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

Abstract

The invention relates to the technical field of attitude control of aerial unmanned aerial vehicles, in particular to a micro unmanned equipment cluster control method and system based on swarm intelligence. According to the actual position, the set position, the windward area, the air density of the environment where each unmanned aerial vehicle is located and the speed and the wind power in the longitude direction, the latitude direction and the height direction of each unmanned aerial vehicle at each moment, the external environment interference resistance, the flight path complexity and the actual flight path fluctuation degree of each unmanned aerial vehicle at the current moment are determined; therefore, the external environment interference response degree of each unmanned aerial vehicle at the current moment is determined, the threat radius of each unmanned aerial vehicle at the current moment is determined, and a control instruction of each unmanned aerial vehicle at the next moment of the current moment is obtained. According to the invention, the real-time threat radius of the unmanned aerial vehicle is analyzed as a control parameter, so that the cluster control effect of the miniature unmanned aerial vehicle is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of attitude control of aerial drones, and particularly to a method and system for controlling a cluster of micro unmanned devices based on swarm intelligence. Background Art

[0002] With the development of artificial intelligence, communication technologies (such as 5G), and low-power sensors, the ability of swarm intelligence systems to achieve efficient cooperation, autonomous decision-making, and flexible adaptation to complex environments among multiple devices has been significantly improved. Currently, this technology has been applied in fields such as disaster rescue, environmental monitoring, logistics distribution, and agricultural inspection, demonstrating powerful task execution capabilities and scalability, and promoting the development of unmanned intelligent systems.

[0003] In the field of swarm intelligence research, clusters of micro unmanned devices encounter many challenges when performing tasks. Due to their constituent micro devices, which usually have a small volume and low power consumption, the limitations of the hardware conditions result in poor attitude stability during task execution in complex environments. Therefore, the accuracy of cluster control may be negatively affected. During task execution, the relative positions of the drones fluctuate greatly. If the distance between them is close, collisions are likely to occur when the environment changes or their trajectories need to be adjusted uniformly, which will reduce the reliability of the task and may result in poor control effects of the cluster of micro unmanned devices. Summary of the Invention

[0004] The present invention provides a method and system for controlling a cluster of micro unmanned devices based on swarm intelligence to solve the existing problems.

[0005] The method and system for controlling a cluster of micro unmanned devices based on swarm intelligence of the present invention adopt the following technical solutions: An 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: During the process of a cluster of micro drones performing a task, obtain the actual position, set position, windward area, air density of the environment where each drone is located, as well as the speed and wind force in the longitude, latitude, and altitude directions respectively at each moment; Determine the external environmental interference resistance of each drone at the current moment according to the windward area, air density of the environment where each drone is located, as well as the speed and wind force in the longitude, latitude, and altitude directions respectively at each moment; Determine the flight trajectory complexity of each drone at the current moment according to the speed difference of each drone in the same direction at adjacent moments; Determine the actual flight trajectory fluctuation degree of each drone at the current moment according to the distance between the actual position and the set position of each drone at each moment; Determine the external environment interference response degree of each drone at the current moment according to the external environment interference resistance, flight trajectory complexity, and actual flight trajectory fluctuation degree of each drone at the current moment; Determine the threat radius of each drone at the current moment according to the flight trajectory complexity, actual flight trajectory fluctuation degree, and external environment interference response degree of each drone at the current moment; use the threat radius of each drone at the current moment as the input of the PID controller, and output the control instruction of each drone at the next moment of the current moment.

[0006] Further, the specific steps for determining the external environment interference resistance of each drone at the current moment are as follows: Calculate the square of the speed of the th drone in the longitude direction at the th moment, the preset air resistance coefficient, the air density of the environment where the th drone is located at the th moment, and half of the product of the windward area, and use it as the wind resistance value of the th drone in the longitude direction at the th moment; Take the sum of the wind force magnitude of the th drone in the longitude direction at the th moment and the wind resistance value as the initial external environment interference resistance of the th drone in the longitude direction at the th moment; Preset a time threshold , record the current moment as the th moment, and record the time period from the th moment to the th moment as the monitoring time period of the current moment; Determine the external environment interference resistance of the th drone at the current moment according to the initial external environment interference resistance of all moments of the th drone in all directions during the monitoring time period of the current moment.

