Unmanned aerial vehicle cluster collaborative search method based on adaptive division

Abstract

The invention discloses an unmanned aerial vehicle cluster collaborative search method based on adaptive division, and the method comprises the following steps: 1, evaluating the performance of an unmanned aerial vehicle: evaluating the comprehensive performance of the unmanned aerial vehicle according to the index of the unmanned aerial vehicle, and obtaining a comprehensive performance index; step 2, task area distribution: distributing unmanned aerial vehicle task areas according to the comprehensive performance indexes; step 3, initial region division: a Voronoi graph algorithm is adopted to divide a task region; and 4, a dynamic adjustment mechanism: monitoring the state of the unmanned aerial vehicle in real time and re-dividing a task area by using a dynamic adaptive division mechanism. According to the method, the limitation of a traditional region division method in a dynamic complex environment is solved, the self-adaptive division and real-time adjustment of the search region are realized, the search efficiency is improved, the resource configuration is optimized, and the comprehensive completion of tasks is ensured.

Description

Technical Field

[0001] The present invention belongs to the technical field of drone clusters, and in particular relates to a drone cluster collaborative search method based on adaptive partitioning. Background Art

[0002] In recent years, UAV technology has developed rapidly and is widely used in military reconnaissance, disaster relief, environmental monitoring and other fields. However, due to the limitations of payload, endurance, sensor capabilities, etc., a single UAV has great limitations when facing wide-area search tasks. For example, when the mission area is wide, the terrain is complex, or the target distribution is irregular, the operating capability of a single UAV cannot complete the mission efficiently. Therefore, in order to improve the efficiency of mission execution, cluster technology in which multiple UAVs work together has gradually become a research focus.

[0003] Drone swarm technology can significantly enhance the intelligence and mission execution capabilities of the system through the collaborative cooperation of multiple drones, thereby improving the overall system efficiency. The performance of this group behavior in complex environments far exceeds the performance of a single drone. Drone swarm search can effectively cover a wide area, detect and report key information in the mission area in real time, and significantly reduce uncertainty in the environment.

[0004] A key issue in the search mission of drone swarm technology is how to reasonably divide the task area and evenly distribute the tasks to different drones. In existing research, common area division methods include grid division and Voronoi diagram division. Although these geometric division methods can effectively divide the task area in simple scenarios, they still have certain limitations in complex environments.

[0005] The grid division method divides the mission area into several regular rectangular or hexagonal grids, and each grid is assigned to a single UAV as a sub-mission area. This division method is simple and intuitive, with a small amount of calculation, but it also has obvious shortcomings:

[0006] (1) Fixed division leads to resource waste: The static nature of grid division makes it difficult to adapt to complex areas or irregularly shaped mission areas. In some scenarios with complex terrain or uneven target distribution, the use of regular grid division may cause some drones to work too hard, while other drones have easy tasks, and resources cannot be fully utilized;

[0007] (2) Inability to make adaptive adjustments: Since the grid division is a static division based on the initial geometric features, it cannot be adjusted in real time according to the drone status or mission progress; therefore, when the status of some drones changes, such as when the battery power drops or the perception ability weakens, the system cannot respond in time, resulting in a decrease in mission execution efficiency.

[0008] The Voronoi diagram partitioning method generates multiple Voronoi diagram polygons based on the initial position of the drone, and each polygon represents the mission area of ​​a drone. This method is relatively reasonable in the initial allocation of drones, but there are still some problems:

[0009] (1) Limitations of static partitioning: Voronoi diagram partitioning depends on the initial deployment position of the UAV and cannot be adjusted dynamically during the mission execution. This means that when the state of the UAV or the distribution of targets in the mission area changes, the Voronoi diagram partitioning cannot respond flexibly, which may cause some UAVs to have too heavy a mission load while other UAVs have a lighter mission load;

[0010] (2) Poor adaptability in complex environments: When faced with environments with complex terrain, irregular mission areas, or uneven target distribution, the Voronoi diagram division is difficult to accurately reflect the complexity of the task and the actual capabilities of the UAV, resulting in reduced search efficiency.

[0011] In summary, the current task area division algorithm has problems such as unbalanced task load and insufficient fault handling capabilities. In the actual task execution process, with the change of drone status, such as battery exhaustion, sensor failure, etc., or the dynamic change of targets in the task area, the overall search efficiency of the drone cluster will be greatly affected. Summary of the invention

[0012] The purpose of the present invention is to provide a UAV cluster collaborative search method based on adaptive division, which solves the limitations of traditional area division methods in dynamic and complex environments, realizes adaptive division and real-time adjustment of search areas, improves search efficiency, optimizes resource allocation, and ensures the comprehensive completion of tasks.

