Task allocation method for multiple large unmanned aerial vehicles based on WI-CBBA algorithm
By introducing an online task weight coefficient and constraints based on the WI-CBBA algorithm, the problem of slow convergence speed and low adaptability of task allocation in UAV swarms is solved, achieving fast and accurate task allocation and improving the execution efficiency and robustness of UAV swarms.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- CHENGDU AIRCRAFT DESIGN INST OF AVIATION IND CORP OF CHINA
- Filing Date
- 2025-12-30
- Publication Date
- 2026-05-12
AI Technical Summary
Existing distributed task allocation algorithms suffer from slow convergence and solution speeds and low adaptability in large-scale UAV swarms and complex task scenarios, making it difficult to meet real-time and robustness requirements.
A UAV task allocation method based on the WI-CBBA algorithm is adopted, which introduces online task weight coefficients and combines range, time window and task package constraints. Clustering and branch and bound methods are used to reduce the search space and improve the traditional consensus outsourcing algorithm to improve the solution efficiency.
It achieves fast and accurate task allocation in complex and large-scale problems, improves the task execution efficiency and robustness of UAV swarms, and reduces the consumption of computing resources.
Smart Images
Figure CN122022264A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of fixed-wing unmanned aerial vehicle (UAV) mission planning technology, and relates to a method for mission allocation of multiple large UAVs based on the WI-CBBA algorithm. Background Technology
[0002] With the continuous advancement of technology, unmanned aerial vehicle (UAV) technology has developed rapidly. As a type of integrated medium-to-high altitude, long-endurance UAV, it plays a crucial role in reconnaissance, surveillance, and target location missions. It possesses numerous advantages, including long range, extended endurance, large combat radius, and strong payload capacity, enabling it to perform diverse tasks in complex environments. To improve combat effectiveness, multiple UAVs often need to cooperate to complete complex tasks. Multi-UAV task allocation is a key link in achieving collaborative cooperation. Its goal is to rationally distribute multiple tasks among multiple UAVs, enabling the entire UAV swarm to complete tasks in an optimal manner, improving mission execution efficiency and success rate while reducing costs and risks. Therefore, how to efficiently solve for the optimal task allocation curve using optimization algorithms for multi-UAV collaborative combat has become a very popular research direction in recent years.
[0003] Generally, task allocation algorithms fall into two main categories: centralized and distributed. Centralized methods, such as graph search algorithms, traditional optimization algorithms, and heuristic algorithms, suffer from excessive computational burden on the central node when dealing with large-scale UAV swarms and complex task scenarios, leading to low computational efficiency and difficulty in meeting real-time requirements. Furthermore, a failure in the central node will affect the task execution of the entire UAV swarm. Distributed methods, such as distributed swarm intelligence algorithms, contract network algorithms, and auction algorithms, address this issue. In distributed methods, each UAV independently generates a task allocation scheme based on its own perception information, and then obtains a global solution for the multi-UAV system through communication negotiation. Therefore, distributed task allocation methods exhibit strong robustness and fault tolerance, making them suitable for multi-UAV collaborative task allocation problems in edge computing scenarios. It is worth noting that distributed task allocation algorithms still have the following shortcomings:
[0004] (1) The algorithm has a slow convergence speed. When using traditional distributed task allocation algorithms to deal with large-scale problems, it often requires a large number of iterations to find the optimal solution. Not only is the convergence speed slow, but it also occupies a lot of computing resources.
[0005] (2) The algorithm is slow to solve. In each iteration, each UAV needs to communicate and negotiate with other UAVs to update its task allocation until global convergence is achieved. This iterative process requires a large number of iterations to obtain a stable result when there are many tasks and complex constraints, which leads to a slow solution speed.
