Unmanned aerial vehicle multi-mode detection netting system

Abstract

The invention relates to the field of unmanned aerial vehicle interception, and discloses an unmanned aerial vehicle multi-mode detection net capturing system. The system is arranged on an interception unmanned aerial vehicle platform and comprises a multi-mode detection module, a maneuvering behavior prediction and capture countermeasure field construction module, a variable topology net capture execution module, a net piece self-sensing and local contraction control module and a flight and execution cooperative control module. The multi-modal detection module fuses the multi-source sensing data to obtain a target state vector; the prediction and countermeasure field construction module predicts a target maneuvering behavior based on the target state vector and a maneuvering strategy model, generates a capture countermeasure field and outputs a capture polyhedron parameter set; the netting execution module controls the unfolding, orientation and boundary control parameters of a plurality of meshes according to the parameter set to form a variable topology netting structure; the self-sensing and contraction control module identifies the contact area and drives the corresponding mesh and the adjacent mesh to locally contract; and the cooperative control module plans a flight path according to the parameter set and coordinates a mesh expansion and contraction time sequence.

Description

Technical Field

[0001] This invention relates to the field of drone interception, specifically to a multimodal detection and capture system for drones. Background Technology

[0002] With the gradual opening of low-altitude airspace and the widespread use of drones in logistics, emergency rescue, patrol mapping, and aerial filming, drone activity in urban near-ground airspace, around important facilities, and within specific controlled areas is becoming more frequent and diverse. To address the identification, tracking, and handling of non-cooperative drone targets, the industry has developed a low-altitude security technology system based on the "detection-location-interception-response" framework, involving multiple technical branches such as radar detection, electro-optical / infrared imaging, radio direction finding, acoustic positioning, visual intelligent recognition, and flight control coordination. These systems are typically deployed on fixed base stations, vehicle-mounted platforms, or airborne platforms, continuously sensing the spatial position, motion characteristics, and appearance features of target drones through different types of sensors, and implementing tracking and response actions under the command and control link.

[0003] Regarding interception and disposal methods, currently widely used technologies include navigation deception, radio jamming, directed energy interference, and physical capture. Among these, physical capture, represented by net capture, utilizes an interceptor carrier equipped with deployable nets. During relative movement with the target drone, the nets form an envelope or entanglement, effectively restricting the target drone. To address the dynamic characteristics of the target drone, such as speed changes, attitude adjustments, and maneuvering, net capture devices are evolving towards multi-net coordination, deployable structures, adjustable attitudes, and controllable boundary tension to adapt to target drones of different sizes, flight attitudes, and maneuvering trajectories. The corresponding control system typically includes net deployment control, net attitude adjustment, boundary rope release and tension adjustment, and is coordinated with the flight control process of the interceptor carrier.

[0004] In target perception and state estimation, the detection and tracking of low-altitude targets has become a technological trend of multi-source information fusion. For example, millimeter-wave radar can provide target relative distance and radial velocity information, visual or depth imaging can provide target contour and three-dimensional pose information, and infrared imaging can provide thermal distribution information related to the dynamic operating state. The integrated application of multimodal sensors on airborne platforms has enabled target state estimation to gradually evolve from single measurement to a unified cross-modal and multi-dimensional representation, and can provide an input basis for subsequent maneuver prediction, interception path planning, and net-capture structure control. To this end, we propose a UAV multimodal detection and net-capture system. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a UAV multimodal detection and capture system to solve the technical problems existing in the prior art.

[0006] The above-mentioned technical objective of the present invention is achieved through the following technical solution: A multimodal detection and capture system for unmanned aerial vehicles (UAVs) includes: [The system is described in the original text, but the translation is incomplete.] The multimodal detection module is used to acquire multi-source perception data characterizing the relative position, motion state, spatial attitude, and power load state of the target UAV, and to fuse the multi-source perception data to obtain the target state vector. The maneuvering behavior prediction and capture strategy field construction module is connected to the multimodal detection module. It is used to predict the maneuvering behavior of the target UAV in a preset time domain based on the target state vector and combined with the maneuvering strategy model, and construct the capture strategy field. The capture strategy field is parameterized into the spatial position, time interval and corresponding net surface control parameters of multiple capture areas to form a capture polyhedron parameter set. The variable topology net capture execution module is connected to the maneuver behavior prediction and capture strategy field construction module. It is used to control the deployment state of multiple deployable net panels arranged around the interceptor UAV platform according to the capture polyhedron parameter set, and to adjust the spatial attitude and boundary control parameters of each net panel so that the multiple net panels form a variable topology net capture structure. The mesh self-sensing and local shrinkage control module is connected to the variable topology mesh capture execution module. It is used to collect the detection data of the sensing units set on each mesh, identify the contact area between each mesh and different parts of the target UAV, and control the corresponding mesh and its adjacent mesh to perform local shrinkage when the trigger condition is met. The flight and execution coordinated control module is connected to the multimodal detection module, the maneuvering behavior prediction and capture strategy field construction module, the variable topology net capture execution module, and the net self-sensing and local contraction control module, respectively. It is used to control the flight trajectory of the intercepting UAV platform according to the capture polyhedron parameter set and coordinate the working timing of the variable topology net capture execution module and the net self-sensing and local contraction control module.