[0007] Further, the specific steps for determining the external environment interference resistance of the th drone at the current moment are as follows: During the monitoring time period of the current moment, calculate the mean value of the initial external environment interference resistance of all moments of the th drone in the longitude direction, and then calculate the The variance of the initial external environmental interference resistance of all drones in the longitude direction at all times, and the normalized value of the product of the mean of the initial external environmental interference resistance and the variance is denoted as the external environmental interference resistance of the -th drone at the current time in the longitude direction; According to the acquisition method of the external environmental interference resistance of the -th drone at the current time in the longitude direction, obtain the external environmental interference resistance of the -th drone at the current time in the latitude direction and the external environmental interference resistance of the -th drone at the current time in the altitude direction; Take the mean of the external environmental interference resistances of the -th drone at the current time in the longitude, latitude, and altitude directions as the

[0008] external environmental interference resistance of the -th drone at the current time. Further, the determination of the flight trajectory complexity of each drone at the current time includes the following specific steps: Preset a time threshold and denote the current time as the -th time. Denote the time period from the -th time to the -th time as the monitoring time period of the current time; Within the monitoring time period of the current time, take the mean of the absolute values of the differences in the speeds of all adjacent times of the -th drone in the longitude direction as the speed change amplitude of the -th drone at the current time in the longitude direction; Determine the flight trajectory change amplitude of the -th drone at the current time according to the speed change amplitudes of the -th drone at the current time in all directions; According to the acquisition method of the flight trajectory change amplitude of the -th drone at the current time, obtain the flight trajectory change amplitude of the

[0009] -th drone at each time; Take the mean of the flight trajectory change amplitudes of all times of the -th drone within the monitoring time period of the current time as the The method for obtaining the amplitude of the speed change of a drone in the longitude direction at the current moment, obtaining the amplitude of the speed change of the th drone in the latitude direction at the current moment and the amplitude of the speed change of the th drone in the altitude direction at the current moment; Taking the normalized value of the mean of the amplitudes of the speed changes of the th drone in the longitude, latitude, and altitude directions at the current moment as the amplitude of the flight trajectory change of the

[0010] th drone at the current moment. Presetting a time threshold , denoting the current moment as the th moment, and denoting the time period from the th moment to the th moment as the monitoring period of the current moment; Within the monitoring period of the current moment, calculating the Euclidean distance between the actual position and the set position of each moment of the th drone as the deviation degree of the flight trajectory of each moment of the th drone, and taking the normalized value of the mean of the deviation degrees of the flight trajectories of all moments of the th drone as the actual flight trajectory fluctuation degree of the th drone at the current moment.

[0011] Furthermore, the method for determining the degree of response of each drone to external environmental interference at the current moment includes the following specific steps: Taking the mean of the external environmental interference resistance and the flight trajectory complexity of the th drone at the current moment as the orbit interference index of the th drone at the current moment; Determining the degree of response of the th drone to external environmental interference at the current moment according to the orbit interference index and the actual flight trajectory fluctuation degree of the th drone at the current moment.

[0012] Furthermore, the method for determining the degree of response of the th drone to external environmental interference at the current moment includes the following specific steps: Taking the normalized value of the difference between the orbit interference index and the actual flight trajectory fluctuation degree of the th drone at the current moment as the The degree of response of an external environmental interference to a drone at the current moment.

[0013] Further, the determining of the threat radius of each drone at the current moment includes the following specific steps: Calculate the ratio of the change amplitude of the flight trajectory of the th drone at the current moment to the complexity of the flight trajectory as a first ratio; Calculate the product of the first ratio, the actual flight trajectory fluctuation degree of the th drone at the current moment, and the degree of response to the external environmental interference as the threat radius of the th drone at the current moment.

