will be further described with reference to the accompanying drawings and examples, wherein it is apparent that the examples described are only some, but not all, of the examples of the present invention. All other embodiments obtained by those skilled in the art are intended to fall within the scope of the embodiments of the present invention.
Aiming at the problem of high time consumption of online accurate calculation of the reachable domain of the unmanned aerial vehicle, the method takes ballistic characteristic factors and reachable domain projection into consideration, and obtains the shape of the real reachable domain by a variable-precision Monte Carlo targeting method to be similar to a sector, so that the reachable domain of the unmanned aerial vehicle is represented by adopting a sector-like shape, the reachable domain of the unmanned aerial vehicle is rapidly represented by the characteristic parameters of the reachable domain of the unmanned aerial vehicle, and region constraint is provided for allocation of a cooperative striking target task. Further, considering the time-consuming influence of the real-time change of the reachable domain of the unmanned aerial vehicle on the task allocation algorithm, the discretization of the reachable domain of the unmanned aerial vehicle is realized by adopting a mode of discretizing the reachable domain of the whole-course continuous change, and a parameter list of the reachable domain of the unmanned aerial vehicle with a fixed period is formed, so that the unmanned aerial vehicle can adjust the allocation scheme in time when a new target appears, and the optimality and completeness of the solution are ensured. Specifically, the present invention provides the following examples:
the simulation hardware is Intel Core i5-6200CPU 2.30GHz,8G memory, and the simulation environment is MATLAB R2022b. Unmanned aerial vehicle formation performs tasks in a 30000m×80000m×1500m three-dimensional environment. The task allocation requirements take the sum of the maximum task benefits as an optimization target, and constraint conditions such as a reachable domain and unmanned aerial vehicle performance are considered to reasonably allocate the target to each unmanned aerial vehicle.
The unmanned aerial vehicle distributed task allocation method considering the reachable domain constraint in the embodiment comprises the following specific implementation steps:
step 1: taking the solving of the reachable domains of each unmanned aerial vehicle and the reference trajectory as targets, and taking the dynamic constraint of the unmanned aerial vehicle into consideration, and establishing an unmanned aerial vehicle mass center motion model, a guiding trajectory motion model and a three-dimensional proportional guiding control rate model; and taking the maximum unmanned aerial vehicle task execution profit sum as an optimization target, and establishing a multi-unmanned aerial vehicle task allocation model by taking the unmanned aerial vehicle reachable domain constraint into consideration.
(1) Multi-unmanned aerial vehicle task allocation model
T α(i) ∈η(i)，i＝1，2，...，N
β i，j ＝100000-[Dis i，j ]-p j
(2) Unmanned aerial vehicle barycenter motion model
(3) Guided ballistic kinematic model
(4) Three-dimensional proportional-steering control rate model
Step 2: and initializing unmanned aerial vehicle parameter information and task environment information.
Unmanned aerial vehicle parameter information include unmanned aerial vehicle serial number, unmanned aerial vehicle flight speed, unmanned aerial vehicle overload, unmanned aerial vehicle pitch angle, unmanned aerial vehicle yaw angle. The task environment information comprises an unmanned aerial vehicle starting point position, a target number, a target point position, an unmanned aerial vehicle communication topological diameter and an unmanned aerial vehicle communication connection relation.
Setting 5 unmanned aerial vehicles and 5 targets which initially exist, sequentially giving numbers of the unmanned aerial vehicles and the targets in sequence, and A in subsequent figures 1 、A 2 、A 3 、A 4 、A 5 Respectively represent five unmanned aerial vehicles, T 1 、T 2 、T 3 、T 4 、T 5 Respectively representing five targets, wherein the flying speed of the unmanned aerial vehicle is 200m/s, the pitch angle and the yaw angle of all unmanned aerial vehicles at the initial moment are set to be 0 degrees, and the lateral overload coefficient range of the unmanned aerial vehicle is set to be [ -0.9,0.9]The longitudinal overload coefficient range is set to be minus 0.9,0.9]. And setting that all unmanned aerial vehicles can communicate, wherein the communication topology diameter is 1, and the unmanned aerial vehicle position and the target position at the initial moment are listed in table 1.
Table 1 drone and target initial position information
Step 3: and calculating the reachable domain eta (i) of each unmanned aerial vehicle at the current moment according to the unmanned aerial vehicle kinematic parameter information, wherein i=1, 2.
