CN112163278A - Heterogeneous multi-airplane cooperative task automatic decomposition method under multi-constraint condition - Google Patents
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Abstract
The invention discloses an automatic decomposition method of heterogeneous multi-airplane cooperative tasks under a multi-constraint condition, which comprises the following steps: step S1, an airplane task capacity analysis stage; drawing up a multi-airplane cooperative capability complementary relation according to a task target, and distributing a task role of each airplane; step S2, multi-agent task decomposition stage; and constructing a subtask set with a priority constraint relation by using a task database, and automatically obtaining a hierarchical task decomposition tree and an airplane action flow with a time window through a program. The invention has the advantages of simple principle, high automation degree, good reliability and effect and the like.
Description
Technical Field
The invention mainly relates to the technical field of control of unmanned aerial vehicles, in particular to an automatic decomposition method for heterogeneous multi-aircraft cooperative tasks under multi-constraint conditions.
Background
With the wide application of unmanned aerial vehicles in various industries and various fields, in order to meet actual requirements, modes such as combined type and multi-unmanned aerial vehicle marshalling are also increasingly popularized. Because the task capability of a single unmanned aerial vehicle is limited, the task can be effectively completed by adopting a cooperative matching mode of various types of unmanned aerial vehicles or a cooperative matching mode of unmanned aerial vehicles and human-machine. However, after a multi-aircraft approach is adopted, the task is usually composed of a plurality of subtasks with time limit, priority, rule and other constraints, so that the automatic generation of the heterogeneous multi-aircraft cooperative task scheme has certain challenges.
In the prior art, bionic optimization algorithms such as genetic algorithm, particle swarm algorithm, ant colony algorithm and the like are widely used for task planning of unmanned aerial vehicles, but the method mainly solves the problem of flight route planning in a flight control level, and the difference between the required capacity of a task and the actual capacity of an airplane is not analyzed deeply, so that capacity complementation and efficient cooperation among heterogeneous multiple airplanes are difficult to realize in a task management level. If a multi-intelligent-system modeling technology is adopted, the problem is expected to be solved, but related research results are still few. Therefore, for the problem, a better solution is not provided in the traditional technology, and better effects on reliability, efficiency and accuracy cannot be achieved.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: aiming at the technical problems in the prior art, the invention provides the method for automatically decomposing the heterogeneous multi-aircraft cooperative tasks under the multi-constraint condition, which has the advantages of simple principle, high automation degree and good reliability and effect.
In order to solve the technical problems, the invention adopts the following technical scheme:
a heterogeneous multi-airplane cooperative task automatic decomposition method under a multi-constraint condition comprises the following steps:
step S1, an airplane task capacity analysis stage; drawing up a multi-airplane cooperative capability complementary relation according to a task target, and distributing a task role of each airplane;
step S2, multi-agent task decomposition stage; and constructing a subtask set with a priority constraint relation by using a task database, and automatically obtaining a hierarchical task decomposition tree and an airplane behavior flow with a time window through a program.
As a further improvement of the process of the invention: the process of step S1 includes:
s101, designing a task scenario, and defining a task overall target and sub-task targets of each stage;
step S102, analyzing constraint relation existing in the task and performing mathematical description on the constraint relation;
step S103, quantizing and representing the task capacity of the single airplane aiming at each subtask;
and S104, drawing up a multi-airplane cooperative ability complementary relation scheme table facing the whole task flow, and primarily distributing the task role of each airplane.
As a further improvement of the process of the invention: in step S102, a constraint relationship existing in the task is analyzed, and the process includes:
task decomposition is established according to different problems, with the aim of ensuring a proper granularity of task decomposition; set the time T for the man-machine to form the task TsTo task TsThe decomposition is divided into independent subtask combinations, which are recorded as:
each subtask obtained after task decomposition
Are not completely independent, and are carried out by combining a timing constraint relation; timing constraints for tasks are referred to
In other words, if only the task t is presentiAfter completion, task tjCan begin to holdLine, then call task tiAnd task tjExistence of a timing constraint relationship Timord (T)i，Tj) (ii) a The task time sequence constraint is determined by the dependency of information among tasks, and the time sequence constraint is described as follows by adopting a partial sequence relation:
ti<tj
in the formula, tiIs tjPrecondition for (1), denoted as ti＝tjp。
As a further improvement of the process of the invention: setting an operation formation to be composed of one man and m unmanned machines, wherein a task generated at the moment T is TsWill TsThe decomposition is as follows:
defining a task allocation matrix Sm*nIs composed of
Wherein i is 1, 2.. multidot.m; j is 1, 2.
