Intelligent obstacle avoidance method and system for quad-rotor unmanned aerial vehicle based on flow deflectors

Abstract

The invention provides a four-rotor unmanned aerial vehicle intelligent obstacle avoidance method and system based on flow deflectors. The method comprises the following steps that S1, an obstacle avoidance system is initialized before an unmanned aerial vehicle works; s2, when the unmanned aerial vehicle works, executing a passive obstacle avoidance mode by default, monitoring an ambient light intensity level R2 and a flight area risk level R3, and judging whether to switch to an active obstacle avoidance mode; s3, after obstacle avoidance is completed, the unmanned aerial vehicle calculates the shortest regression path according to the original route and the current position, the original flight speed is gradually recovered, the system state is reset after the original route is regressed, the method is suitable for various application scenes, effective obstacle avoidance under special conditions can be achieved, the structure is light and stable, and the obstacle avoidance effect is good.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) navigation technology, specifically to an intelligent obstacle avoidance method and system for quadrotor UAVs based on guide vanes. Background Technology

[0002] With the rapid development of drones and automatic control technology, quadcopter drones have been widely used in various fields due to their flexibility and stability. However, drone flight is limited by navigation technology, and various obstacles may appear during navigation. Therefore, drones with obstacle avoidance capabilities have emerged. However, drones with autonomous obstacle avoidance capabilities still have many shortcomings. Currently, single-type sensing solutions generally suffer from insufficient environmental adaptability. For example, visual systems experience performance degradation in low light, smoke, or when facing transparent objects; lidar is limited by cost, size, and reliability in adverse weather conditions; while ultrasonic and millimeter-wave solutions are limited by detection accuracy, anti-interference capabilities, and installation compatibility, respectively. Furthermore, while airflow sensing methods can overcome some environmental constraints, existing designs are still imperfect in terms of accurate obstacle ranging, classification capabilities, and aerodynamic integration with the fuselage.

[0003] In existing technologies, such as Chinese patent application number CN202511881645.5, published on January 13, 2026, a multi-sensor fusion-based UAV autonomous obstacle avoidance and path planning system and method is disclosed. The system includes acquiring dynamic obstacles and linear obstacles within the scanned area at the current moment; normalizing the features of the dynamic obstacles and linear obstacles, and correcting the normalized features to obtain an obstacle score; obtaining the current obstacle avoidance priority based on the obstacle score; and updating the current path based on the obstacle avoidance priority to obtain a first path.

[0004] In the above literature, UAVs achieve autonomous obstacle avoidance by determining the current obstacle avoidance priority and obtaining different paths based on the characteristics of dynamic and linear obstacles. This obstacle avoidance method selects a flight path by identifying obstacles, which is achieved through visual sensors. However, in low-light environments, visual sensors cannot accurately determine the distance to obstacles due to the influence of lighting and visibility. Furthermore, determining obstacles through visual sensors requires identifying the shape of the obstacle from an image, which takes a long time to process, thus making it impossible to determine obstacles in a timely and accurate manner. In addition, the airflow generated during rotor rotation also affects the passive airflow perception method, which is affected by the turbulence of the rotor airflow, resulting in inaccurate obstacle determination. Summary of the Invention

[0005] This invention provides a method and system for intelligent obstacle avoidance of quadcopter UAVs based on guide vanes. It can adapt to most application scenarios and effectively solve the problem of perception failure of existing obstacle avoidance technologies under extreme conditions such as low light, smoke, strong wind, and narrow space. It has good obstacle avoidance and navigation performance.

