A method, system, device and medium for maneuverable unmanned aerial vehicle interception based on double-machine cooperation

Abstract

This invention belongs to the field of UAV countermeasures technology, specifically involving a method, system, equipment, and medium for intercepting mobile UAVs based on dual-UAV collaboration. The ground control station is initialized, and a master-slave formation control algorithm is used. The master UAV tracks a virtual waypoint, while the slave UAVs achieve horizontal parallel formation via an inter-UAV data link, hovering after reaching a predetermined airspace. A PID controller is used to fine-tune the formation. The master UAV tracks the interception route, while the slave UAVs maintain a fixed distance. The two UAVs collaborate to place the interception net in front of the target's path for collision capture. After capture, the target is released to a safe area and autonomously returns to its home location using obstacle perception and a digital elevation model. This overcomes the shortcomings of traditional single-UAV systems, achieving efficient, controllable, and non-destructive capture, shortening response time, reducing skill dependence, and improving system applicability and reliability.

Description

Technical Field

[0001] This invention belongs to the field of drone countermeasure technology, specifically relating to a method, system, equipment, and medium for intercepting mobile drones based on dual-drone cooperation. Background Technology

[0002] With the popularization of drone technology, its applications in aerial photography, logistics, agriculture, and other fields are becoming increasingly widespread. Therefore, efficient and reliable drone countermeasures technology has become a research hotspot. Existing physical drone interception technologies mainly include: capture net launching devices, fixed interception nets, and aerial combat drones. Capture net launching devices: These typically involve launching a capture net from a single drone or ground-based device. However, the net area launched by a single drone is limited, resulting in a small interception success rate and range, and recovery after launch is difficult. Fixed interception nets: These are fixed interception nets deployed in specific areas, lacking mobility and unable to cope with flexible and changing targets. Aerial combat drones: These utilize drones to collide with target drones. This method may cause a secondary crash risk and is costly.

[0003] Currently, Chinese invention patent application CN2024104301904 proposes a four-drone cooperative trawling method for intercepting drones. This method utilizes the spatial encirclement advantage of multiple drone formations, employing a diving net-trapping technique to entangle and capture the target drone, aiming to solve the management challenges of small drones with a low-cost, reusable solution. However, cooperative systems with four or more drones have complex communication topologies, computationally intensive cooperative control algorithms, and are extremely sensitive to communication latency. Delay or malfunction of any single drone can cause the entire interception operation to fail, or even result in a collision during the cooperative maneuver. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention provides the following technical solution: a method for intercepting mobile unmanned aerial vehicles (UAVs) based on dual-machine cooperation, comprising: The ground control station performs system initialization. Based on the master-slave formation control algorithm, the master aircraft tracks the virtual waypoint issued by the ground control station, and the slave aircraft calculates the position through the inter-aircraft data link to achieve horizontal parallel formation. After autonomously flying to the predetermined airspace, it automatically switches to hovering mode. The formation is fine-tuned using a PID controller, and the electromagnetic chuck is energized by a multi-condition intelligent logic gate to enable the magnetic strips to adhere and complete the netting. The millimeter-wave radar and lidar are activated simultaneously, and adaptive weighted fusion is performed based on the quality factor to output the real-time state estimate of the target. The target recognition confidence is calculated based on environmental adaptive weights. The main aircraft tracks and intercepts the flight path, while the slave aircraft maintains a fixed distance and closely follows. The two aircraft cooperate to maneuver and place the deployed interception net in front of the target's flight path for collision capture. Once captured, the interception net is released and the captured target falls into a predetermined safe area. Combining real-time obstacle perception and digital elevation model, the optimal path is planned to achieve autonomous and safe return.

[0005] As a preferred embodiment of the dual-machine cooperative maneuvering UAV interception method described in this invention, the master-slave formation control algorithm is expressed as follows: ; in, Indicates the host's real-time location. Represents the relative displacement vector; The prediction of the target drone is expressed as: ; ; in, Indicates the predicted location of the target drone. Indicates the real-time location of the target drone. Indicates the speed of the target drone. Indicates a time window. Indicates the effective capture range of the interceptor network; The roll and pitch angles of the two drones are monitored in real time by an airborne IMU. Both drones' real-time roll and pitch angles are less than their respective thresholds, as shown below: ; in, This indicates the real-time roll angle of the transport drone. For the real-time pitch angle of the transport drone, The preset roll angle threshold for transporting drones, The preset pitch angle threshold for the transport drone, This is the serial number for the transport drone.

[0006] As a preferred embodiment of the dual-machine cooperative mobile UAV interception method described in this invention, the formation fine-tuning using a PID controller is expressed as follows: ; in, This represents the spacing error at the current moment; The error between the actual distance between the two carrier drones and the commanded interception distance is within the tolerance limit, expressed as: ; in, This indicates the actual distance between the two transport drones. This indicates the command interception distance between the two carrier drones. Indicates the error tolerance.

