CN108664038A - A kind of online mission planning method of multiple no-manned plane distribution contract auction - Google Patents

A kind of online mission planning method of multiple no-manned plane distribution contract auction Download PDF

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CN108664038A
CN108664038A CN201810457812.7A CN201810457812A CN108664038A CN 108664038 A CN108664038 A CN 108664038A CN 201810457812 A CN201810457812 A CN 201810457812A CN 108664038 A CN108664038 A CN 108664038A
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CN108664038B (en
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席建祥
杨杰
杨小冈
范志良
侯博
王�忠
郑堂
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Rocket Force University of Engineering of PLA
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Abstract

The invention discloses a kind of online mission planning methods of multiple no-manned plane distribution contract auction, include the following steps：1) it establishes multiple no-manned plane collaboration and makes an inspection tour path planning model；2) it is solved by the online mission planning algorithm of distributed contract auction and makes an inspection tour path planning model.The present invention fully considers isomery multiple no-manned plane performance and task execution demand, establishes problem model, and proposes that the completely distributed online mission planning algorithm of contract auction solves problem, realizes the planning of multiple no-manned plane fast worktodo.

Description

A kind of online mission planning method of multiple no-manned plane distribution contract auction
Technical field
The invention belongs to multiple no-manned plane cotasking planning fields, and in particular to a kind of multiple no-manned plane distribution contract auction Online mission planning method.
Background technology
Mission planning technology is maked an inspection tour in multiple no-manned plane collaboration, includes mainly cotasking distribution technique and Path Planning Technique. Task distribution refers to according to unmanned plane resource type, number and mission area attribute, under certain constraints, as voyage is navigated When constraint, sensor performance constraint, task time window constraint etc., definition make an inspection tour earnings target function, task sequence is distributed to Different unmanned planes, function to achieve the objective Income Maximum；Collaboration trajectory planning problem refers to known to known, part or unknown In the environment of information, is cooked up in advance from each unmanned plane starting point to corresponding target point, full spectrum of threats area on the way can be bypassed And barrier, safe and reliable, mutual collisionless, and meet unmanned plane itself constraints simultaneously and cooperate with the more of restrict The feasible flight track of item, be similar to multiple traveling salesmen problem (Multiple Travelling Salesman Problem, MTSP).It is an optimization problem that multiple no-manned plane collaboration, which is maked an inspection tour in mission planning question essence, i.e., different tour tasks exists Time spatially most reasonably distributes to every frame unmanned plane, and task is completed with minimum cost high quality.
Invention content
To overcome, the convergence present in the online mission planning algorithm of traditional multiple no-manned plane can not ensure, convergence rate is slow Problem, the present invention provides a kind of online mission planning methods of multiple no-manned plane distribution contract auction, and this method fully considers different Structure multiple no-manned plane performance and task execution demand establish problem model, and propose the online task of distributed contract auction completely Planning algorithm solves problem, realizes the planning of multiple no-manned plane fast worktodo.
The present invention adopts the following technical scheme that realize：
A kind of online mission planning method of multiple no-manned plane distribution contract auction, includes the following steps：
1) it establishes multiple no-manned plane collaboration and makes an inspection tour path planning model；
2) it is solved by the online mission planning algorithm of distributed contract auction and makes an inspection tour path planning model.
The present invention, which further improves, to be, the concrete methods of realizing of step 1) is as follows：
Step 1.1, in this step, is defined as follows：
Define 1：NuFor unmanned plane set；
Define 2：NtFor target collection；
Define 3：Decision variable xij∈ { 0,1 }, xij=1 indicates that unmanned plane i executes task j, xij=0 is other situations；
Define 4：LtFor the task number of each frame unmanned plane to overabsorption；
Define 5：If path river conjunction node set Pe, e=1,2 ..., E；
Define 6：It is a vector, j-th of element is xij；
Define 7：Unmanned plane identity collectionTarget identification collection
Define 8：VectorIndicate the orderly task sequence of unmanned plane i；
Define 9：If unmanned plane i executes task j, p in kth pointiK-th of element be j ∈ J, if unmanned plane i is executed Number of tasks be then less than k
Define 10：Scoring function meets cij(xi,pi)≥0；
Define 11：Lt=1 and cij(xi,pi)≡cijIndependent of xiAnd pi；
Step 1.2, hard objectives function：
Step 1.3, problem model are as follows：
The present invention, which further improves, to be, the concrete methods of realizing of step 2) is as follows：
Step 2.1, each frame unmanned plane only build a task packet, and are updated as task distributes progress, and continuing will Task is added in the packet of oneself until task cannot be added；
