Micro unmanned equipment cluster control method and system based on swarm intelligence

Abstract

The present invention relates to the technical field of attitude control of aerial drones, and more specifically to a swarm intelligence-based control method and system for a micro-unmanned device cluster. The method comprises: during the execution of a mission by a micro-drone cluster, based on each drone's actual position, set position, frontal area, ambient air density, and speed and wind force in the longitude, latitude, and altitude directions, determining the external environmental interference resistance, flight trajectory complexity, and actual flight trajectory fluctuation of each drone at the current moment, thereby determining the external environmental interference response of each drone at the current moment, and thus determining the threat radius of each drone at the current moment, so as to obtain control instructions for each drone at the next moment after the current moment. The present invention improves the control effect of a micro-drone cluster by analyzing the drone's real-time threat radius as a control parameter.

Description

Technical Field

[0001] The present invention relates to the technical field of attitude control of aerial unmanned aerial vehicles (UAVs), and in particular to a cluster control method and system for micro-unmanned equipment based on swarm intelligence. Background Art

[0002] With the advancement of artificial intelligence, communications technologies (such as 5G), and low-power sensors, swarm intelligence systems have significantly improved their ability to achieve efficient collaboration among multiple devices, make autonomous decisions, and flexibly adapt to complex environments. Currently, this technology has been applied in fields such as disaster relief, environmental monitoring, logistics distribution, and agricultural inspections, demonstrating strong task execution capabilities and scalability, driving the development of unmanned intelligent systems.

[0003] In the field of swarm intelligence research, micro-UAV swarms face numerous challenges when performing missions. Because their constituent micro-devices are typically small and energy-efficient, hardware limitations lead to poor attitude stability during mission execution in complex environments. Consequently, the accuracy of swarm control can be negatively impacted. During mission execution, the relative positions of UAVs fluctuate significantly. Close proximity can lead to collisions when the environment changes or their trajectories require unified adjustments. This reduces mission reliability and can result in poor control of the micro-UAV swarm. Summary of the Invention

[0004] The present invention provides a micro unmanned equipment cluster control method and system based on swarm intelligence to solve existing problems.

[0005] The present invention's swarm intelligence-based micro-unmanned equipment cluster control method and system adopts the following technical solutions:

[0006] One embodiment of the present invention provides a method for controlling a cluster of micro unmanned devices based on swarm intelligence, the method comprising the following steps:

[0007] When the micro-UAV cluster is performing a mission, the actual position, set position, windward area, air density of the environment, and speed and wind force in the longitude, latitude and altitude directions of each UAV at each moment are obtained;

[0008] Determine the external environment interference resistance of each drone at the current moment based on its frontal area, air density, speed, and wind speed in the longitude, latitude, and altitude directions at each moment.

[0009] Determine the complexity of each UAV's flight trajectory at the current moment based on the speed difference of each UAV in the same direction at adjacent moments;

[0010] Determine the degree of fluctuation of the actual flight trajectory of each drone at the current moment based on the distance between the actual position of each drone at each moment and the set position;

[0011] Determine the external environment interference response level of each UAV at the current moment based on the external environment interference resistance, flight trajectory complexity, and actual flight trajectory fluctuation level of each UAV;

[0012] The threat radius of each UAV at the current moment is determined based on the complexity of its flight trajectory, the degree of fluctuation of its actual flight trajectory, and the degree of response to external environmental interference. The threat radius of each UAV at the current moment is used as the input of the PID controller, and the control instructions for each UAV at the next moment at the current moment are output.

[0013] Furthermore, the determination of the external environment interference resistance of each UAV at the current moment includes the following specific steps:

[0014] Calculate the The first drone The square of the speed in the longitude direction at the moment, the preset air resistance coefficient, the The drone in Half of the product of the air density of the environment at the moment and the windward area is used as the first The first drone Wind resistance value in the longitude direction at all times;

[0015] The first The first drone The sum of the wind force in the longitude direction at the moment and the wind resistance value is taken as the The first drone The initial external environment interference resistance in the longitude direction at the moment;

[0016] Preset time threshold , record the current moment as moment, the first Time to The time period of the moment is recorded as the monitoring period of the current moment;

[0017] According to the monitoring period at the current moment The initial external environment interference resistance of each UAV at all times and in all directions is determined by The external environment interference resistance of each UAV at the current moment.