[0007] As a preferred embodiment of the dual-machine collaborative mobile UAV interception method described in this invention, the adaptive weighted fusion based on quality factors is expressed as: ; ; ; ; in, This indicates the signal-to-noise ratio of millimeter-wave radar. Indicates the meteorological disturbance index. This indicates the point cloud density of the lidar. This represents the maximum point cloud density of the lidar system. This indicates the final fusion result. Indicates the fusion weights of millimeter-wave radar. Indicates the fusion weights of the lidar. This represents the target state calculated by the millimeter-wave radar. This indicates the target state calculated by the lidar.

[0008] As a preferred embodiment of the dual-machine cooperative mobile UAV interception method described in this invention, wherein: the target recognition confidence score calculated based on environmental adaptive weights is expressed as: ; in, and Represents the weighting coefficient, and .

[0009] In a preferred embodiment of the dual-machine cooperative mobile UAV interception method described in this invention, the meteorological interference index is expressed as: ; ; in, This represents the filter coefficients.

[0010] As a preferred embodiment of the dual-machine cooperative mobile UAV interception method described in this invention, the step of planning the optimal path to achieve autonomous and safe return includes: After confirming that the target has been successfully captured, the ground control station sends instructions to the two carrier drones. The electric release mechanism at the bottom of the two carrier drones is simultaneously de-energized, the magnetic force of the electromagnetic chuck disappears, and the intercept net, along with the captured target drone, falls towards the predetermined safe area under the action of gravity. After releasing the intercept net and the captured target, the two carrier drones rely on their safety systems to return to base autonomously and safely.

[0011] As a preferred embodiment of the dual-machine cooperative mobile unmanned aerial vehicle (UAV) interception system described in this invention, it includes: The flight and payload module is used to carry out flight and maneuver missions, enabling formation flying, hovering, coordinated maneuvering, and return to base. The interception execution module is connected to the flight and carrier module to realize the net setting, capture and release actions; The intelligent sensing and control module enables formation fine-tuning, target perception, fusion recognition, intelligent decision-making, and collaborative control.

[0012] The ground control station planned the interception mission, and two carrier UAVs, each equipped with an ultra-high molecular weight polyethylene fiber interception net, took off autonomously. During their flight to the designated mission airspace, their safety systems continued to operate: lidar performed high-precision 3D environmental modeling to achieve accurate obstacle avoidance, and millimeter-wave radar, with its strong penetration, provided reliable long-range obstacle detection under complex weather conditions such as rain and fog, jointly ensuring the safety of the transfer flight.

[0013] During coordinated maneuvers, the dual-aircraft cooperative control algorithm relies on real-time perception data. At this time, lidar and millimeter-wave radar jointly perform near-range real-time obstacle avoidance, ensuring that the two aircraft can avoid suddenly appearing obstacles during high-speed maneuvers. The millimeter-wave radar can also effectively monitor wind shear caused by sudden changes in wind speed, providing feedforward compensation to the flight control system and maintaining stable formation spacing. The system controls the two aircraft to maneuver cooperatively to the front of the target's flight path. Once the docking edge enters the effective range, the magnetic catch is activated, the electromagnetic chuck is energized, forming a complete arresting net, and capture is achieved.

[0014] After successful capture, the system executes the disconnection procedure, and the carrier drone enters the autonomous return phase. If the area below the mission zone is a sensitive area, the target is not allowed to fall freely. The two drones can maintain the magnetic clasp in the closed state and continue to fly in combined mode, jointly towing the captured target drone to a safe abandoned area (such as open field or water surface) before abandoning it.

[0015] A computer device includes a memory and a processor, the memory storing a computer program, characterized in that the processor executes the computer program to implement the steps of any one of the methods in a dual-machine cooperative mobile unmanned aerial vehicle interception method.

[0016] A computer-readable storage medium having a computer program stored thereon, characterized in that, when the computer program is executed by a processor, it implements the steps of any one of the methods in a dual-machine cooperative mobile unmanned aerial vehicle interception method.

[0017] The beneficial effects of this invention are as follows: First, this invention employs a master-slave formation control algorithm, requiring only two drones to cooperate. The master drone tracks a virtual navigation point, while the slave drones calculate their positions via an inter-drone data link, thus achieving horizontal parallel formation. This two-drone architecture significantly reduces the system's communication topology complexity and the computational load of the cooperative control algorithm, thereby reducing sensitivity to communication latency. It effectively avoids the risk of overall operational failure due to delays or malfunctions of individual drones in multi-drone collaboration, improving the system's robustness and real-time response capabilities.

[0018] Secondly, this invention simultaneously activates millimeter-wave radar and lidar, and performs adaptive weighted fusion based on a quality factor. By combining environmental adaptive weights to calculate target recognition confidence, this scheme can overcome the limitations of a single sensor in complex environments, outputting high-precision real-time target state estimates.

[0019] Thirdly, this invention utilizes a PID controller for fine-tuning of the formation, ensuring that the slave units maintain a fixed spacing and closely follow each other, thus maintaining the stability of the horizontal parallel formation. Simultaneously, it controls the energization of the electromagnetic chucks through multi-condition intelligent logic gates, precisely controlling the timing of the magnetic strip adsorption, so that the interceptor net opens at the optimal moment. This refined control ensures the stability of the interceptor net 3 during deployment, effectively expanding the interception area and solving the problem of limited area for a single-unit launcher.

