Unmanned aerial vehicle task allocation method considering resource limitation

Abstract

The invention provides an unmanned aerial vehicle task allocation method considering resource limitation, and belongs to the technical field of unmanned aerial vehicle command decision. The method specifically comprises the steps of 1, collecting initial situation information of an unmanned aerial vehicle and a task target; 2, quantifying the contribution value of the unmanned aerial vehicle to the task, introducing the contribution value of the unmanned aerial vehicle into a target function, and calculating the contribution rate of the unmanned aerial vehicle to the task under the guidance of the target function; 3, introducing the contribution value into a PI algorithm, and constructing a task bundle by each unmanned aerial vehicle until unmanned aerial vehicle resources are used up or all tasks are allocated; step 4, performing consistency processing by adopting a PI algorithm, and entering step 5 during convergence; 5, judging whether the types and the number of resources required by the task meet the requirements or not, and entering a step 6 when the requirements are met; 6, the unmanned aerial vehicle with the highest contribution rate serves as a long unmanned aerial vehicle, invitations are sent to other unmanned aerial vehicles, an unmanned aerial vehicle alliance meeting the resource type and number requirements is constructed, and a task allocation result is output.

Description

Technical Field

[0001] The present invention belongs to the technical field of unmanned aerial vehicle command decision-making, and in particular relates to a method for allocating unmanned aerial vehicle tasks taking resource constraints into consideration. Background Art

[0002] UAV technology has been widely used in various research fields, such as rescue search, electronic jamming, tracking and identification, control and suppression, forest rescue and other military and civilian fields. Task allocation is one of the key technologies to improve the performance of UAV systems. Compared with the centralized task allocation method, the PI (performance Impact) algorithm is one of the most effective distributed task allocation algorithms. It has the characteristics of high reliability, no need for global information, and small node calculation. The PI algorithm is a hot topic in the field of UAV task allocation.

[0003] When dealing with task allocation with limited UAV resources, the PI algorithm only considers the number of UAVs required by the task, but does not consider the types and number of resources carried by the UAVs, and lacks a quantitative evaluation of the contribution rate of the UAVs to the task. Summary of the invention

[0004] In view of this, the present invention provides a method for allocating UAV tasks taking into account resource constraints. The method implements UAV task allocation by quantifying the contribution values ​​of the types and quantities of resources carried by the UAV to the tasks.

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

[0006] A method for allocating UAV tasks considering resource constraints, the specific process is as follows:

[0007] Step 1: Collect the initial situation information of the UAV and the mission target, including the resources carried by the UAV and the resources required for the mission;

[0008] Step 2: According to the resources carried by the drone and the resources required for the task, the contribution value of the drone to the task is quantified, and the contribution value of the drone is introduced into the objective function, and the contribution rate of the drone to the task is calculated under the guidance of the objective function;

[0009] Step 3: Introduce the contribution value into the PI algorithm, and each drone constructs a task bundle until the drone resources are exhausted or all tasks have been assigned, and obtain the drone task bundle and drone RPI matrix;

[0010] Step 4: Send the task bundle and RPI matrix in step 3 to the adjacent drones, and use the PI algorithm for consistency processing. That is, for the same task, the drone with the largest RPI retains the task, and other drones actively give up the task. Determine whether the task allocation result converges. If not, jump to step 3; if converged, jump to step 5.

[0011] Step 5: Based on the allocation result of step 4, determine whether the resource type and quantity required for the task have met the requirements. If so, output the task allocation result and the task allocation ends; if not, proceed to step 6;

[0012] Step 6: The drone with the highest contribution rate serves as the leader and sends invitations to other drones to build a drone alliance that meets the resource type and number requirements, and outputs the task allocation results, and the task allocation is completed.

[0013] Furthermore, the contribution value η of the present invention is ij for:

[0014]

[0015] Among them, η ij represents the resource contribution rate of drone i to task j, represents the number of k-th resources carried / remaining by drone i, represents the number of k-th resources required by task j, and M represents the number of resource types.

[0016] Furthermore, the objective function of the present invention is:

[0017]

[0018] Among them, t ij represents the time when drone i starts to perform mission j, represents the fixed benefit value of task j.

[0019] Beneficial Effects

[0020] First, the present invention solves the problem of resource-constrained UAV task allocation by quantifying the contribution of the types and quantities of resources carried by the UAV to the task.

[0021] Second, the present invention calculates the contribution value of the drone to the task based on the type and quantity of resources, introduces the contribution rate in the task construction process in step 4, and at the same time, in step 6, through the construction of the drone alliance, further solves the allocation of the number and type of resources carried by the drone. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0023] Figure 1A flowchart of a method for allocating UAV tasks considering resource constraints;

[0024] Figure 2 is the resource satisfaction rate of each task after steps 2 and 3;

[0025] Figure 3 A timing diagram for the UAV to perform tasks;

[0026] Figure 4 is the resource satisfaction rate of each task. DETAILED DESCRIPTION

[0027] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0028] It should be noted that the following embodiments and features in the embodiments may be combined with each other in the absence of conflict; and, based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in the field without making any creative work are within the scope of protection of the present disclosure.

