CN114326827B - Unmanned aerial vehicle cluster multitasking dynamic allocation method and system - Google Patents
Unmanned aerial vehicle cluster multitasking dynamic allocation method and system Download PDFInfo
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Abstract
The invention relates to a method and a system for dynamically distributing tasks of unmanned aerial vehicle clusters, which utilize a design method of two-layer alliances, firstly, a ground station is used as a decision-making main body to initially distribute tasks, then, an unmanned aerial vehicle is used as a decision-making main body to carry out task redistribution, firstly, a ground station task distribution result is obtained and executed, when a task change or the alliance where the unmanned aerial vehicle is located is found, whether the current task can bring maximum benefit or not is judged, if the current task cannot bring maximum benefit, the alliance corresponding to the maximum benefit is searched and added, and meanwhile, the update times are increased once for each conversion task, and the accuracy of corresponding local information is judged by combining the update times of each unmanned aerial vehicle, so that various emergency situations of the unmanned aerial vehicle in the task execution process can be fully considered, and the problem that the emergency situation cannot be considered in the prior art is effectively solved.
Description
Technical Field
The invention relates to the technical field of unmanned aerial vehicles, in particular to an unmanned aerial vehicle cluster multi-task dynamic allocation method and system based on a double-layer alliance.
Background
With the continuous development of technology, unmanned aerial vehicles (Unmanned Aerial Vehicle, UAVs) have the advantages of small size, strong survivability and the like, and are widely applied to various fields such as environment detection, meteorological monitoring, scene rescue, pesticide spraying, topographic mapping, monitoring attack and the like. In many complex scenarios, dangerous tasks that many manpower cannot accomplish can be accomplished using unmanned aerial vehicles. The multi-tube launching device developed by the project of 'low-cost unmanned aerial vehicle bee colony technology' (LOCOSET) jointly researched by the American naval research bureau and the George sub-management university can launch hundreds of small unmanned aerial vehicles from the ground, and can perform tasks such as shielding, patrol or ground attack together in a designated area. The project of the advanced research planning agency (DARPA) of the united states department of defense, which is an "attack on swarm tactics" (OFFSET) developed in 2016, is to consider that up to 250 small combat units, which are composed of unmanned aerial vehicles (UAS) and/or unmanned ground vehicles (UGS), cooperate to perform tasks such as searching and threat cleaning in a complex urban environment with unknown dangers, while achieving man-machine cooperation. These items may also indicate that multi-agent systems such as unmanned aerial vehicle clusters can play a great role in the military field and the like, and are also being increasingly valued by various countries. The task allocation problem of the multi-agent system is an important problem in the current multi-agent research field.
At present, unmanned aerial vehicle task allocation technology at home and abroad has been explored and researched in many aspects, and a plurality of research results can better reach the target in a static environment. However, with the increase of the number of unmanned aerial vehicles, the existing scheme is low in efficiency and convergence speed, and uncertainty of different task targets and possible emergency situations at any time are less considered.
Therefore, a technical solution with high task redistribution efficiency suitable for the situation of dynamic environment and task transformation is needed in the art.
Disclosure of Invention
The invention aims to provide a method and a system for dynamically distributing unmanned aerial vehicle cluster tasks, which utilize a design method of two-layer alliance, firstly take a ground station as a decision-making main body to initially distribute tasks, and then take an unmanned aerial vehicle as the decision-making main body to redistribute tasks, so that various emergency situations of the unmanned aerial vehicle in the task execution process can be fully considered, and the problem that the emergency situations cannot be considered in the prior art is effectively solved.
In order to achieve the above object, the present invention provides the following solutions:
a method for dynamic allocation of unmanned aerial vehicle clusters, the method comprising:
acquiring a ground station task allocation result, and flying to a corresponding task area according to the ground station task allocation result; the ground station task allocation result is obtained by allocating according to the initial task information by the ground station; the ground station task allocation result is that the unmanned aerial vehicle cluster is divided into a plurality of alliances, and each alliance executes a task;
when the task change is detected or the alliance where the unmanned aerial vehicle is located is changed, judging whether the current task can bring the maximum benefit;
if the current task can bring the maximum benefit, continuing to fly to the corresponding task area according to the task allocation result of the ground station;
if the current task cannot bring the maximum benefit, searching the task with the maximum benefit from the task allocation result of the ground station according to an unmanned aerial vehicle benefit formula, adding a alliance corresponding to the task with the maximum benefit, updating local information at the same time, and increasing the updating times for one time to generate a random time stamp; the local information refers to the task allocation results of all unmanned aerial vehicles which can perform information interaction;
broadcasting interaction information to surrounding unmanned aerial vehicles and receiving the interaction information of the surrounding unmanned aerial vehicles; the interaction information comprises: update times, random time stamp and local information;
comparing all the received interactive information of surrounding unmanned aerial vehicles, and selecting the interactive information with the largest updating times;
and updating the interaction information of the unmanned aerial vehicle by using the interaction information with the largest updating times, returning to the step of searching the task with the largest profit in the task allocation result of the ground station according to the profit formula of the unmanned aerial vehicle, adding the alliance corresponding to the task with the largest profit, updating local information, and increasing the updating times once to generate a random time stamp until the unmanned aerial vehicle has the largest updating times.
In some embodiments, when a task change is detected or a federation where the unmanned aerial vehicle is located changes, determining whether the current task can bring the maximum benefit includes:
when the task change is detected or the alliance where the unmanned aerial vehicle is located is changed, judging whether the current task can bring maximum benefit according to a benefit formula of the unmanned aerial vehicle; the unmanned aerial vehicle income formula is:
wherein (1)>
Representing the benefit of unmanned plane i +.>
Representing the number of unmanned aerial vehicles selecting task m, +.>
Is a relatively large random number, d i,m Represents the distance of unmanned plane i from task m, < +.>
Fuel consumption per unit distance of unmanned aerial vehicle, +.>
Represents a set of tasks that unmanned plane i can select, num m Representing the number of unmanned aerial vehicles required for task m, < ->
Representing the benefit of task m when
Less than num m Because the number requirement of unmanned aerial vehicles is not met, alliance income is zero; when->
Greater than or equal to num m The unmanned aerial vehicle quantity demand is satisfied, alliance income is set to task income at this moment.