[0013] In order to achieve the purpose of the present invention, the present invention provides a UAV cluster collaborative search method based on adaptive partitioning, comprising the following steps:

[0014] Step 1, UAV performance evaluation: evaluating the comprehensive performance of the UAV according to the indicators of the UAV to obtain comprehensive performance indicators;

[0015] Step 2, task area allocation: allocating the UAV task area according to the comprehensive performance index;

[0016] Step 3: Initial area division: Use the Voronoi diagram algorithm to divide the task area;

[0017] 3-1. Use the initial position of the drone as the seed point of the Voronoi diagram;

[0018] 3-2. Using a weighted Voronoi diagram, the seed points are assigned weights;

[0019] 3-3. According to the area of ​​the region , combined with the weight factor of the weighted Voronoi diagram, adjust the boundaries of the sub-regions in the diagram to obtain the new sub-region area ;

[0020] 3-4. Optimize the boundary according to the performance of the UAV and the terrain characteristics of the mission area.

[0021] Step 4: Dynamic adjustment mechanism: Use the dynamic adaptive partitioning mechanism to monitor the status of the drone in real time and redivide the mission area;

[0022] 4-1. Data collection: When the drone is performing a mission, it collects drone status data and environmental dynamic change data in real time through sensors and communication networks; the data is aggregated to the mission control center through the network, and the control center analyzes and dynamically adjusts the search area;

[0023] 4-2. Performance update: When the UAV is performing a mission, the state of the UAV will change. By monitoring the dynamic parameters of the UAV and updating the comprehensive performance index according to the parameters, an updated comprehensive performance index is obtained;

[0024] 4-3. Area redivision: Recalculate the area allocation of the task area based on the updated comprehensive performance indicators;

[0025] 4-4. Communication and collaboration: The drone cluster conducts real-time collaboration through the wireless communication network. The new area division information is transmitted to the drones through the wireless communication protocol. Each drone completes the collaborative work based on the real-time communication data.

[0026] Compared with the prior art, the significant progress of the present invention lies in: (1) the present invention improves the Voronoi diagram algorithm to ensure that the area of ​​each sub-region is proportional to the performance of the UAV, and can optimize the shape of the region according to the performance and position of the UAV, so as to ensure that the area allocated to the UAV is more reasonable; (2) through dynamic adaptive partitioning, the search area can be dynamically adjusted according to the real-time performance of the UAV and environmental changes, ensuring that each UAV can perform the task within its optimal performance range, thereby improving the overall search efficiency and resource allocation; (3) through real-time monitoring and dynamic adjustment, the system can quickly respond to environmental changes and fluctuations in UAV performance, improve the system's adaptability and robustness in complex dynamic environments, and ensure the smooth completion of the task.

[0027] In order to more clearly illustrate the functional characteristics and structural parameters of the present invention, further description is given below in conjunction with the accompanying drawings and specific implementation methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0029] Figure 1 is a flow chart of the steps of the present invention;

[0030] Figure 2 (a), (b), and (c) are schematic diagrams of the region update process of the dynamic adjustment mechanism of the present invention;

[0031] Figure 3 It is a simulation result diagram of the present invention. DETAILED DESCRIPTION

[0032] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments; based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0033] The present invention is a UAV cluster collaborative search method based on adaptive partitioning, combined with Figure 1 , including the following steps:

[0034] Step 1: UAV performance evaluation: Evaluate the comprehensive performance of the UAV according to the UAV's indicators to obtain comprehensive performance indicators. ;

[0035] Drones Flight speed (km / h) Battery life (hours) Sensor performance (points) UAV1 60 2 80 UAV2 50 3 70 UAV3 55 2.5 75

[0036] The comprehensive performance index The calculation formula is as follows:

[0037] ;

[0038] in, , , is the weight of each parameter, For drones The maximum flight speed, For drones How long can the flight last when performing a search mission? To reflect the drone Sensor performance in terms of detection range, accuracy, and sensitivity;

[0039] The weight value is dynamically set according to task requirements to ensure the accuracy of the evaluation results;

[0040] when , , When the values ​​are 0.4, 0.3, and 0.4 respectively, the comprehensive performance indicators of each drone are The calculation is as follows:

[0041] ;

[0042] ;

[0043] .

[0044] Step 2: Task area allocation: Combine Figure 2 (a), according to the comprehensive performance indicators , allocate the UAV mission area to ensure that the UAV with higher performance undertakes more tasks;

[0045] The area of ​​the UAV mission area :

[0046] ;

[0047] in, For drones The search area area, is the total mission area, n is the total number of drones, For the The formula ensures that high-performance drones are assigned a larger search area to fully utilize their capabilities;

[0048] Assuming the total mission area square kilometers;

[0049] The overall performance indicators are:

[0050] ;

[0051] The area allocated to each drone is:

[0052] ;

[0053] ;

[0054] .