[0014] The present invention also provides a swarm intelligence-based micro unmanned device cluster control system, including a memory, a processor, and a computer program stored on the memory and executable on the processor. The processor executes the computer program stored in the memory to implement the steps of the aforementioned swarm intelligence-based micro unmanned device cluster control method.

[0015] The beneficial effects of the technical solution of the present invention are as follows: In the embodiments of the present invention, during the process of a micro drone cluster performing a task, according to the windward area of each drone at each moment, the air density of the environment where it is located, and the speed and wind force in the longitude, latitude, and altitude directions respectively, determine the external environmental interference resistance, flight trajectory complexity, and actual flight trajectory fluctuation degree of each drone at the current moment. Thus, through multi-dimensional data during the flight of the drone, analyze the interference of the external environment, flight speed, and anomalies in the trajectory, so as to ensure the accuracy of the subsequent threat radius calculation. Then obtain the degree of response of each drone to the external environmental interference at the current moment, and further determine the interference of the external environment on the drone at the current moment, so as to determine the threat radius of each drone at the current moment. Thus, through the analysis of the external environment and the flight data of the drone itself, an accurate and reliable threat radius is obtained, which is used as a supplementary control parameter to ensure the accuracy of PID control, and thus output accurate and reliable control instructions. So far, the present invention analyzes the real-time threat radius of the drone as a control parameter to adjust the distance between drones during flight to ensure its stability and safety in a complex environment, so as to improve the control effect of the micro drone cluster. Description of the Drawings

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following briefly introduces the accompanying drawings required for the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other accompanying drawings can be obtained based on these drawings without creative efforts.

[0017] Figure 1 FIG. is a flowchart of the steps of the method for controlling a cluster of micro-unmanned devices based on swarm intelligence according to the present invention; Figure 2 FIG. is a schematic diagram of a micro-unmanned aerial vehicle cluster. Specific embodiments

[0018] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the intended invention purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific embodiments, structures, features and effects of the method and system for controlling a cluster of micro-unmanned devices based on swarm intelligence according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention belongs.

[0020] The following specifically describes the specific solutions of the method and system for controlling a cluster of micro-unmanned devices based on swarm intelligence provided by the present invention with reference to the accompanying drawings.

[0021] Please refer to Figure 1 , which shows a flowchart of the steps of the method for controlling a cluster of micro-unmanned devices based on swarm intelligence provided by an embodiment of the present invention. The method includes the following steps: Step S001: During the process of a micro-unmanned aerial vehicle cluster performing a task, obtain the actual position, set position, windward area, air density of the environment where each unmanned aerial vehicle is located at each moment, as well as the speed and wind force magnitude in the longitude, latitude and altitude directions respectively.

[0022] Collect the actual position, set position, windward area, air density of the environment where each unmanned aerial vehicle is located at each moment during the process of a micro-unmanned aerial vehicle cluster performing a task, the speed in the longitude direction, the speed in the latitude direction, the speed in the altitude direction, the wind force magnitude in the longitude direction, the wind force magnitude in the latitude direction, and the wind force magnitude in the altitude direction.

[0023] It should be noted that: The actual position and the set position include longitude, latitude and altitude. The actual position is collected by the Global Positioning System (GPS). During the process of the UAV swarm performing tasks, the position of each UAV at each moment is usually designed in advance. It usually uses computer simulation and programming tools to plan the flight trajectory of the UAV, and thus the set position of each UAV at each moment can be known. The windward area is simulated by using Computational Fluid Dynamics (CFD) software for the flow of the UAV in the wind according to the real-time flight data of the UAV (flight speed, flight altitude, flight attitude, wind speed and wind direction) to obtain the windward area of the UAV at each moment. The air density of the environment where it is located can be calculated according to the ideal gas state equation in combination with the temperature, pressure and humidity data collected in real time. For the collection of the speed in the longitude, latitude and altitude directions, taking the speed in the altitude direction as an example, it is the difference in the altitude of the UAV at adjacent moments divided by the time interval between adjacent moments. For the collection of the wind force magnitude in the longitude, latitude and altitude directions, through the method of wind force decomposition, the wind speed and wind direction data are converted into the wind force components in the longitude, latitude and altitude directions. Among them, the data that can be directly obtained are collected by integrated sensors, and the calculation process of the data that need to be obtained through calculation is a well-known technology, and the specific method is not introduced here. Schematic diagram of the micro-UAV swarm, such as Figure 2 shown.

