Emergency control method and system for a compound wing unmanned aerial vehicle

Abstract

The application provides an emergency control method and system of a compound wing unmanned aerial vehicle; airspace traffic situation awareness data is acquired to determine the position information of the compound wing unmanned aerial vehicle in a complex airspace with communication delay or communication link interruption; according to the position information, a first control parameter matched with a dynamic obstacle avoidance path is generated; according to the flight attitude parameter and the target attitude parameter, the first control parameter is adjusted to generate a second control parameter; when the communication link interruption is detected, the second control parameter is subjected to signal modulation and cluster coding processing to generate a distributed relay transmission signal in an ad hoc network mode, so as to optimize the initial flight path control instruction through the node hopping strategy and the priority routing distribution strategy, and realize the emergency flight path re-planning and the flight attitude cooperative control of the compound wing unmanned aerial vehicle in the communication failure state; the application realizes the multi-aircraft cooperative emergency flight path planning and flight control of the compound wing unmanned aerial vehicle in the complex airspace with the communication link interruption and without external instructions.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of emergency control, and in particular to an emergency control method and system for a compound wing unmanned aerial vehicle. BACKGROUND

[0002] In a complex airspace environment, the compound wing unmanned aerial vehicle may be caused by electromagnetic interference, meteorological mutation or hardware failure when performing tasks, resulting in communication link interruption or serious delay. In such a sudden situation, the compound wing unmanned aerial vehicle needs to perceive the dynamic obstacle distribution in real time and maintain flight stability under the premise of lacking external instruction support, while relying on limited communication resources to complete autonomous route adjustment, which puts high requirements on the emergency response capability and multi-modal control strategy of the system.

[0003] At present, a specific existing scheme adopts a real-time obstacle avoidance and path planning technology based on a centralized communication architecture, which receives sensor data of the compound wing unmanned aerial vehicle in real time through a ground control center, generates an obstacle avoidance path by using a global path planning algorithm, and issues control instructions through a high-bandwidth communication link. However, the existing scheme has significant defects, for example, it is highly dependent on real-time communication of the central node, and once the communication link is interrupted, the global path planning and instruction issuing function will be disabled, and the compound wing unmanned aerial vehicle will be in a passive state without autonomous decision-making capability, which is difficult to meet the emergency control requirements in the communication failure scenario in a complex airspace. The static obstacle map cannot adapt to the dynamic changes of the airspace environment, and lacks a multi-level obstacle avoidance priority layering mechanism, which is prone to path oscillation or collision risk in a dense obstacle scenario. SUMMARY

[0004] The present application provides an emergency control method and system for a compound wing unmanned aerial vehicle, which solves the problems that the prior art is difficult to meet the emergency control requirements in the communication failure scenario in a complex airspace, and cannot adapt to the dynamic changes of the airspace environment, and is prone to path oscillation or collision risk in a dense obstacle scenario.

[0005] In a first aspect, the present application provides an emergency control method for a compound wing unmanned aerial vehicle, comprising:

[0006] Obtaining airspace traffic situation awareness data to determine the position information of the compound wing unmanned aerial vehicle in a complex airspace with communication delay or communication link interruption;

[0007] According to the position information, the geometric boundary of the emergency obstacle avoidance area is drawn to calculate a dynamic obstacle avoidance path, and a first control parameter matching the dynamic obstacle avoidance path is generated;

[0008] According to the axial deviation value of the flight attitude parameter and the target attitude parameter of the compound wing unmanned aerial vehicle, the first control parameter is adjusted to generate a second control parameter, and the second control parameter is used to maintain the emergency flight stability of the compound wing unmanned aerial vehicle.

[0009] signal modulate and cluster encode the second control parameter to generate a distributed relay transmission signal in an ad hoc network mode when detecting a communication link interruption;

[0010] Optimize the initial flight path control instruction of the compound wing unmanned aerial vehicle through the node hopping strategy and priority routing allocation strategy of the distributed relay transmission signal to obtain a target control instruction to realize emergency flight path re-planning and flight attitude cooperative control of the compound wing unmanned aerial vehicle in a communication failure state.

[0011] Optionally, according to the position information, a geometric boundary of an emergency obstacle avoidance area is delineated to calculate a dynamic obstacle avoidance path, and a first control parameter matched with the dynamic obstacle avoidance path is generated, including:

[0012] Obtain the spatial distribution of airspace obstacles in the complex airspace, and combine the position information to delineate the geometric boundary of the emergency obstacle avoidance area;

[0013] According to the shape and distance parameters of the geometric boundary, a multi-level safety buffer zone is constructed with the position of the compound wing unmanned aerial vehicle as the starting point, and each level of the safety buffer zone corresponds to a different obstacle avoidance priority;

[0014] The candidate paths in the multi-level safety buffer zone that have the smallest conflict with the flight direction of the compound wing unmanned aerial vehicle are filtered level by level, and a dynamic obstacle avoidance path is determined according to the curvature continuity and path length of the candidate path;

[0015] Based on the heading angle change rate, height offset and speed adjustment amount of the dynamic obstacle avoidance path, the deflection angle of the rudder surface and the thrust distribution ratio of the propeller of the compound wing unmanned aerial vehicle are adjusted to generate the first control parameter.

[0016] Optionally, the spatial distribution of airspace obstacles in the complex airspace is obtained, and the geometric boundary of the emergency obstacle avoidance area is delineated in combination with the position information, including:

[0017] Obtain a three-dimensional spatial coordinate set of airspace obstacles that the compound wing unmanned aerial vehicle perceives in real time in the complex airspace, and the three-dimensional spatial coordinate set contains position information, size parameters and motion direction parameters of the airspace obstacles;

[0018] Based on the three-dimensional spatial coordinate set, the spatial distribution of the airspace obstacles is spatially topologically segmented to generate a plurality of sub-areas, wherein the boundary of each sub-area is determined by the maximum circumscribed geometric body vertex coordinates of adjacent airspace obstacles;

[0019] According to the position information of the compound wing unmanned aerial vehicle and the relative distance of the sub-regions, a target sub-region conflicting with the flight direction of the compound wing unmanned aerial vehicle is screened out, and the target sub-region is marked as a key conflict region;

[0020] The boundary fitting is performed on the vertex coordinates of the circumscribed geometry of the airspace obstacle of the key conflict region, and a geometric boundary of an emergency obstacle avoidance region is generated.

[0021] Optionally, according to the axial deviation value of the flight attitude parameter of the compound wing unmanned aerial vehicle and the target attitude parameter, the first control parameter is adjusted to generate a second control parameter, including:

[0022] The axial deviation value of the flight attitude parameter of the compound wing unmanned aerial vehicle and the target attitude parameter is calculated, and the flight attitude parameter includes the pitch angle, the roll angle and the yaw angle;

[0023] According to the amplitude component and the direction component of the axial deviation value, the rudder deflection angle control quantity in the first control parameter is adjusted to generate an adjusted rudder deflection angle control quantity, so that the deflection direction of the flap and the aileron of the compound wing unmanned aerial vehicle is consistent with the compensation direction of the direction component;

[0024] Based on the adjusted rudder deflection angle control quantity, the propeller thrust distribution ratio in the first control parameter is synchronously adjusted to generate an adjusted propeller thrust distribution ratio, so that the moment distribution of the compound wing unmanned aerial vehicle on the pitch axis, the roll axis and the yaw axis matches the correction requirement of the amplitude component;

[0025] The adjusted rudder deflection angle control quantity and the adjusted propeller thrust distribution ratio are compensated to generate a second control parameter.

[0026] Optionally, when the communication link interruption is detected, the second control parameter is subjected to signal modulation and cluster coding processing to generate a distributed relay transmission signal in an ad hoc network mode, including:

[0027] When the communication link interruption is detected, the frequency band adaptation characteristics of the second control parameter are extracted to perform signal waveform conversion on the second control parameter to generate a modulation signal matched with different frequency band channel characteristics, and the frequency band characteristic parameters of the modulation signal are extracted;

[0028] Based on the channel stability index and the signal attenuation threshold in the frequency band characteristic parameters, the modulation signal is divided into a plurality of data clusters, wherein each data cluster contains redundancy check information, cluster identification information and priority identification;

[0029] According to the cluster identifier information and the priority identifier of each data cluster, in combination with the link quality parameters and the node load parameters of adjacent composite wing unmanned aerial vehicle nodes in the node topology of the ad hoc network mode, a transmission path weight value is allocated to each data cluster to generate a transmission path sequence;

[0030] Based on the transmission path sequence, a time sequence scheduling transmission is performed on the data cluster through a relay forwarding mechanism between adjacent composite wing unmanned aerial vehicle nodes to generate a distributed relay transmission signal in the ad hoc network mode, wherein the lost or damaged data cluster is real-time retransmitted according to the redundancy check information during the transmission process.