[0006] (3) Low algorithm adaptability. In reality, when multiple UAVs perform tasks in edge environments, there are many factors that affect the task allocation and benefits, such as sudden environmental changes and changes in the opponent's situation. When these situations occur, the UAVs, as intelligent agents, need to comprehensively consider multiple factors before constructing task packages and resolving conflicts to maximize the overall benefit. Therefore, it is necessary to consider the adaptability of the algorithm in such environments. Summary of the Invention
[0007] Purpose of the invention
[0008] The purpose of this invention is to overcome the shortcomings of existing technologies and provide a multi-large UAV task allocation method based on the WI-CBBA algorithm. It establishes a UAV task allocation model under range constraints, time window constraints, and task package constraints. In the reward function, an online task weight coefficient is introduced to represent the weight of the UAV's task execution. The task weight coefficient comprehensively considers both distance and reward factors. Clustering and branch-and-bound methods are used to reduce the search space, thereby improving the solution efficiency of the task allocation problem.
[0009] Technical solution
[0010] This invention targets the specific physical object of a series of drones, abstracting the scenario of multiple drones performing multiple tasks into a task allocation problem.
[0011] A task allocation method for multiple large UAVs based on the WI-CBBA algorithm is proposed. First, a mathematical model of the task allocation problem is established. Then, considering that UAVs perform tasks in edge environments, which are subject to many factors affecting task allocation rewards, such as sudden environmental changes and changes in the opponent's situation, an improvement on the traditional consensus outsourcing algorithm is proposed: the Weight Interval Consensus-Based Bundle Algorithm (WI-CBBA). This algorithm introduces online task weight coefficients into the reward function to represent the weight of the UAV's task execution. These task weight coefficients comprehensively consider both distance and reward factors. This improved CBBA algorithm solves the objective function with a larger average value and a shorter convergence time.
[0012] Includes the following steps:
[0013] S1. Initialize the task set parameters and drone set information.
[0014] S2. Based on graph theory, model the task allocation scenario, establish the objective function and its constraints, and build a mathematical model for task allocation.
[0015] S3, Determine the set of winners. and the winning bid set Check if the number of task packages has changed and does not exceed the maximum number of tasks that the drone can perform. If the number of task packages has not changed or the number of tasks that the drone can perform has reached the maximum number of tasks, it proves that the algorithm has met the convergence requirements and the result is output directly. Otherwise, jump to S4.
[0016] S4. Task package construction: Iterate through each drone to select a task.
[0017] S5, Conflict Resolution: Check if there are multiple drones performing the same task. If so, select the drone with the highest bid to perform the task, while the other drones will release the task and its subsequent tasks from the task package, and then return to S4.
[0018] Furthermore, step S1 includes the following sub-steps:
[0019] S11. Define the type, quantity, and speed range of the drones. Fuel consumption Maximum number of executable tasks and maximum range .
[0020] S12. Define the type, quantity, and rewards of the tasks. Discount Factor and task time .
[0021] Furthermore, step S2 includes the following sub-steps:
[0022] S21. Assume the task scenario graph G is an m×m square region. G contains n tasks to be executed, and the union of these n tasks is denoted as . .
[0023] S22, Use This indicates the task execution order as task To the mission , .,in The execution order is as follows Otherwise, it is 0.
[0024] S23, Define the edge set ,in Indicates task points and mission points The line segment at the endpoint.
[0025] S24. Use the task pack collection This indicates that the drone was selected based on a greedy task. Ordered assignment tasks; path list It is a drone The order in which the missions were executed; the list of winning drones. yes The corresponding winning drone number; It is along the path list drones Based on the total revenue generated from the task.
[0026] S25, Use Representing drones To the target task point The distance is determined, and a new task reward function is defined as follows:
[0027]
[0028] S26, Use Representative task The location, and using Representing drones Execute the task The cost:
[0029]
[0030] S27. Based on the profit function and cost function, the objective function is established as follows:
[0031]
[0032]
[0033] S28. Based on the principle of maximizing the objective function, the objective function and its constraints are established as follows:
[0034]
[0035] in, Indicates drone Complete the task The time required For drones From the previous task point to the next task point The shortest time, For drones Execute the task time; Indicates drone From the task The departure time from the previous mission point restricts the drone's departure time. Execute tasks that satisfy time window constraints.