Step 3.1: and acquiring unmanned aerial vehicle parameter information at the current moment, ensuring that unmanned aerial vehicle parameters are correct, and setting a lateral overload coefficient range of the unmanned aerial vehicle and a longitudinal overload coefficient range of the unmanned aerial vehicle.
Step 3.2: setting that the lateral overload coefficient of the unmanned aerial vehicle and the longitudinal overload coefficient of the unmanned aerial vehicle in the example meet n y 2 +n z 2 =0.81. When the lateral overload coefficient of the unmanned aerial vehicle is respectively taken down to the boundary of-0.9 and the upper boundary of 0.9, the corresponding longitudinal overload coefficient of the unmanned aerial vehicle is obtained to be 0, and two characteristic parameter points P about the reachable domain are calculated according to the unmanned aerial vehicle mass center motion model and the guided trajectory kinematic model 1 ,P 2 The method comprises the steps of carrying out a first treatment on the surface of the The longitudinal overload coefficient of the unmanned aerial vehicle is respectively taken down to be minus 0.9 and the upper boundary to be 0.9, the lateral overload coefficient of the unmanned aerial vehicle is taken to be 0, and two characteristic parameter points P on the upper and lower sides of the reachable domain are calculated according to the mass center motion model and the guide trajectory motion model of the unmanned aerial vehicle 3 ,P 4 . The characteristic parameter points of each unmanned aerial vehicle at the initial time in the example are listed in table 2.
TABLE 2 characteristic parameter points for each unmanned aerial vehicle at initial time
Step 3.3: adopting a sector-like representation unmanned aerial vehicle reachable domain, and taking left and right characteristic points P 1 ,P 2 Is taken as the center of the region according to the three characteristic points P on the left and the right 1 ,P 3 ,P 2 Determination of major axis alpha 2 And minor axis beta 2 Drawing a semi-ellipse epsilon 1 According to the three characteristic points P of left and right 1 ,P 4 ,P 2 Determining sector radius gamma 1 Forming sector-like reachable domains. The reachable domains of each unmanned aerial vehicle are recorded, forming a set of reachable domains η (i), i=1, 2.
Step 4: after considering the reachable domain constraint, performing task allocation on the unmanned aerial vehicle through a distributed auction algorithm to obtain a matching list alpha of the unmanned aerial vehicle and the target fin 。
Step 4.1: and acquiring unmanned aerial vehicle parameter information and task environment information at the current moment. And acquiring the reachable domain of each unmanned aerial vehicle from the reachable domain set eta (i) according to the number of the unmanned aerial vehicle, wherein i=1, 2.
Step 4.2: the three-dimensional proportional guidance control rate model simulates a heterogeneous platform matching idea, the gains of the unmanned aerial vehicle on targets outside the reachable domain are set to be 0, the closer the unmanned aerial vehicle is to the targets in the reachable domain, the higher the gains are, the unmanned aerial vehicle only carries out bidding and bid-winning on the targets in the reachable domain, and the initial price p of the targets is generated according to initial information init And the initial winning bidder beta init The initial price of the target is 0, and the initial winning bid is shown in the following formula, and the asynchronous communication process is simulated according to the communication link.
Step 4.3: each unmanned aerial vehicle sequentially carries out bidding on a target with highest profit in a reachable domain, the unmanned aerial vehicle with highest bidding obtains the target, and before a new bidding round starts, information interaction among unmanned aerial vehicles is determined by communication link parameters, and auction information needed by unmanned aerial vehicle i is input in the process:
(1) alpha (i) represents the target sequence number allocated to the unmanned aerial vehicle i at the current moment and capable of obtaining the maximum net benefit.
②p i,j (j=1, 2,., M) represents the price set for each target at the current time.
③b i,j (j=1, 2,., M), represents the highest numbered drone of the plurality of drones that gave the highest bid for target j, i.e., the winning bidder, among all of the price information received by drone i at the current time. The maximum serial number is selected from a plurality of unmanned aerial vehicles with optimal bidding but equal bidding, so that the confusion of allocation is prevented.