In some embodiments, further comprising:
the ground station performs random sequencing on all unmanned aerial vehicles to obtain a random sequencing result;
the ground station sequentially selects one unmanned aerial vehicle according to the random sequencing result;
the ground station calculates the individual benefit of each task selected by the unmanned aerial vehicle according to the number of unmanned aerial vehicles required by each task in the initial task information;
the ground station distributes tasks corresponding to the maximum individual benefits to the unmanned aerial vehicle, and updates task distribution results;
in the task allocation process of the ground station, each unmanned aerial vehicle judges whether the task allocated to the unmanned aerial vehicle is the task with the largest profit, if so, the unmanned aerial vehicle receives the task allocated to the unmanned aerial vehicle, and if not, the unmanned aerial vehicle switches to the task with the largest profit;
and returning to the step of selecting an unmanned aerial vehicle by the ground station according to the random sequencing result in order until all unmanned aerial vehicles are distributed to tasks corresponding to the maximum individual benefits, and obtaining a ground station task distribution result.
In some embodiments, the task change or the alliance where the unmanned aerial vehicle is located changes, specifically includes: the importance of the task changes, a new task appears, and a plurality of unmanned aerial vehicles in the alliance where the unmanned aerial vehicle is located fail.
The invention also provides a unmanned aerial vehicle cluster multitasking dynamic allocation system, which comprises: unmanned plane;
the unmanned aerial vehicle is used for:
acquiring a ground station task allocation result, and flying to a corresponding task area according to the ground station task allocation result; the ground station task allocation result is obtained by allocating according to the initial task information by the ground station; the ground station task allocation result is that the unmanned aerial vehicle cluster is divided into a plurality of alliances, and each alliance executes a task;
when the task change is detected or the alliance where the unmanned aerial vehicle is located is changed, judging whether the current task can bring the maximum benefit;
if the current task can bring the maximum benefit, continuing to fly to the corresponding task area according to the task allocation result of the ground station;
if the current task cannot bring the maximum benefit, searching the task with the maximum benefit from the task allocation result of the ground station according to an unmanned aerial vehicle benefit formula, adding a alliance corresponding to the task with the maximum benefit, updating local information at the same time, and increasing the updating times for one time to generate a random time stamp; the local information refers to the task allocation results of all unmanned aerial vehicles which can perform information interaction;
broadcasting interaction information to surrounding unmanned aerial vehicles and receiving the interaction information of the surrounding unmanned aerial vehicles; the interaction information comprises: update times, random time stamp and local information;
comparing all the received interactive information of surrounding unmanned aerial vehicles, and selecting the interactive information with the largest updating times;
and updating the interaction information of the unmanned aerial vehicle by using the interaction information with the largest updating times, returning to the step of searching the task with the largest profit in the task allocation result of the ground station according to the profit formula of the unmanned aerial vehicle, adding the alliance corresponding to the task with the largest profit, updating local information, and increasing the updating times once to generate a random time stamp until the unmanned aerial vehicle has the largest updating times.
In some embodiments, further comprising a ground station;
the ground station is used for:
randomly sequencing all unmanned aerial vehicles to obtain a random sequencing result;
selecting one unmanned aerial vehicle according to the random sequencing result in sequence;
calculating the individual benefits of each task selected by the unmanned aerial vehicles according to the number of the unmanned aerial vehicles required by each task in the initial task information;
assigning tasks corresponding to the maximum individual benefits to the unmanned aerial vehicle, and updating task assignment results; in the task allocation process of the ground station, each unmanned aerial vehicle judges whether the task allocated to the unmanned aerial vehicle is the task with the largest profit, if so, the unmanned aerial vehicle receives the task allocated to the unmanned aerial vehicle, and if not, the unmanned aerial vehicle switches to the task with the largest profit;
and returning to the step of selecting an unmanned aerial vehicle by the ground station according to the random sequencing result in order until all unmanned aerial vehicles are distributed to tasks corresponding to the maximum individual benefits, and obtaining a ground station task distribution result.
In some embodiments, when a task change is detected or a federation where the unmanned aerial vehicle is located changes, determining whether the current task can bring the maximum benefit includes:
when the task change is detected or the alliance where the unmanned aerial vehicle is located is changed, judging whether the current task can bring maximum benefit according to a benefit formula of the unmanned aerial vehicle; the unmanned aerial vehicle income formula is:
wherein (1)>
Representing the benefit of unmanned plane i +.>
Representing the number of unmanned aerial vehicles selecting task m, +.>
Is a relatively large random number, d i,m Represents the distance of unmanned plane i from task m, < +.>
Fuel consumption per unit distance of unmanned aerial vehicle, +.>
Represents a set of tasks that unmanned plane i can select, num m Representing the number of unmanned aerial vehicles required for task m, < ->
Representing the benefit of task m when
Less than num m Because the number requirement of unmanned aerial vehicles is not met, alliance income is zero; when->
Greater than or equal to num m The unmanned aerial vehicle quantity demand is satisfied, alliance income is set to task income at this moment.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
according to the unmanned aerial vehicle cluster multi-task dynamic allocation method and system provided by the invention, by utilizing the design method of the two-layer alliance, firstly, the ground station is used as a decision-making main body to perform task initial allocation, then the unmanned aerial vehicle is used as a decision-making main body to perform task re-allocation, firstly, the ground station task allocation result is acquired and executed, when the task change or the alliance where the unmanned aerial vehicle is located is found, whether the current task can bring the maximum benefit is judged, if the maximum benefit cannot be brought, the alliance corresponding to the maximum benefit is searched and added, meanwhile, the update times are increased once for each conversion task, and the accuracy of the corresponding local information is judged by combining the update times of each unmanned aerial vehicle, so that various emergency situations of the unmanned aerial vehicle in the task execution process can be fully considered, and the problem that the emergency situation cannot be considered in the prior art is effectively solved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic diagram of task allocation of an unmanned aerial vehicle cluster according to an embodiment of the present invention.
