CN108664038B - Multi-unmanned aerial vehicle distributed contract auction online task planning method - Google Patents
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Abstract
The invention discloses a multi-unmanned aerial vehicle distributed contract auction online task planning method, which comprises the following steps: 1) establishing a multi-unmanned aerial vehicle collaborative tour path planning model; 2) and solving the patrol path planning model by a distributed contract auction online task planning algorithm. The invention fully considers the performance and task execution requirements of the heterogeneous multi-unmanned aerial vehicles, establishes a problem model, and provides a fully distributed contract auction online task planning algorithm to solve the problem, thereby realizing the rapid task planning of the multi-unmanned aerial vehicles.
Description
Technical Field
The invention belongs to the field of multi-unmanned aerial vehicle collaborative task planning, and particularly relates to a multi-unmanned aerial vehicle distributed contract auction online task planning method.
Background
The multi-unmanned aerial vehicle collaborative patrol task planning technology mainly comprises a collaborative task allocation technology and a path planning technology. Task allocation means that according to the resource type, the number and the task area attribute of the unmanned aerial vehicle, under certain constraint conditions, such as voyage and navigation time constraint, sensor performance constraint, task time window constraint and the like, a tour income objective function is defined, a task sequence is allocated to different unmanned machines, and the maximum income of the objective function is realized; the collaborative flight path planning Problem refers to planning Multiple feasible flight paths from starting points to corresponding target points of all unmanned aerial vehicles in advance in an environment with known, partially known or unknown information, can bypass various threat areas and barriers in the way, is safe, reliable and free of mutual collision, and simultaneously meets self constraint conditions and collaborative constraint limits of the unmanned aerial vehicles, and is similar to a multi-traveler Problem (MTSP). The problem of planning the multi-unmanned aerial vehicle collaborative inspection task is essentially an optimization problem, namely, different inspection tasks are most reasonably distributed to each unmanned aerial vehicle in time and space, and the tasks are finished with minimum cost and high quality.
Disclosure of Invention
In order to solve the problems that the convergence cannot be guaranteed and the convergence speed is low in the traditional multi-unmanned-aerial-vehicle online task planning algorithm, the invention provides a multi-unmanned-vehicle distributed contract auction online task planning method which fully considers the performances of heterogeneous multi-unmanned aerial vehicles and task execution requirements, establishes a problem model, provides a completely distributed contract auction online task planning algorithm to solve the problem and realizes multi-unmanned-aerial-vehicle rapid task planning.
The invention is realized by adopting the following technical scheme:
a multi-unmanned aerial vehicle distributed contract auction online task planning method comprises the following steps:
1) establishing a multi-unmanned aerial vehicle collaborative tour path planning model;
2) and solving the patrol path planning model by a distributed contract auction online task planning algorithm.
The further improvement of the invention is that the specific implementation method of the step 1) is as follows:
step 1.1, in this step, the definition is as follows:
definition 1: n is a radical ofuIs a set of unmanned aerial vehicles;
definition 2: n is a radical oftIs a target set;
definition 3: decision variable xij∈{0,1}，xijDenotes that drone i performs task j, x as 1ij0 is the other case;
definition 4: l istFor each oneThe number of tasks allocated to the unmanned aerial vehicle at most;
definition 5: set P of nodes for setting path intersectione,e＝1,2,…,E；
Definition 6:
is a vector with the jth element xij；
Definition 7: unmanned identity identification set
Target identification set
Definition 8: vector quantity
Representing an ordered task sequence for drone i;
definition 9: if UAV i executes task j, p at k pointiIs J e J, if the number of tasks performed by drone i is less than k
Definition 10: the score function satisfies cij(xi,pi)≥0；
Definition 11: l ist1 and cij(xi,pi)≡cijIndependent of xiAnd pi；
Step 1.2, defining a target function:
step 1.3, the problem model is as follows:
the further improvement of the invention is that the specific implementation method of the step 2) is as follows:
2.1, each unmanned aerial vehicle only constructs one task package, updates along with the progress of task allocation, and continuously adds the tasks into the packages of the unmanned aerial vehicles until the tasks cannot be added;
step 2.2, each unmanned aerial vehicle carries two task lists, namely a task package biAnd path pi，biAnd piThe maximum number of assigned tasks L cannot be exceededt；
Step 2.3,
Following path p for drone iiIf a task j joins the task package biIn, the marginal gain is
Where | represents the dimension of the list,
indicating insertion into the second list immediately after the nth element of the first list;
step 2.4, the score function is initialized to
