A time constraint based distributed task allocation method for unmanned aerial vehicles

Abstract

The application provides a kind of unmanned plane distributed task allocation method based on time constraint, belong to unmanned plane command decision technology field.The specific process is: generating unmanned plane task bundle, the RPI value of all tasks of unmanned plane and unmanned plane sequence, wherein when unmanned plane executes task to meet time constraint, RPI value is calculated according to PI algorithm, when it is not satisfied, RPI value is set to infinity;Unmanned plane communicates with each other, for each task, the unmanned plane with the lowest RPI value is retained to execute the task, and the remaining unmanned plane deletes the task, to realize consistency processing;For the unmanned plane with the largest number of deleted tasks, delete all tasks required to be executed;For the remaining unmanned plane, re-perform conflict task elimination, and update the corresponding RPI value and unmanned plane sequence;If the task allocation result of each unmanned plane no longer changes, the unmanned plane task allocation is finished, otherwise, for the task of unmanned plane not allocated, start a new round of task allocation.

Description

Technical Field

[0001] This invention belongs to the field of UAV command and decision-making technology, specifically relating to a time-constrained distributed task allocation method for UAVs. Background Technology

[0002] Unmanned aerial vehicles (UAVs) possess advantages such as high flexibility, good stealth, and low cost, playing a vital role in military and civilian fields such as reconnaissance and strike, information gathering, environmental monitoring, and search and rescue. UAV task allocation aims to solve the matching problem between UAVs and tasks. Based on the presence or absence of an allocation center, UAV task allocation methods can be divided into centralized and distributed approaches. Centralized task allocation relies on a central node to generate task allocation schemes and distribute the allocation results to all nodes within the cluster system, resulting in drawbacks such as single points of failure and high computational complexity. Distributed task allocation often resolves conflicts through communication and negotiation among individuals within the cluster to maximize the collective benefit of the group. It does not rely on global information and has therefore become the main method for current research on UAV task allocation. The PI (Performance Impact) algorithm is one of the most effective methods for solving UAV task allocation, especially for time-constrained UAV task allocation in known environments, where each task requires a UAV to start execution before a specified time, and a single UAV can continuously complete multiple tasks.

[0003] The Performance Impact (PI) algorithm is one of the most effective methods for solving drone task allocation, especially for time-constrained drone task allocation in known environments. While the PI algorithm can solve the drone task allocation problem when the number of tasks is small, it suffers from a low task completion rate when the number of drones is large, meaning there are unassigned tasks. Therefore, this invention improves the task elimination strategy of the PI algorithm, increasing the number of tasks each drone can execute, thereby improving the drone task allocation completion rate. Summary of the Invention

[0004] In view of this, the present invention provides a distributed task allocation method for UAVs that takes into account time constraints. This method can achieve the most reasonable task allocation and improve the completion rate of UAV task allocation.

[0005] The technical solution for implementing the present invention is as follows:

[0006] A time-constrained distributed task allocation method for UAVs, the specific process of which is as follows:

[0007] Step 1: Collect initial parameters, including the latest start time of the task;

[0008] Step 2: Generate UAV mission bundle p i RPI value of drones for all missions and drone sequence β i The process of generating the RPI value is as follows: when the UAV performs a task that meets the time constraints, the RPI value corresponding to the UAV performing the task is calculated according to the PI algorithm; when it does not meet the time constraints, the RPI value of the UAV performing the task is set to infinity.

[0009] Step 3: The drones communicate with each other. For each task, the drone with the lowest RPI value is retained to execute the task, while the remaining drones delete the task, achieving consistency. The number of tasks θ to be deleted for each drone is then obtained. i RPI value and drone sequence β i ;

[0010] Step 4: For the drone with the largest number of deleted tasks, delete all the tasks it needs to perform, and update its corresponding RPI value and drone sequence.

[0011] Step 5: For the remaining drones, re-examine the conflicting missions and update their corresponding RPI values ​​and drone sequences;

[0012] Step 6: If the task allocation results for each drone no longer change, the drone task allocation ends; otherwise, a new round of task allocation begins for the unassigned drones.

