A cluster distributed cooperative task allocation method applicable to denial environment

Abstract

The application provides a cluster distributed cooperative task allocation method which can be used in denial environment, and relates to the technical field of task planning of unmanned aerial vehicle cluster. The task allocation method provided by the application can be used in large-scale unmanned aerial vehicle cluster by delimiting a task allocation range to reduce the functional complexity; can be used in cooperative task allocation by decomposing the cooperative task and allocating the decomposed cooperative task in the method of internal role division; can guarantee the global task allocation effect while reducing the calculation pressure and time consumption of task allocation by using the architecture of distributed calculation and centralized global decision; and can be used in task allocation in denial environment by using a perfect denial environment processing strategy.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of task planning of unmanned aerial vehicle (UAV) cluster, and particularly provides a cluster distributed cooperative task allocation method which can be used in denial environment. BACKGROUND

[0002] After discovering a target, the UAV cluster generates several combat tasks for the target, and needs to specify specific UAVs to execute to build a complete kill chain. At present, the tasks are allocated by manual designation, but as the cluster size continues to expand, manual designation gradually cannot meet the requirements. There are also optimization methods such as auction algorithm to automatically allocate tasks, but the process needs multiple information interactions within the cluster, which is difficult to use in denial environment. The task allocation algorithm consumes more and more time as the UAV size expands, and it is difficult to be used for large-scale cluster task allocation, and the coordination relationship between tasks is not considered during task allocation, which is difficult to be used for cooperative task allocation.

[0003] Therefore, there is an urgent need for a fast cooperative task allocation method which can be used in denial environment. SUMMARY

[0004] The present application aims to provide a fast cooperative task allocation method which can be used in denial environment, and through functions such as defining allocation range, distributed cost calculation, cooperative task decomposition and decision division, and denial environment self-adaptation, the global effect of task allocation is ensured while the time consumption of task allocation is reduced, and the problem of cooperative task allocation of large-scale UAV cluster in denial environment is solved.

[0005] In order to achieve the above-mentioned purpose, the present application adopts the following technical solution: a cluster distributed cooperative task allocation method which can be used in denial environment, the method comprises:

[0006] S1: calculating task allocation range;

[0007] S2: sending a bidding request to the UAVs participating in task allocation, and the UAVs calculate the task execution cost after receiving the bidding request, and return the cost information to the relay machine;

[0008] S3: after the relay machine receives the cost information, storing to form a task cost matrix;

[0009] S4: calculating the task value according to the target type, and obtaining a task benefit matrix in combination with the task cost;

[0010] S5: initializing a profit matrix, and the profit is the difference between the task benefit and the bid;

[0011] S6: the UAVs in the UAV list start bidding in sequence, according to the new bidding information, updating the profit matrix of all UAVs for the task, the task is successfully bid by the UAV with the highest bid, until all UAVs complete bidding, the UAV successfully bid forms a pair with the task;

[0012] S7: all tasks are traversed to determine whether they are cooperative tasks, if cooperative tasks are included, re-divide according to the cooperative task requirements, calculate the cooperative task time according to the task estimated completion time, and issue task instructions according to the task allocation result.

[0013] The cluster distributed cooperative task allocation method for denial environment provided by the application also has the technical features that S1 includes calculating the task allocation range by screening the UAVs.

[0014] The cluster distributed cooperative task allocation method for denial environment provided by the application also has the technical features that screening the UAVs includes first screening the UAV types, and then screening the UAVs and the target by distance.

[0015] The cluster distributed cooperative task allocation method for denial environment provided by the application also has the technical features that the task execution cost C in S2 is calculated as:

[0016] C=p1d+p2t+p3s

[0017] Wherein, p is a weight coefficient, d is a distance cost, t is a consumption cost, and s is a threat cost.

[0018] The cluster distributed cooperative task allocation method for denial environment provided by the application also has the technical features that S2 further includes, if a UAV does not return the task execution cost within 100 cycles, a relay machine replaces the calculation of the task execution cost, and the relay machine takes the straight-line distance between the lost UAV and the target as the task execution cost of the UAV.