[0024] It should be further noted that: In the process of controlling and monitoring the UAV swarm, obtaining the device monitoring data and constructing the network topology structure of the UAV group are the key foundations for realizing swarm intelligence, autonomous navigation and task execution. During the task execution process, these micro-UAVs cooperate with each other, utilize the data sharing among multiple UAVs, and continuously adjust their own postures and motion modes according to their relative position information to better adapt to environmental changes. In order to improve the reliability of the micro-UAV swarm when performing tasks in a complex environment, it is necessary to evaluate the degree of external environmental interference on any one of the UAVs in the swarm. By combining the intensity of external environmental interference and the complexity of the flight trajectory of the UAV itself, the trajectory interference situation of the micro-UAV individual can be determined. At the same time, combining the actual flight trajectory fluctuation of the UAV within the current time, the degree of influence of the external fluctuation on the micro-UAV individual in the swarm is evaluated.

[0025] Step S002: Determine the external environmental interference resistance of each UAV at the current moment according to the windward area of each UAV at each moment, the air density of the environment where it is located, and the speed and wind force magnitude in the longitude, latitude and altitude directions respectively.

[0026] When a swarm of micro - UAVs is performing a mission, due to the differences in the external environment and the positions of the UAVs, there are certain differences in the degree of interference from the external environment on the UAVs at different positions. For the degree of interference from the external environment, it can be analyzed by monitoring the changes in wind force intensity and direction during the flight of the UAV, and the UAV needs to make reverse motion compensation in a timely manner according to the relevant changes. Therefore, it is necessary to calculate the degree of interference from the external environment on the current UAV based on the changes in the external wind field monitored by the UAV during a certain period of motion.

[0027] The external interference on the UAV during flight is mainly caused by two factors. On the one hand, it is the resistance caused by the corresponding environmental wind speed to its trajectory, and on the other hand, it is the wind resistance value during flight. The combined effect of these two aspects forms the external environmental interference on the UAV during flight.

[0028] Assume that the preset air resistance coefficient is 0.5, and this will be used as an example for description.

[0029] In the swarm of micro - UAVs, taking the th UAV as an example, calculate half of the product of the square of the speed of the th UAV in the longitude direction at the th moment, the preset air resistance coefficient, the air density of the environment where the th UAV is located at the th moment, and the frontal area of the th UAV at the th moment, as the wind resistance value of the th UAV in the longitude direction at the th moment.

[0030] Take the sum of the wind force magnitude of the th UAV in the longitude direction at the th moment and the wind resistance value of the th UAV in the longitude direction at the th moment, as the initial external environmental interference resistance of the th UAV in the longitude direction at the th moment.

[0031] It should be noted that: the wind resistance value of the th UAV in the longitude direction at the th moment is , where is the preset air resistance coefficient, is the speed of the th UAV in the longitude direction at the th moment, is the The air density of the environment where the th drone is located at the th moment, the windward area of the th drone at the

[0032] The preset time threshold is 20, and this will be used as an example for description.

[0033] Record the current moment as the th moment, and record the time period from the th moment to the th moment as the monitoring time period of the current moment.

[0034] According to the above method, the monitoring time period of each moment can be obtained.

[0035] It should be noted that: when is less than or equal to , the monitoring time period corresponding to the th moment is not obtained, and no subsequent analysis is performed. This is because in the initial stage of the takeoff of the UAV cluster, the UAV cluster will be in a relatively stable environment, and the environmental interference at this time is relatively small.

[0036] Regarding the degree of external interference received by a micro UAV at a single location, by analyzing the external interference resistance received during its monitoring time period and the change of its external resistance, when the overall external resistance received during its monitoring time period is relatively small and the corresponding resistance change degree is relatively low, the corresponding external interference degree is relatively weak.