[0036] Furthermore, step S4 includes the following sub-steps:
[0037] S41, Take the data structure obtained from the previous iteration (UAV) The task set, winning bid set, task path set, and winning drone set are used as the starting data for this iteration.
[0038]
[0039]
[0040]
[0041]
[0042] S42. When the number of tasks in the task package is less than the maximum number of tasks the drone can execute, start a loop to calculate the drone's... Execute the task Profit value Search for potential new tasks and verify whether they are suitable for assignment to the drone. The constraints. If the conditions are met, and a task is added. Can give drones The additional benefit is the task The highest bid value in the current task sequence, then the task... Add to drones In the task sequence.
[0043] S43. If the drone wins the bid for the mission, the new mission will be placed in the optimal position in the mission sequence through calculation.
[0044] S44. Update the data structure, including drones. The task sequence, shared vector.
[0045] S45. When the number of tasks in the task package is equal to the upper limit of the tasks that the drone can execute, the current cycle ends and the conflict resolution phase begins.
[0046] Furthermore, step S5 includes the following sub-steps:
[0047] S51. Detect Conflicts: Identify any conflicts in task allocation, such as resource contention or task overlap.
[0048] S52. Conflict Analysis: Analyze the detected conflicts to determine the root causes of the conflicts and the possible solutions.
[0049] S53. Solution Generation: Based on the results of conflict analysis, appropriate solutions are generated to eliminate or mitigate task conflicts. By sharing the task information structure through communication, UAVs exclude invalid and conflicting candidate tasks during task screening, ensuring that the final task allocation result is not interfered with. UAVs exchange lists of winners, bids, and timestamp vectors to facilitate collaboration during task allocation. This stage introduces timestamp vectors to record the time of the most recent information update after communication between UAVs, and these vectors are used as the vectors stored by the UAVs during the allocation process. The update formula for the timestamp vector is shown below.
[0050]
[0051] In the formula, This represents the timestamp vector updated after drone i communicates with drone k; here Indicates the time of information reception; This indicates whether drone i and drone k can communicate; a value of 1 indicates that a communication link can be established, and a value of 0 indicates that communication is not possible. This indicates that drone i and drone m can communicate, and the timestamp vector is updated after drone m communicates with drone k.
[0052] S54. During the conflict resolution phase, when drones Receive drone The information being conveyed includes At that time, drones Will utilize known To determine which drone's information is considered the most up-to-date for the mission. For the mission... drones Three different operations can be selected:
[0053] 1. Update:
[0054]
[0055] 2. Reset
[0056]
[0057] 3. Leave
[0058]
[0059] drones Upon receiving the drone Relevant tasks After receiving the information, corresponding actions are taken based on the consensus mechanism shown in the table below. The values in the first two columns of the table represent the drones. and Evaluation and assessment of bidding tasks The winner, the third column represents the drone. The action to be performed, where the default action is to leave.
[0060] If drone of and After an update occurs following negotiation using the consensus mechanism based on the table below, the task sequence for all drones should be updated. and path order set In the middle, affected and The tasks affected by the value update and their subsequent tasks will be released and reset.
[0061]
[0062] After the drone swarm reaches a consensus on the current task allocation scheme (S55), the algorithm will jump to S4, adding the tasks released during the conflict resolution phase to the drone's local task package. This cycle of the two phases continues until... and It will no longer change.
[0063] The beneficial effects of this application are as follows:
[0064] Therefore, the proposed UAV task allocation method based on the chaotic mapping porcupine optimization algorithm has significant advantages when dealing with complex and large-scale problems. Compared with the existing technology, the beneficial effects of this invention are mainly reflected in the following two aspects:
[0065] 1) A novel UAV task allocation method based on the WI-CBBA algorithm is proposed. Compared with the contract net algorithm, particle swarm algorithm and other methods, this invention introduces a newer and better-performing consensus outsourcing algorithm, which expands the application scenarios of the consensus outsourcing algorithm.