[0019] The cluster distributed cooperative task allocation method for denial environment provided by the application also has the technical features that the task is successfully bid by the UAV with the highest bid, including:

[0020] The UAV that starts bidding bids for the task with the highest profit, if the profit of the UAV for all tasks is not greater than 0, the UAV exits the bidding, otherwise, the bid is increased, and the size of the bid is calculated according to the profit of the task and the profit of the suboptimal task;

[0021] If the task currently bid has been bid by other UAVs before, and the current bid is higher, the UAV that previously bid fails to bid, and the UAV that fails to bid starts bidding again.

[0022] If there is no other UAV bidding before the current bidding task, the bidding starts from the next UAV in the UAV list.

[0023] The cluster distributed cooperative task allocation method for denial environment provided by the application further has the technical features that the re-partitioning in S7 includes: decomposing the cooperative task into corresponding number of single-machine tasks according to the required number of UAVs.

[0024] The cluster distributed cooperative task allocation method for denial environment provided by the application further has the technical features that the method further includes: after the relay machine is lost, the UAV closest to the center position of all UAVs is taken as a temporary relay node to replace the relay machine to plan the task allocation process.

[0025] Advantages

[0026] The task allocation method provided by the application can be used for large-scale UAV clusters by reducing the functional complexity through defining the task allocation range; can be used for cooperative task allocation by decomposing the cooperative task and allocating it in the method of internal role division; can be used for task allocation in denial environment by perfect denial environment processing strategy. BRIEF DESCRIPTION OF DRAWINGS

[0027] To more clearly illustrate the technical solutions of the embodiments of the present disclosure, the following will briefly introduce the drawings needed to be used in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present disclosure, and for those skilled in the art, other drawings can be obtained from these drawings without creative labor.

[0028] Fig. 1 The flow chart of the method provided by the embodiments of the application;

[0029] Fig. 2 The functional architecture diagram of the method provided by the embodiments of the application. DETAILED DESCRIPTION

[0030] The present application will be further described in detail below in combination with the drawings and embodiments. It should be noted that these embodiments are not limitations of the present application, and equivalent changes or substitutions of functions, methods, or structures made by those skilled in the art according to these embodiments are within the protection scope of the present application.

[0031] In the description of the embodiments of the present application, it should be understood that the terms "center", "longitudinal", "transverse", "upper", "lower", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer" and the like indicate the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation on the present application.

[0032] In addition, the terms "first", "second", "third" and the like are only for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the technical features indicated. Therefore, the features defined with "first", "second" and the like can explicitly or implicitly include one or more of the features. In the description of the present application, unless otherwise stated, the meaning of "a plurality of" is two or more.

[0033] The terms "mounting", "connection", "connection" should be broadly understood, for example, it can be fixed connection, or detachable connection, or integral connection; it can be mechanical connection, or electrical connection; it can be directly connected, or indirectly connected through intermediate medium, or internal communication of two elements. For those skilled in the art, the specific meaning of the above terms in the present application can be understood through specific circumstances.

[0034] As Figs. 1-2 shown, the present application provides a cluster distributed cooperative task allocation method which can be used in denial environment, the method comprises:

[0035] S1: calculating the range of task allocation;

[0036] S2: sending a bid request to the unmanned aerial vehicle participating in the task allocation, the unmanned aerial vehicle calculates the task execution cost after receiving the bid request, and returns the cost information to the relay machine;

[0037] S3: after the relay machine accepts the cost information, it stores the task cost matrix;

[0038] S4: calculating the task value according to the target type, combining the task cost, obtaining the task benefit matrix;

[0039] S5: initializing the profit matrix, the profit is the difference between the task benefit and the bid;

[0040] S6: the unmanned aerial vehicles in the unmanned aerial vehicle list start bidding in order, according to the new bid information, update the profit matrix of all unmanned aerial vehicles for the task, the task is successfully bid by the unmanned aerial vehicle with the highest bid, until all unmanned aerial vehicles complete bidding, the unmanned aerial vehicle with successful bidding forms a pair with the task;

[0041] S7: traversing all tasks, judging whether it is a collaborative task, if it includes a collaborative task, re-dividing according to the requirements of the collaborative task, calculating the collaborative task time according to the estimated completion time of the task, and issuing task instructions according to the task allocation result.