[0007] Preferably, the multimodal detection module includes: The millimeter-wave radar unit is used to collect distance data and radial velocity data between the target UAV and the interceptor UAV platform. The visual detection unit is used to acquire visible light and depth images of the target UAV to obtain target contour information and three-dimensional pose information; The infrared detection unit is used to acquire infrared thermal images of the target UAV to obtain target thermal distribution information; The multi-source sensing data is output by the millimeter-wave radar unit, the visual detection unit, and the infrared detection unit, respectively.

[0008] Preferably, the multimodal detection module further includes a time synchronization and coordinate calibration unit, which is used to timestamp-align the multi-source sensing data output by the millimeter-wave radar unit, the visual detection unit, and the infrared detection unit, and to convert the measurement results of each sensor to a unified coordinate system; The multimodal detection module includes a data fusion unit, which is used to fuse distance data, radial velocity data, three-dimensional pose information and heat distribution information according to fusion rules after completing timestamp alignment and coordinate unification, and output the target state vector. The target state vector includes the target UAV's relative position parameters, relative velocity parameters, attitude parameters, and power load parameters.

[0009] Preferably, the maneuver behavior prediction and capture strategy field construction module includes: The maneuver strategy model storage unit is used to store the maneuver strategy model of the target UAV. A maneuver behavior prediction unit is used to generate a predicted state sequence of the target UAV in the time domain based on the target state vector and the maneuver strategy model. A capture game field solving unit is used to generate a capture game field based on the predicted state sequence; The parameterization output unit is used to parameterize the capture game field into a capture polyhedron parameter set.

[0010] Preferably, the maneuver behavior prediction unit recursively calculates the target state vector at a fixed sampling interval in the time domain, and outputs a predicted state sequence sorted by time based on the maneuver strategy model. The predicted state sequence consists of relative position parameters, relative velocity parameters, and attitude parameters. The capture game field solving unit determines multiple capture regions based on the predicted state sequence, and each capture region corresponds to a continuous time interval and a set of spatial constraints. The parameterized output unit outputs parameter items from the capture polyhedron parameter set for each capture region. The parameter items include the spatial position parameter, time interval parameter, mesh normal parameter, and mesh control parameter of the capture region. The spatial constraints consist of relative distance constraints, relative azimuth constraints, and mesh surface normal constraints.

[0011] Preferably, the variable topology net capture execution module includes: The mesh unfolding control unit is used to determine the unfolding time and unfolding degree of each unfoldable mesh based on the captured polyhedron parameter set; The mesh attitude adjustment unit is used to adjust the spatial orientation of each deployable mesh according to the captured polyhedron parameter set; The mesh boundary control unit is used to adjust the boundary control parameters of each deployable mesh according to the captured polyhedron parameter set.

[0012] Preferably, the interceptor drone platform is circumferentially arranged with multiple deployable net panels, each of which corresponds to an independent deployment control channel, attitude adjustment channel, and boundary control channel. The variable topology net capture execution module controls each of the multiple deployable net panels. Each deployable mesh panel has multiple boundary ropes along its boundary. The mesh panel boundary control unit sets the boundary control parameters by adjusting the release length and tension of the corresponding boundary ropes.

[0013] Preferably, the mesh self-sensing and local shrinkage control module includes: The sensor data acquisition unit is used to acquire the detection data of the strain sensor unit and the pressure sensor unit set at the boundary and surface area of ​​each mesh. The contact area identification unit is used to determine the contact area where the mesh and the target drone come into contact based on the strain distribution data of the strain sensing unit and the pressure distribution data of the pressure sensing unit. The local shrinkage control unit is used to output a local shrinkage command to the boundary control channel of the corresponding mesh and its adjacent mesh after the contact area identification unit determines the contact area; The contact area identification unit maps the detection data of each strain sensing unit and pressure sensing unit to the mesh coordinate system, and determines the boundary range and center position of the contact area within the mesh coordinate system. The local contraction control unit triggers the local contraction command when the detection value of any strain sensing unit in the contact area exceeds a first threshold and the detection value of any pressure sensing unit in the contact area exceeds a second threshold. The first and second thresholds are calculated by the calibration unit based on the mesh material parameters and the range of the sensing unit. The local contraction command is used to control the boundary ropes of the corresponding mesh and its adjacent mesh to tighten according to the boundary range of the contact area.

[0014] Preferably, the flight and execution coordinated control module includes: The flight trajectory planning unit is used to generate a reference flight trajectory for the intercepting drone platform based on the spatial position parameters in the captured polyhedron parameter set. An attitude command generation unit is used to generate attitude commands for the interceptor drone platform based on the reference flight trajectory. The execution timing management unit is used to generate the control timing of the variable topology net capture execution module and the net self-sensing and local shrinkage control module based on the time interval parameters in the capture polyhedron parameter set.

