A method for allocating unmanned aerial vehicle (UAV) tasks considering resource constraints

Abstract

The application provides a kind of unmanned aerial vehicle task allocation method considering resource limited, belong to unmanned aerial vehicle command decision-making technical field.The specific process of the method is as follows: step 1: collect the initial situation information of unmanned aerial vehicle and task target;Step 2: quantify the contribution value of unmanned aerial vehicle to task, while introducing the contribution value of unmanned aerial vehicle into the objective function, and calculating the contribution rate of unmanned aerial vehicle to task under the guidance of the objective function;Step 3: introduce the contribution value into PI algorithm, each unmanned aerial vehicle constructs task bundle, until the resource of unmanned aerial vehicle is exhausted or all tasks are allocated;Step 4: consistency processing is carried out by using PI algorithm, and step 5 is entered when convergence;Step 5: judge whether the required resource type and number of task have met the requirements, and enter step 6 when the requirements are met;Step 6: the unmanned aerial vehicle with the highest contribution rate is used as the long-range aircraft, and an invitation is sent to other unmanned aerial vehicles, an unmanned aerial vehicle alliance meeting the requirements of resource type and number is constructed, and the task allocation result is output.

Description

Technical Field

[0001] This invention belongs to the field of UAV command and decision-making technology, specifically relating to a UAV task allocation method that takes into account resource constraints. Background Technology

[0002] Unmanned aerial vehicle (UAV) technology has been widely applied in various research fields, such as rescue and search, electronic jamming, tracking and identification, defense and suppression, and forest rescue, both in military and civilian applications. Task allocation is one of the key technologies for improving the performance of UAV systems. Compared with centralized task allocation methods, the PI (Performance Impact) algorithm, as one of the most effective distributed task allocation algorithms, has the characteristics of high reliability, no need for global information, and low node computational load. The PI algorithm is a hot research topic in the field of UAV task allocation.

[0003] When dealing with resource-constrained task allocation using drones, the PI algorithm only considers the task's requirement for the number of drones, without taking into account the type and number of resources carried by the drones, thus lacking a quantitative assessment of the drones' contribution to the task. Summary of the Invention

[0004] In view of this, the present invention provides a method for allocating drone tasks that takes into account resource constraints. This method achieves drone task allocation by quantifying the contribution of the types and quantities of resources carried by the drone to the task.

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

[0006] A method for allocating unmanned aerial vehicle (UAV) tasks considering resource constraints, the specific process of which is as follows:

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

[0008] Step 2: Based on the resources carried by the UAV and the resources required for the mission, quantify the contribution value of the UAV to the mission, and introduce the contribution value of the UAV into the objective function. Under the guidance of the objective function, calculate the contribution rate of the UAV to the mission.

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

[0010] Step 4: Send the task bundle and RPI matrix from Step 3 to neighboring UAVs. Use the PI algorithm for consistency processing. That is, for the same task, the UAV with the largest RPI retains the task, while other UAVs actively abandon the task. Determine whether the task allocation result has converged. If it has not converged, skip to Step 3; if it has converged, skip to Step 5.

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

[0012] Step 6: The drone with the highest contribution rate becomes the lead drone, which sends invitations to other drones to build a drone alliance that meets the requirements for resource types and numbers, and outputs the task allocation results. The task allocation ends.

[0013] Furthermore, the contribution value η described in this invention ij for:

[0014]

[0015] Where, η ij This represents the resource contribution rate of drone i to task j. This represents the number of the k-th type of resource carried / remaining on drone i. Let M represent the number of the k-th type of resource required for task j, and M represent the number of resource types.

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

[0017]

[0018] Among them, t ij This indicates the time when drone i begins executing task j. This represents the fixed reward value for task j.

[0019] Beneficial effects

[0020] First, this invention solves the problem of allocating drone missions under resource constraints by quantifying the contribution of the types and quantities of resources carried by the drone to the mission.

[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 further solves the allocation of the quantity and type of resources carried by the drone through the construction of a drone alliance in step 6. Attached Figure Description

[0022] 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.

[0023] Figure 1A flowchart illustrating a resource-constrained drone task allocation method;

[0024] Figure 2 This represents the resource satisfaction rate of each task after steps 2 and 3.