[0042] The method provided by the above embodiments performs collaborative task allocation of large-scale clusters in a denial environment through task allocation range definition, distributed cost calculation, auction-style task allocation, collaborative task decomposition and role division, and denial environment adaptive process.

[0043] In some embodiments, S1 includes calculating the task allocation range by screening the unmanned aerial vehicles.

[0044] In some embodiments, screening the unmanned aerial vehicles includes first screening the unmanned aerial vehicle types, and then screening the unmanned aerial vehicles and the target by distance.

[0045] In some embodiments, the task machines participating in the task allocation are screened by task constraints, the influence range of the task allocation function is limited, the interaction times between the relay machines and the task machines are reduced, thereby reducing the bandwidth occupation, and the complexity of the task allocation process can be effectively reduced to ensure the real-time performance of the task allocation process.

[0046] The task constraints include unmanned aerial vehicle type constraints and distance constraints. The unmanned aerial vehicle type constraints are generated by the task scheme generation module, and the unmanned aerial vehicles that do not meet the unmanned aerial vehicle type constraints do not participate in this task allocation. The distance constraint is a configuration parameter, and the default is 20 km. Only the task machines with a distance of less than 20 km from the target participate in this task allocation.

[0047] In some embodiments, the task execution cost C in S2 is calculated as:

[0048] C = p1d + p2t + p3s

[0049] Wherein, p is a weight coefficient, d is a distance cost, t is a consumption cost, and s is a threat cost.

[0050] In the above embodiments, the task execution cost is calculated by distributed calculation. Each unmanned aerial vehicle calculates the cost of executing the task itself, which can effectively reduce the computing pressure of the relay machine, thereby supporting real-time task allocation in a large-scale unmanned aerial vehicle cluster. At the same time, due to the distributed calculation of the task cost, the calculation process of the task cost allows a more complex and more accurate method, so we calculate the task execution cost by deducing the task execution process through real-time route planning. The calculation result is more close to the actual situation and more accurate. Real-time route planning can also output the estimated consumption time of executing the task, which is convenient for subsequent calculation of the collaborative task time to ensure the time consistency of the collaborative task.

[0051] Support user-defined task cost indicators, including voyage, time consumption, number of threats, etc. Support subsequent expansion according to user needs, so that the task allocation result can better meet the user's needs. By default, the voyage is used as the task cost indicator. If the endurance of the task machine is insufficient, the task cost is set to 200, giving the largest task cost, and the unmanned aerial vehicle exits this task allocation. The task cost of non-optical reconnaissance and attack machine increases by 100, and the optical reconnaissance and attack machine is preferred.

[0052] In some embodiments, there are N different unmanned aerial vehicles {U1, U2,... U N}, M tasks {T1, T2,... T M}, and a task allocation matrix X N×M is defined.

[0053]

[0054] Each unmanned aerial vehicle is allowed to perform only one task at the same time, and each task is allowed to be performed by only one unmanned aerial vehicle, i.e. the following constraints are met:

[0055]

[0056] The objective function of the multi-unmanned aerial vehicle cooperative task allocation problem is defined as:

[0057] J(X)=X.*C

[0058] Where C is an N x M allocation basis matrix, c i j represents the cost of U i executing task T j .

[0059] C=p1d+p2t+p3s+…

[0060] Where p is the weight coefficient, d is the distance cost, t is the time cost, and s is the threat cost, which can be continuously expanded according to user needs.

[0061] In some embodiments, the S2 further comprises, if a certain unmanned aerial vehicle does not return within 100 cycles, the task execution cost is replaced by a relay machine, and the relay machine calculates the task execution cost as the straight-line distance between the lost unmanned aerial vehicle and the target.