[0031] Optionally, through the node hopping strategy and the priority routing allocation strategy of the distributed relay transmission signal, the initial flight path control instruction of the composite wing unmanned aerial vehicle is optimized to obtain a target control instruction, comprising:

[0032] The heading angle correction amount, the height correction amount and the speed correction amount are extracted from the flight path parameters in the distributed relay transmission signal, and the execution weight corresponding to each correction amount is determined based on the priority identifier;

[0033] According to the execution weight and the flight attitude parameters of the composite wing unmanned aerial vehicle, multi-axis coupling calculation is performed on the heading angle correction amount, the height correction amount and the speed correction amount to generate an initial flight path control instruction;

[0034] According to the node hopping strategy of the distributed relay transmission signal, the real-time position parameters and the link quality parameters of adjacent composite wing unmanned aerial vehicle nodes, the heading angle change rate, the height offset and the speed adjustment amount in the initial flight path control instruction are adjusted to generate an optimized flight path control instruction;

[0035] According to the priority routing allocation strategy of the distributed relay transmission signal, the execution order of the optimized flight path control instruction is hierarchically divided to generate a target control instruction.

[0036] In a second aspect, the present application provides an emergency control system for a composite wing unmanned aerial vehicle, comprising:

[0037] An acquisition module is configured to acquire airspace traffic situation awareness data to determine the position information of the composite wing unmanned aerial vehicle in a complex airspace with communication delay or communication link interruption;

[0038] An identification module is configured to determine the geometric boundary of an emergency obstacle avoidance area according to the position information, to calculate a dynamic obstacle avoidance path, and to generate a first control parameter matching the dynamic obstacle avoidance path;

[0039] An adjusting module is configured to adjust the first control parameter according to an axial deviation value between the flight attitude parameter and the target attitude parameter of the compound-wing UAV, to generate a second control parameter, which is used to maintain emergency flight stability of the compound-wing UAV.

[0040] A processing module is configured to perform signal modulation and cluster coding processing on the second control parameter when detecting the communication link interruption, to generate a distributed relay transmission signal in an ad hoc network mode.

[0041] An optimization module is configured to optimize an initial route control instruction of the compound-wing UAV by using a node hopping strategy and a priority routing allocation strategy of the distributed relay transmission signal, to obtain a target control instruction, so as to realize emergency route re-planning and flight attitude cooperative control of the compound-wing UAV in a communication failure state.

[0042] In a third aspect, the present application provides a computing device, comprising a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component, to realize the emergency control method of the compound-wing UAV as described in the first aspect.

[0043] In a fourth aspect, the present application provides a computer storage medium, which stores a computer program; when the computer program is executed by a computer, the emergency control method of the compound-wing UAV as described in the first aspect is realized.

[0044] The embodiment of the application acquires airspace traffic situation awareness data to determine the position information of a compound wing unmanned aerial vehicle in a complex airspace with communication delay or communication link interruption; according to the position information, the geometric boundary of an emergency obstacle avoidance area is demarcated to calculate a dynamic obstacle avoidance path, and a first control parameter matched with the dynamic obstacle avoidance path is generated; according to the axial deviation value of the flight attitude parameter and the target attitude parameter of the compound wing unmanned aerial vehicle, the first control parameter is adjusted to generate a second control parameter, and the second control parameter is used to maintain the emergency flight stability of the compound wing unmanned aerial vehicle; when the communication link interruption is detected, the second control parameter is subjected to signal modulation and cluster coding processing to generate a distributed relay transmission signal in a self-organizing network mode; through the node hopping strategy and the priority routing allocation strategy of the distributed relay transmission signal, the initial route control instruction of the compound wing unmanned aerial vehicle is optimized to obtain a target control instruction, so as to realize the emergency route re-planning and flight attitude cooperative control of the compound wing unmanned aerial vehicle in the communication failure state. The application realizes real-time positioning of the accurate position of the compound wing unmanned aerial vehicle in the complex airspace by fusing multi-source perception data, provides dynamic environment input for subsequent obstacle avoidance and path planning, solves the problem of insufficient environmental adaptability caused by the dependence of the traditional scheme on a static map; the geometric boundary is constructed based on real-time obstacle distribution, a dynamic obstacle avoidance path coupled with aerodynamic characteristics is generated, the disconnection between path planning and the maneuvering capability of the compound wing unmanned aerial vehicle is avoided, and the control shock caused by the unexecutable path or frequent adjustment in the traditional scheme is broken through; through dynamic feedback adjustment of the flight attitude deviation, the control parameters of the rudder and the thrust are corrected in real time to ensure the attitude stability in the path tracking process, and the flight instability risk caused by the disconnection between the path and the attitude in the traditional hierarchical control is solved; in the communication failure scenario, through multi-band redundant communication and cluster coding technology, distributed transmission and fault tolerance of the control signal are realized, and the single point failure bottleneck of the traditional centralized communication architecture is broken through; based on the node hopping and priority routing strategy, the route instruction is dynamically optimized to realize autonomous route adjustment and multi-machine cooperative control after the communication interruption, and the route freezing or conflict problem caused by the communication dependence in the traditional scheme is solved. Further, through real-time perception of the spatial distribution of obstacles, a multi-level safety buffer is constructed to hierarchically screen candidate paths, a dynamic obstacle avoidance path is generated by combining the path curvature continuity and path length optimization, and the path parameters are mapped to the rudder and thrust control instructions to realize deep coupling of path planning and physical control. Through hierarchical screening of the multi-level safety buffer, the obstacle avoidance decision efficiency in the complex airspace is improved, and the path shock or local optimal trap in the dense obstacle scenario in the traditional scheme is avoided; based on the path curvature continuity, a smooth obstacle avoidance trajectory is generated, and the dynamic allocation of the rudder and the thrust is combined to ensure that the path parameters match the aerodynamic characteristics of the compound wing unmanned aerial vehicle, and the control delay or overshoot problem caused by the disconnection between path planning and execution in the traditional scheme is solved.

[0045] These and other aspects of the application will become more apparent from the following description of the embodiments. Attached Figure Description

[0046] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0047] Figure 1 A flowchart of an emergency control method for a compound-wing unmanned aerial vehicle provided by the present invention is shown;

[0048] Figure 2 A schematic diagram of the structure of an emergency control system for a compound-wing unmanned aerial vehicle provided by the present invention is shown;

[0049] Figure 3 A schematic diagram of the structure of a computing device provided by the present invention is shown. Detailed Implementation

[0050] To enable those skilled in the art to better understand the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings.

[0051] In some of the processes described in the specification, claims, and accompanying drawings of this invention, multiple operations appearing in a specific order are included. However, it should be clearly understood that these operations may not be executed in the order they appear herein, or may be executed in parallel. The operation numbers, such as 101, 102, etc., are merely used to distinguish different operations and do not represent any execution order. Furthermore, these processes may include more or fewer operations, and these operations may be executed sequentially or in parallel. It should be noted that the descriptions such as "first," "second," etc., in this document are used to distinguish different messages, devices, modules, etc., and do not represent a sequential order, nor do they limit "first" and "second" to different types.

[0052] 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.

[0053] In view of the emergency control problem of a compound wing unmanned aerial vehicle in a complex airspace under communication link interruption or serious delay, the application breaks through the dependence on a central node of a traditional centralized architecture by fusing real-time situation awareness and self-organizing network communication technology. On the basis that the existing technology cannot realize real-time generation of a dynamic obstacle avoidance path, dynamic adjustment of attitude control parameters and autonomous route reconstruction after communication failure, the application realizes adaptive obstacle avoidance path generation based on dynamic obstacle boundary identification by multi-level control parameter iterative optimization, combined with a distributed transmission architecture of clustering coding and node hopping strategy, while realizing cooperative control of flight stability and route re-planning through attitude deviation compensation and priority routing allocation strategy, so as to effectively solve key problems such as system response lag, path conflict and decision blind area under communication interruption.

[0054] Figure 1 A flowchart of an emergency control method of a compound wing unmanned aerial vehicle is provided for the embodiment of the application, as shown in Figure 1 The method comprises the following steps.

[0055] Step 101: Obtain airspace traffic situation awareness data to determine the position information of the compound wing unmanned aerial vehicle in a complex airspace with communication delay or communication link interruption;

[0056] In this step, the airspace traffic situation awareness data refers to multi-dimensional data including obstacle position, size, motion direction and airspace terrain features collected by an onboard sensor, which is used to reflect the real-time airspace environment state. The complex airspace refers to a three-dimensional airspace environment with dynamic obstacles, electromagnetic interference, weather disturbance, communication delay or interruption, which requires a high adaptability control strategy. The position information refers to the three-dimensional coordinates of the compound wing unmanned aerial vehicle based on multi-source data fusion, including longitude, latitude and height parameters.

[0057] In the embodiment of the application, the airspace traffic situation awareness data is obtained by multi-source data fusion technology of an onboard radar, a laser radar and a vision sensor, and the data includes airspace obstacle position, size, motion trajectory and airspace terrain features; the data is fused and aligned in real time based on an extended Kalman filtering algorithm to calculate the accurate position information of the compound wing unmanned aerial vehicle in the complex airspace.

[0058] Step 102: According to the position information, the geometric boundary of the emergency obstacle avoidance area is delineated to calculate a dynamic obstacle avoidance path, and a first control parameter matched with the dynamic obstacle avoidance path is generated;

[0059] In this step, the emergency obstacle avoidance area refers to the dangerous airspace range centered on the compound wing unmanned aerial vehicle and needing real-time obstacle avoidance. The geometric boundary refers to the continuous polygon boundary fitted by the vertex coordinates of the circumscribed cube of the airspace obstacle, used to define the obstacle avoidance decision range. The dynamic obstacle avoidance path refers to the executable path with the optimal combination of heading angle, height and speed generated based on the path planning algorithm. The first control parameter refers to the physical control instruction mapping the path parameters to the rudder deflection angle and the propeller thrust distribution ratio.