[0020] Fourth, this invention employs a dual-aircraft coordinated maneuver to place the deployed interception net in front of the target's flight path for collision capture (i.e., a "static braking" interception method). This method avoids active collisions with the target UAV, greatly reducing the risk of damage to the intercepting UAV itself; simultaneously, upon successful capture, the interception net is released and the captured target falls towards a predetermined safe area, effectively preventing secondary damage to the ground caused by the captured UAV's fall.

[0021] Fifth, after completing the interception mission, this invention combines real-time obstacle perception and a digital elevation model to plan the optimal path for autonomous and safe return. This enables the interception drone to have autonomous obstacle avoidance capabilities even in complex geographical environments, ensuring the safe recovery of the equipment and realizing full-process autonomy from takeoff and interception to return, thereby improving the reusability and economy of the equipment. Attached Figure Description

[0022] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 This is a flowchart of a mobile unmanned aerial vehicle (UAV) interception method based on dual-machine collaboration in an embodiment of the present invention.

[0024] Figure 2 This is a schematic diagram of the overall structure of a mobile unmanned aerial vehicle (UAV) interception system based on dual-machine collaboration in an embodiment of the present invention.

[0025] Figure 3 This is a schematic diagram of the layout of sensors and mechanisms for a transport drone in an embodiment of the present invention.

[0026] Figure 4 This is a schematic diagram of the sensor fusion strategy based on quality factor in an embodiment of the present invention.

[0027] Figure 5 This is a flowchart illustrating the intelligent magnetic opening and closing logic judgment in an embodiment of the present invention.

[0028] Explanation of reference numerals in the attached diagram: 1. Carrier UAV, 2. Ground control station, 3. Interception net, 4. Millimeter-wave radar, 5. LiDAR, 6. O4 image transmission module, 7. 4G / 5G redundant communication module, 8. Electric uncoupling mechanism, 9. Dual braking parachute, 10. Target UAV. Detailed Implementation

[0029] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0030] Example 1 Reference Figures 1-5 This is the first embodiment of the present invention, which provides a method for intercepting mobile unmanned aerial vehicles based on dual-machine cooperation, including: The overall flowchart of this embodiment is as follows: Figure 1 As shown, the specific implementation method is as follows: S1: Initialize the system of ground control station 2. Based on the master-slave formation control algorithm, the master aircraft tracks the virtual waypoint issued by ground control station 2, and the slave aircraft calculates the position through the inter-aircraft data link to achieve horizontal parallel formation. After autonomously flying to the predetermined airspace, it automatically switches to hovering mode.

[0031] It should be noted that the ground control station 2 and the carrier UAV 1, as well as the two carrier UAVs 1, are connected by a two-way communication network consisting of the O4 image transmission module 6 and the 4G / 5G redundant communication module 7. This ensures that control commands and status data can be transmitted in real time and reliably. This is the technical prerequisite for the realization of "the slave device calculating its position through the inter-device data link".

[0032] It should be noted that ground control station 2 plans the interception mission route, and two carrier UAVs 1 take off autonomously after being equipped with interception net 3. They maintain connection with ground control station 2 through O4 image transmission 6 and 4G / 5G redundant communication module 7, and fly to the predetermined mission airspace.

[0033] It should be noted that ground control station 2 plans the interception mission route, and two carrier UAVs 1 take off autonomously after being equipped with interception net 3. They maintain connection with ground control station 2 through O4 image transmission 6 and 4G / 5G redundant communication module 7, and fly to the predetermined mission airspace.

[0034] Furthermore, the airspace and environment loading of ground control station 2, intelligent route generation, and after the mission route is confirmed, it is sent to the two carrier UAVs 1 through a heterogeneous network composed of O4 image transmission module 6 and 4G / 5G redundant communication module 7.

[0035] It should be noted that, as Figure 2 As shown, the interception net 3 is attached to the electric unhooking mechanism 8 on the bottom of the two carrier drones 1 via magnetic strips on their sides, assisted manually or by a robotic arm. After reaching a safe altitude, the carrier drones 1 automatically form a horizontal parallel formation according to a preset master-slave formation control algorithm. One carrier drone 1 acts as the "master," tracking a preset virtual navigation point, while the other carrier drone 1 acts as the "slave," receiving real-time data from the master drone via an inter-drone data link established through the O4 image transmission module 6. The system obtains the aircraft's location information and autonomously calculates its tracking waypoint based on a preset 100-meter spacing constraint, achieving synchronized flight. After the carrier UAV 1 arrives at the designated mission airspace at a preset cruising speed (e.g., 10 m / s), it automatically switches to hovering mode.

[0036] Furthermore, based on the master-slave formation control algorithm, the master drone (leading drone) adjusts to the predetermined hovering point, and the slave drones obtain the position of the master drone in real time through the inter-drone data link, and automatically calculate the target position according to the preset command interception interval.

[0037] It should be noted that the specific implementation of the master-slave formation control algorithm is as follows: one of the carrier UAVs 1 is designated as the master, whose task is to track the virtual master route issued by the ground control station 2; the other carrier UAV 1 is designated as the slave, whose control law includes not only maintaining a preset distance from the master. The core of a PID controller is that its desired position is determined by the real-time position of the host computer. Overlay The conclusion is that The slave device tracks this dynamic change. This allows for synchronized movement with the host machine.

