Task allocation method for multiple unmanned aerial vehicles under inter-vehicle interference constraint

Abstract

According to the multi-unmanned aerial vehicle task allocation method under the inter-unmanned aerial vehicle interference constraint provided by the invention, effective task allocation can be realized, so that the interference among the unmanned aerial vehicles is reduced to the greatest extent, and the overall performance and the task completion efficiency are improved. Comprising the following steps of 1, performing modeling analysis on a multi-unmanned aerial vehicle distribution problem under inter-vehicle interference constraint by using a graph theory, and converting a task completion time minimization problem into a task grouping number minimization problem; 2, representing a relationship between tasks by adopting an undirected graph; step 3, entering top layer circulation; 4, judging an exit condition, and when the number of unallocated tasks is less than 2, judging that task allocation is completed, otherwise, entering the step 5, and carrying out bottom layer circulation to find out an alternative maximum parallelizable group; step 5, entering a bottom loop to find out an alternative maximum parallelizable group of the current image; and 6, calculating the cost of each alternative maximum parallelizable group, updating the cost of the current node and the current updated graph, and finally obtaining K minimum task groups meeting conditions.

Description

Technical Field

[0001] The invention belongs to the technical field of unmanned aerial vehicles and relates to a multi-unmanned aerial vehicle task allocation method under inter-unmanned aerial vehicle interference constraints. Background Art

[0002] In the multi-UAV task allocation problem, the UAV cluster is divided into multiple groups, and each group of UAVs is assigned a series of tasks to be performed. Existing methods are mainly divided into two categories: centralized and distributed. Centralized methods usually rely on a central controller to uniformly allocate tasks, while distributed methods distribute the decision of task allocation to each UAV or group.

[0003] In the distributed task allocation method, each drone needs to allocate tasks independently and autonomously without relying on the assistance of a global network or central server. The drone monitors the status of neighboring drones within its perception range and allocates the best tasks by comparing the task execution capabilities of neighboring drones. This distributed allocation method highlights the autonomy and flexibility of drones, enabling them to make decisions autonomously based on local information, thereby achieving more efficient overall task allocation.

[0004] The centralized task allocation method uses a central processing unit to monitor and summarize the relative position, task status, battery capacity and other information of each drone to determine the drone with the highest task efficiency and allocate tasks. This method can centrally monitor and control the entire task allocation process to ensure the efficiency and accuracy of task allocation. At the same time, it can also better optimize resource allocation and prevent some drones from bearing too heavy a task burden.

[0005] At present, the mainstream multi-UAV task allocation methods mainly include market-based task allocation, behavior-based task allocation, optimization-based task allocation and cluster task allocation. However, most of these task allocation methods are based on static attributes of tasks or dynamic factors, such as the urgency of the task, geographical location and UAV performance. However, when the same group of UAVs are performing tasks, there is still a lack of research on task allocation due to the mutual interference caused by the downwash of the UAVs. Summary of the invention

[0006] The present invention proposes a multi-UAV task allocation method under inter-UAV interference constraints, which can achieve effective task allocation to minimize interference between UAVs and improve overall performance and task completion efficiency.

[0007] The present invention is achieved through the following technical solutions.

[0008] A multi-UAV task allocation method under inter-UAV interference constraints comprises the following steps:

[0009] Step 1: Use graph theory to model and analyze the multi-UAV allocation problem under the constraint of inter-UAV interference, and convert the problem of minimizing the task completion time into the problem of minimizing the number of task groups;

[0010] Step 2: Use an undirected graph G = (V, E) to represent the relationship between tasks, where V represents a set of vertices, and each vertex v i represents a task point, that is, an element in the task set T; E represents the edge set, each edge e ij It represents the connection relationship between two task points, that is, whether the two task points can be executed at the same time. The adjacency matrix A is constructed as follows:

[0011] A=[a ij ] M×M

[0012] If there is an edge e between node i and node j ij , that is, task i and task j are executed simultaneously, then the adjacency matrix element a ij =1; otherwise, if there is no edge e between node i and node j ij Indicates that tasks i and j cannot be executed at the same time, then the adjacency matrix element a ij =0;

[0013] Step 3: Enter the top-level loop, and construct the top-level node Node with the current graph G as the initial value, where Node contains: 1. The cost value of the current node Node.cost 2. The vertex Node.G.V of the current graph 3. The edge Node.G.E of the current graph 4. The adjacency matrix Node.A formed by the current graph, and add the node to the list Openset; enter the loop, and the loop exit condition is that the list Openset is empty, and the current node is given as the node with the smallest cost in Openset;

[0014] Step 4: Determine the exit condition. When the number of unassigned tasks is less than 2, it is determined that the task assignment has been completed. Otherwise, enter the bottom loop of step 5 to find the alternative maximum parallel group.