[0060] In the embodiment of the present application, based on the position information, the spatial distribution of the airspace obstacle is topologically segmented by using the Delaunay triangulation algorithm to generate the geometric boundary of the emergency obstacle avoidance area composed of the vertex coordinates of the circumscribed geometric body of the airspace obstacle. The dynamic obstacle avoidance path with the optimal combination of the heading angle change rate, the height offset and the speed adjustment is searched within the geometric boundary by the path planning algorithm, and the first control parameter is generated based on the adjustment of the rudder deflection angle and the propeller thrust distribution ratio.

[0061] Step 103: adjusting the first control parameter according to the axial deviation value of the flight attitude parameter and the target attitude parameter of the compound wing unmanned aerial vehicle to generate the second control parameter, the second control parameter being used to maintain the emergency flight stability of the compound wing unmanned aerial vehicle;

[0062] In this step, the flight attitude parameter refers to the pitch angle, roll angle and yaw angle data parameters measured in real time by the attitude sensor. The target attitude parameter refers to the target values of the pitch, roll and yaw angles required for the stable flight of the compound wing unmanned aerial vehicle. The second control parameter refers to the amplitude component and direction difference of the flight attitude parameter and the target attitude parameter on the pitch axis, roll axis and yaw axis. The emergency flight stability refers to the ability of the compound wing unmanned aerial vehicle to maintain balanced flight under communication failure or environmental disturbance.

[0063] In the embodiment of the present application, the axial deviation value of the flight attitude parameter and the target attitude parameter of the compound wing unmanned aerial vehicle is calculated in real time by the quaternion attitude solving algorithm. The rudder deflection angle and the thrust distribution ratio in the first control parameter are dynamically feedback adjusted based on the fuzzy PID control algorithm to generate the second control parameter used to maintain the emergency flight stability of the compound wing unmanned aerial vehicle.

[0064] Step 104: when the communication link interruption is detected, the second control parameter is signal modulated and cluster encoded to generate a distributed relay transmission signal in the ad hoc network mode;

[0065] In this step, the ad hoc network mode refers to the distributed communication network architecture constructed autonomously by the compound wing unmanned aerial vehicle node, which does not depend on the central node. The distributed relay transmission signal refers to the communication signal achieving fault-tolerant transmission through multi-node hopping and redundancy checking.

[0066] In the embodiment of the application, when the communication link interruption is detected, the second control parameter is subjected to multi-band signal waveform conversion by using orthogonal frequency division multiplexing technology, and the signal is subjected to cluster coding by using low-density parity-check code, thereby generating a self-organizing network mode distributed relay transmission signal containing redundant check information, cluster identification information and priority identification.

[0067] Step 105: The initial flight path control instruction of the compound wing unmanned aerial vehicle is optimized through the node hopping strategy and the priority routing allocation strategy of the distributed relay transmission signal, and a target control instruction is obtained, so as to realize emergency flight path re-planning and flight attitude cooperative control of the compound wing unmanned aerial vehicle in a communication failure state.

[0068] In this step, the node hopping strategy refers to a communication routing mechanism of dynamically selecting a next hop node based on path weight. The priority routing allocation strategy refers to a decision rule of allocating data transmission priority according to link quality and node load. The flight path control instruction refers to a flight control instruction containing a heading angle, a height and a speed adjustment amount. The target control instruction refers to a final executable control instruction subjected to time-space alignment and priority sorting. The communication failure state refers to a running mode of the communication link interruption between the compound wing unmanned aerial vehicle and a ground control center or other nodes.

[0069] In the embodiment of the application, the node hopping strategy is dynamically allocated based on a shortest path algorithm, the real-time position parameters and the link quality parameters of adjacent compound wing unmanned aerial vehicle nodes are combined, the initial flight path control instruction is adjusted to obtain an optimized flight path control instruction, the optimized flight path control instruction is reconstructed by using the distributed relay transmission signal, and a target control instruction is generated, thereby realizing flight path re-planning and multi-axis attitude cooperative control in a communication failure state.

[0070] The embodiment of the application realizes autonomous obstacle avoidance and flight path generation of the compound wing unmanned aerial vehicle in a complex airspace through multi-source perception data fusion and dynamic path planning. Based on attitude deviation feedback adjustment and distributed communication reconstruction, the communication dependence on a center node in a traditional scheme is broken, the core defects such as disconnection between path planning and execution, poor adaptability to a static environment and low multi-machine cooperative efficiency are solved, and the emergency response capability and flight stability in a communication failure scene are improved.

[0071] Taking the complex wing unmanned aerial vehicle (UAV) performing a task in a complex airspace in a mountainous area encountering strong electromagnetic interference leading to communication interruption as an example, first, the position and motion data of the airspace obstacles are collected in real time by an onboard sensor, the own coordinates are determined through multi-source fusion, and the geometric boundary of the emergency obstacle avoidance area is generated; after the dynamic obstacle avoidance path is planned based on a path planning algorithm, the change rate of the heading angle is converted into the aileron deflection instruction, the height offset is mapped to the elevator control, and the speed adjustment amount is distributed to the propeller thrust proportion to generate the first control parameter; the current pitch angle deviation is measured by an attitude sensor, the deflection angle of the rudder is dynamically adjusted by using a fuzzy PID algorithm to generate the second control parameter; after the communication interruption, the second control parameter is converted into a distributed relay transmission signal by using an orthogonal frequency division multiplexing technology and a low-density parity-check code, and is transmitted to the target node through the node hopping strategy of adjacent complex wing UAVs; after receiving the signal, the flight path instruction is reconstructed based on a priority routing strategy, the real-time flight attitude parameters are combined for space-time alignment, and finally the target control instruction is generated to realize autonomous obstacle avoidance, flight path re-planning and attitude stability control, and complete the safe return in the communication failure state.

[0072] The present application provides one embodiment, step 102, according to the position information, the geometric boundary of the emergency obstacle avoidance area is demarcated, the dynamic obstacle avoidance path is calculated, the first control parameter matched with the dynamic obstacle avoidance path is generated, and specifically includes the following steps:

[0073] Step 201: Obtain the spatial distribution of the airspace obstacles in the complex airspace, and demarcate the geometric boundary of the emergency obstacle avoidance area in combination with the position information;

[0074] In this step, the airspace obstacle refers to the dynamic or static object in the flight airspace of the complex wing UAV, including other aircraft, buildings, mountains. The spatial distribution refers to the topological structure generated based on the three-dimensional coordinate set of the airspace obstacle, reflecting the relative position and geometric relationship between the obstacles. The emergency obstacle avoidance area refers to the dangerous airspace range which needs to be avoided in real time with the complex wing UAV as the center, and the boundary is determined by the extension space of the airspace obstacle.

[0075] In the embodiment of the present application, the three-dimensional coordinate, size and motion speed parameters of all the airspace obstacles in the complex airspace are obtained by the fusion perception technology of millimeter wave radar and laser radar, forming a three-dimensional spatial coordinate set; based on the three-dimensional spatial coordinate set, the Delaunay triangulation algorithm is used to connect the vertex coordinates of the circumscribed cubes of adjacent airspace obstacles, and the geometric boundary of the emergency obstacle avoidance area formed by the splicing of multiple convex polygons is generated, and the vertex coordinates of the geometric boundary match the maximum extension space of the airspace obstacle.

[0076] Step 202: constructing a multi-level safety buffer zone with the position of the compound wing UAV as the starting point according to the shape and distance parameters of the geometric boundary, each level of the multi-level safety buffer zone corresponding to a different obstacle avoidance priority;

[0077] In this step, the distance parameter refers to the straight-line distance from the geometric boundary vertex of the airspace obstacle to the position of the compound wing UAV, which is used for buffer zone level division. The multi-level safety buffer zone refers to a concentric circular region with the compound wing UAV as the center and divided by distance, each level corresponding to a different collision avoidance response level. The obstacle avoidance priority refers to the decision weight set according to the level of the multi-level safety buffer zone, with the first level safety buffer zone > the second level safety buffer zone > the third level safety buffer zone.

[0078] In the embodiment of the present application, a multi-level safety buffer zone is constructed according to the Euclidean distance between the polygon vertex coordinates of the geometric boundary and the position of the compound wing UAV, i.e. the straight-line distance from the vertex of the airspace obstacle to the position of the compound wing UAV. Among them: the first level buffer zone (0-5 meters) corresponds to the highest obstacle avoidance priority, the second level buffer zone (5-15 meters) corresponds to the medium priority, and the third level buffer zone (15-30 meters) corresponds to the low priority; The boundary of each level buffer zone is generated by outward equidistant offset of the obstacle vertex coordinates, and the offset distance is the radius of the buffer zone of this level.

[0079] Step 203: screening the candidate paths in the multi-level safety buffer zone that have the smallest conflict with the flight direction of the compound wing UAV level by level, and determining the dynamic obstacle avoidance path according to the curvature continuity and path length of the candidate path;

[0080] In this step, the candidate path refers to the preliminary screening of the candidate obstacle avoidance trajectory in the multi-level buffer zone, which needs to meet the minimum conflict condition.