[0038] Furthermore, based on the preliminary detection data from millimeter-wave radar 4, the system predicts the motion of the target UAV 10. Based on the real-time position and velocity of the target UAV 10, it predicts its position after the time window and determines that this predicted position is within the effective capture range of the interception network 3, i.e., satisfying the formula: ; ; in, This is represented as the predicted location of target drone 10. This represents the real-time location of the target drone 10. The speed of target drone 10 is represented as follows. Represented as a time window, This represents the effective capture range of interceptor 3, which is half the length of interceptor 3.

[0039] The roll and pitch angles of the two drones were monitored in real time by the airborne IMU. The real-time roll and pitch angles of both carrier drones 1 were less than their respective thresholds, that is, they satisfied the formula: ; in, For the real-time roll angle of the carrier drone 1, For the real-time pitch angle of the carrier UAV 1, The preset roll angle threshold for the carrier UAV 1 is determined based on the flight envelope and control performance of the carrier UAV 1 using the following steps: First, high-fidelity simulation is performed based on the aerodynamic model and flight control law of the carrier UAV 1 to analyze its attitude stability margin under various disturbances and initially determine the range of the threshold; then, through actual flight tests, the effectiveness of the threshold in maintaining formation stability and the safe deployment of the interceptor net 3 is verified under different weather conditions; finally, it is optimized and tuned to 5 degrees. The preset pitch angle threshold for the carrier drone 1, This is designated as the carrier drone number 1.

[0040] S2: The formation is fine-tuned using a PID controller, and the electromagnetic chuck is energized through a multi-condition intelligent logic gate to enable the magnetic strip to adhere and complete the netting. The millimeter-wave radar 4 and lidar 5 are started simultaneously, and adaptive weighted fusion is performed based on the quality factor to output the real-time state estimate of the target.

[0041] It should be noted that the formation control uses a PID (proportional-integral-derivative) controller, and its control output is as follows: ; in, This represents the spacing error at the current moment. =0.6, =0.02, =0.15 is the tuned controller parameter. Set K. p =0.6 is an engineering tuning principle based on the critical proportionality method. In the initial debugging, K is first... i and K d Set K to zero and gradually increase it. p Record the critical gain K at the point when the system reaches a critical oscillation (i.e., constant amplitude oscillation). c To ensure the system has a good response speed while maintaining sufficient stability margin, K was ultimately selected. p The value is approximately 0.6 to 0.8 times K. c After comprehensive consideration, K p =0.6 can effectively avoid excessive overshoot caused by the drone's own inertia while ensuring response speed.

[0042] Too large K i This can cause "integral saturation," leading to sluggish system response and significant overshoot. K i The value of 0.02 is relatively small, aiming to gently eliminate small steady-state errors caused by persistent wind disturbances or model mismatches, and to avoid affecting the dynamic performance of the system (such as causing low-frequency oscillations) due to excessive integral action. This meets the requirements for high-precision formation flight steady-state indicators.

[0043] K d The setting of 0.15 is intended to provide moderate damping for the system, smoothing the UAV's maneuvers. It proactively suppresses excessive overshoot tendencies caused by rapid spacing adjustments, significantly improving formation stability, especially when dealing with sudden gusts of wind or emergency maneuver commands. This value is within the K... p and K i After initial determination, adjustments were made by observing the overshoot of the system's step response.

[0044] It should be noted that, referring to Figure 4The two sensors independently calculate the target state. The system calculates the quality factor of each sensor in real time and dynamically allocates fusion weights accordingly. The final output is a more accurate and reliable estimate of the target state, providing high-quality data for the next interception decision.

[0045] Reference Figure 5 As shown, the carrier UAV 1 will only issue a power-on command to the actuator when all three safety conditions are simultaneously met (using AND logic): "spacing ready" (PID fine-tuning results meet the standard, spacing check in the attached figure), "target within the predicted acquisition range" (target check in the attached figure based on preliminary sensor data), and "dual-aircraft attitude stable" (IMU data is normal, attitude check in the attached figure). This effectively prevents dangerous net-laying behavior under unstable attitude or improper distance conditions.

[0046] Furthermore, the netting action is controlled by a multi-condition coordinated intelligent logic gate. The system sends an energizing command to the electromagnetic suction port of the electric unhooking mechanism 8 only when the following three conditions are met simultaneously: Spacing Readiness Condition: The error between the actual distance between the two carrier UAVs 1 and the commanded interception distance is within the tolerance, that is, it satisfies the formula: ; in, The actual distance between the two transport drones 1 The commanded interception distance between the two transport drones 1 This is the tolerance for error.

[0047] It should be noted that the error tolerance was set to ±1.0m based on the accuracy (±0.1m) of the high-precision positioning system used by the UAV 1, the response characteristics of the flight control system, and the shape requirements of the interceptor net 3. This value ensures the stability and efficiency of the system in maintaining formation spacing.

