CN112528416A - Online real-time distribution algorithm - Google Patents

Online real-time distribution algorithm Download PDF

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CN112528416A
CN112528416A CN202011504772.0A CN202011504772A CN112528416A CN 112528416 A CN112528416 A CN 112528416A CN 202011504772 A CN202011504772 A CN 202011504772A CN 112528416 A CN112528416 A CN 112528416A
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张俊杰
徐骋
李振
梁文宝
张云
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Beijing Research Institute of Mechanical and Electrical Technology
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Abstract

Compared with other task allocation algorithms, the online real-time allocation algorithm has the advantages of low calculation complexity and high operation efficiency. The method is suitable for the online real-time multi-task allocation scene of multiple aircrafts, and can well meet the task allocation requirement.

Description

Online real-time distribution algorithm
Technical Field
The invention relates to the problem of online real-time multi-task allocation of multiple aircrafts, in particular to an online allocation algorithm based on a market mechanism, and belongs to the field of task allocation.
Background
The problem of aircraft mission allocation is an important issue in aircraft applications. In the aircraft allocation problem, the task types are all differentiated. Therefore, under the constraints of considering the performance of the aircraft and the type of the task, how to allocate the aircraft to execute the relevant task to maximize the total system benefit is an urgent problem to be solved by the aircraft cluster system.
Distributed and centralized are two main algorithms for studying the problem of multi-aircraft task assignment. Compared with a centralized algorithm, the distributed algorithm has small dependence on a communication center and good expandability and robustness, and the problem of task allocation of the aircraft based on the distributed algorithm under the condition of limited communication is more and more concerned.
Disclosure of Invention
The invention provides an on-line real-time allocation algorithm, which can quickly realize the on-line task allocation process of multiple aircrafts in the real-time multi-task allocation problem of the multiple aircrafts.
An online real-time distribution algorithm comprising the steps of:
step 1, establishing an online multi-task allocation algorithm mathematical model: establishing a corresponding mathematical model according to the demand of online real-time task allocation of the multiple aircrafts, and setting corresponding constraint conditions according to the allocation demand;
step 2, designing a bidding algorithm: estimating the task profit value of the condition that the aircraft completes each task based on the situation perception result of the aircraft on the environment, the cluster state, the task state and the like; selecting an intentional task based on a task selection strategy according to a task income estimation result; contacting an aircraft with network connection with the aircraft and sending out bids for tasks;
step 3, designing a synchronization algorithm: receiving a task bid, comparing the received task bid evaluation value with the value estimation of the task to determine a trading result; and responding to all the aircrafts in contact with the aircrafts in the operation period, publishing the determined transaction result, completing the synchronization of the aircrafts to the task distribution result and ensuring the consistency of the global distribution result.
Compared with the traditional centralized task allocation algorithm, the online real-time allocation algorithm provided by the invention has the advantages that the dependence on a communication center is weak, the robustness is strong, and the stability is good; compared with other distributed task allocation algorithms, the online real-time allocation algorithm has the advantages of low computational complexity and high operation efficiency. The method is suitable for the online real-time multi-task allocation scene of multiple aircrafts, and can well meet the task allocation requirement.
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The accompanying drawings, which are included to provide a further understanding of the embodiments of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention. It is obvious that the drawings in the following description are only some embodiments of the invention, and that for a person skilled in the art, other drawings can be derived from them without inventive effort.
FIG. 1 is a schematic flow chart of a bidding algorithm;
FIG. 2 is a schematic flow chart of a synchronization algorithm;
FIG. 3 is a schematic diagram of the distribution results in the initial state;
FIG. 4 is a schematic diagram of the assignment after the target location has changed;
FIG. 5 is a schematic diagram of the allocation result after the target is increased;
FIG. 6 is a schematic representation of the results of the assignment after a change in the position of the aircraft;
FIG. 7 is a graph of time spent planning a cycle versus target quantity for different algorithms.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings.
An online real-time allocation algorithm is adopted to solve the problem of online real-time multitask allocation of multiple computers, and the algorithm comprises the following steps:
step 1, establishing an online algorithm mathematical model: establishing a corresponding mathematical model according to the demand of online real-time task allocation of the multiple aircrafts, expressing the mathematical model through a mathematical formula, and setting corresponding constraint conditions according to the allocation demand.