[0018] Furthermore, the determination The specific steps of the external environment interference resistance of each drone at the current moment are as follows:

[0019] During the current monitoring period, calculate the The average value of the initial external environment interference resistance of each drone in the longitude direction at all times, and then calculate the The variance of the initial external environment interference resistance of the UAV in the longitude direction at all times is recorded as the normalized value of the product of the mean value of the initial external environment interference resistance and the variance. The external environment interference resistance of each UAV in the longitude direction at the current moment;

[0020] According to the The method for obtaining the external environment interference resistance of each drone at the current moment in the longitude direction is to obtain the The external environment interference resistance of the UAV at the current moment in the latitude direction and the The external environment interference resistance of each UAV in the altitude direction at the current moment;

[0021] The first The average value of the external environment interference resistance of each drone at the current moment in the longitude, latitude and altitude direction is taken as the first The external environment interference resistance of each UAV at the current moment.

[0022] Furthermore, the determination of the complexity of the flight trajectory of each UAV at the current moment includes the following specific steps:

[0023] Preset time threshold , record the current moment as moment, the first Time to The time period of the moment is recorded as the monitoring period of the current moment;

[0024] During the current monitoring period, The average of the absolute values ​​of the differences in the velocities of the UAVs at all adjacent moments in the longitude direction is taken as the The speed change of each drone in the longitude direction at the current moment;

[0025] According to The speed change of each drone in all directions at the current moment is used to determine the The magnitude of the change in the flight trajectory of each drone at the current moment;

[0026] According to the The method for obtaining the flight trajectory change amplitude of each drone at the current moment is to obtain the The magnitude of the flight trajectory change of each drone at each moment;

[0027] The first The average value of the flight trajectory change of the UAV at all moments in the monitoring period at the current moment is taken as the The complexity of the flight trajectory of each UAV at the current moment.

[0028] Furthermore, the determination The flight trajectory change amplitude of each drone at the current moment includes the following specific steps:

[0029] According to the The method for obtaining the speed change amplitude of each drone in the longitude direction at the current moment is to obtain the The speed change amplitude of each drone in the latitude direction at the current moment and the The speed change of each drone in the altitude direction at the current moment;

[0030] The first The normalized value of the average speed change amplitude of each drone in the longitude, latitude and altitude directions at the current moment is used as the first The flight trajectory change of each UAV at the current moment.

[0031] Furthermore, the determination of the degree of fluctuation of the actual flight trajectory of each UAV at the current moment includes the following specific steps:

[0032] Preset time threshold , and record the current moment as moment, the first Time to The time period of the moment is recorded as the monitoring period of the current moment;

[0033] During the current monitoring period, calculate the The Euclidean distance between the actual position of each drone and the set position at each moment is taken as the The degree of deviation of the flight trajectory of each drone at each moment will be The normalized value of the mean deviation of the flight trajectory of each drone at all times is used as the The actual flight trajectory fluctuation degree of each drone at the current moment.

[0034] Furthermore, the specific steps of determining the external environment interference response level of each UAV at the current moment include the following:

[0035] The first The average value of the external environment interference resistance and flight trajectory complexity of each UAV at the current moment is taken as the The orbit interference index of each UAV at the current moment;

[0036] According to The orbit interference index of each drone at the current moment and the degree of fluctuation of the actual flight trajectory are used to determine the The external environment interference response degree of each drone at the current moment.

[0037] Furthermore, the determination The specific steps involved in determining the external environment interference response level of each drone at the current moment are as follows:

[0038] The first The normalized value of the difference between the orbit interference index of the UAV at the current moment and the actual flight trajectory fluctuation degree is used as the first The external environment interference response degree of each drone at the current moment.