[0048] Furthermore, the system controls the electromagnetic chuck to be energized only when the three conditions of spacing readiness, target within acquisition range, and dual-aircraft attitude stability are met. The two magnetic strips attract each other, and the interception net 3 fully unfolds in the air within 1-2 seconds, and the system enters the "hovering net deployment" state.

[0049] In the "hovering and netting" state, the airborne multi-sensor fusion safety system is fully activated, continuously detecting, identifying, and tracking the target UAV 10. An adaptive weighted fusion strategy based on quality factors is employed, and the specific process is as follows: 1. Independent operation and feature extraction of each sensor: Millimeter-wave radar 4: Emits 77GHz frequency-modulated continuous waves, utilizing the Doppler effect to detect the radial velocity and range of targets. Its advantage lies in its unaffectedness by light and common weather conditions (rain, fog), providing stable data for medium to long ranges.

[0050] LiDAR 5: Generates a high-precision 3D point cloud by emitting a laser beam and receiving the echo. Used for contour recognition and accurate ranging of targets at close range.

[0051] 2. Calculate the quality factor of each sensor: The quality factor dynamically reflects the reliability of each sensor in the current environment.

[0052] 3. Quality factor of millimeter-wave radar 4: ; in, For the signal-to-noise ratio of millimeter-wave radar 4, This is the meteorological disturbance index.

[0053] It should be noted that the system records key meteorological parameters (visibility, precipitation intensity, cloud height / haze concentration index) acquired in real time by sensors and the ground. The acquired raw meteorological data is then mapped to values ​​between 0 and 1 using a preset mapping function. Value. For example: when visibility is 10km, the value is 0; when visibility drops to 1km, the value is 0.9.

[0054] Furthermore, α is an adjustment coefficient that amplifies or reduces meteorological disturbances. The impact on the final millimeter-wave radar quality factor is set to a value between 0 and 1. A value of 0 means that meteorological interference is completely ignored, and the quality factor depends only on the signal-to-noise ratio. A value greater than 0 means that meteorological interference is present. The larger the value, the greater the quality factor of the millimeter-wave radar, and the higher its weight.

[0055] Furthermore, by using ground-based meteorological data links and recording meteorological sensors, visibility and precipitation intensity are obtained. These meteorological parameters are then transformed into independent sub-disturbance indices through a preset mapping function. The mapping rules are as follows: 1. Visibility Mapping: Visibility ≥ 10km, (No interference); visibility ≤ 0.5km, (Maximum interference); linear interpolation is used between 0.5km and 10km. .

[0056] 2. Precipitation intensity mapping: Precipitation intensity = 0 mm / h, Rainfall intensity ≥ 25 mm / h Linear mapping is used between 0-25 mm / h. .

[0057] Final meteorological disturbance index This is determined by taking the maximum value among all sub-disturbance indices to ensure the system's response to the worst-case scenario: ; To avoid The values ​​fluctuate frequently due to sensor noise and data volatility, so a first-order low-pass filter is used for smoothing. ; in, The filter coefficients (usually 0.1-0.3) are tuned according to the system response speed requirements. The filtered result... Used for the final quality factor calculation.

[0058] Quality factor of LiDAR 5: ; in, The cloud density of 5 points for lidar, This represents the maximum cloud density at 5 points on the lidar.

[0059] The weights of each sensor in the fusion are calculated based on the quality factor: ; The final target state estimate is obtained by weighted fusion: ; in, For the final fusion result, For millimeter-wave radar 4 fusion weights, For the fusion weights of LiDAR 5, The target state calculated by millimeter-wave radar 4 The target state calculated by LiDAR 5.

[0060] S3: Calculate the target recognition confidence based on environmental adaptive weights, the main aircraft tracks and intercepts the flight path, the slave aircraft maintains a fixed distance and closely follows, and the two aircraft cooperate to maneuver to place the spread intercept net 3 in front of the target's flight path for collision capture.

[0061] It should be noted that once the target UAV 10 is confirmed, the ground control station 2 or the airborne autonomous system immediately calculates the optimal interception route; the two carrier UAVs 1, according to the instructions, maintain a certain distance and maneuver together to move the deployed interception net 3 in front of the flight path of the target UAV 10 to achieve collision capture. Specifically, target confirmation and optimal interception route calculation: when the fusion system continuously tracks the target and its confidence level is higher than a set threshold, it is determined to be a "confirmed target".

[0062] Confidence calculation: ; in, and It is a weighting coefficient, and Severe weather settings =0.6, =0.4; Sunny weather =0.6, =0.4, and the weights can be adaptively adjusted according to the environment. The confidence level is calculated once in each fusion cycle, and the confidence level threshold is (0.7-0.9). It can also be verified through gravitational testing and actual flight.

[0063] Subsequently, ground control station 2 or the airborne autonomous system immediately initiates interception route calculation. The generated interception route not only includes a virtual "navigation point" (i.e., the predicted interception point), but also explicitly requires the two carrier UAVs 1 to maintain a distance of approximately 10 meters during maneuvering to ensure that the interception net 3 remains effectively deployed. After receiving the interception command, the two carrier UAVs 1 cease hovering and enter a cooperative maneuvering mode. The master UAV is responsible for tracking the calculated optimal interception route, while the slave UAVs obtain the master's position in real time through inter-UAV communication and follow according to a fixed relative position relationship. The two UAVs, with coordinated speed and acceleration, precisely maneuver the deployed interception net 3 to the front of the target UAV 10's flight path. The interception timing is chosen so that the target precisely collides into the central area of ​​the net. After the collision, the rotor of the target UAV 10 will be entangled by the net, thus achieving capture.