[0015] Preferably, the execution timing management unit divides the time interval parameter into a flight approach phase, a mesh deployment phase, and a local contraction control phase. During the flight approach phase, it outputs flight control commands for intercepting the UAV platform. During the mesh deployment phase, it outputs mesh deployment control commands to the variable topology mesh capture execution module. During the local contraction control phase, it outputs local contraction control commands to the mesh self-sensing and local contraction control module. When generating a reference flight trajectory, the flight trajectory planning unit uses the spatial position parameters of multiple capture regions in the capture polyhedron parameter set as trajectory constraint points to constrain the flight path of the interceptor drone platform.

[0016] In summary, the present invention has the following main beneficial effects: By setting up a multimodal detection module consisting of millimeter-wave radar, visual detection, and infrared detection, and fusing distance, radial velocity, three-dimensional pose, and thermal distribution information based on time synchronization and unified coordinate calibration, a unified vectorized representation of the target UAV's relative position, relative velocity, attitude, and power load status is achieved. This enables the interception platform to obtain stable and consistent target status input even in complex backgrounds, obstructions, or high-speed target maneuvers, providing a reliable foundation for subsequent prediction and control, thereby reducing the tracking drift risk caused by error amplification from a single sensor source. By setting up a maneuver behavior prediction and capture strategy field construction module, the target state vector is recursively predicted within a preset time domain, and the prediction results are parameterized into the spatial location, time interval and net surface control parameters of multiple capture areas to form a capture polyhedron parameter set. This transforms the possible maneuver trajectory of the target into a spatiotemporal constraint and control interface that can directly drive the net capture structure, so that the net capture action changes from passive triggering interception to prediction-based feedforward net deployment, thereby improving the lead time arrangement and capture window utilization efficiency for highly maneuverable targets. By setting up a linkage control between the variable topology net capture execution module and the net self-sensing and local contraction control module, and with the flight and execution collaborative control module uniformly managing the timing of net deployment, attitude adjustment, boundary rope control, and local contraction, the net can form a spatial envelope matching the target's attitude and orientation within the capture area. After contact occurs, the contact area and adjacent areas are locally tightened. This allows the net capture structure to maintain topological self-adaptation and force self-equilibrium during the continuous actions of deployment, alignment, contact, and tightening. This improves the practical adaptability to targets of different sizes and attitudes and reduces the probability of escape gaps. Attached Figure Description

[0017] Figure 1 This is a system flowchart of the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Example 1 refer to Figure 1 A multimodal detection and capture system for unmanned aerial vehicles (UAVs) comprises: a multimodal detection module, a maneuvering behavior prediction and capture strategy field construction module, a variable topology capture execution module, a net self-sensing and local contraction control module, and a flight and execution collaborative control module. The modules are sequentially coupled through data and control interfaces to form a closed-loop working link. The multimodal detection module performs millimeter-wave radar, visual, and infrared detection on the target UAV, obtaining distance data, radial velocity data, three-dimensional pose information, and thermal distribution information. After time synchronization and coordinate calibration, the data fusion unit outputs the target state vector. The maneuver behavior prediction and capture strategy field construction module generates a predicted state sequence based on the target state vector and maneuver strategy model, solves the capture strategy field, and outputs the capture polyhedron parameter set. The variable topology net capture execution module controls the deployment time, deployment degree, and spatial orientation of multiple deployable nets arranged circumferentially on the intercepting UAV platform according to the capture polyhedron parameter set, and adjusts the control parameters of the net boundaries. When a net comes into contact with the target UAV, the net self-sensing and local contraction control module identifies the contact area based on the detection data of the net sensing unit and triggers the local contraction of the corresponding net and adjacent nets. The flight and execution coordination control module generates the reference flight trajectory and attitude command of the intercepting UAV platform according to the capture polyhedron parameter set, and manages the timing of net deployment control and local contraction control.

[0020] The multimodal detection module is used to acquire multi-source perception data characterizing the relative position, motion state, spatial attitude, and power load state of the target UAV, and to fuse the multi-source perception data to obtain the target state vector. The maneuvering behavior prediction and capture strategy field construction module is connected to the multimodal detection module. It is used to predict the maneuvering behavior of the target UAV in a preset time domain based on the target state vector and combined with the maneuvering strategy model, and construct the capture strategy field. The capture strategy field is parameterized into the spatial position, time interval and corresponding net surface control parameters of multiple capture areas to form a capture polyhedron parameter set. The variable topology net capture execution module is connected to the maneuver behavior prediction and capture strategy field construction module. It is used to control the deployment state of multiple deployable net panels arranged around the interceptor UAV platform according to the capture polyhedron parameter set, and to adjust the spatial attitude and boundary control parameters of each net panel so that the multiple net panels form a variable topology net capture structure. The mesh self-sensing and local shrinkage control module is connected to the variable topology mesh capture execution module. It is used to collect the detection data of the sensing units set on each mesh, identify the contact area between each mesh and different parts of the target UAV, and control the corresponding mesh and its adjacent mesh to perform local shrinkage when the trigger condition is met. The flight and execution coordinated control module is connected to the multimodal detection module, the maneuvering behavior prediction and capture strategy field construction module, the variable topology net capture execution module, and the net self-sensing and local contraction control module, respectively. It is used to control the flight trajectory of the intercepting UAV platform according to the capture polyhedron parameter set and coordinate the working timing of the variable topology net capture execution module and the net self-sensing and local contraction control module.

