CN112633654A - Multi-unmanned aerial vehicle task allocation method based on improved cluster expansion consistency bundle algorithm - Google Patents

Multi-unmanned aerial vehicle task allocation method based on improved cluster expansion consistency bundle algorithm Download PDF

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CN112633654A
CN112633654A CN202011481261.1A CN202011481261A CN112633654A CN 112633654 A CN112633654 A CN 112633654A CN 202011481261 A CN202011481261 A CN 202011481261A CN 112633654 A CN112633654 A CN 112633654A
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李一兵
孟宪祯
叶方
孙骞
田园
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Abstract

The invention provides a multi-unmanned aerial vehicle task allocation method based on an improved cluster expansion consistency bundle algorithm, which comprises the following steps of 1: clustering the tasks to be distributed by using a density clustering algorithm; step 2: distributing a corresponding number of unmanned aerial vehicles for each cluster according to the number and the type of tasks in each cluster; and step 3: initializing variables; and 4, step 4: using an improved extended consistency bundle algorithm for the task to be distributed and the unmanned aerial vehicle in each cluster to complete the construction of the local task sequence of the unmanned aerial vehicle; and 5: in order to avoid task allocation conflict, carrying out consistency negotiation between unmanned aerial vehicles bidding for the same task according to a consensus rule; step 6: the step 3 to the step 4 are circulated until the task sequence of the unmanned aerial vehicle is not changed any more; and 7: and outputting a final task distribution effect graph and a time table. The invention can solve the problem of large-scale task allocation of the multi-heterogeneous unmanned aerial vehicle and realize the task allocation of the multi-heterogeneous unmanned aerial vehicle with short flight distance and high communication efficiency.

Description

Multi-unmanned aerial vehicle task allocation method based on improved cluster expansion consistency bundle algorithm
Technical Field
The invention relates to a multi-unmanned aerial vehicle task allocation method based on an improved cluster expansion consistency bundle algorithm, and belongs to the technical field of multi-unmanned aerial vehicle task allocation.
Background
Compared with a manned aircraft, an Unmanned Aerial Vehicle (UAV) has the characteristics of small volume, high flexibility, low cost, high survival capacity, easiness in operation and the like, and plays an important role in a future battlefield. In battlefields with increasingly complex environments, the unmanned aerial vehicles jointly execute tasks, have the advantages that a single unmanned aerial vehicle cannot achieve compared with the high robustness and the high task completion degree of the single unmanned aerial vehicle, and well supplement the defects that the single unmanned aerial vehicle is single in function and easy to be interfered by the outside. The unmanned aerial vehicle control technology is part of the increasing popularity and rapid growth in aviation and programming technology, and can be applied to military and civil use. Military, unmanned aerial vehicles are widely used in tasks such as target search, reconnaissance and strike; for civil use, the unmanned aerial vehicle can be applied to industries such as meteorology, agriculture and geology.
The problem of multi-drone task allocation is a combinatorial optimization problem, which is often coupled with the problem of flight path planning. The designated number of the multi-heterogeneous unmanned aerial vehicles distributes known multi-type tasks, the task distribution of the unmanned aerial vehicles directly influences the execution efficiency and the success rate of the tasks, and simultaneously indirectly influences the overall situation of a battlefield. Therefore, the research of the more efficient and intelligent multi-unmanned aerial vehicle task allocation algorithm has important practical significance for the development of unmanned aerial vehicles.
At present, scholars at home and abroad apply a large number of task allocation algorithms which can be summarized into three categories; one is a mathematical programming method, such as a mixed integer linear programming method, hungarian algorithm, and the like. One is an intelligent optimization algorithm, such as a particle swarm optimization algorithm, a genetic algorithm, and the like. The other is a market machine method, such as a contract net algorithm and the like. The extended consistency bundle Algorithm (CBBA) is a distributed Algorithm developed on the basis of a contract network Algorithm and consists of two stages of unmanned aerial vehicle local task sequence construction and unmanned aerial vehicle conflict resolution. The algorithm can rapidly complete conflict-free task allocation, but due to the fact that task time window constraint exists, part of unmanned aerial vehicles can select tasks far away from the unmanned aerial vehicles to execute, and the risk of the unmanned aerial vehicles is increased.
Disclosure of Invention
The invention aims to provide a multi-unmanned aerial vehicle task allocation method based on an improved cluster expansion consistency bundle algorithm, which considers task time window constraint. The method has the advantages that the tasks can be processed preferentially through the density clustering algorithm under the condition that more tasks need to be distributed, and then each cluster is distributed through the improved CBBA algorithm, so that the multi-unmanned aerial vehicle task distribution under the condition is realized.