[0025] Figure 3 A sequence diagram for the drone's mission execution;

[0026] Figure 4 This represents the resource satisfaction rate for each task. 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 the embodiments described below are within the scope of the appended claims. 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 other structures and / or functionalities besides one or more of the aspects set forth herein.

[0030] like Figure 1 As shown in the figure, this application provides a method for allocating drone tasks considering resource constraints. The specific process is as follows:

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

[0032] Step 2: Based on the resources carried by the UAV and the resources required for the task provided in Step 1, quantify the UAV's contribution to the task, ensuring the contribution value ranges from [0,1]. Simultaneously, incorporate the UAV's contribution value into the objective function J, and use the PI algorithm to calculate the UAV's RPI value for each task. By comparing the RPI values, quantify the impact of resource contribution rate on UAV task allocation.

[0033]

[0034]

[0035] Where, η ij This represents the resource contribution rate of drone i to task j. This represents the number of the k-th type of resource carried / remaining on drone i. Let M represent the number of the k-th type of resource required for task j, and M represent the number of resource types. For tasks requiring multi-drone collaboration, the resources carried by a single drone cannot meet the task requirements, i.e. For a task to be completed independently by a single drone, the resources carried by that drone are sufficient to meet the task requirements. therefore This holds true, with a contribution value ∈ [0,1]. t ij This represents the time when drone i begins executing task j. By comparing the amount of resources required for each task with the amount of resources carried by the drone, we can determine the execution capability of each drone for each task, thereby quantifying the contribution of each drone to the task.

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

[0037] Step 4: Send the task bundle construction results from Step 3 to neighboring UAVs, use the PI algorithm for consistency processing, and determine whether the task allocation results have converged. If they have not converged, skip to Step 3; if they have converged, skip to Step 5.

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

[0039] Step 6: The drone with the highest contribution rate becomes the lead drone, which sends invitations to other drones to build a drone alliance that meets the requirements for resource types and numbers, and outputs the task allocation results. The task allocation ends.

[0040] Below, N a drones and N T Taking one objective as an example, the present invention will be described in detail as follows:

[0041] Step 1: Given N a drone location speed Maximum range Maximum number of executable tasks Portable resources N T The location of the target Required resources

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

[0043] Step 3: Introduce the contribution value of the drones from Step 2 into the PI algorithm. Use a sequential greedy algorithm to construct the task bundles for each drone, continuously updating the contribution rate based on remaining resources until drone resources are exhausted or all tasks have been assigned. Let the result of the task bundle construction be the execution task bundle for each drone. and RPI array

[0044] Step 4: Send the task bundle construction results from Step 3 to neighboring UAVs, perform consistency processing using the PI algorithm, and update the execution task bundle for each UAV. and RPI array Determine whether the task allocation result has converged. If it has not converged, skip to step 3; if it has converged, skip to step 5.

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

[0046] Step 6: The drone with the highest contribution rate becomes the lead drone, which sends invitations to other drones to build a drone alliance that meets the requirements for resource types and numbers, and outputs the task allocation results. The task allocation ends.

[0047] The PI algorithm consists of two processes: task bundle construction and consistency processing. Through continuous iteration of these two processes, a certain objective function no longer changes, i.e., the algorithm converges. When step 2 is executed for the first time, the task bundle and RPI matrix Z are empty sets. After consistency processing (step 4), the task bundle and RPI matrix Z are not empty. Based on this, task bundle construction and consistency processing continue.

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

[0049] Example:

[0050] This embodiment uses a UAV task allocation method considering resource constraints provided by the present invention, and the specific execution steps are as follows:

[0051] Step 1: Within a 10km × 10km area, given N a =6 drones executing N T =10 missions, each drone has a speed of 100m / s, a maximum range of 30km, and a maximum of 6 missions. Each drone carries three types of consumable resources, and each mission requires three types of resources. Drone and mission locations, resource quantities, and information are shown in Tables 1 and 2.

[0052] Table 1. Information on the location of drones and the amount 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 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: This indicates that the number of the first, second, and third types of resources carried by UAV 1 are 5, 8, and 10, respectively. This indicates that the number of the first, second, and third types of resources required for Task 1 are 1, 3, and 2, respectively.