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

[0030] like Figure 1 As shown, the embodiment of the present application provides a method for allocating UAV tasks considering resource constraints, and the specific process is as follows:

[0031] Step 1: Given the known initial situation information, including the initial position of the drone, maximum range, speed, maximum number of executable tasks, types and quantities of resources carried, the initial position of the target, and the types and quantities of resources required.

[0032] Step 2: According to the resources carried by the drone and the resources required for the task provided in step 1, quantify the contribution value of the drone to the task, and ensure that the contribution value range ∈ [0,1]. At the same time, introduce the drone contribution value into the objective function J, and calculate the RPI value of the drone for each task in combination with the PI algorithm. By comparing the RPI values, quantify the impact of resource contribution rate on drone task allocation.

[0033]

[0034]

[0035] Among them, η ij represents the resource contribution rate of drone i to task j, represents the number of k-th resources carried / remaining by drone i, represents the number of k-th resources required for task j, and M represents the number of resource types. For tasks that require the collaboration of multiple drones, the resources carried by a single drone cannot meet the task requirements, that is, For a task that a single UAV completes independently, the resources carried by the single UAV can meet the task requirements, that is, therefore Established, contribution value ∈ [0,1]. ij It represents the time when UAV i starts to perform task j. By comparing the number of resources required by the task with the number of resources carried by the UAV, the execution capability of each UAV for each task can be determined, thereby quantifying the contribution of the UAV to the task.

[0036] Step 3: Introduce the contribution rate of the drone in step 2 into the PI algorithm, and use the sequential greedy algorithm to construct the task bundle of each drone until the drone resources are exhausted or all tasks have been assigned;

[0037] Step 4: Send the task bundle construction result in step 3 to the adjacent UAVs, use the PI algorithm for consistency processing, and determine whether the task allocation result converges. If not, jump to step 3; if converged, jump to step 5;

[0038] Step 5: Based on the allocation result of step 4, determine whether the resource type and quantity required for the task have met the requirements. If so, output the task allocation result and the task allocation ends; if not, proceed to step 6;

[0039] Step 6: The drone with the highest contribution rate serves as the leader and sends invitations to other drones to build a drone alliance that meets the resource type and number requirements, and outputs the task allocation results, and the task allocation is completed.

[0040] Below is N a drone and N T Taking the target as an example, the present invention is described in detail:

[0041] Step 1: Given N a Drone location speed Maximum range Maximum number of executable tasks Resources to carry N T The location of the target Resources Required

[0042] Step 2: Based on the resources carried by the drones and the resources required for the tasks provided in step 1, quantify the contribution value and objective function of each drone to all tasks, and calculate the contribution rate of the drone to the tasks;

[0043] Step 3: Introduce the contribution value of the drone in step 2 into the PI algorithm, use the sequential greedy algorithm to construct the task bundle of each drone, and continuously update the contribution rate according to the remaining resources until the drone resources are exhausted or all tasks have been assigned. Let the result of task bundle construction be the execution task bundle of each drone and RPI array

[0044] Step 4: Send the task bundle construction results in step 3 to the adjacent drones, use the PI algorithm for consistency processing, and update the execution task bundle of each drone. and RPI array Determine whether the task allocation result converges. If not, jump to step 3; if converged, jump to step 5;

[0045] Step 5: Based on the allocation result of step 4, determine whether the resource type and quantity required for the task have met the requirements. If so, output the task allocation result and the task allocation ends; if not, proceed to step 6;

[0046] Step 6: The drone with the highest contribution rate serves as the leader and sends invitations to other drones to build a drone alliance that meets the resource type and number requirements, and outputs the task allocation results, and the task allocation is completed.

[0047] The PI algorithm includes two processes: task bundle construction and consistency processing. Through the continuous iteration of the above two processes, a certain objective function no longer changes, that is, the algorithm converges. When step 2 is executed for the first time, the task bundle, RPI matrix Z, etc. are all empty sets. After consistency processing (step 4), the task bundle and RPI matrix Z are not empty. On this basis, the task bundle construction and consistency processing continue.

[0048] Take UAV 1 as an example: when the task execution sequence of UAV 1 is empty, that is, p1 = [], the RPI value of the UAV for Task 2 is a1; when the task execution sequence of UAV 1 is not empty, that is, p1 = [Task 1], the RPI value of the UAV for Task 2 is a2; because a1 is not equal to a2, the results of executing step 3 twice are different.

[0049] Example:

[0050] This embodiment adopts a method for allocating UAV tasks considering resource constraints provided by the present invention, and the specific execution steps are as follows:

[0051] Step 1: In the range of 10km×10km, given N a = 6 drones execute N T = 10 tasks, the speed of each drone is 100m / s, the maximum range of the drone is 30km, and the maximum number of tasks is 6. Each drone carries three consumable resources, and each task requires three resources. The information of drone and task location and resource quantity is shown in Table 1 and Table 2.