[0006] To achieve the above objectives, one aspect of the technical solution provided by the present invention is as follows: The present invention also provides an intelligent obstacle avoidance method for a quadcopter drone based on a guide vane. The drone includes a guide vane disposed on a rotating wing, a guide groove disposed within the guide vane, the guide groove being connected to an active airflow emission module, and a pressure sensor and an angular displacement sensor disposed on the guide vane. The method includes the following steps: S1. Initialize the UAV. The initialization operation includes benchmark calibration and preset angle change threshold and pressure change threshold in passive obstacle avoidance mode, and determine the original flight path and original flight speed. S2. When the UAV is working, it initially executes the passive obstacle avoidance mode. In the passive obstacle avoidance mode, the air pressure value in the airflow groove inside the airflow guide plate is detected by the airflow guide plate pressure sensor, and the airflow angle value affected by the airflow is determined by the angular displacement sensor. The airflow pressure change value and airflow angle change value are determined according to the airflow pressure value and airflow angle value, respectively. The airflow pressure change value is compared with the preset pressure change threshold and the airflow angle change value is compared with the preset angle change threshold to determine whether it is a disturbance of the rotor's own airflow. If not, the passive perception risk level is determined according to the peak value of the airflow pressure change value, the peak value of the airflow angle change value and the change rate, and different obstacle avoidance actions are determined according to the passive perception risk level. At the same time, the ambient light intensity level and the risk level of the flight area are monitored, and it is determined whether to switch to the active obstacle avoidance mode. S3. After obstacle avoidance is completed, the UAV calculates the shortest return path based on the original route and the current position, and gradually restores the original flight speed.

[0007] The above method ensures the system's baseline state and operational reliability through initialization. The drone defaults to passive obstacle avoidance mode, enabling it to sense airflow beneath the rotor in environments with no obvious obstacles or favorable conditions. It detects the pressure of reflected airflow from nearby obstacles and the angle changes caused by the deformation of the guide vanes. These values ​​are first compared with preset pressure and angle change thresholds to determine if the disturbance originates from the rotor's own airflow. If rotor-related disturbances are ruled out, the peak values ​​of the airflow pressure and angle changes, along with their rates, determine the passive obstacle avoidance risk level and initiate obstacle avoidance operations. Simultaneously, the system monitors light levels and flight area conditions in real-time. When these conditions are high, it activates active obstacle avoidance mode for proactive obstacle avoidance. This allows for switching between passive and active obstacle avoidance modes based on different environmental conditions. In passive obstacle avoidance mode, the distance to the obstacle is determined by the reflected airflow on the guide vanes, thus enabling the adoption of different obstacle avoidance methods. By combining pressure and angle changes, the position of the obstacle is comprehensively determined from both pressure and guide vane deformation perspectives, thereby improving detection accuracy.

[0008] Furthermore, step S2, "determining the change in air pressure and the change in air angle based on the air pressure value and the air angle value respectively", includes: filtering the air angle value using wavelet transform filtering to remove the interference frequency part caused by rotor vibration, and extracting the change in guide vane deflection angle Δθ caused by airflow disturbance. The airflow pressure value is filtered using Kalman filtering to remove environmental noise and extract the airflow pressure change ΔP.

[0009] The above settings, after obtaining the airflow angle value through the angular displacement sensor, remove the interference components of the rotor's own airflow and then determine the airflow angle change value. The airflow pressure value is also filtered before determining the airflow pressure change value, thereby reducing the influence of the rotor's own airflow.

[0010] Furthermore, step S2, "determining the passively perceived risk level based on the peak value of the airflow pressure change, the peak value of the airflow angle change, and the rate of change, and determining different obstacle avoidance actions based on the passively perceived risk level," includes: determining the passively perceived risk level R1 based on R1=(ΔP / P0)×0.7+(Δθ / (θ0+0.1))×0.3. If R1 < the preset first passive perception risk level value, it is determined to be low risk, and is identified as a distant obstacle or a small interference object. Only the disturbance warning information is sent to the flight control unit. The flight control unit maintains the original flight attitude and speed, and the data processing unit continuously monitors the disturbance change trend. If the preset first passive perception risk level value < R1 < the preset second passive perception risk level value, it is determined to be a medium risk and identified as a medium-distance obstacle. Based on the distribution of Δθ and ΔP of two or more sets of guide vanes, the location of the obstacle is determined, and the UAV is deflected by a preset angle to maintain its original flight speed. If the preset second passive perception risk level value is <R1, it is determined to be a high risk and identified as a close-range obstacle. The drone is controlled to hover within a preset time and move a preset distance in the direction with the least disturbance. During the movement, airflow disturbance is continuously monitored. If the disturbance disappears, the drone returns to the original flight path. If the disturbance continues, the mode switch is triggered and the active obstacle avoidance mode is activated.