Path and task packet iterative update as
Ji＝arg maxj(cij[bi]×hij)，
hij＝||(cij＞yij)；
Step 2.5, each unmanned aerial vehicle carries four vectors: winningBidding list
Winning unmanned aerial vehicle list
Task package
And corresponding path
Step 2.6, the unmanned aerial vehicle adds the task into the task package according to the current task allocation set, if the bid value of one unmanned aerial vehicle is exceeded, the task is abandoned, and the marginal score of the task added into the task package after the task is not effective any more;
step 2.7, winning Bid List yiAnd a winning drone list ziTime stamp s for task package constructioniAt the update moment of information obtained by the unmanned aerial vehicle from other members in the formation, the three vectors are communicated with each other to realize situation perception consistency;
step 2.8, transmitting the information every moment, wherein the time vector is changed into:
wherein tau isrIs the time of receipt of the information;
step 2.9, when the unmanned aerial vehicle i receives the information of another unmanned aerial vehicle k, for each task, ziAnd siThe information used for determining which unmanned plane is the latest, and the auction unmanned plane i receives three possible results of updating, resetting and leaving of the task j;
step 2.10, if a bid is changed by the decision criteria, each drone checks whether all updated tasks are in the task package, these tasks and all the tasks after them will be released:
wherein b isinRepresents the nth task of the incoming task package, and
step 2.11, if other tasks are added before a certain task, the income value is not increased after the task is finished, and the requirement of meeting the requirement
For all biB, j satisfy
Wherein
Representing a vacant task;
step 2.12, when the income function meets the condition of marginal decrement, the following conditions are necessarily met:
ifn ≤ m, wherein bikIs to enter a task packet b of an unmanned aerial vehicle iiThe k element of (2) because
Satisfy the requirement of
Step 2.13, the time gain is
Wherein λj< 1 is a condition parameter of task j,
is that the unmanned plane i follows the path piThe time to the point j is expected to arrive,
is the static score for performing task j;
step 2.14, the time gain represents the uncertain track scene characteristics, and the expected value of the planning gain of the patrol specific node and the route is reduced along with the time,
that is, a drone follows a longer path, and the time to reach each task is delayed relative to a shorter path, further resulting in inefficient revenue.
The invention has the following beneficial technical effects:
compared with a mixed integer linear programming algorithm, the distributed contract auction online task programming algorithm has better extensibility and lower calculation complexity, and compared with a greedy algorithm, the distributed contract auction online task programming algorithm has higher calculation speed and calculation stability, can ensure the convergence of the algorithm and generates a satisfactory solution within a limited time.
Drawings
Fig. 1 is a schematic diagram of a maneuvering path of an unmanned aerial vehicle.
Detailed Description
The invention is further described below with reference to the accompanying drawings.
The invention provides a multi-unmanned aerial vehicle distributed contract auction online task planning method, which comprises the following steps:
firstly, establishing a multi-unmanned aerial vehicle collaborative tour path planning model
Step 1.1, in this step, the definition is as follows:
definition 1: n is a radical ofuIs a set of unmanned aerial vehicles;
definition 2: n is a radical oftIs a target set;
definition 3: decision variable xij∈{0,1}，xijDenotes that drone i performs task j, x as 1ij0 is the other case;
definition 4: l istThe number of tasks allocated to each unmanned aerial vehicle at most;
definition 5: set P of nodes for setting path intersectione,e＝1,2,…,E；
Definition 6:
is a vector with the jth element xij；
Definition 7: unmanned identity identification set
Target identification set
Definition 8: vector quantity
Representing an ordered task sequence for drone i;
definition 9: if UAV i executes task j, p at k pointiIs J e J, if the number of tasks performed by drone i is less than k
Definition 10: the score function satisfies cij(xi,pi)≥0；
Definition 11: l ist1 and cij(xi,pi)≡cijIndependent of xiAnd pi；
Step 1.2, defining a target function:
step 1.3, the problem model is as follows:
solving patrol path planning model through distributed contract auction online task planning algorithm
2.1, each unmanned aerial vehicle only constructs one task package, updates along with the progress of task allocation, and continuously adds the tasks into the packages of the unmanned aerial vehicles until the tasks cannot be added;
step 2.2, each unmanned aerial vehicle carries two task lists, namely a task package biAnd path pi，biAnd piThe maximum number of assigned tasks L cannot be exceededt；
Step 2.3,
Following path p for drone iiIf a task j joins the task package biIn, the marginal gain is
Where | represents the dimension of the list,
indicating insertion into the second list immediately after the nth element of the first list;
step 2.4, the score function is initialized to
Path and task packet iterative update as
Ji＝argmaxj(cij[bi]×hij)，
hij＝||(cij＞yij)；
Step 2.5, each unmanned aerial vehicle carries four vectors: list of winning bids