The task allocation aims to allocate n tasks to m unmanned aerial vehicles, and the task allocation comprises the following steps:
in the formula: m ismaxTo a task
The number of allocated drones; n ismaxIs shown to unmanned aerial vehicle ViThe maximum amount of tasks allocated.
As a further improvement of the process of the invention: the process of step S2 includes:
step S201, a task domain common sense database is constructed, and all entity types, entity attribute declaration and entity value initialization are defined;
step S202, designing a basic action set to be taken and a method set consisting of partial atomic tasks;
step S203, constructing a cost function which comprises time cost, an undesirable action sequence and an intelligent agent interaction rule for scheduling;
step S204, constructing a subtask set with a priority constraint relation;
s205, selecting a breadth-first search algorithm and setting the upper limit of the running time of the search algorithm;
and S206, running the program to carry out iterative solution on the task decomposition scheme, and automatically obtaining a hierarchical task decomposition tree and an airplane action flow with a time window.
As a further improvement of the process of the invention: in step S204, the constraint conditions are expressed in three ways: firstly, in the aspect of time constraint, a duration function is used as a time scale and is arranged in each atomic task, each atomic task occupies a time unit, and a time stream flows along with the atomic task in a task stream structure; in terms of logic constraints, determined by methods and social rules, the execution environment rules are closed; in the aspect of capacity constraint, values of all entity attributes are set according to the searched relevant capacity parameters of the unmanned aerial vehicle and the manned vehicle, the relative size of the capacity of all entities is ensured, and a task flow can be reasonably planned based on the analysis of the capacity dependence relation table.
As a further improvement of the process of the invention: the priority representation of the tasks translates into an ordering of sub-task decompositions in the method, which may be partially or fully ordered. In the last case, the order must be explicit and coded by the sign > and the numerical Id of the previous task, followed by the name of the subtask.
As a further improvement of the process of the invention: and the step S1 and the step S2 are based on the ROS robot operating system and the HATP intelligent agent programming environment, and the problem of automatic decomposition of the heterogeneous multi-airplane cooperative task under the multi-constraint condition is solved in a symbolic system.
Compared with the prior art, the invention has the advantages that:
the method for automatically decomposing the heterogeneous multi-airplane cooperative tasks under the multi-constraint condition has the advantages of simple principle, high automation degree, good reliability and good effect, and applies the multi-agent system task planning technology to the problem of automatically decomposing the heterogeneous multi-airplane cooperative tasks. The method analyzes the capability difference among the heterogeneous aircrafts aiming at the complex tasks under various constraint conditions such as time constraint, task priority constraint, airplane capability constraint and the like, automatically decomposes the complex tasks into atomic tasks which can be executed by a certain aircraft alone or cooperatively executed by certain aircrafts, and obtains a hierarchical task decomposition tree and an airplane action flow with a time window. The method can effectively improve the efficiency of heterogeneous multi-airplane cooperation, and has higher application value in mission modes such as man-machine-unmanned-machine cooperation, heterogeneous multi-unmanned-machine cooperation, unmanned-machine cluster and the like.
Drawings
FIG. 1 is a schematic flow diagram of the process of the present invention.
Fig. 2 is a diagram illustrating a scenario of heterogeneous multi-aircraft mission in an exemplary embodiment of the present invention.
FIG. 3 is a diagram illustrating a gridding task in an embodiment of the present invention.
FIG. 4 is a diagram illustrating a scenario of a multi-plane interoperability complementation relation in a specific application example.
FIG. 5 is a schematic diagram of a hierarchical task decomposition tree in an embodiment of the present invention.
FIG. 6 is a schematic diagram of an airplane behavior flow with time windows in an embodiment of the present invention.
Detailed Description
The invention will be described in further detail below with reference to the drawings and specific examples.
As shown in fig. 1, the method for automatically decomposing heterogeneous multi-aircraft cooperative tasks under multiple constraint conditions of the present invention includes:
step S1, an airplane task capacity analysis stage; drawing up a multi-airplane cooperative capability complementary relation according to a task target, and distributing a task role of each airplane;
step S2, multi-agent task decomposition stage; and constructing a subtask set with a priority constraint relation by using a task database, and automatically obtaining a hierarchical task decomposition tree and an airplane behavior flow with a time window through a program.
In a specific application example, the stage of analyzing the mission capability of the aircraft in step S1 includes:
and S101, designing a task scenario, and defining a task overall target and sub-task targets of each stage.
And S102, analyzing constraint relations existing in the tasks and performing mathematical description on the constraint relations.
And S103, quantizing the task capacity of the single airplane for each subtask.
And S104, drawing up a multi-airplane cooperative ability complementary relation scheme table facing the whole task flow, and primarily distributing the task role of each airplane.
