An unmanned aerial vehicle group coordination method based on communication state adaptation

Abstract

This invention discloses a UAV swarm collaboration method based on adaptive communication status, belonging to the field of UAV collaborative control technology. The method includes: each UAV monitoring communication quality in real time and generating a comprehensive index, which is compared with a preset threshold to determine the communication status. When the communication status is not lower than the threshold, the UAV swarm enters a communication collaboration mode and executes global task allocation; when the communication status is lower than the threshold, each UAV automatically switches to an autonomous mode, independently executing tasks only within its own responsibility area based on local perception information, employing a distributed mechanism for load balancing and conflict avoidance. In autonomous mode, the communication status is continuously monitored; when communication is detected to be restored, the UAVs synchronously exchange task summary information to update the global task status. This invention can maintain continuous task execution even under conditions of communication obstruction or interruption, improving the robustness and task completion capability of multi-UAV systems in complex electromagnetic environments.

Description

Technical Field

[0001] This invention belongs to the field of UAV cooperative control technology, specifically relating to a UAV swarm cooperative method based on communication state adaptation. Background Technology

[0002] With the rapid development of drone technology, multi-drone collaborative systems are widely used in complex mission scenarios such as regional patrols, disaster relief, and military reconnaissance. Through wireless communication networks, drones can share location information, flight status, and mission progress, thereby achieving centralized or distributed collaborative control.

[0003] However, in practical engineering applications, the environments in which UAV swarms operate are often characterized by complex communication conditions and significant dynamic changes. In densely built-up urban areas, mountain valleys, underground spaces, and scenarios with strong electromagnetic interference, wireless communication links are susceptible to factors such as obstruction, multipath effects, and electromagnetic interference, resulting in problems such as decreased signal-to-noise ratio, increased packet loss rate, and increased communication latency. In some cases, communication may even be intermittently available or completely interrupted.

[0004] Many existing multi-UAV collaborative methods are designed based on the assumption that communication links are continuously available or that communication quality meets certain requirements. Once communication quality deteriorates or is interrupted, the collaborative mechanism becomes difficult to maintain, leading to a significant reduction in task execution efficiency or even collaborative failure. While some existing technologies consider communication constraints, most only introduce communication quality as a control parameter into the collaborative model, or treat communication quality degradation as something that needs to be "optimized and maintained"—for example, by adjusting relay paths, switching network topology, or optimizing resource allocation to try to maintain communication connectivity. However, they do not take the "communication state" itself as a fundamental decision-making basis affecting collaborative behavior patterns, and lack the ability to switch from "communication collaboration" to "non-communication autonomy" behavior patterns.

[0005] Furthermore, existing technologies generally suffer from a high dependence on global information and continuous communication. Most methods require communication to obtain the status information of other drones or the results of global task allocation. When communication is limited, drones cannot obtain complete collaborative information, which can easily lead to problems such as repeated searches, wasted resources, path conflicts, and even collisions. As the scale of drone swarms expands, the communication load problem becomes increasingly prominent, and maintaining collaboration through high-frequency communication faces high system complexity and implementation costs.

[0006] Therefore, how to reduce the dependence of multi-UAV systems on continuous communication links when communication is blocked or even interrupted, so that UAVs can maintain the ability to continuously perform collaborative tasks under conditions of only partial perception information and limited communication, has become a technical problem that urgently needs to be solved in this field. Summary of the Invention

[0007] To address the aforementioned technical problems, this invention provides a UAV swarm collaboration method based on adaptive communication state. According to the real-time communication state of the UAV swarm, it adaptively hard switches between "communication collaboration mode" and "non-communication autonomous mode", enabling UAVs to completely disconnect from the communication link and independently execute tasks relying solely on local perception when communication is blocked or interrupted. After communication is restored, the collaborative state is seamlessly reconstructed through lightweight information synchronization.

[0008] To achieve the above objectives, the present invention adopts the following technical solution:

[0009] A UAV swarm cooperative method based on communication state adaptation includes:

[0010] Step 1: Each drone monitors its own communication quality indicators in real time and generates its own comprehensive communication quality indicators:

[0011] Step 2: Compare the respective comprehensive communication quality indicators with the first preset threshold to determine the current communication status of each UAV. When the comprehensive communication quality indicator is not lower than the preset threshold, the UAV swarm enters the communication cooperation mode and performs global task area division and cooperative task allocation. When the comprehensive communication quality indicator is lower than the preset threshold, each UAV automatically switches to autonomous mode and independently performs tasks based on local perception information only within its assigned responsibility area.