[0039] Furthermore, the determination of the threat radius of each drone at the current moment includes the following specific steps:

[0040] Calculate the The ratio of the flight trajectory change amplitude of each UAV at the current moment to the flight trajectory complexity is used as the first ratio;

[0041] Calculate the first ratio, the The product of the actual flight trajectory fluctuation degree of each drone at the current moment and the external environment interference response degree is used as the The threat radius of each drone at the current moment.

[0042] The present invention also proposes a micro-unmanned equipment cluster control system based on swarm intelligence, including a memory, a processor, and a computer program stored in the memory and runnable on the processor. The processor executes the computer program stored in the memory to implement the steps of the aforementioned micro-unmanned equipment cluster control method based on swarm intelligence.

[0043] The beneficial effects of the technical solution of the present invention are:

[0044] In an embodiment of the present invention, during a micro-UAV swarm's mission execution, the external environmental interference resistance, flight trajectory complexity, and actual flight trajectory fluctuation of each UAV at the current moment are determined based on each UAV's frontal area, ambient air density, and speed and wind force in the longitude, latitude, and altitude directions at each moment. This analysis uses multi-dimensional data from the UAVs during flight to analyze external environmental interference, flight speed, and trajectory anomalies, thereby ensuring the accuracy of subsequent threat radius calculations. The external environmental interference response level of each UAV at the current moment is then obtained to further determine the external environmental interference on the UAV at the current moment, thereby determining the threat radius of each UAV at the current moment. This analysis of the external environment and the UAVs' own flight data yields an accurate and reliable threat radius, which serves as a supplementary control parameter to ensure the accuracy of PID control and thereby output accurate and reliable control instructions. Thus, the present invention analyzes the UAVs' real-time threat radius as a control parameter to adjust the UAV spacing during flight, ensuring stability and safety in complex environments and improving the control effectiveness of the micro-UAV swarm. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0046] Figure 1 This is a flowchart of the steps of the micro unmanned equipment cluster control method based on swarm intelligence of the present invention;

[0047] Figure 2 Schematic diagram of a micro-UAV cluster. DETAILED DESCRIPTION

[0048] To further illustrate the technical means and effectiveness of the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, describes in detail the specific implementation, structure, features, and effectiveness of the swarm intelligence-based micro-unmanned equipment cluster control method and system proposed by the present invention. In the following description, different references to "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.

[0049] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0050] The specific scheme of the micro unmanned equipment cluster control method and system based on swarm intelligence provided by the present invention is described in detail below with reference to the accompanying drawings.

[0051] See also Figure 1 , which shows a flowchart of a method for controlling a cluster of micro-unmanned devices based on swarm intelligence according to an embodiment of the present invention, the method comprising the following steps:

[0052] Step S001: When the micro-UAV cluster is performing a mission, the actual position, set position, windward area, air density of the environment, and speed and wind force of each UAV in the longitude, latitude and altitude directions are obtained at each moment.

[0053] The actual position, set position, windward area, air density of the environment, speed in the longitude direction, speed in the latitude direction, speed in the altitude direction, wind force in the longitude direction, wind force in the latitude direction and wind force in the altitude direction of each drone at each moment during the execution of the mission by the micro-UAV cluster are collected.

[0054] It should be noted that the actual and set positions include longitude, latitude, and altitude. The actual position is collected using the Global Positioning System (GPS). During a UAV swarm mission, the position of each drone at each moment is typically pre-determined. Computer simulations and programming tools are typically used to plan the flight trajectory of the drones, which provides the set position of each drone at each moment. The frontal area is calculated by using computational fluid dynamics (CFD) software to simulate the flow of the drones in the wind based on the drones' real-time flight data (flight speed, altitude, flight attitude, wind speed, and direction). The air density of the surrounding air is calculated using the ideal gas state equation combined with real-time temperature, pressure, and humidity data. For velocity collection in longitude, latitude, and altitude, for example, velocity in altitude is calculated as the difference in altitude between adjacent moments divided by the time interval between those moments. For wind force collection in longitude, latitude, and altitude, wind speed and direction data are converted into wind force components in the longitude, latitude, and altitude directions through wind force decomposition. The data that can be directly obtained are collected by integrated sensors, and the calculation process of the data that needs to be obtained by calculation is a well-known technology, and the specific method will not be introduced here. Figure 2 shown.