[0011] The above settings determine the passive perception risk level by the rate of change of airflow pressure and the rate of change of airflow angle. In the process of determining the passive perception risk level, the changes in airflow pressure value are mainly considered, combined with the changes in airflow angle value, and the passive perception risk level is determined. Different obstacle avoidance methods are executed according to different risk levels. Especially in high-risk situations, after hovering or moving, the system can switch to active obstacle avoidance mode for more accurate obstacle avoidance.

[0012] Furthermore, in step S2, "determining whether to switch to active obstacle avoidance mode" includes: if the passive obstacle avoidance risk level R1 ≥ the first preset threshold or the ambient light intensity level R2 ≤ the second preset threshold or the flight area risk level R3 ≥ the third preset threshold, a mode switching command is immediately sent to switch to active obstacle avoidance mode. If the passive obstacle avoidance risk level R1 is less than the first preset threshold for a continuous preset time, and the ambient light intensity level R2 is greater than the second preset threshold and the flight area risk level R3 is less than the third preset threshold, the passive obstacle avoidance mode will be maintained.

[0013] The above settings can determine whether to switch modes when considering the relationship between the three main elements, thus avoiding both frequent switching and inaccurate mode switching.

[0014] Furthermore, step S2 also includes: the active obstacle avoidance mode includes: emitting stable airflow in a time sequence through an active airflow emission module; collecting reflected airflow signals through pressure sensors and angular displacement sensors; calculating the airflow propagation speed through an airflow speed correction algorithm; calculating the obstacle distance by combining the time difference between emission and reception; calculating the azimuth and pitch angles through a weighted triangulation method; identifying the obstacle type based on the characteristics of the reflected airflow signals; executing a graded obstacle avoidance response based on the obstacle information; and continuously monitoring environmental parameters after active obstacle avoidance to determine whether to switch back to passive obstacle avoidance mode.

[0015] The above settings, by emitting a stable airflow beam and collecting reflected airflow signals, enable the drone to operate independently of ambient light intensity, while also reducing interference from ambient background noise and allowing for accurate calculation of distances between obstacles.

[0016] Furthermore, the ambient light intensity level R2 = actual light intensity / maximum light intensity; the flight area risk level R3 = 1 - distance from high-risk area / safe distance; the distance from high-risk area is calculated in real time by the GPS positioning system as the straight-line distance between the drone fuselage and the nearest high-risk area boundary.

[0017] The above settings allow for the determination of ambient light intensity levels and flight area risk levels based on actual light intensity and the positioning system.

[0018] Furthermore, in the active obstacle avoidance mode, the calculation of obstacle distance based on the time difference between transmission and reception includes: when the pressure sensor detects a sudden change in the airflow pressure signal, determining the acquisition and reception time t2, and calculating the time difference Δt = t2 - t1; where t1 is the start time when the pressure sensor detects the airflow pressure value. In the active obstacle avoidance mode, the obstacle type identification based on the characteristics of reflected airflow signals includes: performing feature analysis on the captured reflected airflow signals, extracting three core feature parameters: rise slope, peak duration, and signal attenuation rate, and determining the obstacle type based on the core feature parameters; In the active obstacle avoidance mode, the obstacle distance is calculated by combining the time difference between transmission and reception. This includes: calculating the obstacle distance D (m) based on the airflow velocity v determined by the airflow velocity correction algorithm and the time difference Δt, using the formula D=(v×Δt) / 2. If two or more sets of guide vanes detect the same obstacle, the average obstacle distance calculated by the two or more sets of guide vanes is taken as the final obstacle distance. The weighted triangulation method is used, with the signal peak value of each guide vane pressure sensor as the weighting factor, to calculate the obstacle's azimuth angle α and pitch angle β. The active obstacle avoidance response level is determined by comparing the obstacle distance with the preset distance.

[0019] The above settings, during active obstacle avoidance, generate a stable airflow through the active airflow emission module, then measure the reflected airflow pressure signal based on the pressure sensor, determine the obstacle type based on the airflow pressure signal, and revise the airflow speed according to the temperature and relative humidity based on the airflow speed correction algorithm. Then, determine the time based on the airflow speed and pressure change formation time, further determine the distance and direction, and determine the active obstacle avoidance response level based on the distance to the obstacle to perform active obstacle avoidance.