[0037] During the monitoring time period of the current moment, calculate the mean value of the initial external environmental interference resistance of the th drone at all moments in the longitude direction, and then calculate the variance of the initial external environmental interference resistance of the th drone at all moments in the longitude direction. Denote the normalized value of the product of this mean value and this variance as the external environmental interference resistance of the th drone at the current moment in the longitude direction.

[0038] Among them, for the normalized value of the product, in this embodiment, the linear normalization function is used to normalize this product to between 0 and 1.

[0039] According to the above method, obtain the external environmental interference resistance of the th drone at the current moment in the latitude direction and the external environmental interference resistance of the th drone at the current moment in the height direction.

[0040] The The mean of the external environmental interference resistance of a drone at the current moment in the longitude, latitude, and altitude directions is used as the external environmental interference resistance of the th drone at the current moment.

[0041] Step S003: Determine the flight trajectory complexity of each drone at the current moment according to the speed difference of each drone in the same direction at adjacent moments.

[0042] Through the above process, by analyzing the degree of external interference of a single drone in the micro-drone cluster, the external interference situation during the monitoring period is obtained. For the interference suffered by drones over a period of time, in addition to the external environmental impact, their own flight trajectories also significantly affect the degree of trajectory deviation during the actual flight process.

[0043] When evaluating the complexity of the flight trajectory, the flight trajectory is also split into different directions for analysis. The complexity of the flight trajectory is evaluated by analyzing the degree of speed change in a single direction within consecutive moments. When the speed in a single direction within consecutive moments maintains a relatively high degree of change amplitude, it is considered that the complexity of the flight trajectory of the drone is relatively high.

[0044] During the monitoring period at the current moment, calculate the absolute value of the difference in speed in the longitude direction between any two adjacent moments of the th drone. Take the mean of the absolute values of the differences in speed in the longitude direction of all adjacent moments of the th drone as the speed change amplitude of the th drone at the current moment in the longitude direction.

[0045] In the above manner, obtain the speed change amplitude of the th drone at the current moment in the latitude direction and the speed change amplitude of the th drone at the current moment in the altitude direction.

[0046] Take the normalized value of the mean of the speed change amplitudes of the th drone at the current moment in the longitude, latitude, and altitude directions as the flight trajectory change amplitude of the th drone at the current moment.

[0047] Among them, for the normalized value of the mean of the speed change amplitudes, in this embodiment, the linear normalization function is used to normalize the mean of the speed change amplitudes to between 0 and 1.

[0048] In the above manner, obtain the flight trajectory change amplitude of each moment of the th drone.

[0049] During the monitoring period at the current moment, the mean value of the change amplitude of the flight trajectory of the nth drone at all moments is used as the flight trajectory complexity of the nth drone at the current moment.

[0050] It should be noted that: taking the average of the change amplitudes of the flight trajectories at consecutive moments, the larger the average value, the greater the degree of change in the current flight trajectory of the drone and the higher the degree of complexity.

[0051] Step S004: Determine the actual flight trajectory fluctuation degree of each drone at the current moment according to the distance between the actual position and the set position of each drone at each moment.

[0052] For a single micro-drone during flight, due to its complex flight trajectory and being affected by the external environment at the same time, the degree of trajectory deviation of drones at different positions varies. Therefore, by comparing the real-time position of the current drone with the planned flight trajectory, the actual flight trajectory fluctuation of the current drone is determined.

[0053] During the monitoring period at the current moment, calculate the Euclidean distance between the actual position and the set position of the nth drone at each moment as the flight trajectory deviation degree of the nth drone at each moment. Take the normalized value of the mean of the flight trajectory deviation degrees of the nth drone at all moments as the actual flight trajectory fluctuation degree of the nth drone at the current moment.

[0054] Among them, for the normalized value of the mean of the flight trajectory deviation degrees, in this embodiment, the linear normalization function is used to normalize the mean of the flight trajectory deviation degrees to between 0 and 1.

[0055] It should be noted that: the greater the flight trajectory deviation degree at each moment, the more the drone deviates from the set flight path, that is, the greater the actual flight trajectory fluctuation degree.