The purpose of the invention is realized as follows: step 1: clustering the tasks to be distributed by using a density clustering algorithm;
step 2: distributing a corresponding number of unmanned aerial vehicles for each cluster according to the number and the type of tasks in each cluster;
and step 3: initializing variables;
and 4, step 4: and (3) using an improved extended consistency bundle algorithm for the task to be distributed and the unmanned aerial vehicle in each cluster to complete the construction of the local task sequence of the unmanned aerial vehicle, wherein the method specifically comprises the following steps:
4.1: unmanned aerial vehicle i attempts to add task j into bundle set b in the process of adding taskiWherein, the beam set biThe task sequence number obtained by the current auction of the unmanned aerial vehicle i is stored; set of paths piThe task sequence number of the unmanned aerial vehicle i to be executed at present is stored;
4.2: if the score of the unmanned aerial vehicle i is increased after the task j is inserted, adding the task j into a beam set b of the unmanned aerial vehicle iiThe last position in;
4.3: insert task j into piA position where the unmanned aerial vehicle performs the task to obtain the maximum score;
4.4: looping through steps 4.2 to 4.3 until drone i reaches a maximum number N of executable tasksi；
And 5: in order to avoid task allocation conflict, carrying out consistency negotiation between unmanned aerial vehicles bidding for the same task according to a consensus rule;
step 6: the step 3 to the step 4 are circulated until the task allocation sequence of the unmanned aerial vehicle is not changed any more;
and 7: and outputting a final task distribution effect graph and a time table.
The invention also includes such structural features:
1. step 4.1, the unmanned aerial vehicle i tries to add the task j into the bundle set b in the process of adding the taskiWherein, the beam set biThe task sequence number obtained by the current auction of the unmanned aerial vehicle i is stored; set of paths piThe task sequence number of the unmanned aerial vehicle i to be executed at present is stored; for the current path set piRespectively inserting task j into all the positions in the database and comparing scores after insertion; the score formula for drone i is constructed as follows:
Figure BDA0002837627480000021
wherein, cij(pi) The total score of unmanned aerial vehicle i execution after adding task j into the sequence;
Figure BDA0002837627480000022
to insert task j into piThe nth position in (a);
Figure BDA0002837627480000023
ordering set p along path for drone iiThe total score of the executed tasks, namely the total score formula obtained by the task allocation, is constructed as follows:
Figure BDA0002837627480000024
wherein r isij(pi) And costijRespectively executing the benefit and cost, r, of task j for UAV iij(pi) The structural formula is as follows:
Figure BDA0002837627480000031
Figure BDA0002837627480000032
wherein task j is executed firstpThen j is executed and task j is executed finallynAnd the execution sequence of the three is adjacent; sigma is a distance reward and punishment coefficient, is determined by the ratio of the last flight path to the next predicted flight path of the current executed task of the unmanned aerial vehicle, and can force the unmanned aerial vehicle to tend to select the task closer to the unmanned aerial vehicle to execute, costijThe structural formula is as follows:
Figure BDA0002837627480000033
compared with the prior art, the invention has the beneficial effects that: 1. according to the invention, a density clustering algorithm and an improved CBBA algorithm are combined, clustering processing is preferentially carried out on the tasks to be distributed, the range of the unmanned aerial vehicle for executing the tasks is limited near the unmanned aerial vehicle, and the communication times among the unmanned aerial vehicles in the subsequent task distribution process are reduced; 2. for the subsequent task allocation problem of the unmanned aerial vehicle, the improved CBBA algorithm is applied, the distance reward and punishment coefficient is introduced into a profit formula, and the unmanned aerial vehicle is further guided to preferentially execute tasks nearby the unmanned aerial vehicle to reduce the voyage.
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FIG. 1 is a flow chart of a method of the present invention;
FIG. 2 is a simulation diagram of task allocation effect of the improved clustering CBBA algorithm of the present invention;
FIG. 3 is a simulation diagram of a task allocation plan view of the improved clustering CBBA algorithm of the present invention;
FIG. 4 is a simulation diagram of the task allocation schedule of the improved clustering CBBA algorithm of the present invention;
FIG. 5 is a simulation diagram of the task allocation effect of the CBBA algorithm;
FIG. 6 is a CBBA algorithm task assignment overhead view simulation diagram;
FIG. 7 is a simulation diagram of a task allocation schedule of the CBBA algorithm.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and specific embodiments.