Step 4.4: the task allocation algorithm converges through repeated iteration until each unmanned aerial vehicle is allocated to a corresponding target and allocation conflict is not generated, and a final matching list alpha of the unmanned aerial vehicle and the target is obtained fin . In this example, the number of pre-allocation iterations is 11, and the pre-allocation result is α fin ＝[1,3,5,4,2]。
Step 5: and calculating the reference strike trajectory and strike time of each unmanned aerial vehicle on the allocated target according to the matching list of the unmanned aerial vehicle and the target.
Step 5.1: and acquiring unmanned aerial vehicle parameter information and task environment information at the current moment. And determining the matching relationship between the unmanned aerial vehicle and the target according to the matching list of the unmanned aerial vehicle and the target at the current moment.
Step 5.2: and inputting parameter information of the current moments of the unmanned aerial vehicle and the target according to the unmanned aerial vehicle mass center motion model, the guidance trajectory kinematics model and the three-dimensional proportional guidance control rate model, and acquiring the reference hit trajectory and hit time of the unmanned aerial vehicle on the allocated target. The calculation formula of the striking time is:
t＝r/V
according to the operation of the formula, in the example, the striking time of each unmanned aerial vehicle for pre-distributing the distributing target is t 0 ＝[95.0,80.4,94.4,71.6,73.2]s。
Step 6: when a new target appears, calculating the position, trajectory deflection angle and trajectory inclination angle information of each unmanned aerial vehicle at the moment, returning to the third calculation of the reachable domain of each unmanned aerial vehicle at the current moment, returning to the fourth calculation of the reachable domain, and performing task redistribution until the striking time of all unmanned aerial vehicles on the distributed target is less than 30 seconds, and stopping the algorithm. In the example, 1-2 new targets are set to randomly appear every 20 seconds in the task scene, the new targets are numbered according to the appearance sequence, the algorithm is finally stopped after 3 rounds of reassignment, and the initial information and reassignment results of the new targets before each round of reassignment are shown in Table 3.
Table 3 new target initial information and reassignment results per round of reassignment
And when the algorithm operation is finished, meeting a convergence condition, and generating a task allocation result which is a multi-unmanned aerial vehicle feasible allocation scheme meeting the reachable domain constraint. In combination with the results of table 3, fig. 3 (a) to 3 (d) are front views of pre-allocation and re-allocation after the appearance of new targets every 20 seconds, respectively; fig. 4 (a) to 4 (d) are top views of pre-allocation and re-allocation after appearance of new targets every 20 seconds, respectively. In the legend, UAV represents the current time position of the unmanned aerial vehicle, target represents the current time position of a Target, attackbaleZone represents the reachable domain of the unmanned aerial vehicle, and Trajectory represents the attack Trajectory of the unmanned aerial vehicle. As can be seen from fig. 3 (a) to 3 (d) and fig. 4 (a) to 4 (d), the task allocation results satisfy the multiple unmanned aerial vehicle target kinematics constraint and the reachable domain constraint.
According to the simulation result and analysis of the target task allocation example of the multiple unmanned aerial vehicles, the task allocation method can provide allocation results meeting the constraint of the reachable domain for each unmanned aerial vehicle, the reachable domain generation speed has high efficiency, and the influence of real-time change of the reachable domain of the unmanned aerial vehicle on time consumption of a task allocation algorithm can be considered to form a parameter list of the reachable domain of the unmanned aerial vehicle with a fixed period. Therefore, the invention has strong engineering practicability and can achieve the expected aim of the invention.
Finally, it should be noted that: the above embodiments are only for illustrating the technical aspects of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the above embodiments, it should be understood by those of ordinary skill in the art that: modifications and equivalents may be made to the specific embodiments of the invention without departing from the spirit and scope of the invention, which is intended to be covered by the claims.