[0081] In the embodiment of the present application, the direction conflict evaluation function, i.e. the cosine value of the angle between the flight direction vector and the candidate path vector, is used to screen the candidate paths in the buffer zone level by level, and the path with a cosine value greater than 0.95 is selected. The candidate path is smoothed by a cubic B-spline curve to ensure curvature continuity, so that the path curvature change rate is less than 0.1 rad or m², and finally the shortest path with curvature continuity is selected as the dynamic obstacle avoidance path.

[0082] Step 204: adjusting the rudder deflection angle and propeller thrust distribution ratio of the compound wing UAV based on the heading angle change rate, height offset, and speed adjustment amount of the dynamic obstacle avoidance path, to generate the first control parameter;

[0083] In this step, the heading angle change rate refers to the heading angle change amount per unit length of the dynamic obstacle avoidance path (unit: degree or meter), which determines the deflection amplitude of the control surface. The height offset refers to the vertical difference between the target height of the dynamic obstacle avoidance path and the current height of the compound wing UAV (unit: meter). The speed adjustment amount refers to the scalar difference between the target speed of the dynamic obstacle avoidance path and the current speed of the compound wing UAV (unit: meter or second). The control surface deflection angle refers to the physical deflection angle of the aileron or elevator (unit: degree), which is linearly mapped to the heading angle change rate or height offset. The propeller thrust distribution ratio refers to the thrust proportion of each propeller in the multi-propeller system, and the total thrust is 100%, which is dynamically related to the speed adjustment amount.

[0084] In the embodiment of the application, the heading angle change rate of the dynamic obstacle avoidance path is mapped to the aileron deflection angle, i.e. the heading angle change rate x 0.8 times coefficient, the height offset is mapped to the elevator deflection angle, i.e. the height offset x 0.5 times coefficient, and the speed adjustment amount is distributed to the propeller thrust ratio, i.e. the left propeller thrust ratio = 50% + adjustment amount x 0.3, and the right propeller is complementary, to generate the first control parameter.

[0085] The embodiment of the application solves the defects of poor static environment adaptability, low obstacle avoidance decision efficiency and unexecutable path in the traditional scheme by geometric boundary generation driven by spatial distribution, multi-level buffer priority layering, path screening with curvature continuity constraint and physical control parameter mapping, significantly improving the obstacle avoidance success rate and control stability in dense obstacle scenarios.

[0086] The application provides a specific embodiment, step 201, obtaining the spatial distribution of airspace obstacles in the complex airspace, and combining the position information to delineate the geometric boundary of the emergency obstacle avoidance area, specifically including the following steps:

[0087] Step 211: Obtain a set of three-dimensional spatial coordinates of the airspace obstacles in the complex airspace that are sensed in real time by the compound wing UAV, which contains the position information, size parameters and motion direction parameters of the airspace obstacles.

[0088] In this step, the set of three-dimensional spatial coordinates refers to the spatial position data set of the airspace obstacles obtained by sensor fusion, which contains longitude, latitude, elevation coordinates, as well as size parameters (length, width, height) and motion direction parameters (velocity vector and azimuth angle).

[0089] In the embodiment of the present application, through the data fusion technology of airborne millimeter wave radar, laser radar and binocular vision sensor, the three-dimensional spatial coordinate set of all airspace obstacles in the complex airspace is collected in real time, which contains the longitude and latitude elevation position information, length, width, height size parameters and motion direction vector (speed size and direction angle) of the airspace obstacles; the time-space synchronization algorithm is used to align the coordinate systems of multiple sensor data to generate the three-dimensional spatial coordinate set of airspace obstacles with unified time stamp.

[0090] Step 212: Based on the three-dimensional spatial coordinate set, the spatial distribution of the airspace obstacles is spatially topologically segmented to generate a plurality of sub-regions, wherein the boundary of each sub-region is determined by the vertex coordinates of the maximum circumscribed geometry of the adjacent airspace obstacles;

[0091] In this step, the sub-region refers to a local cluster group generated based on the spatial topological segmentation of the airspace obstacles, the boundary of which is jointly defined by the vertex coordinates of the maximum circumscribed geometry of the obstacles in the cluster, reflecting the local obstacle dense area. The maximum circumscribed geometry vertex coordinates refer to the 8 (or 4) corner point coordinates of the minimum circumscribed cube (or sphere) wrapping the airspace obstacles, which are used to quantify the spatial occupation range of the airspace obstacles.

[0092] In the embodiment of the present application, based on the three-dimensional spatial coordinate set, the Delaunay triangulation algorithm is used to construct the spatial topological network of the airspace obstacles, and the airspace obstacles with a Euclidean distance less than a set threshold are clustered into the same sub-region; the boundary of each sub-region is determined by the vertex coordinates of the maximum circumscribed cube of all the airspace obstacles contained therein, such as the vertex of the minimum envelope polyhedron of all the circumscribed cube vertices of the airspace obstacles.

[0093] Step 213: According to the position information of the compound wing unmanned aerial vehicle and the relative distance of the sub-region, the target sub-region conflicting with the flight direction of the compound wing unmanned aerial vehicle is screened out, and the target sub-region is marked as a key conflict region;

[0094] In this step, the relative distance refers to the Euclidean distance from the current position coordinate of the compound wing unmanned aerial vehicle to the centroid of the sub-region, which is used for conflict region screening. The target sub-region refers to the sub-region with an included angle less than 45° with the flight direction of the compound wing unmanned aerial vehicle and a relative distance less than a safety threshold, which represents a high-risk region that needs to be preferentially avoided.

[0095] In the embodiment of the present application, the relative distance between the position information of the compound wing unmanned aerial vehicle and the centroid of each sub-region is calculated, and the calculation formula is ( ), and the positional relationship between the flight direction and the sub-region is evaluated: if the included angle between the flight direction vector of the compound wing unmanned aerial vehicle and the vector pointing to the centroid of the sub-region is less than 45°, it is determined that the flight direction is conflicting; all the conflicting sub-regions are screened out as target sub-regions, which are marked as key conflict regions.

[0096] Step 214: boundary fitting is performed on the circumscribed geometry vertex coordinates of the airspace obstacle in the key conflict region to generate a geometric boundary of the emergency obstacle avoidance region;

[0097] In this step, the key conflict region refers to a high-priority obstacle avoidance decision area formed by merging target sub-regions, and the boundary thereof needs to be fitted and generated separately. The circumscribed geometry vertex coordinates refer to a set of corner point coordinates of the minimum circumscribed cube of the airspace obstacle, such as 8 vertices of the cube, which are used for geometric boundary calculation.

[0098] In the embodiment of the application, the circumscribed geometry vertex coordinates of all airspace obstacles in the key conflict region are extracted, a convex hull algorithm is used to perform boundary fitting on the vertex set, and a geometric boundary of the emergency obstacle avoidance region composed of a continuous polygon vertex sequence is generated. The boundary vertices are stored in a closed polygon in a clockwise order.

[0099] The embodiment of the application realizes rapid locking of a key obstacle avoidance region through a set of three-dimensional space coordinates, in combination with a topological segmentation and a direction conflict dynamic screening mechanism. A compact geometric boundary is generated based on convex hull fitting, the real-time performance and accuracy of obstacle avoidance decision in a complex airspace are improved, and the problem of delay in obstacle avoidance response caused by lagging environment modeling and global calculation redundancy in the traditional scheme is solved.

[0100] The application provides a specific embodiment, step 103, adjusting the first control parameter according to the axial deviation value of the flight attitude parameter of the compound wing unmanned aerial vehicle and the target attitude parameter to generate a second control parameter, specifically including the following steps:

[0101] Step 301: calculating the axial deviation value of the flight attitude parameter of the compound wing unmanned aerial vehicle and the target attitude parameter, the flight attitude parameter including a pitch angle, a roll angle and a yaw angle;

[0102] In this step, the pitch angle refers to the angle between the longitudinal axis of the compound wing unmanned aerial vehicle and the horizontal plane, a positive value indicating that the nose is tilted upward, and is used to reflect the climbing or diving attitude. The roll angle refers to the angle between the lateral axis of the compound wing unmanned aerial vehicle and the horizontal plane, a positive value indicating that the right wing is tilted downward, and is used to reflect the rolling attitude. The yaw angle refers to the angle between the projection of the longitudinal axis of the body on the horizontal plane and the true north direction, and is used to reflect the heading direction. The flight attitude parameter refers to a set of real-time attitude data composed of the pitch angle, the roll angle and the yaw angle. The target attitude parameter refers to a preset stable flight state reference value, such as a pitch angle of 0°, a roll angle of 0° and a target heading angle. The axial deviation value refers to the algebraic difference between the flight attitude parameter and the target attitude parameter on the pitch, roll and yaw axes.

[0103] In the embodiment of the present application, the pitch angle, the roll angle and the yaw angle of the compound wing unmanned aerial vehicle are collected in real time by the on-board inertial measurement unit to form the flight attitude parameters; based on the preset target attitude parameters, such as the pitch angle 0°, the roll angle 0° and the yaw angle θ required for stable flight, the axial deviation value is calculated by using the Euler angle deviation formula, specifically: the pitch axis deviation value = current pitch angle-target pitch angle; the roll axis deviation value = current roll angle-target roll angle; the yaw axis deviation value = current yaw angle-target yaw angle.