[0015] Step 5: Enter the bottom loop and use the Bron–Kerbosch algorithm to find the candidate maximum parallel group of the current graph G;

[0016] Step 6: Calculate g for each candidate maximum parallel group m The cost of updating the current node cost Noed.cost and the current updated graph G' is added to the node Node.G and added to Openset. The top-level nodes and Openset groups are updated until the allocation method that meets the exit condition is found. Finally, the minimum task group that meets the condition is K groups. The grouping set is defined as G r={g1, g2, g3, ..., g K}.

[0017] Beneficial effects of the present invention:

[0018] 1. The present invention proposes a multi-task batching strategy based on the Bron-Kerbosch algorithm, which uses a hierarchical framework to divide the tasks to be performed by the drone into multiple batches;

[0019] 2. The present invention fully considers the influence of downwash airflow generated when the UAV is hovering to optimize task batching, thereby improving the overall task completion efficiency;

[0020] 3. The present invention combines relevant knowledge of graph theory to transform the task allocation problem into a graph search problem, thus solving the task allocation problem under distance constraints. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 A schematic diagram of converting the problem of minimizing the task completion time into the problem of minimizing the number of task groups in a specific implementation manner of the present invention;

[0022] Figure 2 This is a schematic diagram of the multi-UAV allocation problem in a specific implementation manner of the present invention;

[0023] Figure 3 This is a schematic diagram of a circle with the current coordinates of the drone as the center and a radius of 3m in a specific embodiment of the present invention;

[0024] Figure 4 It is a schematic diagram showing the relationship between tasks in a specific embodiment of the present invention in a more intuitive graphical form;

[0025] Figure 5 A schematic diagram of an alternative maximum parallelizable group including N tasks in a specific implementation manner of the present invention;

[0026] Figure 6 It is a schematic diagram of Figure G' after updating in a specific implementation manner of the present invention;

[0027] Figure 7 This is a schematic diagram of the minimum task group that finally meets the conditions being K groups in a specific implementation manner of the present invention. DETAILED DESCRIPTION

[0028] The exemplary embodiments of the present invention are described in detail below with reference to the accompanying drawings. It should be understood that the embodiments shown and described in the accompanying drawings are only exemplary and are intended to illustrate the principles and spirit of the present invention, rather than to limit the scope of the present invention.

[0029] like Figure 1As shown, a multi-UAV task allocation method under inter-UAV interference constraints of the present invention specifically includes the following steps:

[0030] Step 1: Use graph theory to model and analyze the multi-UAV allocation problem under the constraint of inter-UAV interference, and convert the problem of minimizing the task completion time into the problem of minimizing the number of task groups;

[0031] The idea of ​​this step is: Figure 2 As shown in the figure, during the task execution process, affected by the downwash effect of drones, the number of drones that can perform tasks simultaneously in the cluster is subject to certain restrictions. In order to minimize the task completion time, the present invention makes the following assumptions about the multi-drone allocation problem:

[0032] 1. Assume that the impact range of the drone wash benefit is a circle with the current coordinates of the drone as the center and a radius of 3m, such as Figure 3 As shown;

[0033] 2. Assume that each mission has a known target location and each drone can only perform one mission at a time;

[0034] 3. Each drone can belong to at most one mission execution group;

[0035] 4. Assume that the time required for drones to perform tasks is the same.