[0057] Step 2: Based on the resources carried by the drones and the resources required for the missions provided in Step 1, quantify the contribution value of each drone to all missions. Taking drone 1 as an example, the calculated contribution value η is... i =[1,1,1,1,1,1,1,1,1,1,1].

[0058] Step 3: Incorporate the contribution values ​​of the drones from Step 2 into the PI algorithm, and use a sequential greedy algorithm to construct task bundles for each drone until drone resources are exhausted or all tasks have been assigned. Result of task bundle construction:

[0059] Drone mission bundle:

[0060]

[0061] Drone RPI array:

[0062]

[0063] Step 4: Send the task bundle construction results from Step 3 to neighboring UAVs, perform consistency processing using the PI algorithm, and update the execution task bundle for each UAV. and RPI array Determine if the task allocation result has converged. If not, proceed to step 3 until the algorithm converges. The consistency processing result is as follows:

[0064] Drone mission bundle:

[0065]

[0066] Drone RPI array:

[0067]

[0068] Step 5: Based on the allocation results in Step 4, determine that the resource requirements of Tasks 5, 9, and 10 are not met. The resource fulfillment rate for each task is as follows: Figure 2 As shown. Since the required resource type and quantity do not meet the requirements, proceed to step 6;

[0069] Step 6: Since Drone 1 has the highest contribution to Task 5, it will act as the leader of Task 5 and send invitations to other drones to form a drone coalition. Since Tasks 9 and 10 have not been assigned drones, the drone with the lowest drone number will be the default leader to form the coalition. 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 diagrams of each UAV's mission execution are as follows: Figure 3 As shown, the resource satisfaction rate for the task is as follows: Figure 4 As shown.

[0077] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for allocating unmanned aerial vehicle (UAV) tasks considering resource constraints, characterized in that, The specific process is as follows: Step 1: Collect initial situation information of unmanned aerial vehicles and task targets, including resources carried by the unmanned aerial vehicles and resources required by the task; Step 2: According to the resources carried by the unmanned aerial vehicles and the resources required by the task, the contribution value of the unmanned aerial vehicles to the task is quantified, and the unmanned aerial vehicle contribution value is introduced into the objective function, and the contribution rate of the unmanned aerial vehicles to the task is calculated under the guidance of the objective function; Step 3: Introduce the contribution value into the PI algorithm, and each unmanned aerial vehicle constructs a task bundle until the unmanned aerial vehicle resources are exhausted or all tasks are allocated, and the unmanned aerial vehicle task bundle and the unmanned aerial vehicle RPI matrix are obtained; Step 4: Send the task bundle and the RPI matrix in step 3 to adjacent unmanned aerial vehicles, and use the PI algorithm for consistency processing, that is, for the same task, the unmanned aerial vehicle with the largest RPI retains the task, and other unmanned aerial vehicles voluntarily abandon the task, and it is judged whether the task allocation result converges or not, if not, jump to step 3; If it converges, jump to step 5; Step 5: According to the allocation result of step 4, it is judged whether the type and number of resources required by the task meet the requirements or not, if yes, the task allocation result is outputted, and the task allocation is ended; If not, go to step 6; Step 6: The unmanned aerial vehicle with the highest contribution rate is used as a long-range aircraft to invite other unmanned aerial vehicles, construct an unmanned aerial vehicle alliance meeting the requirements of resource type and number, and output the task allocation result, and the task allocation is ended.

2. The method of claim 1, wherein, The contribution value η ij is: wherein η ij denotes the resource contribution rate of the UAV i to the task j, denotes the number of the kth resource carried / left by the UAV i, denotes the number of the kth resource required by the task j, and M denotes the number of resource types.

3. The method of claim 2, wherein, The objective function is as follows: where t ij denotes the time at which the drone i starts to perform the task j, denotes the fixed revenue value of the task j.

4. The method of claim 1, wherein, Given the known initial situation information, including the initial position of the unmanned aerial vehicle, the maximum range, the speed, the maximum number of executable tasks, the type and quantity of resources carried, the initial position of the target, the required resource type and quantity.

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