[0052] Table 1 Information on the location of drones and the number of resources they carry

[0053] Drone Number <![CDATA[Resources R carried by the drone U > Drone location km 1 [5,8,10] [6.87,8.27] 2 [3,9,4] [3.85,8.19] 3 [2,6,9] [6.90,1.51] 4 [1,10,6] [7.14,6.73] 5 [9,5,1] [0.78,2.75] 6 [3,8,2] [4.53,5.67]

[0054] Table 2 Task location and required resource number information

[0055] Task Number <![CDATA[Required resource R T > Mission location km 1 [1,3,2] [9.05,8.67] 2 [1,3,2] [5.82,7.98] 3 [3,1,2] [9.48,9.24] 4 [2,3,1] [3.81,0.10] 5 [2,1,3] [9.64,1.33] 6 [2,1,3] [0.33,8.03] 7 [2,1,3] [6.32,9.17] 8 [1,3,2] [6.80,1.92] 9 [3,2,1] [4.58,2.26] 10 [3,1,2] [0.33,2.10]

[0056] Note: It indicates that the number of the first, second and third types of resources carried by UAV 1 are 5, 8 and 10 respectively. It means that the number of the first, second and third types of resources required by task 1 are 1, 3 and 2 respectively.

[0057] Step 2: Quantify the contribution of each drone to all tasks based on the resources carried by the drones and the resources required for the tasks provided in step 1. Taking drone 1 as an example, after calculation, the contribution value η i =[1,1,1,1,1,1,1,1,1,1].

[0058] Step 3: Introduce the contribution value of the drone in step 2 into the PI algorithm, and use the sequential greedy algorithm to construct the task bundles of each drone until the drone resources are exhausted or all tasks have been assigned. The result of task bundle construction is:

[0059] Drone Mission Bundle:

[0060]

[0061] Drone RPI Array:

[0062]

[0063] Step 4: Send the task bundle construction results in step 3 to the adjacent drones, use the PI algorithm for consistency processing, and update the execution task bundle of each drone. and RPI array Determine whether the task allocation result converges. If not, jump to step 3 until the algorithm converges. The consistency processing results are as follows:

[0064] Drone Mission Bundle:

[0065]

[0066] Drone RPI Array:

[0067]

[0068] Step 5: According to the allocation result of step 4, it is determined that the resource requirements of tasks 5, 9 and 10 are not met. The resource satisfaction rate of each task is as follows: Figure 2 As shown. Since the required resource type and quantity do not meet the requirements, go to step 6;

[0069] Step 6: Since drone 1 has the highest contribution rate to mission 5, drone 1 is selected as the leader of mission 5 and invites other drones to form a drone alliance. Since missions 9 and 10 are not assigned to drones, the drone with the smallest number is selected as the leader to form an alliance by default. The final task allocation results are as follows:

[0070] Drone 1: Mission 6-7-5

[0071] Drone 2: Missions 8-10

[0072] Drone 3: Mission 9-5-1

[0073] Drone 4: Mission 2

[0074] Drone 5: Mission 9-4-10

[0075] Drone 6: Mission 3

[0076] The timing diagram of each drone executing tasks is as follows Figure 3 As shown in the figure, the resource satisfaction rate of the task is as follows Figure 4 shown.

[0077] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A method for allocating unmanned aerial vehicle tasks considering resource constraints, characterized in that: The specific process is: Step 1: Collect the initial situation information of the UAV and the mission target, including the resources carried by the UAV and the resources required for the mission; Step 2: According to the resources carried by the drone and the resources required for the task, the contribution value of the drone to the task is quantified, and the contribution value of the drone is introduced into the objective function, and the contribution rate of the drone to the task is calculated under the guidance of the objective function; Step 3: Introduce the contribution value into the PI algorithm, and each drone constructs a task bundle until the drone resources are exhausted or all tasks have been assigned, and obtain the drone task bundle and drone RPI matrix; Step 4: Send the task bundle and RPI matrix in step 3 to the adjacent drones, and use the PI algorithm for consistency processing. That is, for the same task, the drone with the largest RPI retains the task, and other drones actively give up the task. Determine whether the task allocation result converges. If not, jump to step 3. If converged, skip to step 5; Step 5: According to the allocation result of step 4, determine whether the resource type and quantity required for the task have met the requirements. If so, output the task allocation result and the task allocation is completed; If the requirements are not met, proceed to step 6; Step 6: The drone with the highest contribution rate serves as the leader and sends invitations to other drones to build a drone alliance that meets the resource type and number requirements, and outputs the task allocation results, and the task allocation is completed.

2. The method for allocating unmanned aerial vehicle tasks considering resource constraints according to claim 1, characterized in that: The contribution value η ij for: Among them, η ij represents the resource contribution rate of drone i to task j, represents the number of k-th resources carried / remaining by drone i, represents the number of k-th resources required by task j, and M represents the number of resource types.

3. The method for allocating unmanned aerial vehicle tasks considering resource constraints according to claim 2 is characterized in that: The objective function is: Among them, t ij represents the time when drone i starts to perform mission j, represents the fixed benefit value of task j.

4. The method for allocating unmanned aerial vehicle tasks considering resource constraints according to claim 1, characterized in that: Given the known initial situation information, including the initial position of the drone, maximum range, speed, maximum number of executable tasks, types and quantities of resources carried, the initial position of the target, and the types and quantities of resources required.

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