Claims (10)
1. The unmanned aerial vehicle distributed task allocation method taking the reachability domain constraint into consideration is characterized by comprising the following steps of:
step 1: taking the maximum unmanned aerial vehicle task execution profit sum as an optimization target, and taking the unmanned aerial vehicle reachable domain constraint into consideration to establish a multi-unmanned aerial vehicle task allocation model; taking the solving of the reachable domains of each unmanned aerial vehicle and the reference trajectory as targets, and taking the dynamic constraint of the unmanned aerial vehicle into consideration, establishing a missile mass center motion model, a guided trajectory motion model and a three-dimensional proportional guiding control rate model;
step 2: initializing unmanned aerial vehicle parameter information and task environment information; the unmanned aerial vehicle parameter information comprises an unmanned aerial vehicle number, an unmanned aerial vehicle flight speed, an unmanned aerial vehicle overload, an unmanned aerial vehicle pitch angle and an unmanned aerial vehicle yaw angle; the task environment information comprises an unmanned aerial vehicle starting point position, a target number, a target point position, an unmanned aerial vehicle communication topology diameter and an unmanned aerial vehicle communication connection relation;
step 3: according to the unmanned aerial vehicle parameter information, calculating the reachable domain of each unmanned aerial vehicle at the current moment;
step 4: task allocation is carried out on each unmanned aerial vehicle through a distributed auction algorithm, and a matching list of each unmanned aerial vehicle and a target is obtained;
step 5: calculating the reference striking trajectory and striking time of each unmanned aerial vehicle on an allocated target at the current moment according to the matching list of each unmanned aerial vehicle and the target;
step 6: judging whether the striking time of all unmanned aerial vehicles is smaller than a set value; if yes, the algorithm is ended; if not, then a new target appears at random in the scene, the position, the trajectory deflection angle and the trajectory inclination angle information of each unmanned aerial vehicle are calculated, and the steps 3 to 6 are re-executed until the striking time of all unmanned aerial vehicles is smaller than the set value.
2. The method of claim 1, wherein the establishing a multi-unmanned aerial vehicle task allocation model with the maximized unmanned aerial vehicle task execution revenue sum as an optimization objective, taking into account unmanned aerial vehicle reachability domain constraints, comprises:
consider that there are N drones initially, the drone set being denoted u= { U 1 ,U 2 ,...,U N Considering that there are M targets initially, the target set is denoted as T= { T 1 ,T 2 ,...,T M -N is less than or equal to M; the communication link (i, j) between drone i and drone j is bidirectional, the communication topology graph G (t) = (V, epsilon (t)) between drones is a dynamic undirected graph, where v= {1,..n } represents the set of vertices of the drone serial number, e (t) = { (i, j) |i, j e V } represents the set of dynamic communication links between drones;
taking the sum of the maximum unmanned aerial vehicle task execution benefits as an optimization target, and constructing the following multi-unmanned aerial vehicle task allocation model:
T α(i) ∈η(i)，i＝1，2，...，N
β i，j ＝O-[Dis i，j ]-p j
wherein a (i) represents the task assigned to by the unmanned aerial vehicle i,represents an integer set, b i，j Represents the net benefit obtained by the unmanned aerial vehicle i in executing the task j, eta (i) represents the reachable domain of the unmanned aerial vehicle i, O is a very large positive integer, dis i，j Representing the Euclidean distance, p, between unmanned plane i and task j j For the price of task j []Representing an upward rounding.
3. The method of claim 1, wherein the establishing a missile centroid motion model, a guided trajectory motion model, and a three-dimensional proportional-guided-control-rate model with the objective of solving the reachability domain and the reference trajectory of each unmanned aerial vehicle, taking into account unmanned aerial vehicle dynamics constraints, comprises:
taking the motion law of the mass center of the unmanned aerial vehicle into consideration, assuming the unmanned aerial vehicle to fly at a constant speed, establishing a mass center kinematics and dynamics equation set of the unmanned aerial vehicle, and constructing an unmanned aerial vehicle mass center motion model;
taking the guiding trajectory kinematics rule into consideration, constructing a guiding trajectory kinematics model as follows;
and taking the relative motion relation between the unmanned aerial vehicle and the target into consideration, and striking by adopting a proportional guidance method to construct a three-dimensional proportional guidance control rate model.
4. A method according to claim 3, wherein the unmanned aerial vehicle centroid motion model is:
wherein (x, y, z) represents the mass center coordinates of the unmanned aerial vehicle under the ground coordinate system, V represents the speed of the unmanned aerial vehicle, θ represents the ballistic dip angle, ψ V Represents the ballistic deflection angle, n y Represents a longitudinal overload, n z Representing a lateral overload.
5. A method according to claim 3, wherein the guided ballistic kinematic model is:
wherein r represents the distance between the drone and the target, (q y ,q z ) Respectively represent the included angles between the unmanned plane-target connecting line and the longitudinal and lateral surfaces, V T Representative of the target speed, θ T Representing the target trajectoryInclination angle phi VT Representing the target ballistic deflection.