[0020] Furthermore, the airflow velocity correction algorithm corrects the airflow propagation velocity based on temperature and relative humidity, and the correction is based on the following formula: v is the corrected airflow velocity (m / s), T is the collected ambient temperature (°C), and RH is the collected relative humidity (%).

[0021] The above settings allow for the calculation of the corrected airflow velocity using ambient temperature and relative humidity.

[0022] Another aspect of the present invention provides an intelligent obstacle avoidance system for a quadcopter drone based on a guide vane, comprising a drone body, the drone body including a rotor protective cover, and further including a guide vane sensing module, an active airflow emission module, a data processing module, and a flight control module. The guide vane sensing module includes a guide vane, a guide channel system disposed within the guide vane, a pressure sensor, and an angular displacement sensor. The guide vane is connected to the rotor protective cover. The guide channel system includes a main flow channel extending along the length of the guide vane body and branch flow channels located on both sides of the main flow channel. The inlet of the main flow channel is connected to the active airflow emission module, and a pressure sensor is disposed at the outlet end of the branch flow channels. An angular displacement sensor is disposed at the connection between the guide vane and the rotor protective cover. The data processing module includes the UAV initialization operation and passive obstacle avoidance mode. It detects the airflow pressure value in the guide groove of the guide vane through the guide vane pressure sensor, determines the airflow angle value affected by the airflow through the angular displacement sensor, determines the airflow pressure change value and airflow angle change value according to the airflow pressure value and airflow angle value respectively, and determines whether the airflow pressure change value is due to the rotor's own airflow disturbance by comparing the airflow pressure change value with the preset pressure change threshold and the airflow angle change value with the preset angle change threshold. If not, the passive perception risk level is determined according to the peak value of the airflow pressure change value, the peak value of the airflow angle change value and the change rate. The flight control module is signal-connected to the data processing module and is used to determine different obstacle avoidance actions based on the passively perceived risk level. At the same time, it executes graded obstacle avoidance responses based on obstacle information and achieves fault-tolerant control.

[0023] The above configuration integrates a flow deflector sensing module into the side of the corresponding rotor shield of the quadcopter, enabling the drone to simultaneously achieve passive airflow disturbance perception and active airflow reflection perception. An active airflow emission module provides a stable active airflow beam, working in conjunction with the flow deflector sensing module to achieve active obstacle avoidance detection. A data processing module collects data from the working environment through sensors and performs calculations based on the collected information to make corresponding decisions. A flight control module enables the drone to perform graded obstacle avoidance based on the processed data. The passive obstacle avoidance risk level is determined based on the peak values ​​of airflow pressure changes, peak values ​​of airflow angle changes, and the rate of change, and obstacle avoidance operations are performed in passive obstacle avoidance mode. Simultaneously, in passive obstacle avoidance mode, the system monitors the light intensity and flight area level in real time, activating active obstacle avoidance mode when the light intensity and flight area level are high. This allows for switching between passive and active obstacle avoidance modes based on different environmental conditions. In passive obstacle avoidance mode, the distance to the obstacle is determined by the reflected airflow on the guide vanes, thus enabling the adoption of different obstacle avoidance methods. By combining pressure and angle changes, the position of the obstacle is comprehensively determined from both pressure and guide vane deformation perspectives, thereby improving detection accuracy.

[0024] Furthermore, it also includes a temperature and humidity sensor and a vibration sensor; the temperature and humidity sensor is set on a preset groove on the surface of the guide vane body, and a sealant is provided around the preset groove; the vibration sensor is set at the connection between the guide vane and the rotor protective cover by a fixing component.

[0025] The above settings enable the system to measure ambient temperature and relative humidity using a humidity sensor, and also allow the system to collect vibration signals generated by rotor rotation by a vibration sensor, providing interference characteristic data for subsequent data filtering. Attached Figure Description

[0026] Figure 1 This is a schematic diagram of the structure of the quadcopter drone of the present invention.

[0027] Figure 2 This is a schematic diagram of the structure of the guide plate body of the present invention.

[0028] Figure 3 This is a perspective view of the flow guide plate in this invention.

[0029] Figure 4 This is a flowchart illustrating the obstacle avoidance control method of the present invention.