[0055] It is further important to note that in the process of controlling and monitoring drone swarms, acquiring equipment monitoring data and constructing a swarm network topology are key foundations for achieving swarm intelligence, autonomous navigation, and mission execution. During mission execution, these micro-drones collaborate with each other, leverage data sharing between multiple drones, and continuously adjust their posture and motion patterns based on their relative position information to better adapt to environmental changes. To improve the reliability of micro-drone swarms in complex environments, it is necessary to assess the degree of external interference experienced by any drone in the swarm. By combining the intensity of external interference with the complexity of the drone's own flight trajectory, the trajectory interference status of an individual micro-drone can be determined. Furthermore, the impact of external fluctuations on individual micro-drones in the swarm can be assessed based on the actual flight trajectory fluctuations of the drones over time.

[0056] Step S002: Determine the external environment interference resistance of each UAV at the current moment based on the windward area of ​​each UAV at each moment, the air density of the environment, and the speed and wind force in the longitude, latitude and altitude directions.

[0057] When a micro-UAV swarm performs a mission, the degree of interference experienced by each drone varies due to variations in the external environment and the drone's location. This level of interference can be analyzed by monitoring changes in wind strength and direction during flight. The drone must then promptly compensate for these changes by performing reverse motion. This involves calculating the current level of interference based on the observed changes in the external wind field over a period of time.

[0058] The external interference that a drone encounters during flight is mainly caused by two factors: one is the resistance to its trajectory caused by the corresponding ambient wind speed, and the other is the wind resistance value encountered during flight. These two factors work together to form the external environmental interference that the drone encounters during flight.

[0059] The preset air resistance coefficient is 0.5, and this is used as an example for description.

[0060] In the micro-UAV cluster, Take the drone as an example, calculate the The first drone The square of the speed in the longitude direction at the moment, the preset air resistance coefficient, the The drone in The air density of the environment at the moment and the The drone in Half of the product of the frontal areas at the moment The first drone The wind resistance value in the longitude direction at all times.

[0061] The first The first drone The wind speed at the time of the longitude is The first drone The sum of the wind resistance values ​​in the longitude direction at the moment is taken as the The first drone The initial external environment interference resistance in the longitude direction at the moment.

[0062] What needs to be explained is: The first drone The wind resistance value in the longitude direction at this moment is ,in, is the preset air resistance coefficient, For the The first drone The speed in the longitude direction at any moment, For the The drone in The air density of the environment at any moment, For the The drone in The calculation formula of the windward area at the moment and the wind resistance value are well known technologies.

[0063] Preset time threshold The value is 20, and this is used as an example for description.

[0064] Record the current moment as moment, the first Time to The time period at the moment is recorded as the monitoring period of the current moment.

[0065] According to the above method, the monitoring period at each moment can be obtained.

[0066] What needs to be explained is: Less than or equal to When the first The monitoring period corresponding to the moment is not analyzed subsequently. This is because in the initial stage of the drone cluster's takeoff, the drone cluster will be in a relatively stable environment and will be less affected by environmental interference.

[0067] As for the degree of external interference to the micro-UAV in a single position, the external interference resistance it experiences during the monitoring period and the changes in its external resistance are analyzed. When the external resistance it experiences during the monitoring period is generally small and the corresponding resistance change is low, the corresponding external interference degree is relatively weak.