[0055] Step 3: Initial area division: The Voronoi diagram algorithm is used to divide the mission area. The improved Voronoi diagram algorithm can not only ensure that the area of ​​each sub-area is proportional to the performance of the UAV, but also optimize the area shape according to the performance and position of the UAV.

[0056] 3-1. Use the initial position of the drone as the seed point of the Voronoi diagram. The Voronoi diagram (VoronoiDiagram), also known as Voronoi partition or Thiessen Polygons, is a method of dividing a plane into multiple regions. Determine the initial position of each drone, which will be used as the seed point of the Voronoi diagram. These seed points not only determine the responsibility area of ​​each drone, but are also crucial to the subsequent regional division and optimization. In actual operation, these initial position data can be obtained through the GPS positioning system of the drone.

[0057] 3-2. Use weighted Voronoi diagram. The weighted Voronoi diagram is a graph partitioning method that introduces weight factors based on the traditional Voronoi diagram. Each seed point, that is, the position of the drone, is assigned a weight. The adjustment of the weight will affect the boundaries of each sub-region in the Voronoi diagram, thereby achieving control of the area of ​​the region. For each seed point Assign a weight , the partitioning rule of the weighted Voronoi diagram is:

[0058] ;

[0059] in, For the sub-regions, From point x to the seed point The Euclidean distance of For the The weight of the seed point;

[0060] 3-3. According to the area of ​​the region , combined with the weight factor of the weighted Voronoi diagram, adjust the boundaries of the sub-regions in the diagram to obtain the new sub-region area :

[0061] ;

[0062] in, is the area of ​​the region based on the comprehensive performance index, is the weight factor of UAV i;

[0063] During the iterative adjustment process, the following formula is used to approximate the theoretical area:

[0064] ;

[0065] in, The current drone The area of ​​the Voronoi region, is an adjustment factor used to control the adjustment amplitude of each iteration;

[0066] For UAV1, the area on the left side covers approximately 107.2 square kilometers, and the shape is adjusted to be close to a rectangle to adapt to the terrain.

[0067] For UAV2: Cover an area of ​​approximately 92.7 square kilometers in the middle, and expand appropriately to the north and south to cover more terrain features.

[0068] For UAV3: Covers approximately 99.7 square kilometers on the right, with the shape adjusted to accommodate borders and terrain changes.

[0069] 3-4. According to the performance of the UAV and the terrain characteristics of the mission area, the boundary is optimized to match the actual terrain. By adjusting the shape of the area, the path loss caused by the irregular boundary is reduced to ensure that the UAV can cover the target area with a shorter flight distance.

[0070] Step 4: Dynamic adjustment mechanism: Use the dynamic adaptive partitioning mechanism to monitor the status of the drone in real time and redivide the task area to maximize the search efficiency. The core of this mechanism is to optimize the task division in real time according to the dynamic parameters of the drone (such as speed, energy consumption, etc.) and the progress of the task during the task execution;

[0071] 4-1. Data collection: When the drone is performing a mission, it collects drone status data and environmental dynamic change data in real time through sensors and communication networks; the data is aggregated to the mission control center through the communication network, and the control center analyzes and dynamically adjusts the search area;

[0072] The drone status data includes the current position, flight speed, remaining energy, and sensor status information; the environment dynamic change data includes the movement of the target position, terrain changes, and weather condition information;

[0073] Assume that after 30 minutes of task execution, the data is updated as follows:

[0074] Drones Current location (km) Flight speed (km / h) Remaining energy (hours) Sensor status (points) UAV1 (10, 5) 55 1.5 75 UAV2 (15, 7) 45 2.5 65 UAV3 (25, 4) 50 2.2 70

[0075] 4-2. Performance update: When a drone is performing a task, its status will change, such as reduced power, reduced speed, etc., which will affect its comprehensive performance. By monitoring the dynamic parameters of the drone and updating the comprehensive performance indicators according to the parameters, it is ensured that the performance changes can be reflected in the division of the search task in a timely manner;

[0076] Updated comprehensive performance indicators The calculation formula is as follows:

[0077] ;

[0078] in, , , is the updated drone status parameter;

[0079] Also according to the initial weights assigned in step 1, , , Take 0.4, 0.3, 0.3 respectively

[0080] The updated performance indicators are as follows:

[0081] ;

[0082] ;

[0083] ;

[0084] 4-3. Regional redivision: based on updated comprehensive performance indicators , recalculate the area allocation of the task area, combined with Figure 2 (b) Ensure that the new classification reflects the current performance and mission requirements of UAVs;

[0085] 4-4. Communication and collaboration: integration Figure 2 (c) The drone cluster collaborates in real time through a wireless communication network, and transmits new area division information to the drones through wireless communication protocols, ensuring that each drone can respond quickly to task adjustments. Each drone completes collaborative work based on real-time communication data to avoid repeated searches and missed targets.