[0056] Step S005: Determine the external environment interference response degree of each drone at the current moment according to the external environment interference resistance, flight trajectory complexity, and actual flight trajectory fluctuation degree of each drone at the current moment.

[0057] In a cluster, due to differences in the environments where individual micro-unmanned aerial vehicles (UAVs) are located, they will encounter varying degrees of external interference during flight. The responses of these UAVs to interference also differ depending on the location areas they are in, which in turn leads to varying degrees of fluctuations in their flight trajectories. To quantify this impact, the degree of external interference encountered by a single UAV and the complexity of its flight trajectory can be comprehensively considered to determine the trajectory interference index of the UAV. By comparing and analyzing the trajectory interference index with the actual fluctuations of the node orbit, the response degree of a single node to external interference can be evaluated.

[0058] During the process of a UAV cluster performing tasks, there are significant differences in the fluctuation states shown by UAVs at different positions on their flight orbits. UAVs at certain positions may face more severe external interference, resulting in a greater degree of deviation in their flight orbits. To address this issue, it is necessary to determine the orbit interference index of a single UAV during flight in combination with the foregoing analysis results.

[0059] Take the resistance of the external environment interference of the th UAV at the current moment and the mean value of the flight trajectory complexity of the th UAV at the current moment as the orbit interference index of the

[0060] th UAV at the current moment. Subtract the actual flight trajectory fluctuation degree of the th UAV at the current moment from the orbit interference index of the th UAV at the current moment, and take the normalized value of the difference as the response degree of the external environment interference of the

[0061] th UAV at the current moment. Among them, for the normalized value of the difference, in this embodiment, a

[0062] linear normalization function is used to normalize the mean value of the difference to between 0 and 1.

[0063] Step S006: Determine the threat radius of each drone at the current moment based on the flight trajectory complexity, actual flight trajectory fluctuation degree, and external environment interference response degree of each drone at the current moment; use the threat radius of each drone at the current moment as the input of the PID controller, and output the control instruction of each drone at the next moment of the current moment.

[0064] In the above process, by deeply analyzing the historical data of the drone during the flight monitoring period, the responsiveness performance of different drones when encountering external interference can be grasped. Given that micro drones are affected by external environmental changes and flight trajectory complexity during mission execution, resulting in differences in possible trajectory deviations at different time points, in order to ensure the safety and reliability of the flight mission, it is necessary to evaluate the degree of external interference based on the monitoring data and conduct a detailed analysis of the flight trajectory complexity at subsequent moments. Combining the external interference response of the drone at each node, the corresponding threat range can be inferred, and the flight spacing between drones can be adjusted accordingly.

[0065] Calculate the ratio of the flight trajectory change amplitude of the th drone at the current moment to the flight trajectory complexity of the th drone at the current moment as the first ratio, and calculate the product of the first ratio, the actual flight trajectory fluctuation degree of the th drone at the current moment, and the external environment interference response degree of the th drone at the current moment as the threat radius of the th drone at the current moment.

[0066] It should be noted that: the threat radius of the th drone at the current moment is , where is the first ratio, is the actual flight trajectory fluctuation degree of the th drone at the current moment, is the external environment interference response degree of the th drone at the current moment, The larger is, the greater the speed change at the current moment relative to the speed change during the monitoring period at the current moment, and the greater the resulting threat range. The larger is, the greater the deviation from the set flight trajectory at the current moment, and the greater the resulting threat range. The larger

[0067] Thus, the real-time threat radius of any micro UAV can be accurately obtained. During the mission execution of the micro UAV swarm, they will continuously adjust the distance between each other according to the threat radius and collect and monitor flight data in real time. In this way, the UAV swarm can continuously correct the influence degree of external environmental interference and the position response to the interference, ensuring high reliability and safety in mission execution in complex environments.

[0068] The threat radius of each UAV at the current moment and the flight data of each dimension collected at the current moment are input into the PID controller, and the control instruction of each UAV at the next moment of the current moment is output.