[0104] Step 302: adjusting the deflection angle control amount of the control surface in the first control parameter according to the amplitude component and the direction component of the axial deviation value, to generate the adjusted deflection angle control amount of the control surface, so that the deflection directions of the ailerons and the elevators of the compound wing unmanned aerial vehicle are consistent with the compensation direction of the direction component;

[0105] In this step, the amplitude component refers to the absolute value of the axial deviation value, reflecting the severity of the attitude deviation. The direction component refers to the positive or negative sign of the axial deviation value, + or -, indicating the deviation correction direction, including up or down, left or right. The deflection angle control amount of the control surface refers to the instruction value (unit: degree) of controlling the deflection angle of the aileron and the elevator. The adjusted deflection angle control amount of the control surface refers to the control instruction value of the control surface after feedback correction of the axial deviation value. The compensation direction refers to the deflection direction of the control surface determined according to the direction component, such as downward deflection of the aileron for positive deviation.

[0106] In the embodiment of the present application, the axial deviation value is decomposed into the amplitude component and the direction component; based on the fuzzy control rule base, the deflection angle control amount of the control surface in the first control parameter is dynamically adjusted, specifically: according to the direction component, the compensation direction is adjusted: if the direction component is positive, the control surface deflection amount is increased; if it is negative, the control surface deflection amount is decreased; according to the amplitude component, the adjustment amplitude is adjusted: the larger the amplitude, the larger the control surface adjustment amplitude, to generate the adjusted deflection angle control amount of the control surface, so as to ensure that the deflection directions of the ailerons and the elevators are consistent with the compensation direction.

[0107] Step 303: synchronously adjusting the propeller thrust distribution ratio in the first control parameter based on the adjusted deflection angle control amount of the control surface, to generate the adjusted propeller thrust distribution ratio, so that the moment distribution of the compound wing unmanned aerial vehicle on the pitch axis, the roll axis and the yaw axis matches the correction requirement of the amplitude component;

[0108] In this step, the propeller thrust distribution ratio refers to the percentage of the thrust of each propeller in the multi-propeller system, and the total is 100%. The adjusted propeller thrust distribution ratio refers to the thrust distribution instruction value after torque balance adjustment. The pitch axis, roll axis and yaw axis refer to the three-axis control axes corresponding to the body longitudinal axis, transverse axis and vertical axis respectively. The torque distribution refers to the aerodynamic torque value on the three axes (unit: N·m), which needs to be matched with the amplitude component. The correction requirement refers to the target torque value calculated according to the amplitude component, such as 4 N·m torque required for each 1° deviation.

[0109] In the embodiment of the application, the propeller thrust distribution ratio is synchronously adjusted according to the adjusted rudder deflection angle control amount through the torque balance equation, specifically: pitch axis torque = front propeller thrust × front arm - rear propeller thrust × rear arm; roll axis torque = left propeller thrust × left arm - right propeller thrust × right arm; yaw axis torque = (left propeller thrust - right propeller thrust) × yaw arm; the proportion of each propeller thrust is adjusted so that the three-axis torque distribution matches the correction requirement of the amplitude component (such as 20 N·m pitch torque required for a 5° pitch axis deviation amplitude), to generate the adjusted propeller thrust distribution ratio.

[0110] Step 304: Compensate the adjusted rudder deflection angle control amount and the adjusted propeller thrust distribution ratio to generate a second control parameter;

[0111] In the embodiment of the application, the adjusted rudder deflection angle control amount and the adjusted propeller thrust distribution ratio are input into a multi-axis cooperative controller, and a weighted superposition algorithm (such as a rudder weight of 0.6 + a thrust weight of 0.4) is used for compensation calculation to generate a second control parameter integrating the rudder and thrust control instructions.

[0112] The embodiment of the application realizes deep cooperation of rudder control and thrust distribution through decoupling of the component of the axial deviation value and dynamic feedback adjustment: the rudder deflection is adjusted based on the directional component to avoid attitude instability caused by correction direction error; the propeller thrust distribution is quantified according to the amplitude component to eliminate the torque conflict caused by the separation of rudder and thrust control in the traditional scheme; the rudder and thrust instructions are fused through weighted compensation calculation to solve the response lag problem of a single control amount.

[0113] The application provides a specific embodiment, and step 104, when the communication link is detected to be interrupted, signal modulation and cluster coding processing are performed on the second control parameter to generate a distributed relay transmission signal in an ad hoc network mode, specifically including the following steps:

[0114] Step 401: When detecting the communication link interruption, extract the frequency band adaptation characteristics of the second control parameter to perform signal waveform conversion on the second control parameter, generate a modulation signal matched with different frequency band channel characteristics, and extract the frequency band characteristic parameters of the modulation signal;

[0115] In this step, the frequency band adaptation characteristics refer to the adaptability requirements of the second control parameter to the communication frequency band, including minimum bandwidth requirement, maximum tolerable delay, and anti-interference level (i.e. signal-to-interference-and-noise ratio threshold), which are used to match the channel characteristics. The frequency band channel characteristics refer to the physical layer attributes of different communication frequency bands, including the multipath delay, Doppler shift, and path loss model of the 2.4 GHz frequency band (strong diffraction ability but easy to be interfered) and the 5.8 GHz frequency band (large bandwidth but fast attenuation). The frequency band characteristic parameters refer to the physical layer indicators of the modulation signal, including channel signal-to-noise ratio (unit: dB), frequency response flatness (dB / Hz), and bandwidth utilization rate (i.e. actual bandwidth / theoretical bandwidth).

[0116] In the embodiments of the present application, the frequency band adaptation characteristics of the second control parameter are extracted by a spectrum analysis algorithm, and the second control parameter is subjected to signal waveform conversion using orthogonal frequency division multiplexing technology: the high-speed data stream is divided into multiple subcarriers, the subcarrier modulation mode is dynamically allocated according to the channel fading characteristics (such as multipath delay and Doppler shift) of the 2.4 GHz / 5.8 GHz frequency band, a modulation signal matched with different frequency band channel characteristics is generated, and the frequency band characteristic parameters of the modulation signal are extracted.

[0117] Step 402: Based on the channel stability indicators and signal attenuation thresholds in the frequency band characteristic parameters, the modulation signal is divided into multiple data clusters, wherein each data cluster contains redundancy check information, cluster identification information, and priority identification;

[0118] In this step, the modulation signal refers to the conversion of the digital control parameter into an analog waveform signal suitable for wireless transmission. The channel stability indicator refers to a parameter quantifying the reliability of the channel, defined as the probability that the signal-to-noise ratio fluctuation range is less than 3 dB within 1 second, used for clustering decision. The signal attenuation threshold refers to the minimum signal strength (unit: dBm) that can be decoded by the receiving end, below which clustering redundancy protection needs to be triggered. The data cluster refers to a data unit containing the payload, redundancy check information, cluster identification information (i.e. frequency band ID + cluster sequence number), and priority identification. The redundancy check information refers to the error correction check code generated based on Reed-Solomon coding, which can recover up to 2 bytes of error data. The cluster identification information refers to 8-bit encoding (including high 4-bit frequency band ID + low 4-bit cluster sequence number), used for data cluster reorganization and routing addressing. The priority identification refers to a 4-bit binary number (0-15), the smaller the value, the higher the priority, and the 0 level is the emergency attitude control instruction.

[0119] In the embodiment of the present application, based on the channel stability index (stable when the signal-to-noise ratio is greater than 20 dB) and the signal attenuation threshold (the received signal strength is greater than -90 dBm) in the frequency band characteristic parameter, the data clustering algorithm is used to divide the modulation signal into multiple data clusters: each data cluster contains 32 bytes of payload, adds 16-bit cyclic redundancy check information, 8-bit cluster identification information and 4-bit priority identification, and generates a data cluster set with redundant structure.

[0120] Step 403: According to the cluster identification information and priority identification of each data cluster, combined with the link quality parameters and node load parameters of adjacent composite wing unmanned aerial vehicle nodes in the node topology of the ad hoc network mode, a transmission path weight value is assigned to each data cluster to generate a transmission path sequence.

[0121] In this step, the node topology refers to the spatial distribution relationship diagram of the composite wing unmanned aerial vehicle nodes in the ad hoc network, including node ID, position coordinates and neighbor node list. The link quality parameter refers to the reliability index of the communication link, which is represented by a triple as link quality parameter=(bit error rate, delay, packet loss rate), wherein the bit error rate <10 -6 is a high-quality link. The node load parameter refers to the real-time resource state of the composite wing unmanned aerial vehicle node, which is represented by a triple as node load parameter=(node central processor usage rate, node memory occupancy rate), and the node central processor usage rate <70% is low load. The transmission path weight value refers to the numerical value of quantifying the pros and cons of the transmission path, which is calculated according to the bit error rate, delay, node central processor usage rate and node memory occupancy rate. The transmission path sequence refers to the node hop sequence assigned to the data cluster, which is arranged in descending order of weight value.

[0122] In the embodiment of the present application, according to the node topology relationship of the ad hoc network mode, the target transmission node of the data cluster is determined combined with the cluster identification information, the adjacent nodes in the ad hoc network node topology are traversed, the link quality parameters and node load parameters of each target transmission node are obtained, and the transmission path weight value of each candidate path is calculated based on the formula: transmission path weight value=(1 / bit error rate)×(1 / 0.01 delay)×(1-0.01 node central processor usage rate)×(1-0.01 node memory occupancy rate)×weight coefficient corresponding to priority identification, wherein the bit error rate is the proportion of the number of error bits to the total number of transmission bits per unit time, the value range is [0, 1]; the delay is the round-trip time (ms) of signal transmission between nodes; the packet loss rate is the proportion of lost data packets in the transmission process, the value range is [0, 1]; the transmission path sequence is generated in descending order of transmission path weight value, such as transmission path sequence=[path 1, path 2, …, path N], wherein path 1 is the next hop node with the highest transmission path weight value.