[0064] S4: Upon successful capture, release the interception net 3 and the captured target to fall into the predetermined safe area. Combine real-time obstacle perception and digital elevation model to plan the optimal path to achieve autonomous and safe return.

[0065] Once the target has been successfully captured, as follows: Figure 2 As shown, the ground control station (part of the Cooperative Control and Communication Unit) sends a release command to both carrier UAV platforms. This command is transmitted to the UAV platforms via a communication link, which then triggers the electric release mechanism on their undersides (see...). Figure 3 (See annotation). The mechanism performs the unlocking action, collides with the captured target, and releases it as shown in the figure. Afterwards, as... Figure 2 and Figure 3 As shown in the diagram, the two unloaded carrier drone platforms will rely on their integrated safety systems (including lidar / millimeter-wave radar for obstacle avoidance, see...) Figure 3 Combining digital elevation models, it autonomously plans the optimal path to achieve a safe return.

[0066] Specifically, the electric release mechanisms 8 at the bottom of both carrier drones 1 are simultaneously de-energized, the electromagnetic chucks lose their magnetic force, and the interception net, along with the captured target drone, falls towards a predetermined safe area (such as open ground or water) under the influence of gravity. After release, both carrier drones 1 immediately execute the return-to-home procedure. Based on real-time position and preset return-to-home point, combined with a digital elevation model (DEM) and real-time obstacle information (mainly from lidar 5 and millimeter-wave radar 4), the onboard flight controller plans the optimal safe return-to-home path.

[0067] Specifically, the electric release mechanisms 8 at the bottom of both carrier drones 1 are simultaneously de-energized, the electromagnetic chucks lose their magnetic force, and the interception net 3, along with the captured target drone 10, falls towards a predetermined safe area (such as open ground or water) under the influence of gravity. After release, both carrier drones 1 immediately execute the return-to-home procedure. Based on the real-time position and the preset return-to-home point, combined with the digital elevation model (DEM) and real-time obstacle information (mainly from lidar 5 and millimeter-wave radar 4), the onboard flight controller plans the optimal safe return-to-home path.

[0068] It should be noted that the airborne flight controller, based on real-time position and a preset return-to-home point, and combining a digital elevation model (DEM) and real-time obstacle information, implements the following specific method: It integrates multi-source information in real time, including the UAV's current position, preset return-to-home point coordinates, DEM terrain data, and real-time obstacle detection data from LiDAR 5 and millimeter-wave radar 4. Based on this data, the controller uses an optimization algorithm to quickly generate an optimal path that avoids all obstacles and terrain undulations.

[0069] The system acquires the drone's real-time coordinates with centimeter-level accuracy via GPS / INS, and uses these coordinates with a preset return-to-home point to establish the start and end points of the path. Pre-loaded terrain elevation data is used to identify static obstacles such as mountains and buildings, ensuring the path does not collide with the ground or obstacles. A rough path connecting the start and end points is calculated on a topographic map (DEM), avoiding all known static hazards. The global path is then fine-tuned using LiDAR 5 and millimeter-wave radar 4 to avoid dynamic obstacles, ultimately outputting a smooth, flyable 3D trajectory line for the drone to automatically return to home.

[0070] Furthermore, this invention adopts a dual-machine collaborative architecture, which fundamentally reduces the complexity and communication burden of the system. Through a simpler and more reliable control strategy and intelligent decision-making logic, it achieves a higher interception success rate and system robustness.

[0071] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

[0072] Example 2 This is a second embodiment of the present invention, which provides a mobile unmanned aerial vehicle (UAV) interception system based on dual-machine collaboration, characterized by: a flight and carrying module, an interception execution module, and an intelligent sensing and control module.

[0073] This system mainly consists of three parts: 1. The flight and carrier module constitutes the core of the mobile capture system, preferably using two or more quadcopter or hexacopter electric drones with excellent and consistent flight performance as carriers. This drone platform must possess a high load ratio and strong wind resistance stability, with a maximum takeoff weight of no less than 25 kg and an effective payload capacity greater than 8 kg to ensure at least 30 minutes of mission endurance after mounting the interceptor net 3 and various sensors. The carrier drone 1's airframe structure utilizes carbon fiber composite materials to achieve lightweighting and high rigidity. The flight control system incorporates a high-precision inertial navigation system (INS) and a global navigation satellite system (GNSS) receiver, jointly providing centimeter-level real-time positioning and attitude determination data. Its core function is to accurately execute coordinated flight commands, stably mount the interceptor net 3 on one side, and achieve rapid and synchronized coordinated maneuvering during the interception process.