Winning unmanned aerial vehicle list
Task package
And corresponding path
Step 2.6, the unmanned aerial vehicle adds the task into the task package according to the current task allocation set, if the bid value of one unmanned aerial vehicle is exceeded, the task is abandoned, and the marginal score of the task added into the task package after the task is not effective any more;
step 2.7, winning Bid List yiAnd a winning drone list ziTime stamp s for task package constructioniAt the update moment of information obtained by the unmanned aerial vehicle from other members in the formation, the three vectors are communicated with each other to realize situation perception consistency;
step 2.8, transmitting the information every moment, wherein the time vector is changed into:
τris the information reception time;
step 2.9, when the unmanned aerial vehicle i receives the information of another unmanned aerial vehicle k, for each task, ziAnd siThe information used for determining which unmanned plane is the latest, and the auction unmanned plane i receives three possible results of updating, resetting and leaving of the task j;
step 2.10, if a bid is changed by the decision criteria, each drone checks whether all updated tasks are in the task package, these tasks and all the tasks after them will be released:
wherein b isinRepresents the nth task of the incoming task package, and
step 2.11, if other tasks are added before a certain task, the income value is not increased after the task is finished, and the requirement of meeting the requirement
For all biB, j, satisfy
A task representing a vacancy;
step 2.12, when the income function meets the condition of marginal decrement, the following conditions are necessarily met:
ifn≤m，bikis to enter a task packet b of an unmanned aerial vehicle iiThe k element of (2) because
Satisfy the requirement of
Step 2.13, the time gain is
λj< 1 is a condition parameter of task j,
is that unmanned plane i follows path piThe time to the point j is expected to arrive,
is the static score for performing task j;
step 2.14, the time gain can represent the uncertain track scene characteristics, and the tour characteristics are determined along with the timeThe expected value of the node-specific and path planning revenue will decrease,
i.e. a drone following a longer path, the time to reach each task is delayed relative to the shorter path, further resulting in inefficient revenue.
The invention is further explained by combining the attached drawings and simulation experiments. The simulation environment of the invention is an InterCore i5-4590@3.30GHz, 8GRam, Windows 7 system and MATLAB2014a platform. Assuming that there are multiple agents that need to perform multiple tasks on multiple areas in a task environment with a range of 200m × 200m, the characteristic information of each agent and task area is known, which is as follows:
1) intelligent agent parameter setting
There are 2 types of agents in the set task area, 3 agents for type I and type II, respectively, labeled as UAVs11、UAV12、UAV13，UAV21、UAV22、UAV23. The performance parameters of the two classes of agents are shown in the following table:
TABLE 1 Intelligent Performance parameters
| Type of agent | Speed (m/s) | Duration(s) |
| Ⅰ | 15 | 90 |
| Ⅱ | 12 | 100 |
TABLE 2 initial position of agent
| Smart label | Initial coordinate (m) | Smart 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) Task area parameter setting
Assume that there are a total of 2 types of 30 tasks in the task area, where T1～T15The task being of type I, T16～T30Each task is of type ii. The location of the task area is shown in table 3:
TABLE 3 task area location
The communication network between agents is full connectivity, i.e., direct communication between any two agents is possible. The multi-agent task allocation results in the desired situation are shown in table 4, and each agent task allocation plan is expressed in a format of a maneuvering path and a task execution time. It can be seen that the improved method can effectively solve the problem of multi-agent multi-task allocation under complex constraints.
TABLE 4 agent task assignment results
The time-varying maneuver path of an agent is shown in FIG. 1.
Under the same situation, respectively solving by adopting a greedy algorithm (SGA) and a mixed integer linear programming algorithm (MILP), carrying out 200 Monte Carlo simulations on the experiment, comparing the optimal results, setting the upper limit of executable tasks of each intelligent agent to be LtThe total voyage minimum is taken as an objective function, and the running time ratio of 3 algorithms under different parameters is shown in table 5.
TABLE 5 comparison of run times of 3 algorithms under different parameters
From the above results, it can be seen that: by adopting a centralized greedy algorithm, corresponding target tasks can be distributed to the intelligent agents from the perspective of global optimization to be executed, and a better solution of the problem can be obtained; although the MILP method solved by CPLEX software can obtain the accurate optimal solution, the operation time is increased sharply along with the increase of the problem scale; the distributed contract auction online task planning algorithm and the greedy algorithm have better extensibility and lower calculation complexity, and compared with the greedy algorithm, the distributed contract auction online task planning algorithm has higher calculation speed and calculation stability.