Step 2.2, each frame unmanned plane carry two kinds of task lists, task packet biWith path pi, biAnd piNo more than most Big distribution number of tasks Lt；
Step 2.3,It is unmanned plane i along path piAggregate earnings value, if a task j adds task packet bi In, marginal gains isWherein | | indicate the dimension of list, Expression is immediately inserted into after the nth elements of first list in second list；
Step 2.4, scoring function are initialized asPath and task packet iteration are updated to Ji=arg maxj(cij[bi]×hij),hij=| | (cij＞ yij)；
Step 2.5, each frame unmanned plane carry four vectors：Winning bids listTriumph unmanned plane listTask packetAnd corresponding path
Task is added in task packet by step 2.6, unmanned plane according to current task allocation set, if a frame unmanned plane Bid amounts are exceeded, then can abandon the task, be added after the task task marginal point in task packet just there is no Effect；
Step 2.7, winning bids list yiWith triumph unmanned plane list ziIt is built for task packet, timestamp siTable nobody The renewable time for the information that machine is obtained from other members in formation, three vectors, which are in communication with each other, realizes that Situation Awareness is consistent；
Step 2.8 is transmitted per a moment information, and time arrow becomes：Its Middle τrIt is information receiving time；
Step 2.9 receives the information of another frame unmanned plane k, for each task, z as unmanned plane iiAnd siIt is used for Determine the information of which frame unmanned plane be it is newest, auction unmanned plane i receive task j have update, reset, leave these three can The result of energy；
If step 2.10, a bid are changed by decision rule, each all newer tasks of the unmanned machine testing of frame Whether in task packet, these tasks and its all tasks later will be all released： Wherein binIndicate n-th of the entering task packet of the task, and
Step 2.11, if other tasks are added before some task, financial value centainly will not after completing this task It is promoted, is metFor all bi, b, j meet WhereinIndicate the task of vacancy；
Step 2.12, when revenue function meet limit successively decrease condition when, necessarily satisfying for：Ifn≤m, wherein bikIt is to enter unmanned plane i task packets biK-th of element because Meet
Step 2.13, time income areWherein λj＜ 1 is the conditional parameter of task j,It is Unmanned plane i is along path piIt is expected that the time of j points is reached,It is the static score of execution task j；
Step 2.14, time income indicate uncertain flight path scene characteristic, over time, make an inspection tour specific node It can decline with the desired value of path planning income,That is a frame unmanned plane along one more Long path, reaching will postpone for the time relatively short path of each task, further generate inefficient income.
The present invention has following beneficial technique effect：
For the distributed online mission planning algorithm of contract auction is with respect to mixed integer linear programming algorithm, have preferable Ductility and lower computation complexity, opposite greedy algorithm, the distributed online mission planning algorithm of contract auction have Faster calculating speed and computational stability, can ensure convergence, and satisfied solution is generated in finite time.
Description of the drawings
Fig. 1 is UAV Maneuver path schematic diagram.
Specific implementation mode
The present invention is made further instructions below in conjunction with attached drawing.
A kind of online mission planning method of multiple no-manned plane distribution contract auction provided by the invention, including the following contents：
One, it establishes multiple no-manned plane collaboration and makes an inspection tour path planning model
Step 1.1, in this step, is defined as follows：
Define 1：NuFor unmanned plane set；
Define 2：NtFor target collection；
Define 3：Decision variable xij∈ { 0,1 }, xij=1 indicates that unmanned plane i executes task j, xij=0 is other situations；
Define 4：LtFor the task number of each frame unmanned plane to overabsorption；
Define 5：If path river conjunction node set Pe, e=1,2 ..., E；
Define 6：It is a vector, j-th of element is xij；
Define 7：Unmanned plane identity collectionTarget identification collection
Define 8：VectorIndicate the orderly task sequence of unmanned plane i；
Define 9：If unmanned plane i executes task j, p in kth pointiK-th of element be j ∈ J, if unmanned plane i is executed Number of tasks be then less than k
Define 10：Scoring function meets cij(xi,pi)≥0；
Define 11：Lt=1 and cij(xi,pi)≡cijIndependent of xiAnd pi；
Step 1.2, hard objectives function：
Step 1.3, problem model are as follows：
Two, it is solved by the online mission planning algorithm of distributed contract auction and makes an inspection tour path planning model
Step 2.1, each frame unmanned plane only build a task packet, and are updated as task distributes progress, and continuing will Task is added in the packet of oneself until task cannot be added；
Step 2.2, each frame unmanned plane carry two kinds of task lists, task packet biWith path pi, biAnd piNo more than most Big distribution number of tasks Lt；
Step 2.3,It is unmanned plane i along path piAggregate earnings value, if a task j adds task packet bi In, marginal gains isWherein | | indicate the dimension of list, Expression is immediately inserted into after the nth elements of first list in second list；
Step 2.4, scoring function are initialized asPath and task packet iteration are updated to Ji=argmaxj(cij[bi]×hij),hij=| | (cij＞ yij)；
Step 2.5, each frame unmanned plane carry four vectors：Winning bids listTriumph unmanned plane listTask packetAnd corresponding path