[0021] The multimodal detection module acquires multi-source perception data of the target UAV through a combination of various sensors, fuses these data, and outputs the target state vector, which is then used to perform tasks such as maneuver behavior prediction and capture strategy field construction.

[0022] The multimodal detection module includes a millimeter-wave radar unit, which is used to collect distance data and radial velocity data between the target UAV and the interceptor UAV platform.

[0023] This radar unit detects the target's distance based on the reflection of millimeter-wave signals and calculates the target's radial velocity using the Doppler effect.

[0024] Specifically, range data is obtained by measuring the wave propagation time between the target and the interceptor platform, while radial velocity data is obtained by analyzing the frequency shift of the radar return signal.

[0025] Distance calculation formula: ;in, Distance to target; The speed of light; This is the round-trip time of the signal.

[0026] Radial velocity calculation formula: ;in Radial velocity; The received frequency; For transmission frequency; It is the speed of light.

[0027] The visual detection unit is used to acquire visible light and depth images of the target UAV through an optical imaging system, in order to obtain the target's contour information and three-dimensional pose information.

[0028] Visible light images are used to capture the outline of a target.

[0029] Depth images, on the other hand, use stereo vision or structured light technology, combined with multiple camera perspectives, to acquire the three-dimensional shape of the target and infer the target's attitude information (e.g., roll angle, pitch angle, yaw angle).

[0030] The target's three-dimensional pose information is obtained by three-dimensional reconstruction using the depth information of each pixel measured by the vision sensor and the camera's intrinsic and extrinsic parameters.

[0031] The infrared detection unit is used to collect infrared thermal images of the target UAV in order to obtain the target's thermal distribution information.

[0032] Infrared sensors capture the temperature difference between the target body and the surrounding environment, convert it into thermal image data, thereby inferring the target's location and heat source distribution, and further used to determine the target's power load status.

[0033] Multi-source sensing data fusion: The core of the multi-modal detection module lies in the data fusion unit, which fuses multi-source sensing data from millimeter-wave radar, visual detection, and infrared detection units to output a target state vector. In this embodiment, the data fusion follows the following process: Time synchronization and coordinate calibration: Time synchronization and coordinate calibration of multi-source sensing data are crucial for accurate data fusion. To ensure precise alignment of data from different sensors, the following steps are implemented: Timestamp alignment of the output data from each sensor; The measurement results from each sensor are converted to a unified coordinate system.

[0034] Time synchronization: ;in, , , The sampling time for each sensor data point is specified, and the synchronized data will be fused in a unified coordinate system.

[0035] The data fusion unit fuses multi-source sensing data into a target state vector using a weighted average method. The specific fusion process is as follows: Target state vector: ;in, This is the target state vector after fusion; This is the output data of the millimeter-wave radar; This is the output data of the visual detection unit; This is the output data of the infrared detection unit; , , These are weighting coefficients adjusted based on the accuracy and reliability of each sensor.

[0036] Target state vector It includes the following parameters: Relative position parameters: distance information between the target and the intercepting drone provided by millimeter-wave radar, and the three-dimensional position reconstruction results of the visual sensor.

[0037] Relative velocity parameter: Radial velocity information provided by millimeter-wave radar.

[0038] Attitude parameters: Target attitude (including roll angle, pitch angle, and yaw angle) provided by the vision detection unit.

[0039] Dynamic load parameters: The dynamic load of the target is estimated based on the thermal distribution information provided by the infrared detection unit.

[0040] The maneuver behavior prediction and capture strategy field construction module is data-connected to the multimodal detection module. Its input is the target state vector, and its output is the capture polyhedron parameter set. This module consists of a maneuver strategy model storage unit, a maneuver behavior prediction unit, a capture strategy field solving unit, and a parameterized output unit. The maneuver strategy model storage unit pre-stores the target UAV's maneuver strategy model, indicating the set of achievable maneuvers and escape tendencies of the target UAV under different states. The maneuver behavior prediction unit generates a predicted state sequence within a preset time domain based on the target state vector and the maneuver strategy model. The capture strategy field solving unit generates the capture strategy field based on the predicted state sequence and determines multiple capture regions. The parameterized output unit parameterizes each capture region into parameter terms within the capture polyhedron parameter set.