[0012] Step 3: In autonomous mode, continuously monitor the communication status. When communication is detected to have resumed, all UAVs synchronize and exchange task summary information, update the global task status, and remove completed tasks.

[0013] Step 4: Repeat steps 1 to 3 until the overall task completion rate reaches the second preset threshold.

[0014] Furthermore, in step 1, the comprehensive communication quality index is positively correlated with the signal-to-noise ratio and negatively correlated with packet loss rate, latency, and electromagnetic interference intensity.

[0015] Furthermore, in step 2, the first preset threshold is determined using an adaptive method: historical communication status samples are collected and divided into a good communication sample set and a communication blocked sample set. The segmentation value that maximizes the difference between the means of the two sample sets relative to their internal dispersion is selected as the preset threshold.

[0016] Furthermore, in step 2, the responsibility area is constructed based on the initial position of each UAV using a nearest neighbor partitioning method, and each subtask is assigned to the nearest UAV.

[0017] Furthermore, in step 2, in autonomous mode, the UAV constructs a local coverage evaluation function based on obstacle locations and historical coverage levels, and plans a flight search path according to the principle of maximizing the evaluation function.

[0018] Furthermore, when the drone's task completion rate within its area of ​​responsibility reaches a preset area completion threshold, the corresponding drone is allowed to expand its search range to adjacent areas.

[0019] Furthermore, in step 2, in autonomous mode, when the responsibility areas of multiple drones overlap, priority is determined based on the remaining battery power and drone identification number, and drones with lower priority automatically avoid each other.

[0020] Furthermore, in step 3, the task summary information includes coverage area information, target information, and timestamp. After communication is restored, each UAV achieves synchronized updates of the global task status by exchanging the task summary information.

[0021] In a second aspect, the present invention provides an electronic device, comprising: one or more processors; and a memory for storing one or more programs; wherein, when the one or more programs are executed by the one or more processors, the one or more processors implement the aforementioned method for UAV swarm collaboration based on communication state adaptation.

[0022] Thirdly, the present invention provides a computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, enable the processor to implement the aforementioned UAV swarm coordination method based on communication state adaptation.

[0023] The beneficial effects of this invention are as follows:

[0024] This invention introduces a communication state awareness and cooperative behavior mode adaptive mechanism, enabling UAV swarms to dynamically adjust their cooperative mode according to changes in communication conditions. When communication conditions are good, it can give full play to the efficiency advantages of multi-UAV cooperation, and when communication is blocked or interrupted, it can continue to execute tasks by relying on local perception and distributed decision-making mechanisms, thereby significantly reducing the system's dependence on a continuous and stable wireless communication link.

[0025] Under communication-constrained conditions, this invention allows a single UAV to independently execute tasks with only local environmental awareness and limited collaborative information. Through reasonable task area constraints and local decision-making mechanisms, it avoids redundant operations and conflicting behaviors among multiple UAVs, effectively improving the continuity of task execution and overall completion rate. Simultaneously, this mechanism ensures that the system maintains basic collaborative capabilities even when some UAVs lose contact or communication is temporarily interrupted, enhancing the robustness and fault tolerance of the multi-UAV system.

[0026] This invention enables smooth synchronization of mission status and critical information among UAVs when communication status changes or communication conditions recover, avoiding mission status confusion or repetitive execution caused by communication fluctuations, thereby ensuring the continuity and consistency of the collaborative process. This feature gives UAV swarms stronger adaptability and engineering applicability in complex and dynamically changing communication environments.

[0027] This invention employs a collaborative mechanism centered on local perception and distributed decision-making, reducing reliance on centralized control and high-frequency communication, lowering system communication load and implementation complexity, and exhibiting good scalability, making it suitable for medium- to large-scale UAV swarm systems. In application scenarios with complex terrain, electromagnetic interference environments, and limited communication conditions, this invention can effectively improve the mission completion efficiency, energy utilization efficiency, and overall operational reliability of multi-UAV systems, demonstrating high engineering application value. Attached Figure Description

[0028] Figure 1 This is a flowchart of a UAV swarm collaboration method based on communication state adaptation according to the present invention. Detailed Implementation