Task is added in task packet by step 2.6, unmanned plane according to current task allocation set, if a frame unmanned plane Bid amounts are exceeded, then can abandon the task, be added after the task task marginal point in task packet just there is no Effect；
Step 2.7, winning bids list yiWith triumph unmanned plane list ziIt is built for task packet, timestamp siTable nobody The renewable time for the information that machine is obtained from other members in formation, three vectors, which are in communication with each other, realizes that Situation Awareness is consistent；
Step 2.8 is transmitted per a moment information, and time arrow becomes：τr It is information receiving time；
Step 2.9 receives the information of another frame unmanned plane k, for each task, z as unmanned plane iiAnd siIt is used for Determine the information of which frame unmanned plane be it is newest, auction unmanned plane i receive task j have update, reset, leave these three can The result of energy；
If step 2.10, a bid are changed by decision rule, each all newer tasks of the unmanned machine testing of frame Whether in task packet, these tasks and its all tasks later will be all released： Wherein binIndicate n-th of the entering task packet of the task, and
Step 2.11, if other tasks are added before some task, financial value centainly will not after completing this task It is promoted, is metFor all bi, b, j meet Indicate the task of vacancy；
Step 2.12, when revenue function meet limit successively decrease condition when, necessarily satisfying for：Ifn≤m, bik It is to enter unmanned plane i task packets biK-th of element because Meet
Step 2.13, time income areλj＜ 1 is the conditional parameter of task j,It is nobody Machine i is along path piIt is expected that the time of j points is reached,It is the static score of execution task j；
Step 2.14, time income can indicate uncertain flight path scene characteristic, over time, make an inspection tour specific The desired value of node and path planning income can decline,I.e. a frame unmanned plane is along one The longer path of item, reaching will postpone for the time relatively short path of each task, further generate inefficient receipts Benefit.
The present invention is further described with emulation experiment below in conjunction with the accompanying drawings.The simulated environment of the present invention is Inter Core 7 system of i5-4590@3.30GHz, 8GRam, Windows, MATLAB2014a platforms.Assuming that there is multi rack intelligent body to need in model Enclose the feature that in the task environment for 200m × 200m multiple regions are executed with multiple-task, each intelligent body and mission area Information is it is known that specific as follows：
1) intelligent body parameter setting
There is the intelligent body of 2 types in setting mission area, each 3 of I class, II class intelligent body are respectively labeled as UAV11、 UAV12、UAV13, UAV21、UAV22、UAV23.The performance parameter of two class intelligent bodies is as shown in the table：
1 intelligent body performance parameter of table
Intelligent body type Speed (m/s) Cruise duration (s)
Ⅰ 15 90
Ⅱ 12 100
2 intelligent body initial position of table
Intelligent body label Initial coordinate (m) Intelligent body label Initial coordinate (m)
UAV11 [91.27 137.37] UAV21 [47.81 96.68]
UAV12 [149.65 193.06] UAV22 [179.96 107.48]
UAV13 [176.90 15.54] UAV23 [138.09 29.48]
2) mission area parameter setting
Assuming that sharing 2 type, 30 tasks, wherein T in mission area1~T15A task is the Ith type, T16~T30It is a Task is the IIth type.The position of mission area is as shown in table 3：
3 mission area position of table
Communication network between intelligent body is full unicom, i.e., can direct communication between any two intelligent body.Above-mentioned Multiple agent task allocation result is as shown in table 4 under scenario situation, and each intelligent body task plan of distribution uses motor-driven path, appoints The format that business executes the time is expressed.As can be seen that improved method can efficiently solve more intelligence under the conditions of Complex Constraints It can body multi-task planning problem.
4 intelligent body task allocation result of table
The motor-driven path that intelligent body changes over time is as shown in Fig. 1.
Under same scenario situation, using greedy algorithm (SGA) and mixed integer linear programming algorithm (MILP) difference Solved, 200 Monte Carlo simulations taken to experiment, and optimal result therein are taken to be compared, experiment parameter with it is upper State that scenario is consistent, it is L that the executable task upper limit of each intelligent body, which is arranged,t, with the minimum object function of total voyage, different parameters Lower 3 kinds of Riming time of algorithm comparison is as shown in table 5.
The lower 3 kinds of Riming time of algorithm comparison of 5 different parameters of table
From the above results：Can be that each intelligent body distributes phase from global optimization angle using the greedy algorithm of centralization It answers goal task to go to execute, the more excellent solution of problem can be obtained；Although can be obtained using the MILP methods that CPLEX softwares solve Accurate optimal solution, but as problem scale increases, run time sharply increases；The distributed online mission planning of contract auction is calculated Method and greedy algorithm have preferable ductility and lower computation complexity, opposite greedy algorithm, distributed contract auction Online mission planning algorithm has faster calculating speed and computational stability.
The content that the present invention need to protect includes the following：
1, the online mission planning algorithm of distributed contract auction, i.e. algorithm.