Fig. 2 is a flowchart of a method for dynamically assigning unmanned aerial vehicle cluster tasks according to an embodiment of the present invention.
Fig. 3 is a decision flow chart of an unmanned aerial vehicle according to an embodiment of the present invention.
Fig. 4 is a flowchart of a ground station decision process according to a first embodiment of the present invention.
Fig. 5 is a schematic diagram of convergence speed of an initial task allocation method using a ground station as a decision body according to an embodiment of the present invention.
Fig. 6 is a schematic diagram of convergence speed of a task redistribution method using an unmanned aerial vehicle as a decision-making body according to an embodiment of the present invention.
Fig. 7 is a schematic diagram of an initial task allocation simulation with a ground station as a decision body according to an embodiment of the present invention.
Fig. 8 is a schematic diagram illustrating initial task allocation achieving nash stable partition simulation according to a first embodiment of the present invention.
Fig. 9 is a schematic diagram of simulation of an unmanned aerial vehicle affected by task increase according to a first embodiment of the present invention.
Fig. 10 is a schematic diagram of task redistribution simulation using an unmanned aerial vehicle as a decision-making body according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention aims to provide a method and a system for dynamically distributing unmanned aerial vehicle cluster tasks, which utilize a design method of two-layer alliance, firstly take a ground station as a decision-making main body to initially distribute tasks, and then take an unmanned aerial vehicle as the decision-making main body to redistribute tasks, so that various emergency situations of the unmanned aerial vehicle in the task execution process can be fully considered, and the problem that the emergency situations cannot be considered in the prior art is effectively solved.
The invention provides a double-layer alliance-based unmanned aerial vehicle cluster multi-task dynamic allocation scheme, which combines the task allocation of a ground station and the distributed re-allocation of unmanned aerial vehicles to form a unmanned aerial vehicle cluster task allocation problem model by using a hedonic game; the initial task allocation of the ground station is completed by utilizing a alliance formation algorithm, as shown in fig. 1 (a), and the task reallocation is performed by utilizing two stages of task selection and conflict resolution, as shown in fig. 1 (b). The virtual real-time verification platform is built through the Unity3D, a scene of dynamically distributing tasks after task changes (such as task importance changes, tasks are newly added and the like) and other emergencies (such as unmanned aerial vehicle failure and the like) are generated is simulated, the scheme is good in integrity, and the modules are easy to replace, so that the virtual real-time verification platform is a set of valuable system scheme.
According to the invention, after the ground station completes initial allocation according to the known task information, the emergency in the unmanned aerial vehicle execution task is considered, and the task is redistributed by adopting two stages of task selection and conflict resolution. In the task selection stage, each unmanned aerial vehicle selects the best task according to the current local information; in the conflict resolution stage, each unmanned aerial vehicle interacts information with the adjacent unmanned aerial vehicle, and an optimal allocation scheme in the neighborhood is determined according to the respective iteration times and the time stamp, so that task selection conflicts are autonomously resolved. The method has the advantages of high convergence speed and good robustness.
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description.
Embodiment one:
as shown in fig. 2, the present embodiment provides a method for dynamically assigning unmanned aerial vehicle cluster tasks, which is implemented based on a hedonic game, and uses hedonic games to solve the related problems by first creating a hedonic game model and then designing a coalition formation algorithm.
Can be used for enjoying game model
Representation of->
Representing participants, u represents federation revenue. Aiming at the problem of unmanned aerial vehicle cluster multitasking dynamic allocation, participant set +.>
And federation revenue u is specifically defined as follows:
(1) Participant set
The V-frame unmanned aerial vehicle performing the task is taken as a participant, so the participant set in the enjoyment game can be expressed as:
(2) Alliance yield u
Design alliance benefits in consideration of constraint conditions and task benefits, and order
Representing the number of unmanned aerial vehicles selecting task m, num m Representing the number of unmanned aerial vehicles required for task m, < ->
Representing the benefit of task m. When->
Less than num m Because the number requirement of unmanned aerial vehicles is not met, alliance income is zero; when->
Greater than or equal to num m The unmanned aerial vehicle quantity demand is satisfied, alliance income is set to task income at this moment. Thus, for an unmanned aerial vehicle, the alliance benefits corresponding to each task can be expressed as:
wherein, the liquid crystal display device comprises a liquid crystal display device,
representing a set of tasks, the federation yield for an empty task is 0.
Alliance benefits are generally distributed to unmanned aerial vehicles in the alliance on average, and the embodiment sets the unmanned aerial vehicle i benefits as follows:
wherein, the liquid crystal display device comprises a liquid crystal display device,
the unmanned aerial vehicle is a relatively large random number, is a constant formulated randomly, and when the demand of the number of unmanned aerial vehicles is not met, the gains of the unmanned aerial vehicles are larger than those of other tasks, so that the unmanned aerial vehicles can not select other tasks, the number of unmanned aerial vehicles required by the tasks can be brought together as soon as possible, and d i,m Represents the distance of unmanned plane i from task m, < +.>
Fuel consumption per unit distance of unmanned aerial vehicle, +.>
Representing a set of tasks that the drone i may select. In a hedonic game of unmanned aerial vehicle cluster multitasking dynamic allocation system, each unmanned aerial vehicle aims at finding the task that maximizes its own income.
The unmanned aerial vehicle cluster multi-task dynamic allocation method provided by the embodiment comprises the following steps of:
s1, acquiring a ground station task allocation result, and flying to a corresponding task area according to the ground station task allocation result; the ground station task allocation result is obtained by allocating according to the initial task information by the ground station; the ground station task allocation result is that the unmanned aerial vehicle cluster is divided into a plurality of alliances, and each alliance executes a task.