[0066] This study addresses the task allocation problem in multi-task scenarios involving UAVs collaboratively performing tasks. Considering various complex constraints such as UAV performance limitations and time window constraints, as well as the distributed requirements in edge environments, the objective function in the task is reasonably designed, and an improved algorithm is used to achieve a fast and accurate solution to the UAV collaborative task allocation problem. Attached Figure Description
[0067] Figure 1 This is a flowchart of the multi-UAV task allocation method based on the WI-CBBA algorithm of the present invention.
[0068] Figure 2 Spatiotemporal graph of task allocation results based on WI-CBBA algorithm.
[0069] Figure 3 Time allocation diagram for tasks based on the WI-CBBA algorithm. Detailed Implementation
[0070] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions in the embodiments of this invention will be described in more detail below. In the examples, the same or similar reference numerals denote the same or similar components or elements having the same or similar functions throughout. The described embodiments are some, but not all, of the embodiments of this invention. The embodiments described below with reference to reference are exemplary and intended to explain this invention, and should not be construed as limiting the invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention. The embodiments of this invention will be described in detail below.
[0071] like Figure 1 As shown, this invention is a method for task allocation among multiple large unmanned aerial vehicles based on the WI-CBBA algorithm, comprising the following steps:
[0072] S1. Initialize task set parameters and drone set information:
[0073] S11. Define the type, quantity, and speed range of the drones. Fuel consumption Maximum number of executable tasks and maximum range .
[0074] S12. Define the type, quantity, and rewards of the tasks. Discount Factor and task time .
[0075] S2. Modeling the task allocation scenario:
[0076] Suppose the task scenario is an m×m square region G. G contains n tasks to be executed, and the union of these n tasks is denoted as . Once the drone takes off, it can perform each task. This indicates the task execution order as task To the mission , ,in The execution order is as follows Otherwise, it is 0. Edge set , Indicates task points and mission points The endpoints of the line segments, using the task package collection. This indicates that the drone was selected based on a greedy task. Ordered assignment tasks; path list It is a drone The order in which the missions were executed; the list of winning drones. yes The corresponding winning drone number; It is along the path list drones Based on the total revenue generated from the task.
[0077] use Representing drones To the target task point The distance is determined, and a new task reward function is defined as follows:
[0078]
[0079] use Representative task The location, and using Representing drones Execute the task The cost:
[0080]
[0081] Based on the profit function and cost function, the objective function is established as follows:
[0082]
[0083]
[0084] Based on the principle of maximizing the objective function, the objective function and its constraints are established as follows:
[0085]
[0086] S3, Determine the set of winners. and the winning bid set If the number of tasks has not changed and the number of task packages does not exceed the maximum number of tasks the drone can perform, then the algorithm has converged. If the number of tasks has not changed or the number of task packages has reached the maximum number of tasks the drone can perform, then the algorithm has converged and the result is output directly. or Changes Jump to S4.
[0087] S4. Task package construction: Iterate through each drone to select a task.
[0088] S41, Take the data structure obtained from the previous iteration (UAV) The task set, winning bid set, task path set, and winning drone set are used as the starting data for this iteration.
[0089]
[0090]
[0091]
[0092]
[0093] S42. When the number of tasks in the task package is less than the maximum number of tasks the drone can execute, start a loop to calculate the drone's... Execute the task Profit value Search for potential new tasks and verify whether they are suitable for assignment to the drone. The constraints. If the conditions are met, and a task is added. Can give drones The additional benefit is the task The highest bid value in the current task sequence, then the task... Add to drones In the task sequence.