[0013] Furthermore, the time constraint described in this invention is that the time for the UAV to execute the mission is later than the latest time the mission can begin.

[0014] Furthermore, the present invention describes a process for re-eliminating conflicting tasks for the remaining drones. Specifically, let drone i have task d that needs to be deleted. i ;

[0015] Determine which task d needs to be deleted for drone i. i Length |d i Does |>0 hold true? If |d i If | > 0 is not true, the conflict task elimination for drone i ends. If | d i If |>0 is true, drone i enters the conflict task elimination phase, which includes the following steps:

[0016] (1) Calculate the RPI value of UAV i for all tasks according to the PI algorithm.

[0017] (2) Calculate the RPI value With RPI value The maximum difference and the task number corresponding to the maximum difference are denoted as g. ◇ and t q ;

[0018] (3) Determine g ◇ Whether >0 is true, if g ◇ If > 0 is true, then t q From p i and d i Re-enter the drone mission elimination phase, if g ◇ >0 is not true, the drone i-conflict mission elimination ends.

[0019] Furthermore, when constructing a task bundle based on the PI algorithm, the present invention aims to maximize the number of task assignments.

[0020] Furthermore, when there are at least two drones with the largest number of tasks, the present invention selects the drone with the larger number to delete the task.

[0021] Beneficial effects:

[0022] First, the PI algorithm determines whether to remove a task by comparing the RPI (Resource Point Index) of each drone for that task. The basic principle is that the drone with the lowest RPI retains the task, while the others abandon it. Once a task is assigned to a drone, such as U1, it cannot be removed from U1's task sequence unless another drone obtains a lower RPI value, preventing some tasks from being added to U1. This invention ensures that more tasks can be added to drones by clearing and deleting the task bundle of the drone with the largest number of tasks.

[0023] Second, in the PI algorithm, the RPI value of the UAV for a task is closely related to the sequence of tasks executed. Once a task is removed, the RPI value of the tasks following that task will also change. This invention can ensure that the RPI values ​​of the remaining tasks are up-to-date by updating the RPI of all tasks, and can more accurately remove unnecessary tasks. Attached Figure Description

[0024] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 A flowchart of a distributed task allocation method for UAVs that takes time constraints into account.

[0026] Figure 2 This is a diagram showing the distribution of drones and missions. Detailed Implementation

[0027] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0028] It should be noted that, in the absence of conflict, the following embodiments and features can be combined with each other; and, based on the embodiments of this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0029] It should be noted that various aspects of embodiments within the scope of the appended claims are described below. It will be apparent that the aspects described herein can be embodied in a wide variety of forms, and any particular structure and / or function described herein is merely illustrative. Based on this disclosure, those skilled in the art will understand that one aspect described herein can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. For example, any number of aspects set forth herein can be used to implement the device and / or practice the method. Additionally, this device and / or method can be implemented using structures and / or functionalities other than one or more of the aspects set forth herein.

[0030] like Figure 1 As shown in the figure, an embodiment of this application provides a time-constrained distributed task allocation method for unmanned aerial vehicles (UAVs), the specific process of which is as follows:

[0031] Step 1: Given known environmental information, including the location, speed, and number of drones, the location, number, and speed of the mission, and the latest start time.

[0032] Step 2: Each drone uses a greedy algorithm to maximize the number of tasks assigned based on the known environmental information, and constructs a task bundle using the PI algorithm under the condition of task time constraints.