S2, judging whether the current task can bring the maximum benefit when detecting the task change or the alliance where the unmanned aerial vehicle is located changes.
S3, if the current task can bring the maximum benefit, continuing to fly to the corresponding task area according to the task allocation result of the ground station;
s4, if the current task cannot bring the maximum benefit, searching the task with the maximum benefit from the task allocation result of the ground station according to an unmanned aerial vehicle benefit formula, adding a alliance corresponding to the task with the maximum benefit, updating local information at the same time, and increasing the updating times for one time to generate a random time stamp; the local information refers to the task allocation results of all unmanned aerial vehicles which can perform information interaction; the local information in this embodiment refers to the task allocation result that the unmanned aerial vehicle can obtain, and because of the limitation of the communication distance, the unmanned aerial vehicle can only perform information interaction with unmanned aerial vehicles within a certain distance around, and in popular terms, the local information refers to the current global task allocation result that the unmanned aerial vehicle considers.
S5, broadcasting interaction information to surrounding unmanned aerial vehicles and receiving the interaction information of the surrounding unmanned aerial vehicles; the interaction information comprises: update times, random time stamp and local information;
s6, comparing all received interactive information of surrounding unmanned aerial vehicles, and selecting the interactive information with the largest updating times;
and S7, updating the interaction information of the unmanned aerial vehicle by using the interaction information with the largest updating times, returning to the step of searching the task with the largest profit in the task allocation result of the ground station according to the profit formula of the unmanned aerial vehicle, adding the alliance corresponding to the task with the largest profit, updating the local information, and increasing the updating times once to generate a random time stamp until the unmanned aerial vehicle has the largest updating times.
Specifically, the unmanned aerial vehicle flies to the corresponding mission region according to the mission allocated by the ground station. However, during the flight or task execution, the task importance may change, an emergency such as a newly added task or an unmanned aerial vehicle failure may occur, and the ground station is usually far away from the task area, and the task information is not updated timely.
In this layer of federation, each iterative process of task reassignment involves two phases of task selection-conflict resolution. In the task selection stage, each unmanned aerial vehicle selects the best task according to the current local information. The local information refers to a task allocation result considered by the unmanned aerial vehicle and is obtained through information interaction of the unmanned aerial vehicles between adjacent unmanned aerial vehicles. Each unmanned aerial vehicle can only select tasks based on existing local information, subject to the limitation of communication range. For example, unmanned aerial vehicle i and unmanned aerial vehicle j can not directly communicate due to the fact that the distance is too far, in a round of iteration, unmanned aerial vehicle j is distributed to task m in the global task distribution result stored by unmanned aerial vehicle i, but the gain of selecting task n by unmanned aerial vehicle j is larger than task m, unmanned aerial vehicle j does not select task m, task n is selected, and the information unmanned aerial vehicle j can be transmitted to unmanned aerial vehicles in the communication range, but unmanned aerial vehicle i does not know the change in the round of iteration, so that information of unmanned aerial vehicle i is inaccurate. In the conflict resolution stage, each unmanned aerial vehicle interacts information with the adjacent unmanned aerial vehicle. Taking the unmanned aerial vehicle i as an example, the interaction information comprises: iteration number iteration i The iteration times refer to the times of executing task selection phases by the unmanned aerial vehicle; random timestamp stamp of unmanned aerial vehicle i i Wherein a random time stamp is generated between 0-1; state of unmanned plane i i Consists of 0 and 1, wherein 0 is in an unsatisfactory state and 1 is in a satisfactory state. The optimal allocation scheme in the neighborhood is determined according to the iteration times and the time stamp, and the higher the iteration times and the time stamp of a certain unmanned aerial vehicle are,the mastered information is more comprehensive, the task allocation result is more reliable, and the conflict resolution is completed.
As an alternative implementation, as shown in fig. 3, from the perspective of the entire unmanned aerial vehicle group, the specific task reassignment in this embodiment may be implemented by the following steps:
taking unmanned plane i as an example:
(1) initializing II i Equal to the above ground station task decision result, item i Equal to zero, stamp i Equal to zero; when the alliance where the unmanned aerial vehicle i is or the task is detected to be changed, the unmanned aerial vehicle i is in an unsatisfactory state, namely state i =0, otherwise unmanned plane i presents current decision result pi i Satisfaction, i.e. state i ＝1；
(2) If all unmanned aerial vehicles are in a satisfactory state, the task reassignment algorithm converges, the algorithm ends, otherwise, the unmanned aerial vehicle i executes the step (3);
(3) task selection:
a) The unmanned plane i judges whether the unmanned plane i is in a satisfactory state or not according to the formula (2);
b) When state i When=1, this means that the unmanned plane i assigns the result pi i Satisfactorily, step e) is performed;
c) When state i When=0, unmanned plane i assigns the result pi i Dissatisfaction. In II i The task with the greatest benefit is found, and the task is assumed to be m. If m is equal to pi i (i)，Π i (i) Local information pi represented in unmanned plane i i The task of unmanned aerial vehicle i selection recorded in the method means that unmanned aerial vehicle i joins other alliances to obtain higher unmanned aerial vehicle benefits, and leaves the current alliance
Add new alliance->
And update pi i ，iteration i ＝iteration i +1, randomly generated timestamp stamp i =rand (0, 1). If m=pi i (i)，Meaning unmanned aerial vehicle i Currently in alliance->
Is optimal without changing the alliance;
d) As the unmanned plane i selects the task which can bring the maximum benefit, the unmanned plane i becomes a satisfactory state, namely state i ＝1；
e) Unmanned plane i broadcasts message to all neighbor machines i ＝{iteration i ,stamp i ,Π i Comprises the update times of the unmanned plane i i Timestamp stamp i Global task allocation result pi stored by unmanned aerial vehicle i i And receives message of all neighbor machines k ,
Wherein, neighbor i The neighbor of the unmanned plane i is indicated, and is determined by whether communication is possible.