[0068] During the current monitoring period, calculate the The average value of the initial external environment interference resistance of each drone in the longitude direction at all times, and then calculate the The variance of the initial external environment interference resistance of the UAV in the longitude direction at all times is calculated, and the normalized value of the product of the mean and the variance is recorded as The external environment interference resistance of each drone in the longitude direction at the current moment.

[0069] Among them, the normalized value of the product, this embodiment uses A linear normalization function is used to normalize the product to between 0 and 1.

[0070] According to the above method, obtain the The external environment interference resistance of the UAV at the current moment in the latitude direction and the The external environment interference resistance of each UAV in the altitude direction at the current moment.

[0071] The first The average value of the external environment interference resistance of each drone at the current moment in the longitude, latitude and altitude direction is taken as the first The external environment interference resistance of each UAV at the current moment.

[0072] Step S003: Determine the complexity of the flight trajectory of each UAV at the current moment based on the speed difference of each UAV in the same direction at adjacent moments.

[0073] The above process analyzes the external interference level of a single drone in a micro-UAV swarm, capturing the external interference situation during the monitoring period. The interference experienced by a drone over a period of time not only affects the external environment but also its own flight trajectory, significantly influencing the degree of trajectory deviation during actual flight.

[0074] When evaluating the complexity of a flight trajectory, the trajectory is also broken down into different directions for analysis. The complexity of a flight trajectory is assessed by analyzing the degree of speed variation in a single direction over consecutive moments. If the speed in a single direction maintains a high degree of variation over consecutive moments, the UAV's flight trajectory is considered highly complex.

[0075] During the current monitoring period, calculate the The absolute value of the difference in the speed of the UAV in the longitude direction at any two adjacent moments is The average of the absolute values ​​of the differences in the velocities of the UAVs at all adjacent moments in the longitude direction is taken as the The speed change of each drone in the longitude direction at the current moment.

[0076] According to the above method, obtain the The speed change amplitude of each drone in the latitude direction at the current moment and the The speed change of each drone in the height direction at the current moment.

[0077] The first The normalized value of the average speed change amplitude of each drone in the longitude, latitude and altitude directions at the current moment is used as the first The flight trajectory change of each UAV at the current moment.

[0078] Among them, the normalized value of the mean value of the speed change amplitude is used in this embodiment. A linear normalization function is used to normalize the mean value of the speed variation to between 0 and 1.

[0079] According to the above method, obtain the The flight trajectory change of each UAV at each moment.

[0080] During the current monitoring period, The average value of the flight trajectory change amplitude of the UAV at all times is taken as the The complexity of the flight trajectory of each UAV at the current moment.

[0081] It should be noted that the flight trajectory change amplitude at consecutive moments is averaged. The larger the average value, the greater the degree of change in the current UAV flight trajectory and the higher the complexity.

[0082] Step S004: Determine the degree of fluctuation of the actual flight trajectory of each UAV at the current moment based on the distance between the actual position of each UAV at each moment and the set position.

[0083] For a single micro-UAV, its flight trajectory is complex and is also affected by external environmental interference during flight. The degree of trajectory deviation of UAVs in different positions varies. Therefore, the current real-time position of the UAV is compared with the flight trajectory of the mission planning to determine the actual flight trajectory fluctuation of the current UAV.

[0084] During the current monitoring period, calculate the The Euclidean distance between the actual position of each drone and the set position at each moment is taken as the The degree of deviation of the flight trajectory of each drone at each moment will be The normalized value of the mean deviation of the flight trajectory of each drone at all times is used as the The actual flight trajectory fluctuation degree of each drone at the current moment.

[0085] Among them, the normalized value of the mean value of the flight trajectory deviation degree is used in this embodiment. A linear normalization function is used to normalize the mean value of the flight trajectory deviation to between 0 and 1.

[0086] It should be noted that the greater the deviation of the flight trajectory at each moment, the further the drone deviates from the set route, that is, the greater the fluctuation of the actual flight trajectory.

[0087] Step S005: Determine the external environment interference response degree of each UAV at the current moment based on the external environment interference resistance, flight trajectory complexity, and actual flight trajectory fluctuation degree of each UAV at the current moment.