[0041] The maneuver strategy model is built and stored offline. Its input is a subset of the target state vector related to maneuvering, and its output is the upper bound of control and the escape direction constraint of the target UAV in the prediction time domain. In this embodiment, the target state vector at discrete time points... Recorded as: ; in, Let be the relative position parameters of the target UAV with respect to the coordinate system of the interceptor UAV platform; where, , , These represent the target drone at the [number]th [location]. The relative position of each sampling moment with respect to the coordinate system of the intercepting drone platform is in axis, axis, Components in the axial direction, This refers to the relative velocity parameter; For attitude parameters, Euler angles are used in this embodiment. These correspond to roll angle, pitch angle, and yaw angle, respectively. The dynamic load parameters are calculated from the heat distribution information output by the multimodal detection module and the attitude / velocity recursive relationship, and are used to characterize the current propulsion load level of the target UAV. Establishing a platform coordinate system for intercepting drone platforms ,in axis, axis, The axes are pairwise orthogonal and fixed to the interceptor drone platform; in this embodiment, The axis points forward on the platform. The axis points to the right of the platform. The axis points upwards from the platform; Indicates the discrete sampling time number.

[0042] The maneuvering strategy model defines the upper bound of the target UAV's achievable acceleration and the escape direction constraint at time t. The model outputs the maximum achievable acceleration scalar. and the escape direction unit vector ,in, This represents the upper bound of the maximum acceleration corresponding to the dynamic load parameters; The escape direction is determined by both attitude parameters and relative velocity parameters; the feasible set of accelerations for the target UAV is defined as: ; in, For the target drone at any time The relative acceleration control quantity; It is a norm 2; This indicates that the direction of acceleration is not opposite to the direction of escape, and is used to constrain the target's maneuvering to conform to its escape tendency; The maneuver behavior prediction unit in the preset time domain With fixed sampling interval Perform recursion, the number of sampling steps is The predicted state sequence is denoted as ,in = Position and velocity are predicted using a discrete kinematics model: ； ; in, , These are the predicted relative position parameters and relative velocity parameters, respectively. To satisfy the prediction constraint of the most unfavorable escape maneuver, this embodiment uses an extreme value selection method to determine the optimal method. : ; This formula represents selecting the acceleration that maximizes the predicted relative distance increment from the set of feasible accelerations, thus obtaining the most unfavorable escape prediction sequence for the intercepting drone platform. Attitude parameters are recursively derived using a constant angular velocity model:

[0043] in, In this embodiment, the angular velocity values ​​are within the upper bound constraint of the angular velocity given by the maneuver strategy model. Similarly, the most unfavorable escape direction is selected so that the change in the normal direction of the target rotor plane tends to reduce the net capture alignment margin.

[0044] The capture game field solver determines multiple capture regions based on the predicted state sequence. For each discrete time step... Solve for the following geometric quantities:

[0045] in, To predict relative distance; It is the azimuth angle; The pitch angle; , , for Components; based on the mesh normal constraint, using the predicted attitude parameters Calculate the normal to the target rotor plane In Euler angles representation, Normal of the rotor plane in the machine system After attitude rotation, the following was obtained: in, This is a rotation matrix composed of Euler angles. In this embodiment, the spatial constraints consist of relative distance constraints, relative azimuth constraints, and mesh normal constraints. For each time step... When the following conditions are met:

[0046]

[0047]

[0048] These three conditions determine that the moment belongs to the captureable set, among which , This is the distance constraint threshold; , This is the azimuth constraint threshold; The candidate mesh normals at the corresponding time point; The threshold value is the angle between the mesh normal and the rotor normal; the capture game field solving unit merges the set of consecutive moments satisfying the constraints according to temporal adjacency to obtain multiple capture regions. Output captures parameter items within the polyhedron parameter set : ; in, To capture the center location parameters of the region, the mean of the predicted relative positions within that region is used; For time interval parameters; The normal parameters of the mesh surface are determined by the angle constraints within the region. Determined by taking the mean or median; This is a set of mesh control parameters used to characterize the mesh unfolding control and boundary control quantities corresponding to the capture area. In this embodiment... Pick: ,in, This refers to the unfolding orientation angle of the corresponding mesh sheet; To allow for advance preparation; For the mesh opening size parameters; These are the boundary tension force parameters; the above parameters are obtained by the capture game field solver in the region. The internal constraint relaxation is determined by minimizing the spatial constraint relaxation amount, and follows... The data is then output to the variable topology network capture execution module.

[0049] The maneuver behavior prediction and capture strategy field construction module in this embodiment does not determine the trigger time based solely on a single location prediction. Instead, it takes the target state vector as input, constrains the target reachable maneuver set with the maneuver strategy model, generates a capture strategy field based on the "most unfavorable escape maneuver" prediction sequence, and further determines the capture area with three types of spatial constraints: distance, azimuth angle, and mesh surface normal, and outputs the capture polyhedron parameter set, forming a parameterized interface consistent with subsequent mesh deployment, attitude adjustment, and boundary control.