As shown in fig. 1, the multi-drone task allocation method based on the improved cluster expansion consistency bundle algorithm of the present invention includes the following steps:
step 1: clustering the tasks to be distributed by using a density clustering algorithm;
step 2: distributing a corresponding number of unmanned aerial vehicles for each cluster according to the number and the type of tasks in each cluster;
and step 3: initializing variables;
taking one of the clusters as an example, there is mtType is totally M unmanned aerial vehicle goes to carry out MtType n tasks to be distributed, namely, a set of unmanned planes V ═ V1,V2,…,VMUnmanned plane i belongs to V and is formed by typei,veli,fulei,NiFour sets of data representations. Wherein, typeiIs the type of unmanned aerial vehicle i, type is more than or equal to 1j≤mtAnd typej∈N；veliThe flight speed of unmanned aerial vehicle i; fulliIs the fuel consumption of drone i; n is a radical ofiThe maximum number of executable tasks for drone i and only one type of task per drone can be executed. Simultaneous task set T ═ T1,T2,…,TnJ belongs to T and posj,typej,valuej,startj,endj,durj,λjSeven sets of data represent. Wherein, posjIs the location of task j; typejIs the type of task j; valuejIs the value of task j; starting timejAnd endjThe earliest and latest executable times of task j, respectively; durjThe time required to perform task j; lambda [ alpha ]jFor the value decay factor of task j, 0 < lambdajIf the number is less than 1, punishing the unmanned aerial vehicle to execute the task j in a late time;
and 4, step 4: using an improved CBBA algorithm for the tasks to be distributed in each cluster and the unmanned aerial vehicle to complete the construction of the local task sequence of the unmanned aerial vehicle;
and 5: in order to avoid task allocation conflict, carrying out consistency negotiation between unmanned aerial vehicles bidding for the same task according to a consensus rule;
and an unmanned aerial vehicle k is taken as an information sender, an unmanned aerial vehicle i is taken as an information receiver, and an unmanned aerial vehicle m is communicated with the unmanned aerial vehicle k in a communication topology. And the receiver integrates the acquired relevant values in the z, y and s sets to judge which action is taken by the receiver. Wherein the set of winning parties zi＝{zi1,zi2,…,zin}，
Figure BDA0002837627480000041
Consider for unmanned aerial vehicle i which unmanned aerial vehicle performs task ns(ii) a Winning party bid set y ═ yi1,yi2,…,yin}，
Figure BDA0002837627480000042
For all pairs of tasks n in the dronesThe highest bid in the auction; set of timestamps si＝{si1,si2,…,siM}，
Figure BDA0002837627480000043
For unmanned aerial vehicle i to receive unmanned aerial vehicle M when communicating with other unmanned aerial vehicle informationsThe time of the latest information. The man-machine i can take the following three actions on the task j, and the formulas are respectively constructed as follows:
updating: y isij＝ykj,zij＝zkj
Resetting:
Figure BDA0002837627480000044
leaving: y isij＝yij,zij＝zij
The consensus rules between drones are shown in table 1.
TABLE 1 consensus rules for task j between UAV k and UAV i
Figure BDA0002837627480000045
Figure BDA0002837627480000051
Step 6: the step 3 to the step 4 are circulated until the task sequence of the unmanned aerial vehicle is not changed any more;
and 7: and outputting a final task distribution effect graph and a time table.
In the step 4, the method specifically comprises the following steps,
step 4.1: unmanned aerial vehicle i attempts to add task j into bundle set b in the process of adding taskiWherein, the beam set biThe task sequence number currently obtained by the unmanned aerial vehicle i is stored; set of paths piThe task sequence number of the unmanned aerial vehicle i to be executed at present is stored;
for the current path set piInsert task j and compare the post-insertion scores for all positions in (a).
The score formula for drone i is constructed as follows:
Figure BDA0002837627480000052
wherein, cij(pi) The total score of unmanned aerial vehicle i execution after adding task j into the sequence;
Figure BDA0002837627480000053
to insert task j into piThe nth position in (a);
Figure BDA0002837627480000054
ordering set p along path for drone iiThe total score of the executed tasks, namely the total score obtained by the task allocation, is formed as follows:
Figure BDA0002837627480000055
wherein r isij(pi) And costijRespectively executing the benefit and cost, r, of task j for UAV iij(pi) The structural formula is as follows:
Figure BDA0002837627480000056
Figure BDA0002837627480000057
wherein task j is executed firstpThen j is executed and task j is executed finallynAnd the execution sequence of the three is adjacent; sigma is a distance reward and punishment coefficient, is determined by the ratio of the last flight path and the next predicted flight path of the current executed task of the unmanned aerial vehicle, and can force the unmanned aerial vehicle to tend to select the task closer to the unmanned aerial vehicle to execute.
costijThe structural formula is as follows:
Figure BDA0002837627480000058
step 4.2: if the score of the unmanned aerial vehicle i is increased after the task j is inserted, adding the task j into a beam set b of the unmanned aerial vehicle iiThe last position in;
bithe update formula of (2) is constructed as follows:
Figure BDA0002837627480000061
step 4.3: insert task j into piA position where the unmanned aerial vehicle performs the task to obtain the maximum score;
step 4.4: looping through steps 3.2 to 3.4 until drone i reaches a maximum number N of executable tasksi；
The following examples are provided to further illustrate the beneficial effects of the present invention.