[0123] Step 404: based on the transmission path sequence, the data clusters are time-scheduled and transmitted through a relay forwarding mechanism between adjacent composite wing unmanned aerial vehicle nodes to generate a distributed relay transmission signal in an ad hoc network mode, wherein the lost or damaged data clusters are real-time retransmitted according to the redundancy check information during the transmission process.

[0124] In this step, the relay forwarding mechanism refers to the transmission of data between nodes according to the path sequence hop by hop, and the receiving party forwards to the next hop after verification, or requests retransmission if it fails.

[0125] In the embodiment of the application, based on the transmission path sequence, a time-scheduling algorithm is used to transmit the data cluster set through the relay forwarding mechanism of adjacent composite wing unmanned aerial vehicle nodes: the sending node transmits the data cluster within the transmission window, the receiving node verifies the integrity through the redundancy check information (if the cyclic redundancy check fails, a negative acknowledgement retransmission is triggered), and the lost cluster is real-time retransmitted; all data clusters are recombined into a distributed relay transmission signal after reaching the target node.

[0126] The embodiment of the application improves the transmission reliability of control signals in a communication interruption scenario through frequency band adaptive modulation, stability driven intelligent clustering, load sensing dynamic routing and fault tolerance transmission of redundancy check. Specifically, it solves the problem of single frequency band failure in traditional schemes in an interference environment; dynamically adjusts the redundancy strength based on channel stability to optimize resource utilization; avoids transmission bottlenecks caused by node overload and reduces end-to-end delay; quickly recovers lost data through check information to ensure the reachability of critical control instructions.

[0127] The application provides a specific embodiment, step 105, which optimizes the initial flight path control instruction of the composite wing unmanned aerial vehicle through the node hopping strategy and priority routing allocation strategy of the distributed relay transmission signal to obtain a target control instruction, specifically including the following steps:

[0128] Step 501: extract the heading angle correction amount, height correction amount and speed correction amount from the flight path parameters in the distributed relay transmission signal, and determine the execution weight corresponding to each correction amount based on the priority identifier;

[0129] In this step, the heading angle correction amount refers to the flight control data packet encapsulated in the distributed relay transmission signal, and contains three core fields of the heading angle correction amount (unit: degree), the height correction amount (unit: meter), and the speed correction amount (unit: meter / second). The height correction amount refers to the change value of the heading angle to be adjusted (positive value for right turn, negative value for left turn), which is calculated based on the difference between the target heading and the current heading. The speed correction amount refers to the change value of the vertical height to be adjusted (positive value for climbing, negative value for descending), which is determined by the difference between the target height and the actual height. The execution weight refers to the correction coefficient (0.1-1.0) allocated according to the instruction priority, and the higher the priority, the greater the weight, including the heading angle correction weight, the height correction weight, and the speed correction weight, which are used to quantify the execution urgency of different correction amounts.

[0130] In the embodiment of the present application, the heading parameter in the distributed relay transmission signal is extracted by the signal analysis module, the execution weight of the heading angle correction amount, the height correction amount, and the speed correction amount is determined based on the priority identification (such as 4-bit binary value) of the signal packet header, and the preset weight mapping table (wherein, the priority 0-15 corresponds to the weight 1.0-0.1), such as the heading angle correction weight of the priority 0=1.0, the height correction weight=0.8, and the speed correction weight=0.6.

[0131] Step 502: According to the execution weight and the flight attitude parameter of the compound wing unmanned aerial vehicle, the heading angle correction amount, the height correction amount, and the speed correction amount are calculated by multi-axis coupling to generate an initial flight path control instruction;

[0132] In this step, the initial flight path control instruction refers to the primary control instruction generated by multi-axis collaborative optimization, which contains three instruction parameters of the heading angle change rate (unit: degree / second), the height offset (unit: meter), and the speed adjustment amount (unit: meter / second).

[0133] In the embodiment of the present application, the execution weight (including the heading angle correction weight, the height correction weight, and the speed correction weight) and the flight attitude parameter (including the pitch angle, the roll angle, and the yaw angle) are input into the multi-axis collaborative optimizer for coupling calculation, specifically: first, multi-axis control amount distribution is performed to obtain the pitch axis distribution amount, the roll axis distribution amount, and the yaw axis distribution amount; combined with the attitude deviation vector, coupling compensation is performed to obtain the heading angle coupling correction amount, the height coupling correction amount, and the speed coupling correction amount; the heading angle change rate=the heading angle coupling correction amount÷the control period, the height coupling correction amount is generated as the height offset, and the speed coupling correction amount is generated as the speed adjustment amount, finally generating the initial flight path control instruction containing the heading angle change rate, the height offset, and the speed adjustment amount, which can eliminate control conflicts.

[0134] Step 503: According to the node hopping strategy of the distributed relay transmission signal, the real-time position parameters of the adjacent compound wing unmanned aerial vehicle node, and the link quality parameters, the heading angle change rate, the height offset, and the speed adjustment amount in the initial flight path control instruction are adjusted to generate an optimized flight path control instruction;

[0135] In this step, the heading angle change rate refers to the change rate of the heading angle per unit time, such as 3° / s, which means turning 3 degrees per second, and determines the turning sensitivity. The height offset refers to the instantaneous difference between the target height and the actual height, such as +20 meters, which means climbing 20 meters. The speed adjustment amount refers to the instantaneous difference between the target speed and the actual speed, such as +5 m / s, which means accelerating 5 m / s. The optimized flight path control instruction refers to the control instruction (α', h', v') adjusted dynamically according to the node state, which adapts to the real-time communication environment.

[0136] In the embodiment of the application, the next hop node is selected according to the node hopping strategy, and the real-time position parameters (including longitude, latitude, and height) and the link quality parameters of the node are combined. The heading angle change rate adjustment is α = α × (1 + offset distance / 100) if the node horizontal direction offset is greater than 50 meters. The height offset adjustment is h = h + height difference × 0.5 if the node height difference is greater than 20 meters. The speed adjustment amount adjustment is v = v × (1 - delay / 200) if the link delay is greater than 100 ms. The optimized flight path control instruction (α', h', v') is generated.

[0137] Step 504: The execution order of the optimized flight path control instruction is hierarchically divided according to the priority routing allocation strategy of the distributed relay transmission signal to generate a target control instruction;

[0138] In the embodiment of the application, the optimized flight path control instruction is hierarchically divided based on the priority routing allocation strategy: level 1 (emergency obstacle avoidance instruction): α' change rate > 5° / s or |h'| > 30 meters; level 2 (flight path correction instruction): 2° / s < α' ≤ 5° / s or 10 meters < |h'| ≤ 30 meters; level 3 (speed optimization instruction): α' ≤ 2° / s and |h'| ≤ 10 meters; the target control instruction sequence is generated in order of level.

[0139] The embodiment of the application solves the defects in the traditional scheme, such as the disconnection between the flight path instruction and the real-time environment, the conflict between multiple instructions, and the delay in emergency response, by priority-driven weight allocation, multi-axis optimization coupled with posture, node-adaptive parameter adjustment, and emergency degree hierarchical instruction sequencing. Specifically, the instruction parameters are optimized in real time based on the node position and link quality to improve the adaptability in complex airspace. The heading, height, and speed correction amounts are avoided from conflicting with each other through weight allocation and coupling calculation. The high-priority instruction is ensured to be executed first through hierarchical division, thereby reducing the collision risk.

[0140] The application provides a specific embodiment, step 502, according to the execution weight and the flight attitude parameter of the compound wing unmanned aerial vehicle, the heading angle correction amount, the height correction amount and the speed correction amount are calculated in multi-axis coupling, and the initial route control instruction is generated, which specifically comprises the following steps:

[0141] Step 511: based on the execution weight, the heading angle correction amount, the height correction amount and the speed correction amount are respectively distributed to the control channel of the pitch axis, the roll axis and the yaw axis of the compound wing unmanned aerial vehicle, and the correction amount distribution proportion corresponding to each control channel is generated;

[0142] In this step, the control channel refers to the independent control link of the three-axis attitude of the compound wing unmanned aerial vehicle, including the pitch axis (controlling the elevator), the roll axis (controlling the aileron) and the yaw axis (controlling the rudder), which is used to receive the distributed correction amount. The correction amount distribution proportion refers to the intermediate parameter that the heading angle, the height and the speed correction amount are distributed to the three-axis control channel in proportion according to the execution weight, which reflects the correction demand strength of each channel.

[0143] In the embodiment of the application, based on the execution weight of the heading angle correction amount, the execution weight of the height correction amount and the execution weight of the speed correction amount, the heading angle correction amount, the height correction amount and the speed correction amount are respectively distributed to the pitch axis control channel, the roll axis control channel and the yaw axis control channel of the compound wing unmanned aerial vehicle through weighted distribution calculation: specifically, height correction amount x height correction weight = pitch axis distribution amount, heading angle correction amount x 0.5 heading correction weight = roll axis distribution amount, heading angle correction amount x 0.5 heading correction weight = yaw axis distribution amount, and finally the corresponding correction amount distribution proportion composed of the pitch axis distribution amount, the roll axis distribution amount and the yaw axis distribution amount is generated.