[0069] Among them, the PID controller is a well-known technology, and the specific method will not be introduced here. Thus, the control instruction of each UAV in the micro UAV cluster at the next moment of the current moment is obtained, and the control of the micro unmanned device cluster based on swarm intelligence is completed.

[0070] So far, the present invention is completed.

[0071] In summary, in the embodiment of the present invention, during the mission execution of the micro UAV cluster, according to the actual position, set position, windward area, air density of the environment where each UAV is located, and the speed and wind force in the longitude, latitude, and altitude directions respectively, the external environmental interference resistance, flight trajectory complexity, and actual flight trajectory fluctuation degree of each UAV at the current moment are determined, so as to determine the external environmental interference response degree of each UAV at the current moment, and thus determine the threat radius of each UAV at the current moment, so as to obtain the control instruction of each UAV at the next moment of the current moment. The present invention analyzes the real-time threat radius of the UAV as a control parameter to improve the control effect of the micro UAV cluster.

[0072] The present invention also provides a control system for a micro unmanned device cluster based on swarm intelligence, including a memory, a processor, and a computer program stored on the memory and executable on the processor. The processor executes the computer program stored in the memory to implement the steps of the method for controlling a micro unmanned device cluster based on swarm intelligence described above.

[0073] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for controlling a cluster of micro unmanned devices based on swarm intelligence, characterized in that, The method includes the following steps: During the process of the micro UAV swarm performing tasks, obtain the actual position, set position, windward area, air density of the environment where each UAV is located, as well as the speed and wind force in the longitude, latitude, and altitude directions respectively at each moment. According to the windward area, air density of the environment where each UAV is located, as well as the speed and wind force in the longitude, latitude, and altitude directions respectively at each moment, determine the external environmental interference resistance of each UAV at the current moment. According to the speed difference of each UAV in the same direction at adjacent moments, determine the flight trajectory complexity of each UAV at the current moment. According to the distance between the actual position and the set position of each UAV at each moment, determine the actual flight trajectory fluctuation degree of each UAV at the current moment. According to the external environmental interference resistance, flight trajectory complexity, and actual flight trajectory fluctuation degree of each UAV at the current moment, determine the external environmental interference response degree of each UAV at the current moment. According to the flight trajectory complexity, actual flight trajectory fluctuation degree, and external environmental interference response degree of each UAV at the current moment, determine the threat radius of each UAV at the current moment; use the threat radius of each UAV at the current moment as the input of the PID controller, and output the control instruction of each UAV at the next moment of the current moment.

2. The method for controlling a swarm of micro-unmanned devices based on swarm intelligence according to claim 1, wherein The specific steps included in determining the external environmental interference resistance of each UAV at the current moment are as follows: Calculate the square of the velocity of the th drone in the longitude direction at the th moment, the preset air resistance coefficient, the product of the air density of the environment where the th drone is located at the th moment and the windward area, and take half of the product as the wind resistance value of the th drone in the longitude direction at the moment; Take the sum of the wind force in the longitude direction at the th moment of the th drone and the wind resistance value as the initial external environmental interference resistance of the th drone at the th moment in the longitude direction; Preset time threshold , record the current moment as the moment, and record the time period from the moment to the moment as the monitoring time period of the current moment; Determine the external environmental interference resistance of the th drone at the current moment based on the initial external environmental interference resistance of all moments of the th drone in all directions during the monitoring period at the current moment.

3. The method for controlling a cluster of micro unmanned devices based on swarm intelligence according to claim 2, wherein Said determining the external environmental interference resistance of the drone at the current moment includes the following specific steps: During the monitoring period at the current moment, calculate the mean of the initial external environmental interference resistance in the longitude direction for all moments of the th drone, and then calculate the variance of the initial external environmental interference resistance in the longitude direction for all moments of the th drone. Denote the normalized value of the product of the mean of the initial external environmental interference resistance and the variance as the external environmental interference resistance of the th drone at the current moment in the longitude direction; According to the acquisition method of the external environmental interference resistance of the th drone in the longitude direction at the current moment, obtain the external environmental interference resistance of the th drone in the latitude direction at the current moment and the external environmental interference resistance of the th drone in the altitude direction at the current moment; Take the mean value of the external environmental interference resistance of the th drone at the current moment in the longitude, latitude, and altitude directions as the external environmental interference resistance of the th drone at the current moment.