(4) Conflict resolution stage:
a) Comparing all received messages by the unmanned aerial vehicle i, selecting the message with the largest updating times, and assuming that the message is sourced from the neighbor k;
b) Unmanned plane i compares message i And message k When the time is k >iteration i Or an item k ＝iteration i 、stamp k ＞stamp i When it means the local information pi of unmanned plane k k Pi pi i More effectively, the unmanned aerial vehicle i changes the update times, the time stamp, the local information and the state of the unmanned aerial vehicle i, and the following operations are executed: item rate i ＝iteration k ,stamp i ＝stamp k ,Π i ＝Π k ,state i ＝0
Wherein pi (n) k The task of the unmanned aerial vehicle i is not necessarily the task that makes the profit the most, so the state of the unmanned aerial vehicle i becomes unsatisfactory. When the item is k <iteration i Or an item k ＝iteration i 、stamp k ≤stamp i When it means unmanned aerial vehiclei local information pi i Most efficient in the vicinity of the drone, the drone i does not need to perform any operations. Based on the step, the unmanned aerial vehicle i completes conflict resolution in the adjacent machine range, and the task decision result is agreed. Returning to the step (2).
When all unmanned aerial vehicles are in a satisfactory state, the task reassignment algorithm reaches convergence.
In this embodiment, before each unmanned aerial vehicle obtains a ground station task allocation result and flies to a corresponding task area according to the ground station task allocation result, the ground station completes initial allocation by adopting a coalition forming algorithm according to known initial task information, and the specific steps are as follows:
and the ground station performs random sequencing on all unmanned aerial vehicles to obtain a random sequencing result.
And the ground station sequentially selects one unmanned aerial vehicle according to the random sequencing result.
The ground station calculates the individual benefit of the drone to select each task using equation (2) in combination with the number of drones required for each task in the initial task information. Here, the ground station stands at an overall angle, and tasks are allocated to each unmanned aerial vehicle one by one.
The ground station distributes tasks corresponding to the maximum individual benefits to the unmanned aerial vehicle, and updates task distribution results; in the task allocation process of the ground station, each unmanned aerial vehicle judges whether the task allocated to the unmanned aerial vehicle is the task with the largest benefit by using the formula (2), if so, the unmanned aerial vehicle receives the task allocated to the unmanned aerial vehicle, and if not, the unmanned aerial vehicle switches to the task with the largest benefit. In addition, each unmanned plane stands at the angle of the unmanned plane, and whether the task allocated to the unmanned plane is the largest in benefit is judged.
And returning to the step of selecting an unmanned aerial vehicle by the ground station according to the random sequencing result in order until all unmanned aerial vehicles are distributed to tasks corresponding to the maximum individual benefits, and obtaining a ground station task distribution result.
As an alternative implementation manner, the ground station in this embodiment completes the task initial allocation according to the following link. Firstly, randomly sequencing all unmanned aerial vehicles by a ground station, and setting each unmanned aerial vehicle task to be empty; then, the ground station iteratively performs the following links: the ground station sequentially selects one unmanned aerial vehicle, calculates the individual benefit of each task in the initial task information of the unmanned aerial vehicle according to a formula (2), selects the task which enables the unmanned aerial vehicle to be favored (the individual benefit is the largest), distributes the task to the unmanned aerial vehicle, updates the task distribution result, judges whether the task distribution result is a Nash stable partition (when all unmanned aerial vehicles satisfy own partition, nash stable partition is achieved), and the unmanned aerial vehicle which selects the same task can be regarded as a alliance, wherein the task distribution is to divide a group of unmanned aerial vehicles into a plurality of alliances, namely a plurality of areas. If the Nash stable partition is achieved, stopping iteration and outputting an allocation result, otherwise, the ground station continues to select an unmanned aerial vehicle according to the sequence generated by random sequencing, calculates individual benefits of each task selected by the unmanned aerial vehicle according to a formula (2), selects a task which enables the unmanned aerial vehicle to be favored (the individual benefits are the largest), allocates the task to the unmanned aerial vehicle, and updates the task allocation result.
Let pi denote the current task partition, take unmanned plane i as an example, in order to form a stable partition, unmanned plane i will continuously execute task switching, and change the task to be executed into the task with the biggest benefit. The step of judging whether the unmanned plane i is a Nash stable partition is as follows, and when the unmanned plane i prefers to the partition where the unmanned plane i is currently located, the method meets the following conditions
Then the allocation result is called to reach Nash stable partition, wherein ≡ i Indicating a weak preference, pi (i) indicating the task selected by unmanned plane i, ++>
Representing a set of drones selecting task pi (i).
In this layer of federation, the ground station assigns a suitable drone to each known task based on the known task information. The main flow is shown in fig. 4.
The embodiment is an unmanned aerial vehicle cluster multi-task dynamic distribution system based on double-layer alliance. Firstly, comprehensively considering the influence of constraints such as task demands, task types, unmanned aerial vehicle capability and the like, and building a problem model by taking the maximum benefit loss ratio as a target. Secondly, designing an initial allocation method taking a ground station as a decision-making main body, and completing a first layer of alliance; and (3) considering the situations of task change (such as task importance change, task addition and the like) and other emergencies (such as unmanned aerial vehicle failure and the like) of the unmanned aerial vehicle in the task execution process, designing a task redistribution method taking the unmanned aerial vehicle as a decision-making main body, and completing the second-layer alliance. And finally, building a virtual real-time verification platform, and performing simulation test on the dynamic task allocation process of the clustered unmanned aerial vehicle to verify the effectiveness and feasibility of the scheme. The convergence speed of the initial task allocation method taking the ground station as a decision main body is verified, and the statistical results of the convergence under different unmanned aerial vehicle scales and task scales are shown in fig. 5. The convergence rate and effectiveness of the task redistribution method taking unmanned aerial vehicles as decision bodies are verified, and the statistical result of the convergence under the change of the number of unmanned aerial vehicles and the number of tasks is shown in fig. 6.