Step 2, designing a bidding algorithm: the bidding algorithm has the function of estimating the task profit value of the condition that the aircraft completes each task based on the situation perception result of the aircraft on the environment, the cluster state, the task state and the like; selecting an intentional task based on a task selection strategy according to a task income estimation result; and contacting the aircraft with network connection to send out bids for the tasks.
Step 3, designing a synchronization algorithm: the function of the synchronization algorithm is to receive the task bid, compare the received task bid estimate with the value estimate of the task itself, and determine the result of the transaction; and responding to all the aircrafts in contact with the aircrafts in the operation period, publishing the determined transaction result, completing the synchronization of the aircrafts to the task distribution result and ensuring the consistency of the global distribution result.
Step 4, establishing feasibility of a corresponding simulation environment verification algorithm: in order to research the actual application effect of the algorithm, a specific case is designed to carry out simulation verification on the performance index. And judging the feasibility of the algorithm through a simulation result.
Further, the step 1 specifically includes:
based on a task allocation principle and an aircraft cluster task model, establishing a mathematical optimization model as follows:
Figure BDA0002844569380000041
S.t.
Figure BDA0002844569380000042
Figure BDA0002844569380000043
Figure BDA0002844569380000044
Figure BDA0002844569380000045
in the above formula: y denotes the return of the aircraft to perform the mission, N is the number of missions, M is the number of aircraft, R (S)UAV(j),Stask(i) Execution of a revenue calculation function for task i for aircraft j, SUAV(j) As a function of the state of the jth aircraft, Stask(i) Is a state function of the ith task, ziNumber of aircraft required for ith mission, zaThe state function for the number of aircraft performing the mission is expressed as follows:
SUAV(j)＝{PUAV(j),V,Cj} (4)
Stask(i)＝{ptask(i),Ii,Ti,Zi} (5)
task profit computation function R (S)UAV(j),Stask(i) Is proportional to-Dij、Ii、
Figure BDA0002844569380000046
-Cj、-TiThe revenue function is built as follows:
Figure BDA0002844569380000047
in the above formula; pUAV(j) Is the position coordinate of the jth aircraft, V is the speed of the aircraft, CjDegree of wear, P, for the jth aircrafttask(i) Position coordinates for the ith task, IiIs the importance of the ith task, TiDegree of threat for the ith task, DijDistance of jth aircraft to ith mission, RA,RBFor varying and basic benefits of the task, p, e1、ε2、ε3、ε4、ε5Are weight coefficients.
Further, the step 2 specifically includes:
the operation of each cycle of the bidding algorithm is based on the judgment of the situation change, namely, the bidding algorithm is operated when the situation changes; potential profit R (S) of the aircraft for each task all the time in the whole task processUAV(j),Stask(1))、R(SUAV(j),Stask(2))、R(SUAN(j),Stask(3))…、R(SUAV(j),Stask(M)) and in the profit calculation formula, SUAV(j) Indicating the status of the jth aircraftFunction, stask(i) A state function representing the ith task; then judging whether the situation of all the current aircrafts changes or not, and judging whether the held income meets the following conditions or not:
Figure BDA0002844569380000051
the meaning of equation (1) is that the current profit for the jth aircraft is lower than the maximum profit for the current mission and the aircraft is not assigned to the mission;
the decision condition is to find a task that is more profitable than the existing task of the aircraft, and if such a task does not exist, return to the beginning of the algorithm.
Further, step 3 specifically includes:
the operation of each cycle of the synchronization algorithm is based on periodic synchronization points, i.e. synchronization is performed at intervals;
in the whole communication process, the structure of the communication network is required to be a complete network, namely the topology of the network can cover all aircrafts;
before each iteration is started, the task price is updated through the aircraft connected with the task price, before each price updating, the updated price of the next round is the maximum price of the previous round, and the price updating formula is as follows:
Figure BDA0002844569380000061
wherein,
Figure BDA0002844569380000062
representing the price of the jth task stored by the ith aircraft before the start of the τ th iteration.
Further, if the entire network is able to connect, by constantly communicating with neighboring nodes, the highest bid that can eventually be obtained.
For a further understanding of the present invention, an online real-time allocation algorithm of the present invention is described in detail below with reference to the accompanying drawings.
FIG. 1 shows a flow chart of the bidding algorithm. The operation of each cycle of the bidding algorithm is based on judgment of situation change, namely the bidding algorithm is operated when the situation changes, and the aircraft always obtains the potential income R (S) of each task in the whole task processUAV(j),Stask(1))、R(SUAV(j),Stask(2))、R(SUAN(j),Stask(3))…、R(SUAV(j),Stask(M)) and in the profit calculation formula, SUAV(j) Representing the state function of the jth aircraft, stask(i) Representing the state function of the ith task. Then judging whether the situation of all the current aircrafts changes or not, and judging whether the held income meets the following conditions or not:
Figure BDA0002844569380000063
the expression of equation (1) means that the current profit for the jth aircraft is lower than the maximum profit for the current mission and that the aircraft is not assigned to the mission.