4. The method for controlling a cluster of micro-unmanned devices based on swarm intelligence according to claim 1, wherein The specific steps included in determining the flight trajectory complexity of each UAV at the current moment are as follows: Preset time threshold , record the current moment as the moment, and record the time period from the moment to the moment as the monitoring period of the current moment; During the monitoring period at the current moment, take the average value of the absolute values of the differences in the velocities of all adjacent moments of the th drone in the longitude direction as the velocity change amplitude of the th drone at the current moment in the longitude direction; According to the speed change amplitude of the th drone in all directions at the current moment, determine the flight trajectory change amplitude of the th drone at the current moment; According to the method for obtaining the variation range of the flight trajectory of the th drone at the current moment, obtain the variation range of the flight trajectory of the th drone at each moment; Take the mean of the variation amplitudes of the flight trajectories of the th drone at all times during the monitoring period at the current moment as the th drone's flight trajectory complexity at the current moment.

5. The method for controlling a swarm of micro-unmanned devices based on swarm intelligence according to claim 4, wherein The determination of the degree of change in the flight trajectory of the drone at the current moment includes the following specific steps: According to the method for obtaining the velocity change amplitude of the th drone in the longitude direction at the current moment, obtain the velocity change amplitude of the th drone in the latitude direction at the current moment and the velocity change amplitude of the th drone in the altitude direction at the current moment; Take the normalized value of the mean of the velocity change magnitudes in the longitude, latitude, and altitude directions of the th drone at the current moment as the flight trajectory change magnitude of the th drone at the current moment.

6. The method for controlling a cluster of micro-unmanned devices based on swarm intelligence according to claim 1, characterized in that The specific steps included in determining the actual flight trajectory fluctuation degree of each UAV at the current moment are as follows: Preset time threshold , record the current moment as the moment, and record the time period from the moment to the moment as the monitoring time period of the current moment; During the monitoring period at the current moment, calculate the Euclidean distance between the actual position and the set position of each moment of the th drone as the deviation degree of the flight trajectory of each moment of the th drone. Take the normalized value of the mean of the deviation degrees of the flight trajectories of all moments of the th drone as the actual flight trajectory fluctuation degree of the th drone at the current moment.

7. The method for controlling a swarm of micro-unmanned devices based on swarm intelligence according to claim 1, wherein The specific steps included in determining the external environmental interference response degree of each UAV at the current moment are as follows: Take the mean of the external environmental interference resistance and flight trajectory complexity of the th drone at the current moment as the th drone's orbit interference index at the current moment; According to the orbit interference index and the degree of actual flight trajectory fluctuation of the th drone at the current moment, determine the external environment interference response degree of the th drone at the current moment.

8. The method for controlling a swarm of micro-unmanned devices based on swarm intelligence according to claim 7, characterized in that, The determination of the degree of response of the th drone to external environmental interference at the current moment includes the following specific steps: Normalize the difference between the orbit interference index of the th drone at the current moment and the actual flight trajectory fluctuation degree, and use it as the external environment interference response degree of the th drone at the current moment.

9. The method for controlling a swarm of micro-unmanned devices based on swarm intelligence according to claim 4, wherein The specific steps included in determining the threat radius of each UAV at the current moment are as follows: Calculate the ratio of the magnitude of the change in the flight trajectory to the complexity of the flight trajectory of the th drone at the current moment as the first ratio; Calculate the product of the first ratio and the degree of actual flight trajectory fluctuation and the degree of external environment interference response of the nth drone at the current moment, and use it as the threat radius of the nth drone at the current moment.

10. A cluster control system for micro unmanned device clusters based on swarm intelligence, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the computer program is executed by a processor, it implements the steps of the swarm intelligence-based control method for a micro unmanned device swarm according to any one of claims 1-9.

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