In order to verify the effectiveness of the unmanned aerial vehicle cluster multi-task dynamic allocation scheme based on the double-layer game, the embodiment builds a virtual real-time verification platform, and the simulation computer operates the task allocation method, gives out a real-time control instruction according to parameter information sent by the vision computer, and transmits the real-time control instruction back to the vision computer. A view computer is matched with a high-performance display card to operate view software and a database management module, wherein view demonstration software is developed based on a Unity3D game engine, and a vivid three-dimensional visual virtual scene is constructed; based on MySQL development database management module, data such as task allocation scheme and instruction in the process of executing tasks by unmanned aerial vehicle are stored and managed. The vision computer completes the information updating of the unmanned aerial vehicle and the task according to the received control instruction, as shown in fig. 7-10, fig. 7 is initial task allocation taking a ground station as a decision main body, fig. 8 is initial task allocation reaching Nash stable partition, fig. 9 is the unmanned aerial vehicle affected by task increase, and fig. 10 is task redistribution taking the unmanned aerial vehicle as a decision main body.
The unmanned aerial vehicle cluster multi-task dynamic distribution system based on the double-layer alliance, provided by the embodiment of the invention, can effectively solve the problem of cooperative cooperation among all intelligent agents in a large-scale scene, improves the intelligence of the intelligent agents, and has great application value and very wide application prospect. Applications in the military field may include multi-agent collaborative investigation strikes, multi-agent collaborative radar interference, multi-agent collaborative area patrol, and the like. The system can also be used for emergency rescue in the fields of traffic detection, aerospace and the like. In a complex unmanned dynamic environment, tasks can be reasonably distributed according to the field environment and rescue resources, and the multi-agent system of the rescue robot is controlled to effectively complete rescue tasks in the shortest time.
Embodiment two:
the embodiment provides a unmanned aerial vehicle cluster multitasking dynamic allocation system, which comprises: unmanned plane;
the unmanned aerial vehicle is used for:
acquiring a ground station task allocation result, and flying to a corresponding task area according to the ground station task allocation result; the ground station task allocation result is obtained by allocating according to the initial task information by the ground station; the ground station task allocation result is that the unmanned aerial vehicle cluster is divided into a plurality of alliances, and each alliance executes a task;
when the task change is detected or the alliance where the unmanned aerial vehicle is located is changed, judging whether the current task can bring the maximum benefit;
if the current task can bring the maximum benefit, continuing to fly to the corresponding task area according to the task allocation result of the ground station;
if the current task cannot bring the maximum benefit, searching the task with the maximum benefit from the task allocation result of the ground station according to an unmanned aerial vehicle benefit formula, adding a alliance corresponding to the task with the maximum benefit, updating local information at the same time, and increasing the updating times for one time to generate a random time stamp; the local information refers to the task allocation results of all unmanned aerial vehicles which can perform information interaction;
broadcasting interaction information to surrounding unmanned aerial vehicles and receiving the interaction information of the surrounding unmanned aerial vehicles; the interaction information comprises: update times, random time stamp and local information;
comparing all the received interactive information of surrounding unmanned aerial vehicles, and selecting the interactive information with the largest updating times;
and updating the interaction information of the unmanned aerial vehicle by using the interaction information with the largest updating times, returning to the step of searching the task with the largest profit in the task allocation result of the ground station according to the profit formula of the unmanned aerial vehicle, adding the alliance corresponding to the task with the largest profit, updating local information, and increasing the updating times once to generate a random time stamp until the unmanned aerial vehicle has the largest updating times.
The unmanned aerial vehicle cluster multi-task dynamic distribution system further comprises a ground station;
the ground station is used for:
randomly sequencing all unmanned aerial vehicles to obtain a random sequencing result;
selecting one unmanned aerial vehicle according to the random sequencing result in sequence;
calculating the individual benefits of each task selected by the unmanned aerial vehicles according to the number of the unmanned aerial vehicles required by each task in the initial task information;
assigning tasks corresponding to the maximum individual benefits to the unmanned aerial vehicle, and updating task assignment results; in the task allocation process of the ground station, each unmanned aerial vehicle judges whether the task allocated to the unmanned aerial vehicle is the task with the largest profit, if so, the unmanned aerial vehicle receives the task allocated to the unmanned aerial vehicle, and if not, the unmanned aerial vehicle switches to the task with the largest profit;
and returning to the step of selecting an unmanned aerial vehicle by the ground station according to the random sequencing result in order until all unmanned aerial vehicles are distributed to tasks corresponding to the maximum individual benefits, and obtaining a ground station task distribution result.
When detecting the task change or the alliance where the unmanned aerial vehicle is located changes, judging whether the current task can bring the maximum benefit or not, and specifically comprising the following steps:
when the task change is detected or the alliance where the unmanned aerial vehicle is located is changed, judging whether the current task can bring maximum benefit according to a benefit formula of the unmanned aerial vehicle; the unmanned aerial vehicle income formula is:
wherein (1)>
The representation of the unmanned aerial vehicle i is provided,
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other.
The principles and embodiments of the present invention have been described herein with reference to specific examples, the description of which is intended only to assist in understanding the methods of the present invention and the core ideas thereof; also, it is within the scope of the present invention to be modified by those of ordinary skill in the art in light of the present teachings. In view of the foregoing, this description should not be construed as limiting the invention.