[0050] The variable topology net capture execution module receives the capture polyhedron parameter set from the parameterized output unit mentioned above and performs segmented control on the multiple deployable net panels arranged circumferentially on the interceptor UAV platform. Each deployable net panel corresponds to an independent deployment control channel, attitude adjustment channel, and boundary control channel. These three channels are respectively connected to the net panel deployment drive mechanism, the net panel attitude execution mechanism, and the net panel boundary winding / tensioning execution mechanism to achieve decoupled adjustment of the deployment state, spatial orientation, and boundary control parameters of the same net panel.

[0051] The mesh unfolding control unit generates the unfolding command time and unfolding target amount for each mesh based on the time series parameters in the captured polyhedron parameter set. The unfolding command time is determined by the time interval parameters of each captured area and the unfolding lead, while the unfolding target amount is constrained by the opening size parameters. The unfolding control unit sends the corresponding unfolding command to the unfolding control channel of the corresponding mesh, causing the mesh to enter the unfolded state from the folded state at the command time and maintain the unfolded state to the degree corresponding to the opening size during the unfolding process.

[0052] The mesh attitude adjustment unit determines the target spatial orientation of the mesh based on the mesh surface normal parameters from the captured polyhedral parameter set, and drives the mesh attitude actuator to adjust the mesh attitude through the corresponding attitude adjustment channel, so that the mesh normal is consistent with the mesh surface normal parameters. The attitude adjustment unit completes the target orientation setting before the mesh is deployed and continuously outputs attitude maintenance commands during the deployment process until the time interval of the captured area ends.

[0053] Each deployable mesh panel's boundary is composed of multiple boundary ropes, with the release length and tension of each rope adjusted by its corresponding boundary control channel. The mesh boundary control unit, based on the opening size parameters and boundary tension parameters from the captured polyhedron parameter set, adjusts the boundary tension of the first mesh panel. The first piece of the network The boundary rope is set to the target length. Tension with the target The target release length is determined according to the mesh geometric calibration model and the opening size parameters:

[0054] in, This is the reference release length of the boundary rope in the retracted state. The dimensional calibration coefficients are used for the mesh unfolding mechanism; the target tension force is directly given by the boundary tension force parameters. The boundary control unit collects the actual length of ropes released at each boundary in real time. With actual tension and according to a fixed control cycle Perform closed-loop regulation and output winding control quantity. With tension control amount In this embodiment, the winding and tensioning are controlled by proportional and integral methods, respectively: ; ; in, , This is the control coefficient for the winding channel; , This is the control coefficient for the tensioning channel; Acting on the winding actuator; It acts on the tensioning actuator; during execution, the variable topology net capture execution module uses the capture polyhedron parameter set as the unified scheduling basis to perform sequential linkage of net deployment, attitude adjustment and boundary control for different capture areas; multiple nets within the same capture area are synchronously adjusted according to the corresponding target orientation and boundary control parameters, so that multiple nets form the net capture topology corresponding to the capture area in space; when the capture area is switched, the module updates the deployment sequence, net target orientation and boundary control parameters, driving the net capture topology to switch with the change of the capture polyhedron parameter set.

[0055] The mesh self-sensing and local shrinkage control module includes a sensor data acquisition unit, a contact area identification unit, and a local shrinkage control unit, which are sequentially coupled. The sensor data acquisition unit operates at a fixed sampling period. The outputs of the strain sensing unit and the pressure sensing unit are sampled; The first piece of the network The sampled values ​​of each strain sensing unit are denoted as , No. The first piece of the network The sampled values ​​of each touch pressure sensing unit are denoted as: .

[0056] The contact area identification unit pre-stores a mapping table between the mesh coordinate system and the sensor unit placement positions. The mesh coordinate system is denoted as... ,in The shaft is set along the unfolding direction of the mesh. The axis is positioned transversely along the mesh. The positions of the strain sensing unit and the pressure sensing unit in the mesh coordinate system are denoted as follows: and At the sampling time Calculate the strain trigger set and the pressure trigger set: ; in, For strain trigger set, The first threshold; ; in, For pressure-triggered sets, This is the second threshold.

[0057] when and If it is not empty, determine the first... When the meshes come into contact, the boundary of the contact area is defined by the smallest bounding rectangle of the union of the two set point clouds: ; in, The boundary range of the contact area. The minimum bounding rectangle operation; by The contact area is obtained in shaft and The projection intervals along the axial direction are denoted as follows: The center of the contact area is determined by the centroid: ; in, The center position of the contact area; the local contraction control unit obtains Then a local contraction command is generated, and the first... The mesh panels and their adjacent mesh panels in the circumferential arrangement act synchronously. For the first... The first piece of the network One boundary rope, tightening the target increment is: ,in To tighten the target increment; This is the local shrinkage ratio coefficient; From the corresponding boundary node to the center of the contact area The distance between the mesh planar surfaces. After local contraction is triggered, the length of the rope target released at this boundary is updated to: ,in The target length to be extended before local contraction; Release the length for the updated target. The boundary ropes of adjacent meshes are updated according to the same rule, with their distance terms calculated from their respective boundary nodes to... The planar distance is determined.