[0029] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0030] This invention provides a UAV swarm collaboration method based on adaptive communication state. This method can be applied to complex task scenarios such as regional patrol, disaster relief, emergency response, and environmental monitoring, and is particularly suitable for engineering environments with complex and variable communication conditions, such as densely built-up urban areas, mountain valleys, underground spaces, and areas with strong electromagnetic interference. The core concept of this invention lies in: by monitoring the communication quality between the UAVs and the communication network in real time, dynamically judging the current communication state based on a preset threshold, and adaptively switching the collaborative behavior mode of the UAV swarm based on this communication state—when the communication quality is good, a communication collaboration mode is adopted to achieve global task allocation and multi-UAV collaboration; when the communication quality is below the threshold, it automatically switches to an autonomous mode, allowing each UAV to independently execute tasks within its responsibility area based only on local perception information, and seamless reconstruction of the collaborative state is achieved through lightweight task summary synchronization after communication is restored. Through the above-mentioned communication state-driven adaptive behavior mode switching mechanism, this invention can significantly reduce the dependence of the UAV swarm on continuous wireless communication links, ensuring the continuous execution capability of tasks under conditions of communication obstruction or interruption. Specifically, as shown below... Figure 1 As shown, the system initialization and task modeling are performed first:

[0031] The task area is discretized into several sub-regions or grid cells to form a task set. :

[0032] ,

[0033] Each subtask For each spatial region or target search unit, j∈[1,M], M represents the total number of spatial regions or target search units.

[0034] Assume a drone ensemble for:

[0035] .

[0036] After setting the initial state, the method of the present invention includes:

[0037] Step 1: Each drone monitors its own communication quality indicators in real time and generates a comprehensive communication quality indicator:

[0038] Each drone periodically tests its communication quality index vector. :

[0039] ,

[0040] Where SNR is the signal-to-noise ratio, PRL is the packet loss rate, Delay is the time delay, I is the electromagnetic interference intensity index, and the subscript i corresponds to the UAV number index.

[0041] Constructing a comprehensive communication quality index :

[0042] ,

[0043] In the formula, , , , These correspond to the weights of each communication quality index vector.

[0044] Step 2: Compare the comprehensive communication quality index with a preset threshold to determine the current communication status of each UAV. When the comprehensive communication quality index is not lower than the preset threshold, the UAV swarm enters the communication coordination mode and performs global task area division and collaborative task allocation. When the comprehensive communication quality index is lower than the preset threshold, each UAV automatically switches to autonomous mode and independently performs tasks based on local perception information only within its assigned responsibility area.

[0045] Set a threshold And determine the current communication status of each drone. Is it below the preset threshold?

[0046] ,

[0047] If multiple drones are currently in communication status Not lower than the preset threshold At this time, the drone swarm enters a communication and coordination mode, executing global task area division and collaborative task allocation; threshold An adaptive calculation method is used to obtain the optimal segmentation threshold based on historical samples. Specifically:

[0048] First, set the system's overall communication status indicators. The calculation formula is as follows: Let be the comprehensive communication quality index of the i-th UAV at time t.

[0049] ,

[0050] Among them, setting N represents the number of drones.

[0051] Let the set of historical communication state samples be... The samples were divided into "good communication sets". "and "communication blocked set" The threshold θ is determined by the principle that optimizes inter-class separation:

[0052] ,

[0053] ,

[0054] ,

[0055] Here, Indicates the initial reference threshold;

[0056] Optimal threshold The discriminant is:

[0057] ,

[0058] in, , express The number of elements, i.e., the number of samples with good communication. This represents the corresponding mean. , express The number of elements, i.e., the number of samples where communication was blocked. This represents the corresponding mean.

[0059] Based on the initial position of the drone, construct the area of ​​responsibility for each drone. :

[0060] ,

[0061] in, .

[0062] If communication is blocked, that is If the communication fails, the system switches to a weak or no-communication autonomous mode. Each drone does not rely on external communication and operates only within its area of ​​responsibility. It independently performs target search tasks, uses onboard sensors to acquire its own state and surrounding environment information, and constructs a local environment model and a local coverage evaluation function. :

[0063] ,

[0064] in, Location of the obstacle. For the historical coverage of this location, , This represents the weight of the corresponding item.

[0065] At this point, the drone plans its search path based on the principle of maximizing local utility:

[0066] ,

[0067] in, This indicates the flight search path planned by the drone within its area of ​​responsibility.

[0068] When the task completion rate within the area of ​​responsibility reaches the threshold At the same time, drones are allowed to extend their search range to adjacent areas. :

[0069] , For the area of ​​drone i, Let i be the neighborhood of UAV i.

[0070] At the same time, priority-based conflict resolution rules are introduced:

[0071] ,

[0072] in, That is the remaining battery power of the i-th drone. It is the drone's identification number. , As the weight of the corresponding item, when two drones overlap in area, the one with lower priority will automatically give way.