Claims (5)
1. A method for dynamically assigning unmanned aerial vehicle clusters to multiple tasks, the method comprising:
acquiring a ground station task allocation result, and flying to a corresponding task area according to the ground station task allocation result; the ground station task allocation result is obtained by allocating according to the initial task information by the ground station; the ground station task allocation result is that the unmanned aerial vehicle cluster is divided into a plurality of alliances, and each alliance executes a task;
when the task change is detected or the alliance where the unmanned aerial vehicle is located is changed, judging whether the current task can bring the maximum benefit;
if the current task can bring the maximum benefit, continuing to fly to the corresponding task area according to the task allocation result of the ground station;
if the current task cannot bring the maximum benefit, searching the task with the maximum benefit from the task allocation result of the ground station according to an unmanned aerial vehicle benefit formula, adding a alliance corresponding to the task with the maximum benefit, updating local information at the same time, and increasing the updating times for one time to generate a random time stamp; the local information refers to the task allocation results of all unmanned aerial vehicles which can perform information interaction;
broadcasting interaction information to surrounding unmanned aerial vehicles and receiving the interaction information of the surrounding unmanned aerial vehicles; the interaction information comprises: update times, random time stamp and local information;
comparing all the received interactive information of surrounding unmanned aerial vehicles, and selecting the interactive information with the largest updating times;
updating the interaction information of the unmanned aerial vehicle by using the interaction information with the largest updating times, returning to the step of searching the task with the largest profit in the task allocation result of the ground station according to the profit formula of the unmanned aerial vehicle, adding the alliance corresponding to the task with the largest profit, updating local information, and increasing the updating times once to generate a random time stamp until the unmanned aerial vehicle has the largest updating times;
when detecting the task change or the alliance where the unmanned aerial vehicle is located changes, judging whether the current task can bring the maximum benefit or not, and specifically comprising the following steps:
when the task change is detected or the alliance where the unmanned aerial vehicle is located is changed, judging whether the current task can bring maximum benefit according to a benefit formula of the unmanned aerial vehicle; the unmanned aerial vehicle income formula is:
wherein (1)>
Representing the benefit of unmanned plane i +.>
Representing the number of unmanned aerial vehicles selecting task m, +.>
Is a relatively large random number, d i,m Represents the distance of unmanned plane i from task m, < +.>
Fuel consumption per unit distance of unmanned aerial vehicle, +.>
Represents a set of tasks that unmanned plane i can select, num m Representing the number of unmanned aerial vehicles required for task m, < ->
Representing the benefit of task m when
Less than num m Because the number requirement of unmanned aerial vehicles is not met, alliance income is zero; when->
Greater than or equal to num m The unmanned aerial vehicle quantity demand is satisfied, alliance income is set to task income at this moment.
2. The unmanned aerial vehicle cluster multitasking dynamic allocation method of claim 1, further comprising:
the ground station performs random sequencing on all unmanned aerial vehicles to obtain a random sequencing result;
the ground station sequentially selects one unmanned aerial vehicle according to the random sequencing result;
the ground station calculates the individual benefits of each task selected by the unmanned aerial vehicles according to the number of the unmanned aerial vehicles required by each task in the initial task information;
the ground station distributes tasks corresponding to the maximum individual benefits to the unmanned aerial vehicle, and updates task distribution results;
in the task allocation process of the ground station, each unmanned aerial vehicle judges whether the task allocated to the unmanned aerial vehicle is the task with the largest profit, if so, the unmanned aerial vehicle receives the task allocated to the unmanned aerial vehicle, and if not, the unmanned aerial vehicle switches to the task with the largest profit;
and returning to the step of selecting an unmanned aerial vehicle by the ground station according to the random sequencing result in order until all unmanned aerial vehicles are distributed to tasks corresponding to the maximum individual benefits, and obtaining a ground station task distribution result.
3. The unmanned aerial vehicle cluster multi-task dynamic allocation method according to claim 1, wherein the task change or the change of the alliance where the unmanned aerial vehicle is located specifically comprises: the importance of the task changes, a new task appears, and a plurality of unmanned aerial vehicles in the alliance where the unmanned aerial vehicle is located fail.
4. A unmanned cluster multi-tasking dynamic allocation system, the system comprising: unmanned plane;
the unmanned aerial vehicle is used for:
acquiring a ground station task allocation result, and flying to a corresponding task area according to the ground station task allocation result; the ground station task allocation result is obtained by allocating according to the initial task information by the ground station; the ground station task allocation result is that the unmanned aerial vehicle cluster is divided into a plurality of alliances, and each alliance executes a task;
when the task change is detected or the alliance where the unmanned aerial vehicle is located is changed, judging whether the current task can bring the maximum benefit;
if the current task can bring the maximum benefit, continuing to fly to the corresponding task area according to the task allocation result of the ground station;
if the current task cannot bring the maximum benefit, searching the task with the maximum benefit from the task allocation result of the ground station according to an unmanned aerial vehicle benefit formula, adding a alliance corresponding to the task with the maximum benefit, updating local information at the same time, and increasing the updating times for one time to generate a random time stamp; the local information refers to the task allocation results of all unmanned aerial vehicles which can perform information interaction;
broadcasting interaction information to surrounding unmanned aerial vehicles and receiving the interaction information of the surrounding unmanned aerial vehicles; the interaction information comprises: update times, random time stamp and local information;
comparing all the received interactive information of surrounding unmanned aerial vehicles, and selecting the interactive information with the largest updating times;
updating the interaction information of the unmanned aerial vehicle by using the interaction information with the largest updating times, returning to the step of searching the task with the largest profit in the task allocation result of the ground station according to the profit formula of the unmanned aerial vehicle, adding the alliance corresponding to the task with the largest profit, updating local information, and increasing the updating times once to generate a random time stamp until the unmanned aerial vehicle has the largest updating times;
when detecting the task change or the alliance where the unmanned aerial vehicle is located changes, judging whether the current task can bring the maximum benefit or not, and specifically comprising the following steps:
when the task change is detected or the alliance where the unmanned aerial vehicle is located is changed, judging whether the current task can bring maximum benefit according to a benefit formula of the unmanned aerial vehicle; the unmanned aerial vehicle income formula is:
wherein (1)>
Representing the benefit of unmanned plane i +.>
Representing the number of unmanned aerial vehicles selecting task m, +.>
Is a relatively large random number, d i,m Represents the distance of unmanned plane i from task m, < +.>
Fuel consumption per unit distance of unmanned aerial vehicle, +.>
Represents a set of tasks that unmanned plane i can select, num m Representing the number of unmanned aerial vehicles required for task m, < ->
Representing the benefit of task m when
Less than num m Because the number requirement of unmanned aerial vehicles is not met, alliance income is zero; when->
Greater than or equal to num m The unmanned aerial vehicle quantity demand is satisfied, alliance income is set to task income at this moment.