[0033] The following example demonstrates how to generate a drone mission bundle p under initial conditions, using drone i as an example. i RPI value of drones for all missions UAV sequence β i For example, p1 represents the sequence of tasks that drone 1 can perform, such as {task 1, task 3}. This represents the RPI values ​​obtained by UAV 1 in sequentially executing tasks in p1, such as {RPI_1, RPI_2, RPI_3}. If the execution time of UAV 1 for tasks 1 and 3 meets the time constraint, then RPI_1 and RPI_3 are calculated according to the PI algorithm; if the execution time of UAV 2 does not meet the time constraint, the corresponding RPI value is set to infinity. β1 represents the task allocation situation as perceived by UAV 1. For example, β1 = {1, 2, 1} means that UAV 1 believes that tasks 1 and 3 are assigned to UAV 1, and task 2 is assigned to UAV 2.

[0034] Step 3: Drone i sends the results and timestamps of the task bundle construction from Step 2 to neighboring drones. For the same task, the drone with the lower RPI retains the task, while other drones discard it; therefore, after completing the above consistency processing, calculate the number θ of tasks that drone i needs to delete. i RPI value UAV sequence β i .

[0035] Step 4: Based on the consistency processing results in Step 3, determine the drone with the most deleted tasks, i.e., θ in Step 3. i The drone corresponding to the maximum value; let the drone with the most deleted tasks be numbered k.

[0036] Determine if drone i has the most deleted tasks. If so, i.e., k = i, then clear drone i's task bundle p. i Update drone i and β i The conflict task elimination for drone i is completed; if the current drone i is not the drone with the most deleted tasks, i.e., k≠i, proceed to step 5; if there are multiple corresponding drone numbers, select the drone with the larger drone number.

[0037] Step 5: Determine the task d that needs to be deleted for drone i. i Length |d i Does |>0 hold true? If |d i If | > 0 is not true, the conflict task elimination for drone i ends. If | d i If |>0 is true, drone i enters the conflict mission elimination phase, which includes the following sub-steps.

[0038] Sub-step 5.1: Calculate the RPI value of UAV i for all tasks using the PI algorithm.

[0039] Sub-step 5.2: Calculate the result in sub-step 5.1 Compared with the RPI value in step 2 The maximum difference and the task number corresponding to the maximum difference are respectively denoted as g. ◇ and t q ;

[0040] Sub-step 5.3: Determine g ◇ Is >0 true? If g ◇ If > 0 is true, then t q From p i and d i Repeat step 5 if g ◇ >0 is not true, the drone i-conflict mission elimination ends.

[0041] Step 6: If the task allocation results for each drone no longer change, the algorithm converges, and the drone task allocation ends. Otherwise, skip to step 2 and start a new round of task allocation.

[0042] Below, N a A drone against N T Taking the execution of a single target task as an example, the method of the present invention will be described in detail below:

[0043] Step 1, N a drone location drone speed N T The location of the target Target speed Latest execution time

[0044] Step 2: Each drone, based on known environmental information, employs a greedy algorithm to maximize the number of tasks assigned, and constructs a task bundle using the PI algorithm while satisfying task time constraints. Each drone generates a task bundle p. i RPI value of drones for all missions UAV sequence β i ,

[0045] Step 3, Drone i, The result of constructing the task bundle in step 2, p i timestamp i Send to nearby drones Consistency processing was completed based on the PI algorithm for each drone. Update the number of tasks to be deleted θ i RPI value UAV sequence β i ;

[0046] Step 4: Calculate i for each drone. The set of tasks d to be removed i θ i Find θ i The largest drone, k. Drone k clears the mission bundle, updates the RPI value and drone sequence, i.e.

[0047] Step 5: Identify other drones i, i≠k|d i Does |>0 hold true? If |d i If | > 0 is not true, the conflict task elimination for drone i ends. If | d iIf |>0 is true, drone i enters the conflict mission elimination phase, which includes the following sub-steps.

[0048] Sub-step 5.1: Calculate UAV i using the PI algorithm. i≠k RPI value for all tasks

[0049] Sub-step 5.2: Calculate the result in sub-step 5.1 Compared with the RPI value in step 2 The maximum difference and the task number corresponding to the maximum difference are respectively denoted as g. ◇ and t q ;

[0050] Sub-step 5.3: Determine g ◇ Is >0 true? If g ◇ If > 0 is true, then t q From p i and d i Remove from the list and repeat step 5. If g ◇ >0 is not true, the drone i-conflict mission elimination ends.