In a specific application example, the multi-agent task decomposition stage in step S2 includes:
step S201, a task domain common sense database is constructed, and all entity types, entity attribute declaration and entity value initialization are defined;
step S202, designing a basic action set to be taken and a method set consisting of partial atomic tasks.
And S203, constructing a cost function comprising time cost, an undesirable action sequence, an intelligent agent interaction rule for scheduling and the like.
And step S204, constructing a subtask set with a priority constraint relation.
And S205, selecting a breadth-first search algorithm and setting the upper limit of the running time of the search algorithm.
And S206, running the program to carry out iterative solution on the task decomposition scheme, and automatically obtaining a hierarchical task decomposition tree and an airplane action flow with a time window.
In a specific application example, as shown in fig. 2, the embodiment performs automatic decomposition of heterogeneous multi-aircraft cooperative tasks under the following scenario: heterogeneous multi-machine formation comprises a manned reconnaissance unmanned aerial vehicle and an unmanned aerial vehicle with executive capability, carries out tasks such as aggregation, reconnaissance and other operations on a plurality of targets in a designated area, automatically distributes ground targets to different airplanes according to constraint conditions such as task priority, task capability requirements, airplane capability and time, and requires to complete the tasks at minimum cost.
In the embodiment, the method is based on the ROS robot operating system and the HATP intelligent agent programming environment, and solves the problem of automatic decomposition of heterogeneous multi-airplane cooperative tasks under the multi-constraint condition in a symbolic system.
The method specifically comprises the following steps: the solution is divided into two stages of airplane task capacity analysis and multi-agent task decomposition:
the aircraft mission capacity analysis stage mainly comprises the following steps:
step I-1: designing task scenarios, and defining the overall task target and the subtask targets of each stage.
As shown in fig. 2, the five-pointed star at the lower right corner is the starting point of the base, the five-pointed star unmanned aerial vehicle and the manned machine at the lower left corner start from the starting point of the base and fly to the target at a certain speed at a specified position, the three red four-pointed stars at the upper corner represent the operation execution target at a fixed position, the supplementary operation after the manned/unmanned aerial vehicle team reconnaissance and operation execution is set, and after the operation of the target is completed, all the targets return to the base end point, namely the task is considered to be completed.
In order to make the geometric modeling of the task environment more concise and clear, the relative positions of the bases and the target points are drawn into a grid map: a regional grid map is used with the origin of coordinates in the lower left corner. Wherein Bi is the base of takeoff/landing of the airplane of our party (coordinates are (1,1) and (5,1), respectively), and Tj is the destination ground target (coordinates are (2,4), (3,2), (5, 3))).
Step I-2: and analyzing constraint relations existing in the tasks and performing mathematical description on the constraint relations.
Task decomposition needs to be established according to different issues, with the goal of ensuring that the task decomposition has the proper granularity. Set the time T for the man-machine to form the task TsTo be tasked withTsThe decomposition is divided into independent subtask combinations, which are recorded as:
each subtask obtained after task decomposition
They are not completely independent, but have a certain relationship, including a concurrency relationship, a timing relationship, a promotion relationship, a suppression relationship, and the like. The invention mainly considers the time sequence constraint relation directly influencing the task execution. Timing constraints for tasks are referred to
In other words, if only the task t is presentiAfter completion, task tjCan only start to execute, then the task t is callediAnd task tjExistence of a timing constraint relationship Timord (T)i，Tj). The task timing constraint is mainly determined by the dependency of information between tasks, and the timing constraint can be described as a partial order relation
ti<tj
In the formula, tiIs tjPrecondition for (1), denoted as ti＝tjp。
Setting up a job formation to be composed of a man-machine and m unmanned aerial vehicles, wherein the task generated at the moment T is TsWill TsThe decomposition is as follows:
defining a task allocation matrix Sm*nIs composed of
Wherein i is 1, 2.. multidot.m; j is 1, 2.
The task allocation aims to allocate n tasks to m unmanned aerial vehicles, so that formation achieves the maximum combat effect.
To ensure the accuracy and high efficiency of task completion, the rationality of task allocation needs to be considered:
in the formula: m ismaxTo a task
The number of allocated drones; n ismaxIs shown to unmanned aerial vehicle ViThe maximum amount of tasks allocated.
Step I-3: for each subtask, a task capability representing a single aircraft is quantified.