The decision condition is to find a task that is more profitable than the existing task of the aircraft, and if such a task does not exist, return to the beginning of the algorithm.
Preferably, before designing the bidding algorithm flowchart, the following steps should be included:
based on the principle of a market mechanism and an aircraft cluster task model, the following mathematical optimization model is established:
Figure BDA0002844569380000071
S.t.
Figure BDA0002844569380000072
Figure BDA0002844569380000073
Figure BDA0002844569380000074
Figure BDA0002844569380000075
in the above formula: y denotes the return of the aircraft to perform the mission, N is the number of missions, M is the number of aircraft, R (S)UAV(j),Stask(i) Execution of a revenue calculation function for task i for aircraft j, SUAV(j) As a function of the state of the jth aircraft, Stask(i) Is a state function of the ith task, ziNumber of aircraft required for ith mission, zaThe number of aircraft to perform the mission. The state function can be expressed as follows:
SUAV(j)＝{PUAV(j),V,Cj} (4)
Stask(i)＝{ptask(i),Ii,Ti,Zi} (5)
task profit computation function R (S)UAV(j),Stask(i) Is proportional to-Dij、Ii、
Figure BDA0002844569380000076
-Cj、-Ti. The revenue function is established as follows:
Figure BDA0002844569380000077
in the above formula; pUAV(j) Is the position coordinate of the jth aircraft, V is the speed of the aircraft, CjDegree of wear, P, for the jth aircrafttask(i) Position coordinates for the ith task, IiIs the weight of the ith taskDegree of importance, TiDegree of threat for the ith task, DijDistance of jth aircraft to ith mission, RA,RBFor varying and basic benefits of the task, p, e1、ε2、ε3、ε4、ε5Are weight coefficients.
Fig. 2 shows a flow chart of the synchronization algorithm. The execution of each cycle of the synchronization algorithm is based on periodic synchronization points, i.e. synchronization is performed at intervals.
As shown in fig. 3, a network communication diagram between clusters of aircraft is shown. During the whole communication process, the structure of the communication network is required to be a complete network, namely, the topology of the network can cover all the aircrafts. Under the optimal environment, the communication network can realize the point-to-point communication between any two aircrafts in a short time.
In the fully distributed algorithm of the invention, the aircraft is not aware of the global maximum price of the task, since no single bureaucratic plane is designated to collect and broadcast the price information of all tasks, it can only update the price of the task by the aircraft connected to it before the start of each iteration. The price update formula is as follows:
Figure BDA0002844569380000081
the formula means that before each price updating round, the updated price of the next round is the maximum price of the previous round.
Wherein,
Figure BDA0002844569380000082
representing the price of the jth task stored by the ith aircraft before the start of the ith iteration, it is clear that the highest bid that can eventually be obtained by constantly communicating with neighboring nodes if the entire network can be connected.
In order to research the actual application effect of the algorithm, the following cases are designed to simulate the performance indexes. And the aircraft command center receives the superior instruction and dispatches the aircraft cluster to execute the task. The range of the set area is 200 x 200, the speed of the aircraft is 200m/s, and the specific parameters of other aircraft and targets are not detailed here. According to the actual application environment, the algorithm is subjected to simulation verification, and the specific simulation result is shown in the following figure.
As shown in fig. 3, the result of the assignment in the initial state during the simulation is shown. A corresponding application scene is designed according to an algorithm, 3 task targets are provided in an initial state, 10 aircrafts are provided, and the optimal distribution effect can be obtained by running the algorithm.
As shown in fig. 4, the assignment result is the target position after being changed. When the position of the task object is changed, the assignment result is also changed with respect to the initial situation.
As shown in fig. 5, the allocation result after the target is increased. When a mission objective is added, the corresponding distribution results of 4 mission objectives and 10 aircrafts are also changed.
As shown in fig. 6, the result of the assignment after the change of the position of the aircraft. When the position of the aircraft changes, the distribution results all change.
As shown in fig. 7, which is a graph showing a relationship between time consumed by planning a period and a target quantity under different algorithms, i can see that when the number of target tasks is less than 5, the time consumed by the algorithm of the present invention is not particularly superior to the time consumed by the conventional centralized allocation algorithm, but when the number of target tasks is greater than 5, the time consumed by the algorithm of the present invention in the specified period is significantly superior to that of the conventional algorithm.
Compared with the traditional centralized task allocation algorithm, the algorithm has weak dependence on a communication center, strong robustness and good stability; compared with other distributed task allocation algorithms, the online real-time allocation algorithm has the advantages of low computational complexity and high operation efficiency. The method is suitable for the online real-time multi-task allocation scene of multiple aircrafts, and can well meet the task allocation requirement.