[0051] Step 6: If drone i, If the task allocation result no longer changes, it indicates that the algorithm has converged and the drone task allocation is complete. Otherwise, skip to step 2 and start a new round of task allocation.

[0052] Example:

[0053] This embodiment employs a time-constrained distributed task allocation method for UAVs provided by the present invention. By improving the conflict task elimination mechanism of the PI algorithm, the completion rate of UAV task allocation is increased. The specific execution steps are as follows:

[0054] Step 1: Consider 3 drones performing 10 tasks. Each task requires one drone to execute before its latest start time. Each drone can perform multiple tasks. The drone speed is [100-200] m / s. The latest start time for each task is randomly generated within the range [0, 300] s. The drones are uniformly distributed along the X-axis. The task location information is randomly generated within a 10km × 10km square area. Figure 2 As shown.

[0055] Step 2: For each drone, based on its position, speed, and latest start time, a greedy algorithm is used to maximize the number of tasks assigned, while satisfying the task time constraints, to construct a task bundle using the PI algorithm. The constructed task bundle is as follows:

[0056] p1=[7 2 5 9 3 1 10 6 4 8];

[0057] p2=[8 4 2 7 10 1 3 9 5 6];

[0058] p3=[8 4 2 5 9 6 1 3 10 7];

[0059] β1=[1 1 1 1 1 1 1 1 1 1];

[0060] β2=[2 2 2 2 2 2 2 2 2 2];

[0061] β3=[3 3 3 3 3 3 3 3 3 3];

[0062] Step 3: Send the results of task bundle construction in Step 2, timestamps, and the number of tasks to be deleted to nearby drones. Perform consistency processing based on the PI algorithm, and update the number of tasks to be deleted, θ. i RPI value UAV sequence β i Since a task can only be completed by one drone, according to the PI algorithm, for the same task, the drone with the lower RPI retains the task, while other drones abandon it. Therefore, after consistency processing, the drone task bundle p i UAV sequence β i as follows:

[0063] p1=[7 2 5 9 3 1 10 6 4 8];

[0064] p2=[8 4 2 7 10 1 3 9 5 6];

[0065] p3=[8 4 2 5 9 6 1 3 10 7];

[0066] β1=[1 2 2 3 3 1 1 3 1 2];

[0067] β2=[1 2 2 3 3 1 1 3 1 2];

[0068] β3=[1 2 2 3 3 1 1 3 1 2];

[0069] The number of tasks that the drone needs to delete are 6, 7, and 7 respectively.

[0070] Step 4: Because UAV 3 has a large number of tasks to remove and a high sequence number, it has the most invalid RPI values ​​in its task sequence. Therefore, UAV 3 removes all tasks, and the UAV task bundle p... i UAV sequence β i as follows:

[0071] p1=[7 2 5 9 3 1 10 6 4 8];

[0072] p2=[8 4 2 7 10 1 3 9 5 6];

[0073] p3 = [];

[0074] β1=[1 2 2 3 3 1 1 3 1 2];

[0075] β2=[1 2 2 3 3 1 1 3 1 2];

[0076] β3=[0 0 0 0 0 0 0 0 0 0];

[0077] Step 5: Since the number of tasks to be deleted in drone 1, d1 = 6 > 0, tasks are removed sequentially according to sub-steps 5.1-5.3. Similarly, the number of tasks to be deleted in drone 2, d2 = 7 > 0, is removed sequentially according to sub-steps 5.1-5.3. According to step 5, after a task is deleted, step 5.1 is recalculated. Step 5.2 Recalculate g ◇ The value of g, if ◇ The condition > 0 is not met, and the drone conflict task removal process is complete. Therefore, not all tasks in d1 or d2 may be deleted. After removing conflicting tasks, the drone task bundle p... i UAV sequence β i as follows:

[0078] p1 = [7 9 1 6];