[0036] Based on the above assumptions, this embodiment uses graph theory to mathematically model the problem. In the model, M task sets are defined as T = {t1, t2, t3, ..., t M}, where each t i Including the two-dimensional coordinates of the task point (x i ,y i ), the drones will be divided into K groups, and the group set is defined as G r ={g1, g2, g3, ..., g K}; Rationally divide the M tasks into K groups to ensure that there is no conflict between the tasks within the group, and on this basis minimize the overall completion time of the tasks. Since the time required for drones to perform tasks is the same, the problem of minimizing the task completion time is converted into the problem of minimizing the number of task groups;

[0037] Minimize K

[0038] Step 2: Use an undirected graph G = (V, E) to represent the relationship between tasks, where V represents a set of vertices, and each vertex v i represents a task point, that is, an element in the task set T; E represents the edge set, each edge e ijIt represents the connection relationship between two task points, that is, whether the two task points can be executed at the same time. The adjacency matrix A is constructed based on this. Its mathematical expression is as follows:

[0039] A=[a ij ] M×M

[0040] If there is an edge e between node i and node j ij , that is, task i and task j are executed simultaneously, then the adjacency matrix element a ij =1; otherwise, if there is no edge e between node i and node j ij Indicates that tasks i and j cannot be executed at the same time, then the adjacency matrix element a ij =0.

[0041] like Figure 4 As shown, through this step, the relationship between tasks can be expressed in a more intuitive graphical form.

[0042] Step 3: Enter the top-level loop, and construct the top-level node Node with the current graph G as the initial value, where Node contains: 1. The cost value of the current node Node.cost 2. The vertex Node.G.V of the current graph 3. The edge Node.G.E of the current graph 4. The adjacency matrix Node.A formed by the current graph, and add the node to the list Openset; enter the loop, and the loop exit condition is that the list Openset is empty, and the current node is given as the node with the smallest cost in Openset;

[0043] Step 4: Determine the exit condition. When the number of unassigned tasks is less than 2, it is determined that the task assignment has been completed. Otherwise, enter the bottom loop of step 5 to find the alternative maximum parallel group.

[0044] Step 5: Enter the bottom loop and use the Bron–Kerbosch algorithm to find the candidate maximum parallel group of the current graph G. The specific steps are as follows:

[0045] 5.1 Given three sets (R, P, X);

[0046] Among them: P (Potential Candidates): candidate vertex set, which represents the vertices that can be added to the current group;

[0047] R (Current Clique): The clique currently being constructed;

[0048] X (Excluded Vertices): The set of vertices excluded from the current group;

[0049] 5.2 Start from the empty current clique R, then add the vertices in P to R one by one, and update these vertices with P and X accordingly. In each recursive process, P and X are constantly adjusted, and then continue to try to add vertices until P is empty, indicating that no new vertices can be added to the current clique. At this time, R contains a maximum clique, and then the algorithm backtracks to gradually add the previously excluded vertices to R to continue to find more maximum cliques, and finally obtains the alternative maximum parallelizable group g containing N tasks. m ,like Figure 5 As shown;

[0050] g m ={t i1 ,t i2 ,t i3 ,...,t in ,...,t iN}, where t in ∈T,N <M

[0051] Step 6: Calculate g for each candidate maximum parallel group m The cost of updating the current node cost Noed.cost, as well as the current updated graph G', such as Figure 6 As shown, add Node.G to the Openset, update the top-level nodes and Openset groups, until the allocation method that meets the exit conditions is found, and finally the minimum task group that meets the conditions is K groups, and the group set is defined as G r ={g1, g2, g3, ..., g K}.like Figure 7 shown.

[0052] In summary, the above are only preferred embodiments of the present invention and are not intended to limit the protection scope of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

[0053] It is obvious to those skilled in the art that the embodiments of the present invention are not limited to the details of the above exemplary embodiments, and that the embodiments of the present invention can be implemented in other specific forms without departing from the spirit or basic features of the embodiments of the present invention. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-restrictive, and the scope of the embodiments of the present invention is limited by the attached claims rather than the above description, so it is intended to include all changes that fall within the meaning and scope of the equivalent elements of the claims in the embodiments of the present invention. Any figure mark in the claims should not be regarded as limiting the claims involved. In addition, it is obvious that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units, modules or devices stated in the system, device or terminal claims can also be implemented by the same unit, module or device through software or hardware. The words first, second, etc. are used to indicate names, and do not indicate any particular order.