Claims (5)

1. An online real-time distribution algorithm, comprising the steps of:
step 1, establishing an online multi-task allocation algorithm mathematical model: establishing a corresponding mathematical model according to the demand of online real-time task allocation of the multiple aircrafts, and setting corresponding constraint conditions according to the allocation demand;
step 2, designing a bidding algorithm: estimating the task profit value of the condition that the aircraft completes each task based on the situation perception result of the aircraft on the environment, the cluster state, the task state and the like; selecting an intentional task based on a task selection strategy according to a task income estimation result; and contacting the aircraft with network connection to send out bids for the tasks.
2. The online real-time distribution algorithm of claim 1, further comprising the step of 3: designing a synchronization algorithm: receiving a task bid, comparing the received task bid evaluation value with the value estimation of the task to determine a trading result; and responding to all the aircrafts in contact with the aircrafts in the operation period, publishing the determined transaction result, completing the synchronization of the aircrafts to the task distribution result and ensuring the consistency of the global distribution result.
3. The online real-time distribution algorithm according to claim 2, wherein the step 1 specifically comprises:
based on the principle of a market mechanism and an aircraft cluster task model, the following mathematical optimization model is established:
Figure FDA0002844569370000011
S.t.
Figure FDA0002844569370000012
Figure FDA0002844569370000013
Figure FDA0002844569370000014
Figure FDA0002844569370000015
in the above formula: y denotes the return of the aircraft to perform the mission, N is the number of missions, M is the number of aircraft, R (S)UAV(j),Stask(i) Execution of a revenue calculation function for task i for aircraft j, SUAV(j) As a function of the state of the jth aircraft, Stask(i) Is a state function of the ith task, ziNumber of aircraft required for ith mission, zaThe state function for the number of aircraft performing the mission is expressed as follows:
SUAV(j)＝{PUAV(j),V,Cj} (4)
Stask(i)＝{ptask(i),Ii,Ti,Zi} (5) 。
4. the online distribution algorithm based on market mechanism according to claim 3, wherein the step 2 specifically comprises:
the operation of each cycle of the bidding algorithm is based on the judgment of the situation change, namely, the bidding algorithm is operated when the situation changes; potential profit R (S) of the aircraft for each task all the time in the whole task processUAV(j),Stask(1))、R(SUAV(j),Stask(2))、R(SUAN(j),Stask(3))…、R(SUAV(j),Stask(M)) and in the profit calculation formula, SUAV(j) Representing the state function of the jth aircraft, stask(i) Representing the ith taskA state function of (a); then judging whether the situation of all the current aircrafts changes or not, and judging whether the held income meets the following conditions or not:
Figure FDA0002844569370000021
the meaning of equation (1) is that the current profit for the jth aircraft is lower than the maximum profit for the current mission and the aircraft is not assigned to the mission;
the decision condition is to find a task that is more profitable than the existing task of the aircraft, and if such a task does not exist, return to the beginning of the algorithm.
5. The online distribution algorithm based on market mechanism according to claim 2, wherein step 3 specifically comprises:
the operation of each cycle of the synchronization algorithm is based on periodic synchronization points, i.e. synchronization is performed at intervals;
in the whole communication process, the structure of the communication network is required to be a complete network, namely the topology of the network can cover all aircrafts;
before each iteration is started, the task price is updated through the aircraft connected with the task price, before each price updating, the updated price of the next round is the maximum price of the previous round, and the price updating formula is as follows:
Figure FDA0002844569370000031
wherein,
Figure FDA0002844569370000032
representing the price of the jth task stored by the ith aircraft before the start of the τ th iteration.
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