[0144] Step 512: calculating the attitude deviation vector of the flight attitude parameter of the compound wing unmanned aerial vehicle and the target attitude parameter;

[0145] In this step, the attitude deviation vector refers to a three-dimensional vector composed of a pitch deviation value, a roll deviation value and a yaw deviation value, which quantifies the deviation direction and amplitude of the current attitude and the target attitude parameter.

[0146] In the embodiment of the application, the deviation between the current pitch angle, the current roll angle and the current yaw angle of the compound wing unmanned aerial vehicle and the target pitch angle, the target roll angle and the target yaw angle is calculated in real time through the quaternion attitude solving algorithm: current pitch angle-target pitch angle=pitch deviation value, current roll angle-target roll angle=roll deviation value, current yaw angle-target yaw angle=yaw deviation value, and the three deviation components constitute a three-dimensional attitude deviation vector.

[0147] Step 513: based on the association relationship between the correction amount distribution ratio and the attitude deviation vector, performing multi-axis coupling calculation on the heading angle correction amount, the height correction amount and the speed correction amount to generate a heading angle coupling correction amount, a height coupling correction amount and a speed coupling correction amount;

[0148] In this step, the association relationship refers to a mathematical mapping rule of the correction amount distribution ratio and the attitude deviation vector, which is used to eliminate multi-axis control conflicts. The heading angle coupling correction amount refers to a heading correction value compensated by the attitude deviation value, which eliminates the interference of the roll deviation on the heading control. The height coupling correction amount refers to a height correction value fused with the pitch deviation value compensation, which suppresses the influence of the pitch attitude change on the height adjustment. The speed coupling correction amount refers to a speed correction value superimposed with the yaw deviation value, which dynamically adjusts the speed demand according to the heading deviation.

[0149] In the embodiment of the present application, the mathematical association relationship between the correction amount distribution ratio set and the attitude deviation vector is established: specifically, the yaw axis distribution amount-0.3 times the roll deviation value=the heading angle coupling correction amount, the pitch axis distribution amount+0.2 times the pitch deviation value=the height coupling correction amount, and the speed correction amount*(1+0.1 times the absolute value of the yaw deviation)=the speed coupling correction amount. The multi-axis control conflicts are eliminated through the coupling calculation.

[0150] Step 514: performing multi-axis superposition on the heading angle coupling correction amount, the height coupling correction amount and the speed coupling correction amount to generate the initial flight path control instruction;

[0151] In the embodiment of the present application, the heading angle coupling correction amount is divided by the unit time to convert into an initial heading angle change rate, the height coupling correction amount is directly used as an initial height offset amount, and the speed coupling correction amount is directly used as an initial speed adjustment amount. Through the multi-axis superposition controller, the three parameters are integrated to finally generate the initial flight path control instruction containing the initial heading angle change rate, the initial height offset amount and the initial speed adjustment amount.

[0152] First, the physical quantity conversion of the coupling correction amount is carried out, specifically, the coupling correction amount of the heading angle needs to be converted into the heading angle change rate, and the specific conversion method is: heading angle change rate = heading angle coupling correction amount ÷ control period; the coupling correction amount of the height is directly used as the height offset; the coupling correction amount of the speed is directly used as the speed adjustment. After the conversion is completed, a weighted superposition algorithm is used to superimpose each parameter on multiple axes, and the specific rules are: new heading angle change rate = original heading angle change rate x first weight coefficient + 0.3 x height offset x second weight coefficient - 0.2 x speed adjustment x third weight coefficient; new height offset = original height offset x fourth weight coefficient + 0.1 x original heading angle change rate x fifth weight coefficient; new speed adjustment = original speed adjustment x sixth weight coefficient - 0.15 x original heading angle change rate x seventh weight coefficient + 0.05 x original height offset x eighth weight coefficient, wherein the first weight coefficient to the eighth weight coefficient are preset according to the aerodynamic characteristics of the compound wing unmanned aerial vehicle, for example, the first weight coefficient can be 0.8, the second weight coefficient can be 0.2, and the third weight coefficient can be 0.1. After the above processing, the initial route control instruction is presented in a three-tuple structure as (new heading angle change rate, new height offset, new speed adjustment), wherein the new heading angle change rate is used to represent the heading adjustment rate; the new height offset is used to represent the target height offset; and the new speed adjustment is used to represent the target speed adjustment.

[0153] The embodiment of the present application solves the mutual interference problem of the heading, height and speed control amount when coupling is executed by using the attitude deviation vector to dynamically correct the distribution ratio; the coupling correction amount is adjusted based on the real-time attitude deviation to enhance the control robustness of the compound wing unmanned aerial vehicle under the disturbance of turbulence, crosswind and the like; and the initial route control instruction which can be directly executed is generated through physical quantity conversion to reduce the response delay of the traditional hierarchical control architecture.

[0154] Figure 2 A structure diagram of an emergency control system of a compound wing unmanned aerial vehicle is provided for the embodiment of the present application, as shown in Figure 2 The system comprises:

[0155] The acquisition module 21 is used to acquire airspace traffic situation awareness data to determine the position information of the compound wing unmanned aerial vehicle in a complex airspace with communication delay or communication link interruption;

[0156] The identification module 22 is used to demarcate the geometric boundary of the emergency obstacle avoidance area according to the position information, calculate a dynamic obstacle avoidance path, and generate a first control parameter matched with the dynamic obstacle avoidance path;

[0157] The adjusting module 23 is configured to adjust the first control parameter according to the axial deviation value of the flight attitude parameter of the compound wing unmanned aerial vehicle and the target attitude parameter, to generate a second control parameter, and the second control parameter is used to maintain the emergency flight stability of the compound wing unmanned aerial vehicle.

[0158] The processing module 24 is configured to perform signal modulation and cluster coding processing on the second control parameter when the communication link is detected to be interrupted, to generate a distributed relay transmission signal in the ad hoc network mode.

[0159] The optimization module 25 is configured to optimize the initial flight path control instruction of the compound wing unmanned aerial vehicle through a node hopping strategy and a priority routing allocation strategy of the distributed relay transmission signal, to obtain a target control instruction, so as to realize the emergency flight path re-planning and flight attitude cooperative control of the compound wing unmanned aerial vehicle in the communication failure state.

[0160] Figure 2 The emergency control system of the compound wing unmanned aerial vehicle can perform Figure 1 The emergency control method of the compound wing unmanned aerial vehicle in the embodiment is not repeated in terms of implementation principle and technical effects. The specific operation modes of each module and unit of the emergency control system of the compound wing unmanned aerial vehicle in the above embodiment have been described in detail in the embodiment related to the method, and will not be described in detail here.

[0161] In one possible design, Figure 2 The emergency control system of the compound wing unmanned aerial vehicle in the embodiment can be implemented as a computing device, such as Figure 3 As shown, the computing device can include a storage component 31 and a processing component 32.

[0162] The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are called and executed by the processing component 32.

[0163] The processing component 32 is configured to perform the above Figure 1 The emergency control method of the compound wing unmanned aerial vehicle in the embodiment.

[0164] The processing component 32 can include one or more processors to execute computer instructions to complete all or part of the steps in the above method. Of course, the processing component can also be one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic elements, for executing the above method.

[0165] The storage component 31 is configured to store various types of data to support the operation of the terminal. The storage component can be implemented by any type of volatile or nonvolatile storage devices, or a combination thereof, such as a static random access memory (SRAM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a read-only memory (ROM), a magnetic storage, a flash memory, a magnetic disk, or a optical disk.

[0166] Of course, the computing device can also necessarily include other components, such as an input or output interface, a display component, a communication component, etc.

[0167] The input or output interface provides an interface between the processing component and a peripheral interface module, which can be an output device, an input device, etc.

[0168] The communication component is configured to facilitate wired or wireless communication between the computing device and other devices, etc.

[0169] The computing device can be a physical device or an elastic computing host provided by a cloud computing platform, and the processing component, the storage component, etc. can be a basic server resource rented or purchased from the cloud computing platform.

[0170] The embodiment of the application further provides a computer storage medium, which stores a computer program, and the computer program can realize the above-mentioned Figure 1 An emergency control method of the composite wing unmanned aerial vehicle.

[0171] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the above-mentioned system, device and unit can refer to the corresponding process in the foregoing method embodiment, and will not be repeated here.

[0172] The device embodiments described above are only schematic, and the units described as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, that is, they can be located in one place, or distributed on multiple network units. According to actual needs, part or all of the modules can be selected to achieve the purpose of the embodiment scheme. Those skilled in the art can understand and implement without creative labor.

[0173] Those skilled in the art can clearly understand the technical solutions of the various embodiments from the above description of the embodiments, and the various embodiments can be implemented by means of software with the necessary general hardware platforms, and of course, can also be implemented by hardware. Based on such understanding, the above technical solutions, essentially or in other words, the part of the prior art that makes a contribution, can be embodied in the form of a software product, which can be stored in a computer readable storage medium, such as a ROM or a RAM, a magnetic disk, an optical disk, and the like, and includes a number of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute the methods described in the various embodiments or some parts of the embodiments.