Claims (3)

1. a kind of online mission planning method of multiple no-manned plane distribution contract auction, which is characterized in that include the following steps：
1) it establishes multiple no-manned plane collaboration and makes an inspection tour path planning model；
2) it is solved by the online mission planning algorithm of distributed contract auction and makes an inspection tour path planning model.
2. a kind of online mission planning method of multiple no-manned plane distribution contract auction according to claim 1, feature exist In the concrete methods of realizing of step 1) is as follows：
Step 1.1, in this step, is defined as follows：
Define 1：NuFor unmanned plane set；
Define 2：NtFor target collection；
Define 3：Decision variable xij∈ { 0,1 }, xij=1 indicates that unmanned plane i executes task j, xij=0 is other situations；
Define 4：LtFor the task number of each frame unmanned plane to overabsorption；
Define 5：If path river conjunction node set Pe, e=1,2 ..., E；
Define 6：It is a vector, j-th of element is xij；
Define 7：Unmanned plane identity collectionTarget identification collection
Define 8：VectorIndicate the orderly task sequence of unmanned plane i；
Define 9：If unmanned plane i executes task j, p in kth pointiK-th of element be j ∈ J, if appointing of executing of unmanned plane i Business number be then less than k
Define 10：Scoring function meets cij(xi,pi)≥0；
Define 11：Lt=1 and cij(xi,pi)≡cijIndependent of xiAnd pi；
Step 1.2, hard objectives function：
Step 1.3, problem model are as follows：
3. a kind of online mission planning method of multiple no-manned plane distribution contract auction according to claim 2, feature exist In the concrete methods of realizing of step 2) is as follows：
Step 2.1, each frame unmanned plane only build a task packet, and are updated as task distributes progress, continue task It is added in the packet of oneself until task cannot be added；
Step 2.2, each frame unmanned plane carry two kinds of task lists, task packet biWith path pi, biAnd piNo more than maximum point With number of tasks Lt；
Step 2.3,It is unmanned plane i along the aggregate earnings value of path pi, if a task j adds task packet biIn, side Border gain isWherein | | indicate the dimension of list,It indicates It is immediately inserted into after the nth elements of first list in second list；
Step 2.4, scoring function are initialized asPath and task packet iteration are updated to Ji=argmaxj(cij[bi]×hij),hij=| | (cij＞ yij)；
Step 2.5, each frame unmanned plane carry four vectors：Winning bids listTriumph unmanned plane list Task packetAnd corresponding path
Task is added in task packet by step 2.6, unmanned plane according to current task allocation set, if the bid of a frame unmanned plane Value is exceeded, then can abandon the task, and the task marginal point in task packet is added after the task with regard to no longer valid；
Step 2.7, winning bids list yiWith triumph unmanned plane list ziIt is built for task packet, timestamp siTable unmanned plane from The renewable time of the information obtained in other members in formation, three vectors, which are in communication with each other, realizes that Situation Awareness is consistent；
Step 2.8 is transmitted per a moment information, and time arrow becomes：Wherein τr It is information receiving time；
Step 2.9 receives the information of another frame unmanned plane k, for each task, z as unmanned plane iiAnd siFor determining The information of which frame unmanned plane is newest, and auction unmanned plane i, which receives task j, to be had update, reset, leave these three possible knots Fruit；
If step 2.10, a bid are changed by decision rule, whether each all newer tasks of the unmanned machine testing of frame In task packet, these tasks and its all tasks later will be all released： Wherein binIndicate n-th of the entering task packet of the task, and
Step 2.11, if other tasks are added before some task, financial value will not centainly be promoted after completing this task, MeetFor all bi, b, j meetWherein Indicate the task of vacancy；
Step 2.12, when revenue function meet limit successively decrease condition when, necessarily satisfying for：Wherein bikIt is to enter unmanned plane i task packets biK-th of element because Meet
Step 2.13, time income areWherein λj＜ 1 is the conditional parameter of task j,It is unmanned plane I is along path piIt is expected that the time of j points is reached,It is the static score of execution task j；
Step 2.14, time income indicate uncertain flight path scene characteristic, over time, make an inspection tour specific node and road The desired value of diameter planning gain can decline,I.e. a frame unmanned plane is along a longer road Diameter, reaching will postpone for the time relatively short path of each task, further generate inefficient income.
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