The heterogeneous multi-aircraft team includes X aircraft. When X is 3, it includes an manned MAV1, a scout unmanned UAV1_ Z, and an unmanned UAV2_ ZD. The capability of each airplane is required to be quantitatively described, and the capability description mainly comprises the following three aspects:
navigation capability Cap _ dist. Generally, a manned flight path is larger than a unmanned aerial vehicle. For example, model data is searched, where the initial navigation capability of the unmanned aerial vehicle Cap _ dist (MAV1) ═ 20, and the initial navigation capability of the unmanned aerial vehicle Cap _ dist (UAV1_ Z) ═ Cap _ dist (UAV2_ ZD) ═ 15.
Job capability Cap _ attk. The difference in the components of the manned and unmanned aerial vehicle installations is taken into account. Assuming that the capacity to be operated of the ground target Tj is Cap _ defend ═ 3, the target can be completed only when the operation capacity is greater than the band operation capacity, that is, one unmanned aerial vehicle performs the task, or two unmanned aerial vehicles perform the task together.
Step I-4: and aiming at the whole task flow, drawing up a multi-airplane cooperative ability complementary relation scheme table, and primarily distributing the task role of each airplane. As shown in fig. 4.
In a specific application example, the specific implementation steps of the multi-agent task decomposition stage are as follows:
step II-1: constructing a task domain common sense database, and defining all entity types, declaring entity attributes and initializing values of entities;
step II-1-1: defining all entity types
Each airplane agent is the default entity in the HATP, and the attributes of the destination point entity and the base entity need to be defined.
define entityTypeTaskLoc；
{
static atom bool isHostile；
dynamic atom bool isReconed；
dynamic atom bool isClear；
dynamic atom bool isStriked；
dynamic atom number capdefend；}
Step II-1-2: an entity attribute is declared.
TL1＝new TaskLoc；
AP1＝new TaskLoc；
MAV1＝new Agent；
Step II-1-3: and initializing the value of the entity.
MAV1.type＝"MAV"；
MAV1.isAt＝AP1；
MAV1.isInAir＝false；
MAV1.missileNum＝2；
MAV1.capattak＝4；
Step II-2: the basic set of actions taken by the design and the set of methods that are made up of part of the atomic tasks.
Step II-2-1: the basic set of actions taken by the design.
action TakeOff(Agent A,TaskLoc T)
Step II-2-2: a methodology set consisting of part of the atomic tasks is designed.
methodGather_in_air(Agent A1,Agent A2,Agent A3,TaskLoc AP)
method Finish_all_task(Agent A1,Agent A2,Agent A3,TaskLoc T,TaskLocAP,TaskLoc BP)
Step II-3: and constructing a cost function, wherein the cost function comprises time cost, undesirable action sequences, intelligent agent interaction rules for scheduling and the like.
float cost100() { return 100.; the }// cost function
pair < double > duration1() { return make _ pair (1, 1); function of }// duration
undesirableSeqPenalty
computeControlOfIntricacy
computeWastedTime
computeEffortBalancing.
Step II-4: and constructing a subtask set with a priority constraint relation.
Regarding the constraint, the representation is divided into three aspects. Firstly, in the aspect of time constraint, a duration function is used as a time scale and is arranged in each atomic task, each atomic task occupies a time unit, and a time flow flows along with the atomic tasks in the structure of a task flow; in the aspect of logic constraint, the method and the social rule determine the logic constraint, and the logic constraint is close to the real combat environment rule as much as possible; in the aspect of capability constraint, the value of each entity attribute is mainly set according to the searched relevant capability parameters of the unmanned aerial vehicle and the manned vehicle, the relative size of each entity capability is mainly ensured, and a task flow can be reasonably planned based on the analysis of the capability dependency relationship table.
The priority representation of the task translates into an ordering of sub-task decompositions in the method, which may be partially or fully ordered. In the last case, the order must be explicit and coded by the sign > and the numerical Id of the previous task, followed by the name of the subtask.
Step II-5: selecting a breadth-first search algorithm, and setting the upper limit of the running time of the search algorithm. The time limit is set to 1 second.
Step II-6: and running the program to carry out iterative solution on the task decomposition scheme, and automatically obtaining a hierarchical task decomposition tree and an airplane action flow with a time window. The results of the operation are shown in fig. 5 and 6.
The above is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above embodiment, and any technical solutions belonging to the idea of the present invention belong to the protection scope of the present invention. It should be noted that modifications and embellishments within the scope of the invention may be made by those skilled in the art without departing from the principle of the invention.
Claims (8)
1. A heterogeneous multi-airplane cooperative task automatic decomposition method under a multi-constraint condition is characterized by comprising the following steps:
step S1, an airplane task capacity analysis stage; drawing up a multi-airplane cooperative capability complementary relation according to a task target, and distributing a task role of each airplane;
step S2, multi-agent task decomposition stage; and constructing a subtask set with a priority constraint relation by using a task database, and automatically obtaining a hierarchical task decomposition tree and an airplane action flow with a time window through a program.