[0094] S43. If the drone wins the bid for the mission, the new mission will be placed in the optimal position in the mission sequence through calculation.
[0095] S44. Update the data structure, including drones. The task sequence, shared vector.
[0096] S45. When the number of tasks in the task package is equal to the upper limit of the tasks that the drone can execute, the current cycle ends and the conflict resolution phase begins.
[0097] S5, Conflict Resolution: Check if there are multiple drones performing the same task. If so, select the drone with the highest bid to perform the task, while the other drones will release the task and its subsequent tasks from the task package, and then return to S4.
[0098] S51. Detect Conflicts: Identify any conflicts in task allocation, such as resource contention or task overlap.
[0099] S52. Conflict Analysis: Analyze the detected conflicts to determine the root causes of the conflicts and the possible solutions.
[0100] S53. Solution Generation: Based on the results of conflict analysis, appropriate solutions are generated to eliminate or mitigate task conflicts. By sharing the task information structure through communication, UAVs exclude invalid and conflicting candidate tasks during task screening, ensuring that the final task allocation result is not interfered with. UAVs exchange lists of winners, bids, and timestamp vectors to facilitate collaboration during task allocation. This stage introduces timestamp vectors to record the time of the most recent information update after communication between UAVs, and these vectors are used as the vectors stored by the UAVs during the allocation process. The update formula for the timestamp vector is shown below.
[0101]
[0102] In the formula, This represents the timestamp vector updated after drone i communicates with drone k; here Indicates the time of information reception; This indicates whether drone i and drone k can communicate; a value of 1 indicates that a communication link can be established, and a value of 0 indicates that communication is not possible. This indicates that drone i and drone m can communicate, and the timestamp vector is updated after drone m communicates with drone k.
[0103] S54. During the conflict resolution phase, when drones Receive drone The information being conveyed includes At that time, drones Will utilize known To determine which drone's information is considered the most up-to-date for the mission. For the mission... drones Three different operations can be selected:
[0104] 1. Update:
[0105]
[0106] 2. Reset
[0107]
[0108] 3. Leave
[0109]
[0110] drones Upon receiving the drone Relevant tasks After receiving the information, corresponding actions are taken based on the consensus mechanism shown in the table below. The values in the first two columns of the table represent the drones. and Evaluation and assessment of bidding tasks The winner, the third column represents the drone. The action to be performed, where the default action is to leave.
[0111] If drone of and After an update occurs following negotiation using the consensus mechanism based on the table below, the task sequence for all drones should be updated. and path order set In the middle, affected and The tasks affected by the value update and their subsequent tasks will be released and reset.
[0112]
[0113] After the drone swarm reaches a consensus on the current task allocation scheme (S55), the algorithm will jump to S4, adding the tasks released during the conflict resolution phase to the drone's local task package. This cycle of the two phases continues until... and It will no longer change.
[0114] In this embodiment, the mission scenario is a square area G of 30km × 30km, containing 15 tasks to be executed. The task types are reconnaissance or adversarial, with tasks 1 to 7 being reconnaissance tasks and tasks 8 to 15 being adversarial tasks to assess the benefits of reconnaissance. =100, Discount Factor =0.3, task time =10; the benefit of confrontation =150, Discount Factor =0.4, task time =12, each mission location corresponds to a two-dimensional coordinate, and the number of drones is... arrive There are a total of 8 aircraft, of two types, including arrive Type 1, speed range =[350 km / h, 750 km / h], fuel consumption =1, maximum number of executable tasks =10, maximum range =500 km; arrive Type 2, speed range =[400 km / h, 850 km / h], fuel consumption =2, maximum number of executable tasks =12, maximum range =500 km;
[0115] Based on the above task scenarios and parameter settings, the task allocation results are shown in Table 1. From the relevant charts of the task allocation results, it can be seen that the UAV performed the task without omission and met all the constraints in the mathematical model. The simulation results confirm that the WI-CBBA algorithm can achieve conflict-free task allocation.