[0073] Step 3: In autonomous mode, continuously monitor the communication status. When communication is detected to have resumed, all UAVs synchronize and exchange task summary information, update the global task status, and remove completed tasks.

[0074] If communication is restored or briefly available, the drones exchange and synchronize mission summary information. :

[0075] ,

[0076] in, It is coverage area information. It is target information. It's a timestamp.

[0077] Simultaneously mark completed tasks and remove them from the task set. Remove from the list. If communication is not restored, retain the original state.

[0078] Step 4: Repeat steps 1 to 3 until the overall task completion rate reaches the second preset threshold.

[0079] Set a task completion percentage threshold. , This indicates that the task has been completed. If the task is completed, the process ends. Otherwise, the task is not completed, and the process returns to step 1 and repeats.

[0080] In a second aspect, the present invention provides an electronic device, comprising: one or more processors; and a memory for storing one or more programs; wherein, when the one or more programs are executed by the one or more processors, the one or more processors implement the aforementioned method for UAV swarm collaboration based on communication state adaptation.

[0081] Thirdly, the present invention provides a computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, enable the processor to implement the aforementioned UAV swarm coordination method based on communication state adaptation.

[0082] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for UAV swarm cooperation based on adaptive communication state, characterized in that, include: Step 1: Each drone monitors its own communication quality indicators in real time and generates its own comprehensive communication quality indicators: Step 2: Compare the respective comprehensive communication quality indicators with the first preset threshold to determine the current communication status of each UAV. When the comprehensive communication quality indicator is not lower than the preset threshold, the UAV swarm enters the communication cooperation mode and performs global task area division and cooperative task allocation. When the comprehensive communication quality indicator is lower than the preset threshold, each UAV automatically switches to autonomous mode and independently performs tasks based on local perception information only within its assigned responsibility area. Step 3: In autonomous mode, continuously monitor the communication status. When communication is detected to have resumed, all UAVs synchronize and exchange task summary information, update the global task status, and remove completed tasks. Step 4: Repeat steps 1 to 3 until the overall task completion rate reaches the second preset threshold.

2. The UAV swarm cooperation method based on communication state adaptation according to claim 1, characterized in that, In step 1, the comprehensive communication quality index is positively correlated with the signal-to-noise ratio and negatively correlated with packet loss rate, latency and electromagnetic interference intensity.

3. The UAV swarm cooperation method based on communication state adaptation according to claim 1, characterized in that, In step 2, the first preset threshold is determined using an adaptive method: historical communication status samples are collected and divided into a good communication sample set and a communication blocked sample set. The segmentation value that maximizes the difference between the means of the two sample sets relative to their internal dispersion is selected as the preset threshold.

4. The UAV swarm cooperation method based on communication state adaptation according to claim 1, characterized in that, In step 2, the responsibility area is constructed based on the initial position of each UAV using a nearest neighbor partitioning method, and each subtask is assigned to the nearest UAV.

5. The UAV swarm cooperation method based on communication state adaptation according to claim 1, characterized in that, In step 2, under autonomous mode, the UAV constructs a local coverage evaluation function based on obstacle locations and historical coverage levels, and plans a flight search path according to the principle of maximizing the evaluation function.

6. The UAV swarm cooperation method based on communication state adaptation according to claim 5, characterized in that, When the drone's task completion rate within its area of ​​responsibility reaches a preset area completion threshold, the corresponding drone is allowed to expand its search range to adjacent areas.

7. The UAV swarm cooperation method based on communication state adaptation according to claim 1, characterized in that, In step 2, under autonomous mode, when the responsibility areas of multiple drones overlap, priority is determined based on the remaining battery power and drone identification number, and drones with lower priority automatically avoid each other.

8. The UAV swarm cooperation method based on communication state adaptation according to claim 1, characterized in that, In step 3, the task summary information includes coverage area information, target information, and timestamp. After communication is restored, each UAV exchanges the task summary information to achieve synchronous updates of the global task status.

9. An electronic device, characterized in that, include: One or more processors; Memory, used to store one or more programs; When one or more programs are executed by the one or more processors, the one or more processors implement the UAV swarm collaboration method based on communication state adaptation as described in any one of claims 1-8.

10. A computer-readable storage medium, characterized in that, It stores executable instructions that, when executed by a processor, enable the processor to implement the UAV swarm collaboration method based on communication state adaptation as described in any one of claims 1-8.

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