[0062] In some embodiments, if the task machine is lost after the task allocation is completed and the task machine is instructed according to the task allocation result, the nearest idle task machine to the target is dispatched to execute the task. If the task machine is also lost, the above process is repeated until the task instruction is successfully issued.

[0063] In some embodiments, the task is successfully bid by the UAV with the highest bid price, including:

[0064] The UAV that starts the bidding places a bid on the task with the highest profit. If the profit of all tasks is less than 0, the UAV stops bidding. Otherwise, the UAV increases the bid price, and the size of the increase is calculated based on the profit of the task and the profit of the sub-optimal task.

[0065] If the task being bid on has been previously bid on by another UAV, and the current bid is higher, the previous bidder loses the bid, and the bidding starts over with the next UAV in the list.

[0066] If the task being bid on has not been previously bid on, the bidding starts over with the next UAV in the list.

[0067] In the above embodiments, the task allocation uses an auction algorithm, in which the UAVs bid on tasks that they are interested in, and the task is allocated to the UAV with the highest bid. For UAVs that do not win the bid (other UAVs bid higher), they can either increase the bid or change the task they want to execute and bid again. When a UAV finds that the profit of all tasks is less than 0, it stops bidding. The process ends when all UAVs have either won a task or stopped bidding. The combination of UAVs and tasks that have won the bid forms the final task allocation scheme. The auction algorithm converges faster than traditional combinatorial optimization algorithms, takes less time to allocate, and is less likely to be limited to local optima, resulting in a better allocation.

[0068] The value of a task is determined based on the type of target. Time-sensitive targets have a higher value of 100, and non-time-sensitive targets have a lower value of 50. Time-sensitive targets are prioritized for attack. The cost of a task represents the cost of executing the task, which is calculated based on the distributed task cost. The income of a task is the difference between the value and the cost of the task

[0069] income = value - cost

[0070] The profit of a task is the difference between the income and the current bid price of the task

[0071] profit = income - offer

[0072] The UAV bids on the task with the highest profit. The value of the increase in the bid price, π, is calculated as follows

[0073] π = profit - (income - offer) + ε s s ​​

[0074] where profit is the profit of the task, income s -offer s is the profit of the suboptimal task, and ε is a constant parameter to avoid too small price increase each time, which is usually taken as 1 / n, n is the number of UAVs.

[0075] If the task has been bid by other UAVs, and the UAV has a higher bid, the original UAV needs to bid higher or change the task to bid. When the profit of the UAV to all targets is less than 0, the UAV exits the bidding. When all UAVs get the task or exit the bidding, the task allocation ends. For all UAVs that bid for the task, for example, UAV U i The task to be executed is T i , all U i The task T i The task allocation matrix X of the task allocation problem can be combined N×M , that is, the optimal task allocation scheme.

[0076] In some embodiments, the re-division in S7 includes: decomposing the cooperative task into a corresponding number of single-machine tasks according to the required number of UAVs, for example, a cooperative task requiring two UAVs to attack and one UAV to evaluate is decomposed into two attack tasks and one evaluation task. After single-machine task allocation, among UAVs undertaking the same cooperative task, the tasks are re-allocated according to the execution time sequence, and are sequentially allocated to the task machines closest to the target from near to far to meet the time sequence constraints of the cooperative task. For example, the cooperative evaluation task is a follow-up evaluation, and the UAV executing the evaluation task needs to arrive first to shoot the entire attack process. Among the three UAVs, the UAV closest to the target executes the evaluation task, and the other two UAVs execute the attack task.

[0077] The time consistency of the cooperative task refers to the UAVs executing the cooperative task agreeing on a time to start executing the cooperative task simultaneously to ensure the execution effect of the cooperative task. The task cost has calculated the time required by the UAV to execute the task, which includes the time to reach the target point, and the execution time of the cooperative task can be determined accordingly. The estimated time of all UAVs to reach the target point in the same cooperative task is calculated, and the latest time among them is increased by 5 seconds as the execution time of the cooperative task to meet the time consistency requirement of the cooperative task.