[0116] Table 1 shows the task allocation results.
[0117] Table 1
[0118]
[0119] Furthermore, unless otherwise defined, the technical or scientific terms used in this application description shall have the ordinary meaning understood by one of ordinary skill in the art to which this application pertains. The terms "upper," "lower," "left," "right," "center," "vertical," "horizontal," "inner," and "outer," etc., used in this application description to indicate relative direction or positional relationship are used only to indicate relative orientation or positional relationship, and do not imply that the device or component must have a specific orientation, or be constructed and operated in a specific orientation. When the absolute position of the described object changes, its relative positional relationship may also change accordingly, and therefore should not be construed as a limitation on this application. The terms "first," "second," "third," and similar terms used in this application description are used only for descriptive purposes to distinguish different components, and should not be construed as indicating or implying relative importance. The terms "a," "one," or "the," etc., used in this application description should not be construed as an absolute limitation on quantity, but should be construed as indicating the existence of at least one. The terms "including," "comprising," etc., used in this application description mean that the element or object preceding the word covers the element or object listed after the word and its equivalents, without excluding other elements or objects.
[0120] Furthermore, it should be noted that, unless otherwise explicitly specified and limited, terms such as “installation,” “connection,” and “linkage” used in the description of this application should be interpreted broadly. For example, a connection can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium; or it can be a connection within two components. Those skilled in the art can understand its specific meaning in this application according to the specific circumstances.
[0121] The above description is merely a specific embodiment of the present invention and is not intended to limit the present invention. Within the spirit and principles of the present invention, any person skilled in the art may use the above-disclosed technical content to make changes or modifications to equivalent embodiments and apply them to other fields. However, any simple modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention, as well as any modifications, equivalent substitutions, improvements, etc., should be included within the protection scope of the present invention.
Claims
1. A method for task allocation among multiple large unmanned aerial vehicles based on the WI-CBBA algorithm, characterized in that, First, a mathematical model for the task allocation problem is established, and a weighted time-distance consistency package algorithm is proposed. An online task weight coefficient is introduced into the reward function to represent the weight of the UAV's task execution. The task weight coefficient comprehensively considers both distance and reward factors. The improved CBBA algorithm has a larger average value of the objective function and a shorter convergence time.
2. The method as described in claim 1, characterized in that, Includes the following steps: S1. Initialize the task set parameters and UAV set information; S2. Model the task allocation scenario based on graph theory, establish the objective function and its constraints, and build a mathematical model for task allocation; S3, Determine the set of winners. and the winning bid set Check whether the changes have occurred and whether the number of task packages does not exceed the maximum number of tasks that the drone can execute. If the changes have not occurred or the number of task packages has reached the maximum number of tasks that the drone can execute, it proves that the algorithm has met the convergence requirements and outputs the result directly. Otherwise, jump to S4. S4. Task package construction: Iterate through each drone to select a task. S5, Conflict Resolution: Check if there are multiple drones performing the same task. If so, select the drone with the highest bid to perform the task, while the other drones will release the task and its subsequent tasks from the task package, and then return to S4.
3. The method as described in claim 2, characterized in that, S1 includes the following sub-steps: S11. Define the type, quantity, and speed range of the drones. Fuel consumption Maximum number of executable tasks and maximum range ; S12. Define the type, quantity, and rewards of the tasks. Discount Factor and task time .