[0058] The flight trajectory planning unit uses the spatial position parameters of each captured region within the captured polyhedral parameter set as trajectory constraint points, sorts them according to the start time of their corresponding time intervals to form a sequence of constraint points, and constructs fifth-order polynomial trajectory segments between adjacent constraint points: ; in, For the first Segment reference position trajectory; The vector of polynomial coefficients for the trajectory segment; This is the duration of the segment.

[0059] The polynomial coefficients are solved by considering the continuity constraints of endpoint positions, velocities, and accelerations.

[0060]

[0061]

[0062] in, The reference velocity constraint is set at the constraint point; It is a zero vector.

[0063] The attitude command generation unit calculates the platform's attitude commands based on the reference flight trajectory. The reference velocity direction unit vector is: ; in, The reference velocity direction is the unit vector; It is a norm 2; The desired yaw and pitch angles are determined by the velocity direction: ; in, The desired yaw angle; , for of , Quantity; This is the arctangent operation with quadrants.

[0064] ; in, The desired pitch angle; for of Quantity; The desired roll angle is determined by the lateral component of the reference acceleration: ; in, The platform expects the attitude command; the execution timing management unit coordinates the conflict between the two types of execution commands within the corresponding time interval of the capture region, and outputs the final value of boundary control using a command fusion method: ; in, For the first The first in the capture area The final boundary control command for the boundary rope; Boundary control commands output by the variable topology net capture execution module; Tightening control commands output by the mesh self-sensing and local shrinkage control module; This is a local contraction trigger flag; it is set to 1 when triggered and 0 otherwise.

[0065] Through the aforementioned trajectory constraint point generation, intra-segment polynomial programming, attitude command derivation from reference trajectory, and three-stage timing and priority coordination control link, the flight and execution collaborative control module controls the flight trajectory of the interceptor UAV platform and completes the timing coordination of net deployment control and local contraction control.

[0066] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A multimodal detection and capture system for unmanned aerial vehicles (UAVs), characterized in that, Including those set up on interceptor drone platforms: The multimodal detection module is used to acquire multi-source perception data characterizing the relative position, motion state, spatial attitude, and power load state of the target UAV, and to fuse the multi-source perception data to obtain the target state vector. The maneuvering behavior prediction and capture strategy field construction module is connected to the multimodal detection module. It is used to predict the maneuvering behavior of the target UAV in a preset time domain based on the target state vector and combined with the maneuvering strategy model, and construct the capture strategy field. The capture strategy field is parameterized into the spatial position, time interval and corresponding net surface control parameters of multiple capture areas to form a capture polyhedron parameter set. The variable topology net capture execution module is connected to the maneuver behavior prediction and capture strategy field construction module. It is used to control the deployment state of multiple deployable net panels arranged around the interceptor UAV platform according to the capture polyhedron parameter set, and to adjust the spatial attitude and boundary control parameters of each net panel so that the multiple net panels form a variable topology net capture structure. The mesh self-sensing and local shrinkage control module is connected to the variable topology mesh capture execution module. It is used to collect the detection data of the sensing units set on each mesh, identify the contact area between each mesh and different parts of the target UAV, and control the corresponding mesh and its adjacent mesh to perform local shrinkage when the trigger condition is met. The flight and execution coordinated control module is connected to the multimodal detection module, the maneuvering behavior prediction and capture strategy field construction module, the variable topology net capture execution module, and the net self-sensing and local contraction control module, respectively. It is used to control the flight trajectory of the intercepting UAV platform according to the capture polyhedron parameter set and coordinate the working timing of the variable topology net capture execution module and the net self-sensing and local contraction control module.

2. The UAV multimodal detection and capture system according to claim 1, characterized in that, The multimodal detection module includes: The millimeter-wave radar unit is used to collect distance data and radial velocity data between the target UAV and the interceptor UAV platform. The visual detection unit is used to acquire visible light and depth images of the target UAV to obtain target contour information and three-dimensional pose information; The infrared detection unit is used to acquire infrared thermal images of the target UAV to obtain target thermal distribution information; The multi-source sensing data is output by the millimeter-wave radar unit, the visual detection unit, and the infrared detection unit, respectively.

3. The UAV multimodal detection and capture system according to claim 2, characterized in that, The multimodal detection module also includes a time synchronization and coordinate calibration unit, which is used to timestamp-align the multi-source sensing data output by the millimeter-wave radar unit, visual detection unit and infrared detection unit, and convert the measurement results of each sensor to a unified coordinate system; The multimodal detection module includes a data fusion unit, which is used to fuse distance data, radial velocity data, three-dimensional pose information and heat distribution information according to fusion rules after completing timestamp alignment and coordinate unification, and output the target state vector. The target state vector includes the target UAV's relative position parameters, relative velocity parameters, attitude parameters, and power load parameters.