Example (b): under the Matlab simulation condition, a plurality of tasks exist in an area of 100km × 100km, and the performance parameters of the unmanned aerial vehicle are shown in table 2.
TABLE 2 UAV Performance parameters
Figure BDA0002837627480000062
The task parameters are shown in table 3.
TABLE 3 task parameters
Figure BDA0002837627480000063
There are 65 types of tasks in a region, mt2, n is 65. And according to the number of tasks and the maximum executable number of the unmanned aerial vehicles, 5 tasks are expected to be allocated to each unmanned aerial vehicle, so that 13 unmanned aerial vehicles are selected to be dispatched to take off to execute the tasks. The location and time window for each task in the simulation is randomly generated. For recording, the tasks of type I are sorted according to the rule of recording the tasks with smaller sequence numbers in each cluster.
The improved clustering CBBA algorithm and the CBBA algorithm are used for simulation, a task distribution effect diagram, a top view and a time table of the improved clustering CBBA algorithm are respectively shown in figures 2, 3 and 4, and a task distribution effect diagram, a top view and a time table of the CBBA algorithm are respectively shown in figures 5, 6 and 7. The improved clustering CBBA assignment results are shown in table 4.
TABLE 4 improved clustering CBBA assignment results
Figure BDA0002837627480000064
Figure BDA0002837627480000071
The CBBA assignment results are shown in table 5.
TABLE 5CBBA assignment results
Figure BDA0002837627480000072
The experimental data in the simulation are shown in table 6.
TABLE 6 Experimental data
Figure BDA0002837627480000073
As can be seen from table 5, the number of tasks that can be executed by each drone using the CBBA algorithm exceeds the maximum number of tasks that can be executed by each drone, and the number of tasks allocated by each drone is different from the number of tasks allocated by each drone using the improved clustered CBBA algorithm. As can be seen by comparing the table 6, the fig. 3 and the fig. 6, the improved clustering CBBA algorithm effectively avoids the situation that the unmanned aerial vehicle shuttles back and forth in a battlefield, and reduces the range and the communication times of the unmanned aerial vehicle, thereby enhancing the safety of the unmanned aerial vehicle.
The result of the embodiment shows that the method can solve the problem of large-scale task allocation of the multi-heterogeneous unmanned aerial vehicle, and can realize the task allocation of the multi-heterogeneous unmanned aerial vehicle with short flight distance and high communication efficiency.
Finally, it should be noted that the above examples are only intended to describe the technical solutions of the present invention and not to limit the technical methods, the present invention can be extended in application to other modifications, variations, applications and embodiments, and therefore all such modifications, variations, applications, embodiments are considered to be within the spirit and teaching scope of the present invention.
To sum up, the invention relates to a multi-unmanned aerial vehicle task allocation method based on an improved cluster expansion consistency bundle algorithm, which comprises the following steps of 1: clustering the tasks to be distributed by using a density clustering algorithm; step 2: distributing a corresponding number of unmanned aerial vehicles for each cluster according to the number and the type of tasks in each cluster; and step 3: initializing variables; and 4, step 4: using an improved extended consistency bundle algorithm for the task to be distributed and the unmanned aerial vehicle in each cluster to complete the construction of the local task sequence of the unmanned aerial vehicle; and 5: in order to avoid task allocation conflict, carrying out consistency negotiation between unmanned aerial vehicles bidding for the same task according to a consensus rule; step 6: the step 3 to the step 4 are circulated until the task sequence of the unmanned aerial vehicle is not changed any more; and 7: and outputting a final task distribution effect graph and a time table. The invention can solve the problem of large-scale task allocation of the multi-heterogeneous unmanned aerial vehicle and realize the task allocation of the multi-heterogeneous unmanned aerial vehicle with short flight distance and high communication efficiency.