[0074] 2. The interception execution module is the core component of the passive interception function. Its mesh is woven from ultra-high molecular weight polyethylene fiber, a material with extremely high specific strength and excellent impact and wear resistance. Flexible, elongated permanent magnet strips or magnetically conductive metal strips are fixed to the two long sides of the mesh using a high-strength stitching process; while the two short sides remain free or are reinforced with edge binding to facilitate full unfolding in the air and enhance the entanglement effect. The entire interception net is lightweight, flexible, foldable, and easy to deploy quickly.

[0075] 3. The collaborative control and communication unit is the "nerve center" and "decision-making brain" of the entire device, responsible for information flow, intelligent decision-making, and command execution. Its specific composition is as follows: Ground Control Station 2: Serving as the command center of the entire system, it typically consists of a high-performance computer, dual-screen displays, a remote controller, and communication radios. It runs dedicated mission planning and control software, providing functions such as electronic map display, airspace management, mission route planning, real-time status monitoring, and manual intervention command issuance. Operators can gain a comprehensive understanding of the entire mission process through Ground Control Station 2.

[0076] Airborne flight controller: A high-performance embedded computing unit mounted on each UAV1, typically based on ARM or PowerPC architecture and running a real-time operating system. It is responsible for calculating flight control laws and outputting control quantities to the ESCs and motors; it incorporates the master-slave formation control algorithm, intelligent magnetic opening and closing logic, and multi-sensor fusion algorithm described in this invention, enabling it to process various sensor data in real time and make autonomous decisions.

[0077] Communication Link: A heterogeneous, highly reliable data link is constructed using an O4 image transmission module 6 and a 4G / 5G redundant communication module 7. The O4 image transmission module 6 provides a low-latency, high-bandwidth dedicated data link for transmitting critical control commands, high-definition first-person view (FPV) images, and some sensor data. The 4G / 5G public network communication serves as a redundant backup, ensuring uninterrupted transmission of basic control commands and status information when the O4 image transmission module 6 is obstructed or interfered with, thanks to extensive network coverage. The two links seamlessly switch via an intelligent routing algorithm, jointly constructing a bidirectional, highly reliable data link between the UAV and ground control station 2, as well as between UAVs themselves.

[0078] Electric release mechanism 8: This mechanism is the actuator for attaching and releasing the interceptor net 3, precisely installed at the bottom center of gravity of the carrier drone 1. It features conventional mechanical hook locking / releasing functions and integrates a high-force, low-power controllable electromagnetic chuck. When the interceptor net 3 needs to be attached, the electromagnetic chuck is energized, generating a strong magnetic field that firmly attracts the magnetic strip pre-fixed to the side of the interceptor net 3. When release is required, simply de-energize the electromagnetic chuck; the magnetic force disappears instantly, and the interceptor net 3 separates from the drone under gravity. This dual "mechanical + magnetic" fixing method ensures that the interceptor net 3 will not accidentally detach during high-speed maneuvers, while release is quick and reliable.

[0079] Multi-sensor fusion safety system: This system is crucial for ensuring the safe flight of the flight and payload modules themselves, and for accurately perceiving targets and the environment. It consists of a sensing network composed of multiple sensors, specifically including: LiDAR 5: Employs a 16-line or 32-line mechanical or solid-state LiDAR 5, typically operating at wavelengths of 905nm or 1550nm. It generates high-density 3D point cloud data by emitting laser beams and receiving echoes, primarily used for high-precision real-time 3D environment modeling around aircraft, enabling precise obstacle avoidance at close range, especially crucial during takeoff, landing, and low-altitude hovering.

[0080] Millimeter-wave radar 4: Typically, a frequency-modulated continuous wave radar module in the 77GHz or 24GHz band is selected. Due to its wavelength being much longer than visible light, millimeter waves have strong penetrating power against rain, fog, smoke, and dust particles, providing stable and reliable mid-to-long-range obstacle detection capabilities under complex weather conditions. Its detection data is unaffected by light, effectively compensating for the performance shortcomings of visual and lidar 5 in adverse weather conditions.

[0081] Dual-Braking Parachute 9: This system includes two independent parachute packs, a primary and a backup, an ejection device, and a trigger controller. When the onboard flight controller detects an irreparable catastrophic malfunction in the drone, it immediately issues a command to first detonate the primary parachute ejection device; if the primary parachute fails to open, the backup parachute detonates after a very short delay. The parachute area is carefully calculated to ensure the drone and its payload land at a safe speed, minimizing damage to people and property on the ground.