4. The method as described in claim 2, characterized in that, S2 includes the following sub-steps: S21. Assume the task scenario graph G is an m×m square region; G contains n tasks to be executed, and the union of the n tasks is denoted as . ; S22, Use This indicates the task execution order as task To the mission , .,in The execution order is as follows Otherwise, it is 0; S23, Define the edge set ,in Indicates task points and mission points The line segment at the endpoint; S24. Use the task pack collection This indicates that the drone was selected based on a greedy task. Ordered assignment tasks; path list It is a drone The order in which the missions were executed; the list of winning drones. yes The corresponding winning drone number; It is along the path list drones Based on the total revenue generated from the task; S25, Use Representing drones To the target task point The distance is determined, and a new task reward function is defined as follows: , S26, Use Representative task The location, and using Representing drones Execute the task The cost: , S27. Based on the profit function and cost function, the objective function is established as follows: , ， S28. Based on the principle of maximizing the objective function, the objective function and its constraints are established as follows: ，， in, Indicates drone Complete the task The time required For drones From the previous task point to the next task point The shortest time, For drones Execute the task time; Indicates drone From the task The departure time from the previous mission point restricts the drone's departure time. Execute tasks that satisfy time window constraints.
5. The method as described in claim 2, characterized in that, S4 includes the following sub-steps: S41. Take the data structure obtained from the previous iteration, and use the drone... The task set, winning bid set, task path set, and winning drone set are used as the starting data for this iteration; ， ， ， ， S42. When the number of tasks in the task package is less than the maximum number of tasks the drone can execute, start a loop to calculate the drone's... Execute the task Profit value Search for potential new tasks and verify whether they are suitable for assignment to the drone. The constraints; if the conditions are met, and a task is added. Can give drones The additional benefit is the task The highest bid value in the current task sequence, then the task... Add to drones In the task sequence; S43. If the drone successfully bids for the mission, the new mission will be placed in the optimal position in the mission sequence through calculation. S44. Update the data structure, including drones. The task sequence, sharing vectors; S45. When the number of tasks in the task package is equal to the upper limit of the tasks that the drone can execute, the current cycle ends and the conflict resolution phase begins.
6. The method as described in claim 2, characterized in that, S5 includes the following sub-steps: S51. Detect conflict: Identify any conflicts in task allocation, such as resource contention or task overlap. S52. Conflict Analysis: Analyze the detected conflicts to determine the root causes of the conflicts and the possible solutions. S53. Solution Generation: Based on the results of conflict analysis, generate appropriate solutions to eliminate or mitigate task conflicts; By sharing the task information structure through communication, the UAV eliminates invalid and conflicting candidate tasks when screening tasks, ensuring that the final task allocation result is not affected. The drones exchange lists of winners, bids, and timestamp vectors to facilitate collaboration during task allocation. This stage introduces timestamp vectors to record the time of the most recent information update after communication between drones, and these vectors are used as the vectors stored by the drones during the allocation process. The update formula for the timestamp vectors is shown below: , In the formula, This represents the timestamp vector updated after drone i communicates with drone k; here Indicates the time of information reception; This indicates whether drone i and drone k can communicate; a value of 1 indicates that a communication link can be established, and a value of 0 indicates that communication is not possible. This indicates that drone i and drone m can communicate, and the timestamp vector is updated after drone m communicates with drone k. S54. During the conflict resolution phase, when drones Receive drone The information being conveyed includes At that time, drones Will utilize known To determine which drone's information is considered the most up-to-date for the mission.
7. The method as described in claim 6, characterized in that, For the task drones Three different operations can be selected: 1) Update: , 2) Reset , 3) Leave , drones Upon receiving the drone Relevant tasks After receiving the information, corresponding actions are taken based on the consensus mechanism shown in the table below; the values in the first two columns of the table represent the drones. and Evaluation and assessment of bidding tasks The winner, the third column represents the drone. The action to be performed, where the default action is to leave; If drone of and After an update occurs following negotiation using the consensus mechanism based on the table below, the task sequence for all drones should be updated. and path order set In the middle, affected and The tasks affected by the value update and their subsequent tasks will be released and reset.
8. The method as described in claim 7, characterized in that, After the drone swarm reaches a consensus on the current task allocation scheme (S55), the algorithm will jump to S4, adding the tasks released during the conflict resolution phase to the drone's local task package. This cycle of the two phases continues until... and It will no longer change.