[0088] In a swarm, individual micro-UAVs in different locations experience varying degrees of external interference during flight due to their diverse environments. Their response to interference also varies depending on their location, leading to varying degrees of fluctuation in their flight trajectories. To quantify this impact, we can comprehensively consider the degree of external interference encountered by a single UAV and the complexity of its flight trajectory to determine its trajectory interference index. By comparing the trajectory interference index with actual node trajectory fluctuations, we can assess the response of individual nodes to external interference.

[0089] During a drone swarm mission, drones in different locations exhibit significant differences in the fluctuations in their flight paths. Drones in certain locations may face more severe external interference, causing their flight paths to deviate significantly. To address this issue, we need to combine the aforementioned analysis results to determine the trajectory interference index for a single drone during flight.

[0090] The first The external environment interference resistance of each UAV at the current moment is The mean of the complexity of the flight trajectory of the UAVs at the current moment is taken as the The orbit interference index of each UAV at the current moment.

[0091] The first The orbit interference index of the UAV at the current moment minus the The normalized value of the difference in the actual flight trajectory fluctuation degree of each drone at the current moment is used as the first The external environment interference response degree of each drone at the current moment.

[0092] Among them, the normalized value of the difference, this embodiment uses A linear normalization function is used to normalize the mean of the difference to between 0 and 1.

[0093] What needs to be explained is that the degree of external environment interference response of the current UAV is measured by obtaining the difference between the orbit interference index and the actual flight trajectory fluctuation degree. For a UAV, when it is subject to a large degree of external interference, if its flight trajectory deviates to a large extent, it is considered that the UAV responds relatively quickly. When its flight trajectory is subject to large external interference, it still maintains a relatively stable posture, then the UAV is considered to have a weak external interference response and a strong anti-interference ability.

[0094] Step S006: Determine the threat radius of each UAV at the current moment based on the complexity of the flight trajectory of each UAV at the current moment, the degree of fluctuation of the actual flight trajectory, and the degree of response to external environmental interference; use the threat radius of each UAV at the current moment as the input of the PID controller, and output the control instructions of each UAV at the next moment at the current moment.

[0095] In this process, through in-depth analysis of historical drone flight monitoring data, we can understand the responsiveness of different drones to external interference. Given that micro-UAVs are subject to environmental fluctuations and the complexity of their flight trajectories during mission execution, their trajectory deviations may vary at different points in time. To ensure the safety and reliability of flight missions, it is necessary to assess the extent of external interference based on monitoring data and conduct a detailed analysis of the complexity of the flight trajectories at each subsequent moment. By combining the drone's response to external interference at each node, we can infer the corresponding threat range and adjust the flight spacing between drones accordingly.

[0096] Calculate the The flight trajectory change of the current moment of the UAV is The ratio of the complexity of the flight trajectory of the UAV at the current moment is used as the first ratio, and the first ratio and the second ratio are calculated. The actual flight trajectory fluctuation degree of each drone at the current moment, The product of the external environment interference response degree of each drone at the current moment is used as the first The threat radius of each drone at the current moment.

[0097] What needs to be explained is: The threat radius of the drone at the current moment is ,in is the first ratio, For the The actual flight trajectory fluctuation degree of each drone at the current moment, For the The external environment interference response degree of each drone at the current moment, The larger the value is, the greater the speed change at the current moment is relative to the speed change during the monitoring period at the current moment, and the larger the threat range caused. The larger the value, the greater the deviation from the set flight trajectory at the current moment, and the greater the threat range caused. The larger it is, the greater the response to external environmental interference at the current moment, and the larger the scope of the threat.

[0098] This allows for precise real-time threat radius analysis of any micro-UAV. During a swarm's mission, they continuously adjust their distances based on the threat radius and collect and monitor flight data in real time. This allows the swarm to continuously adjust the impact of external interference and its positional response to interference, ensuring high reliability and safety in complex environments.