[0079] p2 = [2 10 3];

[0080] p3 = [];

[0081] β1=[1 2 2 3 3 1 1 3 1 2];

[0082] β2=[1 2 2 3 3 1 1 3 1 2];

[0083] β3=[0 0 0 0 0 0 0 0 0 0];

[0084] Step 6: Since the drone task allocation is incomplete, return to Step 2 and repeat the above task allocation process until the task allocation results converge. The final task allocation result is:

[0085] p1 = [7 9 3 1];

[0086] p2 = [2 10 9 5];

[0087] p3 = [8 4];

[0088] This invention can increase the number of time-constrained drone missions assigned and improve the drone mission assignment completion rate. The reason for taking the above measures is:

[0089] First, the PI algorithm determines whether to remove a task by comparing the RPI (Replacement Point Indicator) of each drone for that task. The basic principle is that the drone with the lowest RPI retains the task, while the others abandon it. Once a task is assigned to a drone, such as U1, it cannot be removed from U1's task sequence unless another drone obtains a lower RPI value, preventing some tasks from being added to U1. Step 4, by clearing the task bundle of the drone with the largest number of deleted tasks, ensures that more tasks can be added to drones.

[0090] Second, in the PI algorithm, the RPI value of the UAV for a task is closely related to the sequence of tasks executed. Once a task is removed, the RPI value of the tasks following that task will also change. By updating the RPI of all tasks in sub-step 5.1, it can be ensured that the RPI values ​​of the remaining tasks are up-to-date, and unnecessary tasks can be removed more accurately.

[0091] In summary, the above are merely embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A time-constrained distributed task allocation method for unmanned aerial vehicles (UAVs), characterized in that, The specific process is as follows: Step 1: Collect initial parameters, including the latest start time of the task; Step 2: Generate UAV mission bundle RPI value of drones for all missions and drone sequences The process of generating the RPI value is as follows: when the UAV performs a task and the time constraint is met, the RPI value corresponding to the UAV performing the task is calculated according to the PI algorithm; when the time constraint is not met, the RPI value of the UAV performing the task is set to infinity; the time constraint is that the time when the UAV performs the task is later than the latest time when the task starts. Step 3: The drones communicate with each other. For each task, the drone with the lowest RPI value is retained to execute the task, while the remaining drones delete the task, achieving consistency. This yields the number of tasks that each drone needs to delete. RPI value and drone sequence ; Step 4: For the drone with the largest number of deleted tasks, delete all the tasks it needs to perform, and update its corresponding RPI value and drone sequence. Step 5: For the remaining drones, re-examine the conflicting missions and update their corresponding RPI values ​​and drone sequences; Step 6: If the task allocation results for each drone no longer change, the drone task allocation ends; otherwise, a new round of task allocation begins for the unassigned drones.

2. The time-constrained distributed task allocation method for unmanned aerial vehicles according to claim 1, characterized in that, The process of re-eliminating conflicting tasks for the remaining drones is as follows: Assume drones... The tasks that need to be deleted are ; Determine the drone Tasks to be deleted length Whether it is true or not, if This is not true; drones Conflict task elimination is complete, if Established, drones The conflict task elimination phase includes the following steps: (1) Calculate the unmanned aerial vehicle (UAV) based on the PI algorithm RPI values ​​for all tasks ; (2) Calculate the RPI value With RPI value The maximum difference and the task number corresponding to the maximum difference are denoted as follows: and ; (3) Judgment Whether it is true or not, if Established, will from and Removed, re-entering the drone mission removal phase, if This is not true; drones The conflict task removal process is now complete.

3. The time-constrained distributed task allocation method for unmanned aerial vehicles according to claim 1, characterized in that, When constructing a task bundle based on the PI algorithm, the goal is to maximize the number of tasks assigned.

4. The time-constrained distributed task allocation method for unmanned aerial vehicles according to claim 1, characterized in that, When there are at least two drones with the largest number of tasks, select the drone with the larger number to delete the task.

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