[0174] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, rather than limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for some technical features therein; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. An emergency control method for a compound-wing unmanned aerial vehicle (UAV), characterized in that, include: Acquire airspace traffic situational awareness data to determine the location information of compound-wing UAVs in complex airspace with communication delays or communication link interruptions; Based on the location information, the geometric boundary of the emergency obstacle avoidance area is delineated to calculate the dynamic obstacle avoidance path and generate a first control parameter that matches the dynamic obstacle avoidance path. Based on the axial deviation between the flight attitude parameters of the compound wing UAV and the target attitude parameters, the first control parameter is adjusted to generate the second control parameter, which is used to maintain the emergency flight stability of the compound wing UAV. When a communication link interruption is detected, the frequency band adaptation characteristics of the second control parameter are extracted to convert the signal waveform of the second control parameter, generate a modulation signal that matches the channel characteristics of different frequency bands, and extract the frequency band feature parameters of the modulation signal. Based on the channel stability index and signal attenuation threshold in the frequency band feature parameters, combined with the link quality parameters and node load parameters of adjacent compound wing UAV nodes in the node topology of the self-organizing network mode, a distributed relay transmission signal in the self-organizing network mode is generated. By using the node hopping strategy and priority routing allocation strategy of the distributed relay transmission signal, the initial route control command of the compound wing UAV is optimized to obtain the target control command, so as to realize the emergency route replanning and flight attitude cooperative control of the compound wing UAV in the case of communication failure.

2. The method according to claim 1, characterized in that, Based on the channel stability index and signal attenuation threshold in the frequency band characteristic parameters, and combined with the link quality parameters and node load parameters of adjacent compound-wing UAV nodes in the node topology of the ad hoc network mode, a distributed relay transmission signal in the ad hoc network mode is generated, including: Based on the channel stability index and signal attenuation threshold in the frequency band characteristic parameters, the modulated signal is divided into multiple data clusters, wherein each data cluster contains redundancy check information, cluster identification information and priority identification. Based on the cluster identifier and priority identifier of each data cluster, and combined with the link quality parameters and node load parameters of adjacent compound-wing UAV nodes in the node topology of the self-organizing network mode, a transmission path weight value is assigned to each data cluster to generate a transmission path sequence. Based on the transmission path sequence, the data clusters are transmitted in a time-series scheduling manner through a relay forwarding mechanism between adjacent composite wing UAV nodes, generating a distributed relay transmission signal in the self-organizing network mode. During the transmission process, lost or damaged data clusters are retransmitted in real time according to the redundancy check information.

3. The method according to claim 1, characterized in that, By optimizing the node hopping strategy and priority routing allocation strategy of the distributed relay transmission signal, the initial flight path control command of the compound-wing UAV is obtained, including the target control command: The heading angle correction, altitude correction, and speed correction are extracted from the route parameters in the distributed relay transmission signal, and the execution weight corresponding to each correction is determined based on the priority identifier. Based on the execution weights and the flight attitude parameters of the compound wing UAV, multi-axis coupling calculations are performed on the heading angle correction, altitude correction, and speed correction to generate initial route control commands. Based on the node switching strategy of the distributed relay transmission signal, the real-time position parameters of adjacent compound wing UAV nodes, and the link quality parameters, the heading angle change rate, altitude offset, and speed adjustment in the initial route control command are adjusted to generate an optimized route control command. The execution order of the optimized route control command is hierarchically divided according to the priority routing allocation strategy of the distributed relay transmission signal to generate the target control command.

4. The method according to claim 3, characterized in that, Based on the execution weights and the flight attitude parameters of the compound-wing UAV, multi-axis coupled calculations are performed on the heading angle correction, altitude correction, and speed correction to generate initial route control commands, including: Based on the execution weights, the heading angle correction, altitude correction, and speed correction are respectively allocated to the control channels of the pitch axis, roll axis, and yaw axis of the compound wing UAV, generating a correction allocation ratio corresponding to each control channel; Calculate the attitude deviation vector between the flight attitude parameters of the compound wing UAV and the target attitude parameters; Based on the correlation between the correction amount allocation ratio and the attitude deviation vector, multi-axis coupling calculations are performed on the heading angle correction, altitude correction, and speed correction to generate heading angle coupling correction, altitude coupling correction, and speed coupling correction. The heading angle coupling correction, altitude coupling correction, and speed coupling correction are superimposed on multiple axes to generate the initial route control command.

5. The method according to claim 1, characterized in that, Based on the location information, the geometric boundaries of the emergency obstacle avoidance area are defined to calculate the dynamic obstacle avoidance path, and first control parameters matching the dynamic obstacle avoidance path are generated, including: Obtain the spatial distribution of airspace obstacles in complex airspace, and combine the location information to delineate the geometric boundaries of the emergency obstacle avoidance area; Based on the shape and distance parameters of the geometric boundary, a multi-level safety buffer zone is constructed with the position of the compound wing UAV as the starting point. Each level of the multi-level safety buffer zone corresponds to a different obstacle avoidance priority. The candidate paths within the multi-level safety buffer zone that have the least conflict with the flight direction of the compound wing UAV are selected step by step, and the dynamic obstacle avoidance path is determined based on the curvature continuity and path length of the candidate paths. Based on the heading angle change rate, altitude offset, and speed adjustment of the dynamic obstacle avoidance path, the control surface deflection angle and thruster distribution ratio of the compound wing UAV are adjusted to generate the first control parameters.

6. The method according to claim 5, characterized in that, Obtain the spatial distribution of airspace obstacles in the complex airspace, and, in conjunction with the location information, delineate the geometric boundaries of the emergency obstacle avoidance area, including: The three-dimensional spatial coordinate set of airspace obstacles perceived in real time by the compound wing UAV in the complex airspace is obtained. The three-dimensional spatial coordinate set includes the position information, size parameters and motion direction parameters of the airspace obstacles. Based on the set of three-dimensional spatial coordinates, the spatial distribution of the airspace obstacles is spatially topologically segmented to generate multiple sub-regions, wherein the boundary of each sub-region is determined by the coordinates of the vertex of the largest outer geometry of the adjacent airspace obstacles. Based on the relative distance between the position information of the compound wing UAV and the sub-region, target sub-regions that conflict with the flight direction of the compound wing UAV are filtered out, and the target sub-regions are marked as key conflict areas; Boundary fitting is performed on the coordinates of the circumscribed geometric vertices of the airspace obstacles in the critical conflict area to generate the geometric boundary of the emergency obstacle avoidance area.

7. The method according to claim 1, characterized in that, Based on the axial deviation between the flight attitude parameters of the compound-wing UAV and the target attitude parameters, the first control parameter is adjusted to generate the second control parameter, including: Calculate the axial deviation between the flight attitude parameters of the compound wing UAV and the target attitude parameters, wherein the flight attitude parameters include pitch angle, roll angle and yaw angle; Based on the amplitude and directional components of the axial deviation value, the control amount of the control surface deflection angle in the first control parameter is adjusted to generate the adjusted control surface deflection angle, so that the deflection direction of the flaps and ailerons of the compound wing UAV is consistent with the compensation direction of the directional component. Based on the adjusted control surface deflection angle, the thrust distribution ratio of the first control parameter is adjusted synchronously to generate the adjusted thrust distribution ratio of the thruster, so that the torque distribution of the compound wing UAV on the pitch axis, roll axis and yaw axis matches the correction requirements of the amplitude component. The adjusted control surface deflection angle is compensated by the adjusted thrust distribution ratio of the propeller to generate the second control parameter.

8. An emergency control system for a compound-wing unmanned aerial vehicle (UAV), characterized in that, include: The acquisition module is used to acquire airspace traffic situational awareness data to determine the location information of the compound-wing UAV in complex airspace with communication delays or communication link interruptions. The identification module is used to delineate the geometric boundary of the emergency obstacle avoidance area based on the location information, calculate the dynamic obstacle avoidance path, and generate a first control parameter that matches the dynamic obstacle avoidance path. The adjustment module is used to adjust the first control parameter and generate a second control parameter based on the axial deviation between the flight attitude parameters of the compound wing UAV and the target attitude parameters. The second control parameter is used to maintain the emergency flight stability of the compound wing UAV. The processing module is used to extract the frequency band adaptation characteristics of the second control parameter when a communication link interruption is detected, to perform signal waveform conversion on the second control parameter, generate a modulation signal that matches the channel characteristics of different frequency bands, and extract the frequency band feature parameters of the modulation signal. Based on the channel stability index and signal attenuation threshold in the frequency band feature parameters, combined with the link quality parameters and node load parameters of adjacent compound-wing UAV nodes in the node topology of the self-organizing network mode, a distributed relay transmission signal in the self-organizing network mode is generated. The optimization module is used to optimize the initial route control command of the compound-wing UAV through the node hopping strategy and priority routing allocation strategy of the distributed relay transmission signal, and obtain the target control command, so as to realize the emergency route replanning and flight attitude cooperative control of the compound-wing UAV in the state of communication failure.

9. A computing device, characterized in that, It includes a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are invoked and executed by the processing component to implement an emergency control method for a compound-wing unmanned aerial vehicle as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that, The device contains a computer program that, when executed by a computer, implements an emergency control method for a compound-wing unmanned aerial vehicle as described in any one of claims 1 to 7.

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