[0099] The threat radius of each UAV at the current moment and the collected flight data of each dimension at the current moment are input into the PID controller, and the control instructions of each UAV at the next moment at the current moment are output.

[0100] The PID controller is a well-known technology and its specific method will not be introduced here. In this way, the control instructions of each drone in the micro-drone cluster at the current moment and the next moment are obtained, completing the micro-drone cluster control based on swarm intelligence.

[0101] So far, the present invention is completed.

[0102] In summary, in an embodiment of the present invention, when a micro-UAV cluster is performing a task, the external environment interference resistance, flight trajectory complexity, and actual flight trajectory fluctuation of each UAV at the current moment are determined based on the actual position, set position, windward area, air density of the environment, and speed and wind force of each UAV in the longitude, latitude, and altitude directions at each moment. In this way, the external environment interference response degree of each UAV at the current moment is determined, thereby determining the threat radius of each UAV at the current moment, so as to obtain the control instructions of each UAV at the next moment of the current moment. The present invention improves the control effect of the micro-UAV cluster by analyzing the real-time threat radius of the UAV as a control parameter.

[0103] The present invention also provides a micro-unmanned equipment cluster control system based on swarm intelligence, including a memory, a processor, and a computer program stored in the memory and runnable on the processor. The processor executes the computer program stored in the memory to implement the steps of the aforementioned micro-unmanned equipment cluster control method based on swarm intelligence.

[0104] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A micro unmanned equipment cluster control method based on swarm intelligence, characterized in that: The method comprises the following steps: When the micro-UAV cluster is performing a mission, the actual position, set position, windward area, air density of the environment, and speed and wind force in the longitude, latitude and altitude directions of each UAV at each moment are obtained; Determine the external environment interference resistance of each drone at the current moment based on its frontal area, air density, speed, and wind speed in the longitude, latitude, and altitude directions at each moment. Determine the complexity of each UAV's flight trajectory at the current moment based on the speed difference of each UAV in the same direction at adjacent moments; Determine the degree of fluctuation of the actual flight trajectory of each drone at the current moment based on the distance between the actual position of each drone at each moment and the set position; Determine the external environment interference response level of each UAV at the current moment based on the external environment interference resistance, flight trajectory complexity, and actual flight trajectory fluctuation level of each UAV; The threat radius of each UAV at the current moment is determined based on the complexity of the UAV's current flight trajectory, the degree of actual flight trajectory fluctuation, and the degree of response to external environmental interference. The threat radius of each UAV at the current moment is used as the input of the PID controller, which outputs the control instructions for each UAV at the next moment from the current moment. The specific steps of determining the complexity of the flight trajectory of each UAV at the current moment are as follows: Preset time threshold , and record the current moment as moment, the first Time to The time period of the moment is recorded as the monitoring period of the current moment; During the current monitoring period, The average of the absolute values ​​of the differences in the velocities of the UAVs at all adjacent moments in the longitude direction is taken as the The speed change of each drone in the longitude direction at the current moment; According to The speed change of each drone in all directions at the current moment is used to determine the The magnitude of the change in the flight trajectory of each drone at the current moment; According to the The method for obtaining the flight trajectory change amplitude of each drone at the current moment is to obtain the The magnitude of the flight trajectory change of each drone at each moment; The first The average value of the flight trajectory change of the UAV at all moments in the monitoring period at the current moment is taken as the The complexity of the UAV’s flight trajectory at the current moment; The determination of The flight trajectory change amplitude of each drone at the current moment includes the following specific steps: According to the The method for obtaining the speed change amplitude of each drone in the longitude direction at the current moment is to obtain the The speed change amplitude of each drone in the latitude direction at the current moment and the The speed change of each drone in the altitude direction at the current moment; The first The normalized value of the average speed change amplitude of each drone in the longitude, latitude and altitude directions at the current moment is used as the first The flight trajectory change of each UAV at the current moment.