Claims (2)

1. A multi-unmanned aerial vehicle task allocation method based on an improved cluster expansion consistency bundle algorithm is characterized in that: the method comprises the following steps:
step 1: clustering the tasks to be distributed by using a density clustering algorithm;
step 2: distributing a corresponding number of unmanned aerial vehicles for each cluster according to the number and the type of tasks in each cluster;
and step 3: initializing variables;
and 4, step 4: and (3) using an improved extended consistency bundle algorithm for the task to be distributed and the unmanned aerial vehicle in each cluster to complete the construction of the local task sequence of the unmanned aerial vehicle, wherein the method specifically comprises the following steps:
4.1: unmanned aerial vehicle i attempts to add task j into bundle set b in the process of adding taskiWherein, the beam set biThe task sequence number obtained by the current auction of the unmanned aerial vehicle i is stored; set of paths piThe task sequence number of the unmanned aerial vehicle i to be executed at present is stored;
4.2: if the score of the unmanned aerial vehicle i is increased after the task j is inserted, adding the task j into a beam set b of the unmanned aerial vehicle iiThe last position in;
4.3: insert task j into piA position where the unmanned aerial vehicle performs the task to obtain the maximum score;
4.4: looping through steps 4.2 to 4.3 until drone i reaches a maximum number N of executable tasksi；
And 5: in order to avoid task allocation conflict, carrying out consistency negotiation between unmanned aerial vehicles bidding for the same task according to a consensus rule;
step 6: the step 3 to the step 4 are circulated until the task allocation sequence of the unmanned aerial vehicle is not changed any more;
and 7: and outputting a final task distribution effect graph and a time table.
2. The multi-unmanned-aerial-vehicle task allocation method based on the improved cluster expansion consistency bundle algorithm according to claim 1, wherein the task allocation method comprises the following steps: step 4.1, the unmanned aerial vehicle i tries to add the task j into the bundle set b in the process of adding the taskiWherein, the beam set biThe task sequence number obtained by the current auction of the unmanned aerial vehicle i is stored; set of paths piThe task sequence number of the unmanned aerial vehicle i to be executed at present is stored; for the current path set piRespectively inserting task j into all the positions in the database and comparing scores after insertion; the score formula for drone i is constructed as follows:
Figure FDA0002837627470000011
wherein, cij(pi) The total score of unmanned aerial vehicle i execution after adding task j into the sequence;
Figure FDA0002837627470000021
to insert task j into piThe nth position in (a);
Figure FDA0002837627470000022
ordering set p along path for drone iiThe total score of the executed tasks, namely the total score formula obtained by the task allocation, is constructed as follows:
Figure FDA0002837627470000023
wherein r isij(pi) And costijRespectively executing the benefit and cost, r, of task j for UAV iij(pi) The structural formula is as follows:
Figure FDA0002837627470000024
Figure FDA0002837627470000025
wherein task j is executed firstpThen j is executed and task j is executed finallynAnd the execution sequence of the three is adjacent; sigma is a distance reward and punishment coefficient, is determined by the ratio of the last flight path to the next predicted flight path of the current executed task of the unmanned aerial vehicle, and can force the unmanned aerial vehicle to tend to select the task closer to the unmanned aerial vehicle to execute, costijThe structural formula is as follows:
Figure FDA0002837627470000026
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CN113268082A (en) * 2021-06-03 2021-08-17 一飞(海南)科技有限公司 Method and system for fast downloading, storing and acquiring dance step waypoints in formation of unmanned aerial vehicles
CN113657718A (en) * 2021-07-20 2021-11-16 香港中文大学（深圳） Multi-robot dynamic alliance task allocation method and related device
CN113657718B (en) * 2021-07-20 2024-04-19 香港中文大学（深圳） Multi-robot dynamic alliance task allocation method and related device
CN113723805A (en) * 2021-08-30 2021-11-30 上海大学 Unmanned ship composite task allocation method and system
CN113723805B (en) * 2021-08-30 2023-08-04 上海大学 Unmanned ship compound task allocation method and system
CN113934228A (en) * 2021-10-18 2022-01-14 天津大学 Cluster quad-rotor unmanned aerial vehicle task planning method based on negotiation consensus
CN113934228B (en) * 2021-10-18 2023-12-19 天津大学 Task planning method for clustered four-rotor unmanned aerial vehicle based on negotiation consensus
CN115329595A (en) * 2022-08-31 2022-11-11 哈尔滨工业大学 Unmanned aerial vehicle cluster task planning method and system based on knowledge and experience
CN116205464A (en) * 2023-03-21 2023-06-02 哈尔滨工程大学 Water surface multi-unmanned-ship task allocation method based on expansion consistency beam algorithm under multi-obstacle environment
CN116205464B (en) * 2023-03-21 2023-11-24 哈尔滨工程大学 Water surface multi-unmanned-ship task allocation method based on expansion consistency beam algorithm under multi-obstacle environment
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Application publication date: 20210409