2. The method for automatically decomposing the heterogeneous multi-aircraft cooperative task under the multi-constraint condition according to claim 1, wherein the process of the step S1 includes:
s101, designing a task scenario, and defining a task overall target and sub-task targets of each stage;
step S102, analyzing constraint relation existing in the task and performing mathematical description on the constraint relation;
step S103, quantizing and representing the task capacity of the single airplane aiming at each subtask;
and S104, drawing up a multi-airplane cooperative ability complementary relation scheme table facing the whole task flow, and primarily distributing the task role of each airplane.
3. The method for automatically decomposing the heterogeneous multi-aircraft cooperative task under the multi-constraint condition according to claim 2, wherein in the step S102, constraint relationships existing in the task are analyzed, and the process includes:
task decomposition is established according to different problems, with the aim of ensuring a proper granularity of task decomposition; set the time T for the man-machine to form the task TsTo task TsThe decomposition is divided into independent subtask combinations, which are recorded as:
each subtask obtained after task decomposition
Are not completely independent, and are carried out by combining a timing constraint relation; timing constraints for tasks are referred to
In other words, if only the task t is presentiAfter completion, task tjCan only start to execute, then the task t is callediAnd task tjExistence of a timing constraint relationship Timord (T)i，Tj) (ii) a The task time sequence constraint is determined by the dependency of information between tasks, and the time sequence constraint is described as follows by adopting a partial sequence relation:
ti<tj
in the formula, tiIs tjPrecondition for (1), denoted as ti＝tjp。
4. The method for automatically decomposing the heterogeneous multi-aircraft cooperative tasks under the multi-constraint condition according to claim 3, wherein a job formation is setConsists of one man-machine and m unmanned aerial vehicles, and the task generated at the moment T is TsWill TsThe decomposition is as follows:
defining a task allocation matrix Sm*nIs composed of
Wherein i is 1, 2.. multidot.m; j is 1, 2.
The task allocation aims to allocate n tasks to m unmanned aerial vehicles, and the task allocation comprises the following steps:
in the formula: m ismaxTo a task
The number of allocated drones; n ismaxIs shown to unmanned aerial vehicle ViThe maximum amount of tasks allocated.
5. The method for automatically decomposing the heterogeneous multi-aircraft cooperative task under the multi-constraint condition according to claim 1, wherein the process of the step S2 includes:
step S201, a task domain common sense database is constructed, and all entity types, entity attribute declaration and entity value initialization are defined;
step S202, designing a basic action set to be taken and a method set consisting of partial atomic tasks;
step S203, constructing a cost function which comprises time cost, an undesirable action sequence and an intelligent agent interaction rule for scheduling;
step S204, constructing a subtask set with a priority constraint relation;
s205, selecting a breadth-first search algorithm and setting the upper limit of the running time of the search algorithm;
and S206, running the program to carry out iterative solution on the task decomposition scheme, and automatically obtaining a hierarchical task decomposition tree and an airplane action flow with a time window.
6. The method for automatically decomposing the heterogeneous multi-aircraft cooperative task under the multi-constraint condition according to claim 5, wherein in the step S204, the constraint condition is expressed in three aspects: firstly, in the aspect of time constraint, a duration function is used as a time scale and is arranged in each atomic task, each atomic task occupies a time unit, and a time flow flows along with the atomic tasks in the structure of a task flow; in terms of logical constraints, determined by methods and social rules, proximate to the execution environment rules; in the aspect of capacity constraint, the value of each entity attribute is set according to the searched relevant capacity parameters of the unmanned aerial vehicle and the manned vehicle, the relative size of each entity capacity is ensured, and a task flow can be reasonably planned based on the analysis of the capacity dependency relationship table.
7. The method for automatically decomposing the heterogeneous multi-aircraft cooperative tasks under the multi-constraint condition according to claim 6, wherein the priority expression of the tasks is converted into the sequencing of the decomposition of the subtasks in the method, and the subtasks can be partially or completely ordered; in the last case, the order must be explicit and coded by the sign > and the numerical Id of the previous task, followed by the name of the subtask.
8. The method for automatically decomposing the heterogeneous multi-airplane cooperative tasks under the multi-constraint condition according to any one of claims 1 to 7, wherein the steps S1 and S2 are based on an ROS robot operating system and an HATP intelligent programming environment, and the problem of automatically decomposing the heterogeneous multi-airplane cooperative tasks under the multi-constraint condition is solved in a symbolic system.
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