2. The micro unmanned equipment cluster control method based on swarm intelligence according to claim 1 is characterized in that: The specific steps of determining the external environment interference resistance of each UAV at the current moment are as follows: Calculate the The first drone The square of the speed in the longitude direction at the moment, the preset air resistance coefficient, the The drone in Half of the product of the air density of the environment at the moment and the windward area is used as the first The first drone Wind resistance value in the longitude direction at all times; The first The first drone The sum of the wind force in the longitude direction at the moment and the wind resistance value is taken as the The first drone The initial external environment interference resistance in the longitude direction at the moment; Preset time threshold , and record the current moment as moment, the first Time to The time period of the moment is recorded as the monitoring period of the current moment; According to the monitoring period at the current moment The initial external environment interference resistance of each UAV at all times and in all directions is determined by The external environment interference resistance of each UAV at the current moment.

3. The micro unmanned equipment cluster control method based on swarm intelligence according to claim 2 is characterized in that: The determination of The specific steps of the external environment interference resistance of each drone at the current moment are as follows: During the current monitoring period, calculate the The average value of the initial external environment interference resistance of each drone in the longitude direction at all times, and then calculate the The variance of the initial external environment interference resistance of the UAV in the longitude direction at all times is recorded as the normalized value of the product of the mean value of the initial external environment interference resistance and the variance. The external environment interference resistance of each UAV in the longitude direction at the current moment; According to the The method for obtaining the external environment interference resistance of each drone at the current moment in the longitude direction is to obtain the The external environment interference resistance of the UAV at the current moment in the latitude direction and the The external environment interference resistance of each UAV in the altitude direction at the current moment; The first The average value of the external environment interference resistance of each drone at the current moment in the longitude, latitude and altitude direction is taken as the first The external environment interference resistance of each UAV at the current moment.

4. The micro unmanned equipment cluster control method based on swarm intelligence according to claim 1 is characterized in that: The specific steps of determining the actual flight trajectory fluctuation degree of each UAV at the current moment include the following: Preset time threshold , and record the current moment as moment, the first Time to The time period of the moment is recorded as the monitoring period of the current moment; During the current monitoring period, calculate the The Euclidean distance between the actual position of each drone and the set position at each moment is taken as the The degree of deviation of the flight trajectory of each drone at each moment will be The normalized value of the mean deviation of the flight trajectory of each drone at all times is used as the The actual flight trajectory fluctuation degree of each drone at the current moment.

5. The micro unmanned equipment cluster control method based on swarm intelligence according to claim 1 is characterized in that: The specific steps of determining the external environment interference response level of each UAV at the current moment include the following: The first The average value of the external environment interference resistance and flight trajectory complexity of each UAV at the current moment is taken as the The orbit interference index of each UAV at the current moment; According to The orbit interference index of each drone at the current moment and the degree of fluctuation of the actual flight trajectory are used to determine the The external environment interference response degree of each drone at the current moment.

6. The micro unmanned equipment cluster control method based on swarm intelligence according to claim 5 is characterized in that: The determination of The specific steps involved in determining the external environment interference response level of each drone at the current moment are as follows: The first The normalized value of the difference between the orbit interference index of the UAV at the current moment and the actual flight trajectory fluctuation degree is used as the first The external environment interference response degree of each drone at the current moment.

7. The micro unmanned equipment cluster control method based on swarm intelligence according to claim 1 is characterized in that: The specific steps of determining the threat radius of each drone at the current moment are as follows: Calculate the The ratio of the flight trajectory change amplitude of each UAV at the current moment to the flight trajectory complexity is used as the first ratio; Calculate the first ratio, the The product of the actual flight trajectory fluctuation degree of each drone at the current moment and the external environment interference response degree is used as the The threat radius of each drone at the current moment.

8. A micro-unmanned equipment cluster control system based on swarm intelligence, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the computer program is executed by a processor, the steps of the micro-unmanned equipment cluster control method based on swarm intelligence as described in any one of claims 1 to 7 are implemented.

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