5. The unmanned aerial vehicle cluster multitasking dynamic allocation system of claim 4, further comprising a ground station;
the ground station is used for:
randomly sequencing all unmanned aerial vehicles to obtain a random sequencing result;
selecting one unmanned aerial vehicle according to the random sequencing result in sequence;
calculating the individual benefits of each task selected by the unmanned aerial vehicles according to the number of the unmanned aerial vehicles required by each task in the initial task information;
assigning tasks corresponding to the maximum individual benefits to the unmanned aerial vehicle, and updating task assignment results; in the task allocation process of the ground station, each unmanned aerial vehicle judges whether the task allocated to the unmanned aerial vehicle is the task with the largest profit, if so, the unmanned aerial vehicle receives the task allocated to the unmanned aerial vehicle, and if not, the unmanned aerial vehicle switches to the task with the largest profit;
and returning to the step of selecting an unmanned aerial vehicle by the ground station according to the random sequencing result in order until all unmanned aerial vehicles are distributed to tasks corresponding to the maximum individual benefits, and obtaining a ground station task distribution result.
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Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106506657A (en) * | 2016-11-21 | 2017-03-15 | 黑龙江省科学院自动化研究所 | One kind distributes method of adjustment based on multiobject cloud computing virtual machine |
| CN110083173A (en) * | 2019-04-08 | 2019-08-02 | 合肥工业大学 | The optimization method of unmanned plane formation patrol task distribution |
| CN110308740A (en) * | 2019-06-28 | 2019-10-08 | 天津大学 | A kind of unmanned aerial vehicle group dynamic task allocation method towards mobile target tracking |
| CN111199360A (en) * | 2020-01-13 | 2020-05-26 | 西安电子科技大学 | Unmanned aerial vehicle task allocation planning method |
| CN111311049A (en) * | 2019-12-04 | 2020-06-19 | 南京理工大学 | Multi-agent task allocation method based on income maximization |
| CN112910716A (en) * | 2021-03-08 | 2021-06-04 | 北方工业大学 | Mobile fog calculation loss joint optimization system and method based on distributed DNN |
| CN113009934A (en) * | 2021-03-24 | 2021-06-22 | 西北工业大学 | Multi-unmanned aerial vehicle task dynamic allocation method based on improved particle swarm optimization |
| EP3842890A1 (en) * | 2019-12-26 | 2021-06-30 | UVify Inc. | Unmanned aerial vehicle for fleet operation and fleet operation system |
| WO2021135336A1 (en) * | 2020-05-06 | 2021-07-08 | 农业农村部南京农业机械化研究所 | Cluster job task assignment method and device for plant-protection unmanned aerial vehicles |
| WO2021179550A1 (en) * | 2020-03-11 | 2021-09-16 | 五邑大学 | Task dispatch decision making method and system for unmanned aerial vehicle cluster |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US11163608B2 (en) * | 2019-03-13 | 2021-11-02 | Tata Consultancy Services Limited | Method and system for privacy enabled task allocation |
| CN111240211B (en) * | 2020-03-19 | 2020-08-28 | 北京航空航天大学 | Dynamic recovery method for unmanned aerial vehicle group |
-
2022
- 2022-01-12 CN CN202210029987.4A patent/CN114326827B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106506657A (en) * | 2016-11-21 | 2017-03-15 | 黑龙江省科学院自动化研究所 | One kind distributes method of adjustment based on multiobject cloud computing virtual machine |
| CN110083173A (en) * | 2019-04-08 | 2019-08-02 | 合肥工业大学 | The optimization method of unmanned plane formation patrol task distribution |
| CN110308740A (en) * | 2019-06-28 | 2019-10-08 | 天津大学 | A kind of unmanned aerial vehicle group dynamic task allocation method towards mobile target tracking |
| CN111311049A (en) * | 2019-12-04 | 2020-06-19 | 南京理工大学 | Multi-agent task allocation method based on income maximization |
| EP3842890A1 (en) * | 2019-12-26 | 2021-06-30 | UVify Inc. | Unmanned aerial vehicle for fleet operation and fleet operation system |
| CN111199360A (en) * | 2020-01-13 | 2020-05-26 | 西安电子科技大学 | Unmanned aerial vehicle task allocation planning method |
| WO2021179550A1 (en) * | 2020-03-11 | 2021-09-16 | 五邑大学 | Task dispatch decision making method and system for unmanned aerial vehicle cluster |
| WO2021135336A1 (en) * | 2020-05-06 | 2021-07-08 | 农业农村部南京农业机械化研究所 | Cluster job task assignment method and device for plant-protection unmanned aerial vehicles |
| CN112910716A (en) * | 2021-03-08 | 2021-06-04 | 北方工业大学 | Mobile fog calculation loss joint optimization system and method based on distributed DNN |
| CN113009934A (en) * | 2021-03-24 | 2021-06-22 | 西北工业大学 | Multi-unmanned aerial vehicle task dynamic allocation method based on improved particle swarm optimization |
Non-Patent Citations (4)
| Title |
|---|
| A Game Theoretic Approach for Mobility Prediction Clustering in Unmanned Aerial Vehicle Networks;Na Xing 等;《IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY》;第68卷(第10期);9963-9973 * |
| Dynamic Task Allocation for Formation Air-to-ground Attack;An Zhang 等;《2013 Sixth International Conference on Advanced Computational Intelligence》;119-123 * |
| 考虑协同航路规划的多无人机任务分配;王然然 等;《航空学报》;第41卷(第S2期);1-12 * |
| 面向无人机集群任务分配的分布式算法研究;林晨;《中国优秀硕士学位论文全文数据库工程科技Ⅱ辑》(第1期);C031-244 * |
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