[0078] In some embodiments, the method further comprises: after the relay machine is lost, the UAV closest to the center position of all UAVs is used as a temporary relay node to replace the relay machine to coordinate the task allocation process. Through a perfect abnormal handling mechanism, the stability and robustness of the task allocation process are guaranteed.

[0079] The above description is only the preferred embodiment of the present application, and is not used to limit the present application, any modification, equivalent replacement and improvement made within the spirit and principle of the present application should be included in the protection scope of the present application. The above description is only the preferred embodiment of the present application, and it should be pointed out that, for ordinary skilled in the art, without departing from the technical principles of the present application, a number of improvements and variations can be made, and these improvements and variations should be considered as the protection scope of the present application.

Claims

1. A cluster distributed cooperative task allocation method that can be used in a denial environment, characterized by, The method comprises: S1: calculating a task allocation range; S2: sending a bid request to a UAV participating in task allocation, the UAV calculating a task execution cost after receiving the bid request and returning the cost information to a relay machine; S3: the relay machine storing a task cost matrix after accepting the cost information; S4: calculating a task value according to a target type, combining the task cost to obtain a task benefit matrix; S5: initializing a profit matrix, the profit being the difference between the task benefit and the bid; S6: the UAVs in a UAV list starting bidding in order, updating the profit matrix of all UAVs for the task according to new bid information, the task being successfully bid by the UAV with the highest bid, until all UAVs complete bidding, the UAV successfully bidding forming a pairing with the task; S7: traversing all tasks to determine whether they are cooperative tasks, if including cooperative tasks, re-dividing the tasks according to cooperative task requirements, calculating a cooperative task time according to the estimated completion time of the task, and issuing a task instruction according to the task allocation result.

2. The cluster distributed cooperative task allocation method applicable to denial environment according to claim 1, characterized in that, The S1 comprises calculating the task allocation range by screening the UAVs.

3. The cluster distributed cooperative task allocation method for denial environment as claimed in claim 2 wherein, The screening of the UAVs comprises screening the UAV types first and then screening the UAVs and the target according to the distance.

4. The cluster distributed cooperative task allocation method for denial environment as claimed in claim 1 wherein, The task execution cost C in the S2 is calculated as follows: C = p1d + p2t + p3s wherein p is a weight coefficient, d is a distance cost, t is a consumption cost, and s is a threat cost.

5. The cluster distributed cooperative task allocation method for denial environment as claimed in claim 1 wherein, The S2 further comprises, if a UAV does not return the task execution cost within 100 cycles, the relay machine replacing the calculation of the task execution cost, the relay machine taking the straight-line distance between the out-of-contact UAV and the target as the task execution cost of the UAV.

6. The cluster distributed cooperative task allocation method for denial environment as claimed in claim 1 wherein, The task being successfully bid by the UAV with the highest bid comprises: the UAV starting bidding bids for the task with the highest profit, if the profit of the UAV for all tasks is not greater than 0, the UAV exits bidding, otherwise, the UAV increases the bid, the size of the increased bid being calculated according to the profit of the task and the profit of the suboptimal task; if the task being bid has been bid by other UAVs before and the current bid is higher, the UAVs previously bidding fail to bid, and the bidding is restarted by the UAVs that fail to bid; if the task being bid has not been bid by other UAVs before, the next UAV in the UAV list starts bidding.

7. The cluster distributed cooperative task allocation method for denial environment as claimed in claim 1 wherein, The re-dividing in the S7 comprises: decomposing the cooperative task into corresponding number of single-machine tasks according to the required number of UAVs.

8. The cluster distributed cooperative task allocation method for denial environment as claimed in claim 1 wherein, The method further comprises: after the relay machine is out of contact, taking the UAV closest to the center position of all UAVs as a temporary relay node to replace the relay machine in the overall task allocation process.

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