4. The UAV multimodal detection and capture system according to claim 3, characterized in that, The maneuver behavior prediction and capture strategy field construction module includes: The maneuver strategy model storage unit is used to store the maneuver strategy model of the target UAV. A maneuver behavior prediction unit is used to generate a predicted state sequence of the target UAV in the time domain based on the target state vector and the maneuver strategy model. A capture game field solving unit is used to generate a capture game field based on the predicted state sequence; The parameterization output unit is used to parameterize the capture game field into a capture polyhedron parameter set.

5. The UAV multimodal detection and capture system according to claim 4, characterized in that, The maneuver behavior prediction unit recursively calculates the target state vector at a fixed sampling interval in the time domain, and outputs a predicted state sequence sorted by time based on the maneuver strategy model. The predicted state sequence consists of relative position parameters, relative velocity parameters, and attitude parameters. The capture game field solving unit determines multiple capture regions based on the predicted state sequence, and each capture region corresponds to a continuous time interval and a set of spatial constraints. The parameterized output unit outputs parameter items from the capture polyhedron parameter set for each capture region. The parameter items include the spatial position parameter, time interval parameter, mesh normal parameter, and mesh control parameter of the capture region. The spatial constraints consist of relative distance constraints, relative azimuth constraints, and mesh surface normal constraints.

6. The UAV multimodal detection and capture system according to claim 5, characterized in that, The variable topology network capture execution module includes: The mesh unfolding control unit is used to determine the unfolding time and unfolding degree of each unfoldable mesh based on the captured polyhedron parameter set; The mesh attitude adjustment unit is used to adjust the spatial orientation of each deployable mesh according to the captured polyhedron parameter set; The mesh boundary control unit is used to adjust the boundary control parameters of each deployable mesh according to the captured polyhedron parameter set.

7. The UAV multimodal detection and capture system according to claim 6, characterized in that, The interceptor drone platform is circumferentially arranged with multiple deployable net panels, each corresponding to an independent deployment control channel, attitude adjustment channel, and boundary control channel. The variable topology net capture execution module controls each of the multiple deployable net panels. Each deployable mesh panel has multiple boundary ropes along its boundary. The mesh panel boundary control unit sets the boundary control parameters by adjusting the release length and tension of the corresponding boundary ropes.

8. The UAV multimodal detection and capture system according to claim 7, characterized in that, The mesh self-sensing and local shrinkage control module includes: The sensor data acquisition unit is used to acquire the detection data of the strain sensor unit and the pressure sensor unit set at the boundary and surface area of ​​each mesh. The contact area identification unit is used to determine the contact area where the mesh and the target drone come into contact based on the strain distribution data of the strain sensing unit and the pressure distribution data of the pressure sensing unit. The local shrinkage control unit is used to output a local shrinkage command to the boundary control channel of the corresponding mesh and its adjacent mesh after the contact area identification unit determines the contact area; The contact area identification unit maps the detection data of each strain sensing unit and pressure sensing unit to the mesh coordinate system, and determines the boundary range and center position of the contact area within the mesh coordinate system. The local contraction control unit triggers the local contraction command when the detection value of any strain sensing unit in the contact area exceeds a first threshold and the detection value of any pressure sensing unit in the contact area exceeds a second threshold. The first and second thresholds are calculated by the calibration unit based on the mesh material parameters and the range of the sensing unit. The local contraction command is used to control the boundary ropes of the corresponding mesh and its adjacent mesh to tighten according to the boundary range of the contact area.

9. A UAV multimodal detection and capture system according to claim 8, characterized in that, The flight and execution coordinated control module includes: The flight trajectory planning unit is used to generate a reference flight trajectory for the intercepting drone platform based on the spatial position parameters in the captured polyhedron parameter set. An attitude command generation unit is used to generate attitude commands for the interceptor drone platform based on the reference flight trajectory. The execution timing management unit is used to generate the control timing of the variable topology net capture execution module and the net self-sensing and local shrinkage control module based on the time interval parameters in the capture polyhedron parameter set.

10. A UAV multimodal detection and capture system according to claim 9, characterized in that, The execution timing management unit divides the time interval parameters into a flight approach phase, a mesh deployment phase, and a local contraction control phase. During the flight approach phase, it outputs flight control commands to intercept the UAV platform. During the mesh deployment phase, it outputs mesh deployment control commands to the variable topology mesh capture execution module. During the local contraction control phase, it outputs local contraction control commands to the mesh self-sensing and local contraction control module. When generating a reference flight trajectory, the flight trajectory planning unit uses the spatial position parameters of multiple capture regions in the capture polyhedron parameter set as trajectory constraint points to constrain the flight path of the interceptor drone platform.

Images