[0082] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

[0083] Example 3 This embodiment also provides an electronic device applicable to a method for intercepting mobile drones based on dual-machine cooperation, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to realize the method for intercepting mobile drones based on dual-machine cooperation as proposed in the above embodiment. This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements a method for intercepting a mobile unmanned aerial vehicle based on dual-machine cooperation as proposed in the above embodiments. The storage medium proposed in this embodiment and the method for implementing a mobile UAV interception based on dual-machine cooperation proposed in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments. Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0084] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for intercepting mobile unmanned aerial vehicles (UAVs) based on dual-machine cooperation, characterized in that: include: The ground control station performs system initialization. Based on the master-slave formation control algorithm, the master aircraft tracks the virtual waypoint issued by the ground control station, and the slave aircraft calculates the position through the inter-aircraft data link to achieve horizontal parallel formation. After autonomously flying to the predetermined airspace, it automatically switches to hovering mode. The formation is fine-tuned using a PID controller, and the electromagnetic chuck is energized through a multi-condition intelligent logic gate, which causes the magnetic strip to attract and open the interception net. The millimeter-wave radar and lidar are activated simultaneously, and adaptive weighted fusion is performed based on the quality factor to output the real-time state estimate of the target. The target recognition confidence is calculated based on environmental adaptive weights. The main aircraft tracks and intercepts the flight path, while the slave aircraft maintains a fixed distance and closely follows. The two aircraft cooperate to maneuver and place the deployed interception net in front of the target's flight path for collision capture. Once captured, the interception net is released and the captured target falls into a predetermined safe area. Combining real-time obstacle perception and digital elevation model, the optimal path is planned to achieve autonomous and safe return.

2. The method for intercepting mobile unmanned aerial vehicles based on dual-machine cooperation as described in claim 1, characterized in that: The master-slave formation control algorithm is expressed as follows: ； in, Indicates the target position of the slave unit. Indicates the host's real-time location. Represents the relative displacement vector; The prediction of the target drone is represented as: ； ； in, Indicates the predicted location of the target drone. Indicates the real-time location of the target drone. Indicates the speed of the target drone. Indicates a time window. Indicates the effective capture range of the interceptor network; The roll and pitch angles of the two drones are monitored in real time by an airborne IMU. Both drones' real-time roll and pitch angles are less than their respective thresholds, as shown below: ； in, This indicates the real-time roll angle of the transport drone. For the real-time pitch angle of the transport drone, The preset roll angle threshold for transporting drones, The preset pitch angle threshold for the transport drone, This is the serial number for the transport drone.

3. The method for intercepting mobile unmanned aerial vehicles based on dual-machine cooperation as described in claim 2, characterized in that: The formation fine-tuning using a PID controller is represented as follows: ； in, This represents the spacing error at the current moment; The control quantity is the amount of change that needs to be adjusted. This is the proportional gain coefficient. This is the integral gain coefficient. The differential gain coefficient, In order to be in Timing deviation, Due to historical error, Here, k is the error value from the previous moment, k is the current sampling moment, and j is the loop variable for the summation operation; The error between the actual distance between the two carrier drones and the commanded interception distance is within the tolerance limit, expressed as: ； in, This indicates the actual distance between the two transport drones. This indicates the command interception distance between the two carrier drones. Indicates the error tolerance.

4. The method for intercepting mobile unmanned aerial vehicles based on dual-machine cooperation as described in claim 3, characterized in that: The adaptive weighted fusion based on quality factors is expressed as: ； ； ； ； in, This indicates the signal-to-noise ratio of millimeter-wave radar. Indicates the meteorological disturbance index. This indicates the point cloud density of the lidar. This represents the maximum point cloud density of the lidar system. This indicates the final fusion result. Indicates the fusion weights of millimeter-wave radar. Indicates the fusion weights of the lidar. This represents the target state calculated by the millimeter-wave radar. This indicates the target state calculated by the lidar. The quality factor of millimeter-wave radar. This represents the quality factor of the lidar.

5. The method for intercepting mobile unmanned aerial vehicles based on dual-machine cooperation as described in claim 4, characterized in that: The target recognition confidence score calculated based on environmental adaptive weights is expressed as follows: ； in, and Represents the weighting coefficient, and , Confidence level for target identification.

6. The method for intercepting mobile unmanned aerial vehicles based on dual-machine cooperation as described in claim 5, characterized in that: The meteorological disturbance index is expressed as: ； ； in, Represents the filter coefficients. This represents the signal attenuation coefficient caused by reduced visibility due to fog and dust. This indicates signal attenuation caused by rainfall. This is the meteorological interference index after filtering. This represents the meteorological disturbance index after filtering at the previous moment. This represents the instantaneous meteorological disturbance index at the current sampling time.

7. The method for intercepting mobile unmanned aerial vehicles based on dual-machine cooperation as described in claim 6, characterized in that: The planned optimal path for autonomous and safe return includes: After confirming that the target has been successfully captured, the ground control station sends instructions to the two carrier drones. The electric release mechanism at the bottom of the two carrier drones is simultaneously de-energized, the magnetic force of the electromagnetic chuck disappears, and the interception net, along with the captured target drone, falls towards the predetermined safe area under the action of gravity. After releasing the interception net, the two carrier drones rely on their safety systems to return to base autonomously and safely.

8. A system based on the dual-machine cooperative mobile unmanned aerial vehicle interception method according to any one of claims 1-7, characterized in that: include, The flight and payload module is used to carry out flight and maneuver missions, enabling formation flying, hovering, coordinated maneuvering, and return to base. The interception execution module is connected to the flight and carrier module to realize the net setting, capture and release actions; The intelligent sensing and control module enables formation fine-tuning, target perception, fusion recognition, intelligent decision-making, and collaborative control.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the mobile unmanned aerial vehicle interception method based on dual-machine cooperation as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the mobile unmanned aerial vehicle interception method based on dual-machine cooperation as described in any one of claims 1 to 7.

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