[0086] The area division information is transmitted to each drone through the wireless communication protocol to ensure that each drone can respond quickly to the mission adjustment. The drone sends the new area division information to UAV1, UAV2 and UAV3 through the wireless communication protocol. Each message contains: the unique identifier of the drone and the coordinates of the newly assigned search area boundary. After receiving the new search area information, UAV1 adjusts its flight path to cover the newly assigned left side. Area, UAV2 and UAV3 also adjust their flight paths to cover their new area allocations.

[0087] Combination Figure 3According to the above-mentioned UAV equipment and related terrain information, an improved weighted Voronoi diagram algorithm can be used to obtain a search area affected by multiple performance factors such as the UAV flight speed and endurance time, and it can ensure that the boundary of the area is optimized to a certain extent, reducing the overall UAV flight distance. UAVs can collaborate to complete the search task of the entire area.

[0088] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0089] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A UAV cluster collaborative search method based on adaptive partitioning, characterized in that: The following steps are involved: Step 1: UAV performance evaluation: Evaluate the comprehensive performance of the UAV according to the UAV's indicators to obtain comprehensive performance indicators. ; Step 2: Task area allocation: Based on the comprehensive performance indicators , allocate the area of ​​the UAV mission zone; Step 3: Initial area division: Use the Voronoi diagram algorithm to divide the task area; Step 4: Dynamic adjustment mechanism: Use the dynamic adaptive partitioning mechanism to monitor the drone status in real time and redivide the mission area.

2. The method for cooperative search of drone clusters based on adaptive partitioning according to claim 1 is characterized in that: The comprehensive performance index of step 1 The formula is as follows: ； in, , , is the weight of each parameter, For drones The maximum flight speed, For drones How long can the flight last when performing a search mission? To reflect the drone Sensor performance in terms of detection range, accuracy, and sensitivity; The weights are dynamically set according to task requirements.

3. The method for cooperative search of drone clusters based on adaptive partitioning according to claim 2 is characterized in that: The area of ​​the drone mission area in step 2 : ； in, For drones The search area area, is the total mission area, n is the total number of drones, For the The comprehensive performance of the UAV.

4. The method for cooperative search of drone clusters based on adaptive partitioning according to claim 3 is characterized in that: The step 3 is specifically as follows: 3-1. Use the initial position of the drone as the seed point of the Voronoi diagram; 3-2. Using a weighted Voronoi diagram, the seed points are assigned weights; 3-3. According to the area of ​​the region , combined with the weight factor of the weighted Voronoi diagram, adjust the boundaries of the sub-regions in the diagram to obtain the new sub-region area ; 3-4. Optimize the boundary according to the performance of the UAV and the terrain characteristics of the mission area.

5. The method for cooperative search of drone clusters based on adaptive partitioning according to claim 4 is characterized in that: The weighted Voronoi diagram in step 3-2 is a graph partitioning method that introduces weight factors based on the Voronoi diagram: ； in, For the sub-regions, From point x to the seed point The Euclidean distance of For the The weight of the seed point.

6. The method for cooperative search of drone clusters based on adaptive partitioning according to claim 5 is characterized in that: The new sub-region area in step 3-3 : ； in, is the area of ​​the region based on the comprehensive performance index, is the weight factor of UAV i; In the iterative adjustment process, the sub-region is made close to the new sub-region area. : ； in, For current drones The area of ​​the Voronoi region, is the adjustment factor, which is used to control the adjustment amplitude of each iteration.

7. The method for cooperative search of drone clusters based on adaptive partitioning according to claim 1 is characterized in that: The step 4 is specifically as follows: 4-1. Data collection: When the drone is performing a mission, it collects drone status data and environmental dynamic change data in real time through sensors and communication networks; the data is aggregated to the mission control center through the network, and the control center analyzes and dynamically adjusts the search area; 4-2. Performance update: When the UAV is performing a mission, the state of the UAV will change. By monitoring the dynamic parameters of the UAV and updating the comprehensive performance index according to the parameters, the updated comprehensive performance index is obtained. ; 4-3. Regional redivision: based on updated comprehensive performance indicators , recalculate the area allocation of the task area; 4-4. Communication and collaboration: The drone cluster conducts real-time collaboration through the wireless communication network. The new area division information is transmitted to the drones through the wireless communication protocol. Each drone completes the collaborative work based on the real-time communication data.

8. The method for cooperative search of drone clusters based on adaptive partitioning according to claim 5 is characterized in that: The drone status data of step 4-1 is the current position, flight speed, remaining energy, and sensor status information; the dynamic environment change data is the movement of the target position, terrain changes, and weather condition information.

9. The method for cooperative search of drone clusters based on adaptive partitioning according to claim 5 is characterized in that: The updated comprehensive performance index in step 4-2 The formula is as follows: ； in, , , is the updated drone state parameter, , , is the weight of each parameter.

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