[0054] Finally, it should be noted that the above implementation modes are only used to illustrate the technical solutions of the embodiments of the present invention and are not intended to limit them. Although the embodiments of the present invention have been described in detail with reference to the above preferred implementation modes, those skilled in the art should understand that the technical solutions of the embodiments of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A multi-UAV task allocation method under inter-UAV interference constraints, characterized in that: The following steps are involved: Step 1: Use graph theory to model and analyze the multi-UAV allocation problem under the constraint of inter-UAV interference, and convert the problem of minimizing the task completion time into the problem of minimizing the number of task groups; Step 2: Use an undirected graph G = (V, E) to represent the relationship between tasks, where V represents a set of vertices, and each vertex v i represents a task point, that is, an element in the task set T; E represents the edge set, each edge e ij It represents the connection relationship between two task points, that is, whether the two task points can be executed at the same time. The adjacency matrix A is constructed based on this. Its mathematical expression is as follows: A=[a ij ] M×M If there is an edge e between node i and node j ij , that is, task i and task j are executed simultaneously, then the adjacency matrix element a ij =1; On the contrary, if there is no edge e between node i and node j ij Indicates that tasks i and j cannot be executed at the same time, then the adjacency matrix element a ij =0; Step 3: Enter the top-level loop; Step 4: Determine the exit condition. When the number of unassigned tasks is less than 2, it is determined that the task assignment has been completed. Otherwise, enter the bottom loop to find the alternative maximum parallel group.

2. The method for allocating tasks among multiple UAVs under the constraint of inter-UAV interference as claimed in claim 1, characterized in that: The problem of minimizing the task completion time is converted into the problem of minimizing the number of task groups, specifically in the following way: In the model, a set of M tasks is defined as T = {t1, t2, t3, ..., t M }, where each t i Including the two-dimensional coordinates of the task point (x i ,y i ), the drones will be divided into K groups, and the group set is defined as G r ={g1, g2, g3, ..., g K }; Rationally divide the M tasks into K groups to ensure that there is no conflict between tasks within the group, and on this basis minimize the overall completion time of the tasks. Since the time required for drones to perform tasks is the same, the problem of minimizing the task completion time is converted into the problem of minimizing the number of task groups; Minimize K.

3. The method for allocating multi-UAV tasks under inter-UAV interference constraints as claimed in claim 1 or 2, characterized in that: The Bron-Kerbosch algorithm is used to find the candidate maximum parallel group of the current graph G. The specific steps are as follows: 5.1 Given three sets (R, P, X); Among them: P (Potential Candidates): candidate vertex set, which represents the vertices that can be added to the current group; R (Current Clique): The clique currently being constructed; X (Excluded Vertices): The set of vertices excluded from the current group; 5.2 Start from the empty current clique R, then add the vertices in P to R one by one, and update these vertices with P and X accordingly. In each recursive process, P and X are constantly adjusted, and then continue to try to add vertices until P is empty, indicating that no new vertices can be added to the current clique. At this time, R contains a maximum clique, and then the algorithm backtracks to gradually add the previously excluded vertices to R to continue to find more maximum cliques, and finally obtains the alternative maximum parallelizable group g containing N tasks. m ; g m = {t i1 , t i2 , t i3 , …, t in , …, t iN}, where t in ∈T, N < M.

4. The method for allocating multi-UAV tasks under inter-UAV interference constraints as claimed in claim 1 or 2, characterized in that: Step 3 The top-level loop is specifically implemented as follows: Construct the top-level node Node with the current graph G as the initial value, where Node contains:

1. The cost value of the current node Node.cost 2. The vertex Node.GV of the current graph 3. The edge Node.G.E of the current graph 4. The adjacency matrix Node.A formed by the current graph, and add the node to the list Openset; enter the loop, the loop exit condition is that the list Openset is empty, given that the current node is the node with the smallest cost in Openset.

5. The method for allocating multi-UAV tasks under inter-UAV interference constraints as claimed in claim 1 or 2, characterized in that: After step 4, it also includes: Step 5: Enter the bottom loop and use the Bron-Kerbosch algorithm to find the candidate maximum parallel group of the current graph G; Step 6: Calculate g for each candidate maximum parallel group m The cost of updating the current node cost Noed.cost and the current updated graph G' is added to the node Node.G and added to Openset. The top-level nodes and Openset groups are updated until the allocation method that meets the exit condition is found. Finally, the minimum task group that meets the condition is K groups. The grouping set is defined as G r ={g1, g2, g3, ..., g K }.

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