6. A method according to claim 3, wherein the three-dimensional proportional navigational control model is:
where N represents the proportional pilot control rate coefficient.
7. The method according to claim 1, wherein the step 3 comprises:
step 31: acquiring unmanned aerial vehicle parameter information at the current moment, and presetting a lateral overload coefficient range of the unmanned aerial vehicle and a longitudinal overload coefficient range of the unmanned aerial vehicle;
step 32: the lateral overload coefficient of the unmanned aerial vehicle and the longitudinal overload coefficient of the unmanned aerial vehicle meet the equation constraint n y 2 +n z 2 =c, C represents a constant; the lateral overload coefficients of the unmanned aerial vehicle are respectively taken down from the boundary and the upper boundary, the corresponding longitudinal overload coefficients of the unmanned aerial vehicle are obtained, and two characteristic parameter points P about the reachable domain are calculated according to the mass center motion model and the guide trajectory kinematic model of the unmanned aerial vehicle 1 ,P 2 The method comprises the steps of carrying out a first treatment on the surface of the The longitudinal overload coefficient of the unmanned aerial vehicle respectively takes down the boundary and the upper boundary, the lateral overload coefficient of the unmanned aerial vehicle takes 0, and two characteristic parameter points P above and below the reachable domain are calculated according to the mass center motion model and the guide trajectory kinematic model of the unmanned aerial vehicle 3 ,P 4 ；
Step 33: adopting a sector-like representation unmanned aerial vehicle reachable domain, and taking left and right characteristic points P 1 ,P 2 Is taken as the center of the region according to the three characteristic points P on the left and the right 1 ,P 3 ,P 2 Determination of major axis alpha 2 And minor axis beta 2 Drawing a semi-ellipse epsilon 1 According to the three characteristic points P of left and right 1 ,P 4 ,P 2 Determining sector radius gamma 1 Forming a fan-like reachable domain; recording the accessibility of each unmanned aerial vehicleDomains, forming a reachable domain set η (i), i=1, 2.
8. The method according to claim 1, wherein the step 4 comprises:
step 41: acquiring unmanned aerial vehicle parameter information and task environment information at the current moment, and acquiring a reachable domain of each unmanned aerial vehicle according to the number of the unmanned aerial vehicle from a reachable domain set eta (i), i=1, 2.
Step 42: the income of the unmanned aerial vehicle on the targets outside the reachable domain is reduced to be an extremely small integer D, the unmanned aerial vehicle is ensured to bid and bid for the targets in the reachable domain only, and an initial price set p of the targets is generated according to initial information init And initial winning bid set beta init Simulating an asynchronous communication process according to the communication link;
step 43: each unmanned aerial vehicle sequentially carries out bidding on a target with highest income in a reachable domain, the unmanned aerial vehicle with highest bidding obtains the target, and before a new round of bidding begins, information interaction between unmanned aerial vehicles is determined by communication link parameters;
step 44: the task allocation algorithm converges through repeated iteration until each unmanned aerial vehicle is allocated to a corresponding target and allocation conflict is not generated, and a final matching list alpha of the unmanned aerial vehicle and the target is obtained fin 。
9. The method of claim 8, wherein the unmanned aerial vehicle's return to targets outside its reachable domain is reduced to a minimum integer D using the formula:
in the above step 43, auction information required for the unmanned aerial vehicle i is input with: target sequence number alpha (i) capable of obtaining maximum net benefit and allocated to unmanned plane i at current moment, and price set p of each target at current moment i,j The unmanned plane i gives the highest bidding price to the target j in all the price information received at the current moment, and the serial number is the highest among a plurality of unmanned planesLarge unmanned aerial vehicle, i.e. bid winner b i,j ，j＝1,2,…,M。
10. The method according to claim 1, wherein the step 5 comprises:
step 51: acquiring unmanned aerial vehicle parameter information and task environment information at the current moment, and determining a matching relationship between the unmanned aerial vehicle and the target according to a matching list of the unmanned aerial vehicle and the target at the current moment;
step 52: and inputting parameter information corresponding to the current moment of the unmanned aerial vehicle and the target according to the unmanned aerial vehicle mass center motion model, the guidance trajectory kinematics model and the three-dimensional proportional guidance control rate model, and acquiring the reference hit trajectory and hit time of the unmanned aerial vehicle on the allocated target.
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