The contents to be protected of the invention comprise the following points:
1. and (3) a distributed contract auction online task planning algorithm.
Claims (1)
1. A multi-unmanned aerial vehicle distributed contract auction online task planning method is characterized by comprising the following steps:
1) establishing a multi-unmanned aerial vehicle collaborative tour path planning model; the specific implementation method comprises the following steps:
step 1.1, in this step, the definition is as follows:
definition 1: n is a radical ofuIs a set of unmanned aerial vehicles;
definition 2: n is a radical oftIs a target set;
definition 3: decision variable xij∈{0,1}，xijDenotes that drone i performs task j, x as 1ij0 is the other case;
definition 4: l istThe number of tasks allocated to each unmanned aerial vehicle at most;
definition 5: set P of nodes for setting path intersectione,e＝1,2,…,E；
Definition 6:
is a vector with the jth element xij；
Definition 7: unmanned identity identification set
Target identification set
Definition 8: vector quantity
Representing an ordered task sequence for drone i;
definition 9: if UAV i executes task j, p at k pointiIs J e J, if the number of tasks performed by drone i is less than k
Definition 10: the score function satisfies cij(xi,pi)≥0；
Definition 11: l ist1 and cij(xi,pi)≡cijIndependent of xiAnd pi；
Step 1.2, defining a target function:
step 1.3, the problem model is as follows:
2) solving a tour path planning model through a distributed contract auction online task planning algorithm; the specific implementation method comprises the following steps:
2.1, each unmanned aerial vehicle only constructs one task package, updates along with the progress of task allocation, and continuously adds tasks into the packages until the tasks cannot be added;
step 2.2, each unmanned aerial vehicle carries two task lists,task Package biAnd path pi，biAnd piThe maximum number of tasks to allocate L cannot be exceededt；
Step 2.3,
Following path p for drone iiIf a task j joins the task package biIn, the marginal gain is
Where | represents the dimension of the list,
indicating insertion into the second list immediately after the nth element of the first list;
step 2.4, the score function is initialized to
Path and task packet iterative update as
Ji＝arg maxj(cij[bi]×hij)，
hij＝||(cij＞yij)；
Step 2.5, each unmanned aerial vehicle carries four vectors: list of winning bids
Winning unmanned aerial vehicle list
Task package
And corresponding path
Step 2.6, the unmanned aerial vehicle adds the task into the task package according to the current task allocation set, if the bid value of one unmanned aerial vehicle is exceeded, the task is abandoned, and the marginal score of the task added into the task package after the task is not effective any more;
step 2.7, winning Bid List yiAnd a winning drone list ziTime stamp s for task package constructioniRepresenting the update time of information obtained by the unmanned aerial vehicle from other members in the formation, and realizing situation perception consistency by mutual communication of the three vectors;
step 2.8, transmitting the information every moment, wherein the time vector is changed into:
wherein tau isrIs the time of receipt of the information;
step 2.9, when the unmanned aerial vehicle i receives the information of another unmanned aerial vehicle k, for each task, ziAnd siThe information used for determining which unmanned plane is the latest, and the auction unmanned plane i receives three possible results of updating, resetting and leaving of the task j;
step 2.10, if a bid is changed by the decision criteria, each drone checks whether all updated tasks are in the task package, these tasks and all the tasks after them will be released:
wherein b isinRepresents the nth task of the incoming task package, and
step 2.11, if other tasks are added before a certain task, the income value is not increased after the task is finished, and the requirement of meeting the requirement
For all biB, j satisfy
Wherein
A task representing a vacancy;
step 2.12, when the income function meets the condition of marginal decrement, the following conditions are necessarily met:
ifn ≤ m, wherein bikIs to enter a task packet b of an unmanned aerial vehicle iiThe k element of (2) because
Satisfy the requirement of
Step 2.13, the time gain is
Wherein λj< 1 is a condition parameter of task j,
is that drone i follows path piThe time to the point j is expected to arrive,
is the static score for performing task j;
step 2.14, the time gain represents the uncertain track scene characteristics,over time, the expectation of patrolling a particular node and path planning revenue may decrease,
that is, a drone follows a longer path, and the time to reach each task is delayed relative to a shorter path, further resulting in inefficient revenue.
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