[0030] Figure 5 This is a flowchart illustrating the passive obstacle avoidance mode of the present invention.

[0031] Figure 6 This is a flowchart illustrating the active obstacle avoidance mode of the present invention.

[0032] Explanation of reference numerals in the attached drawings: 1-UAV; 10-Rotor shield; 11-Guide vane; 120-Main flow channel; 121-Sub-flow channel; 122-Airflow interface. Detailed Implementation

[0033] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments.

[0034] Example 1.

[0035] like Figures 1 to 3As shown, a quadcopter drone intelligent obstacle avoidance system based on a guide vane includes a drone 1. The drone 1 includes a rotor shield 10, a guide vane sensing module, an active airflow emission module, a data processing module, a flight control module, and a power management module. The guide vane sensing module includes a guide vane 11, a guide channel system, and multi-dimensional sensing elements. The guide vane 11 is connected to the rotor shield 10, and the guide channel system is disposed inside the guide vane 11.

[0036] The guide vane 11 is connected to the rotor guard 10, and the guide vane 11 is arranged in an inwardly concave arc shape.

[0037] The flow guide system includes a main flow guide 120, a secondary flow guide 121, an airflow interface 122, and drainage holes. The main flow guide 120 is a rectangular channel extending along the length of the flow guide vane. The secondary flow guide 121 is symmetrically arranged at one end of the main flow guide 120, away from the center of the rotor shield. The airflow interface 122 is located at the other end of the main flow guide 120, close to the center of the rotor shield, and is also provided with an annular sealing strip. The bottom of the main flow guide 120 is provided with one or more drainage holes (not shown in the figure), which are respectively located at one end and the other end of the main flow guide.

[0038] In this embodiment, the guide vane is made of carbon fiber reinforced polyetheretherketone composite material. The surface of the guide vane is anodized to form an oxide film with a thickness of 0.05 mm, which reduces the frictional resistance during airflow. At the same time, a hydrophobic coating is sprayed on the surface to prevent rainwater and fog droplets from adhering and affecting the accuracy of airflow sensing.

[0039] In this embodiment, the main flow channel has a width of 2.5 mm, a depth of 2 mm, a length of 22 mm, and a wall thickness of 0.3 mm. This ensures structural strength while maximizing the airflow transmission cross-section. The main flow channel is diamond-polished to reduce surface roughness, resulting in less energy loss and turbulence during airflow transmission. Simultaneously, the channel wall is coated with a polytetrafluoroethylene (PTFE) coating to further reduce airflow friction resistance. The branch flow channels have a width of 1 mm, a depth of 1.5 mm, and a length of 5 mm, forming a 30° angle with the main flow channel. The two branch flow channels are symmetrically arranged about the main flow channel. This forms a "Y"-shaped airflow channel. One end of the branch guide channel is the outlet, which is shaped like a trumpet with an expansion angle of 15° to enhance the diffusion of the airflow. The other end of the branch guide channel is the inlet, which connects to the air supply pipe of the active airflow emission module. The connection interface is made of polytetrafluoroethylene, with an inner diameter of 1.2mm, an outer diameter of 2mm, and a length of 3mm. An annular sealing groove with a width of 0.5mm and a depth of 0.3mm is also provided to hold a sealing ring for sealing. Two micro drainage holes with a diameter of 0.3mm are set at the bottom of the main guide channel.

[0040] The multi-dimensional sensing elements include pressure sensors, angular displacement sensors, temperature and humidity sensors, and vibration sensors. In this embodiment, there are four pressure sensors: one at the inlet of the main flow channel to collect the active airflow emission pressure, one at the outlet of the main flow channel to collect the residual pressure after the active airflow emission, and two at the ends of the branch flow channels to collect the reflected airflow pressure. The pressure sensors are fixed to the reserved grooves on the inner wall of the flow channels using conductive silver paste. The angular displacement sensor is a miniature capacitive angular displacement sensor with a measurement range of ±30°, a resolution of 0.002°, and a sampling frequency of 2kHz. It adopts a non-contact measurement method to avoid mechanical wear affecting accuracy. The angular displacement sensor is located at the rotation axis between the guide vane and the rotor shield. By connecting one end of the angular displacement sensor to the rotor shield and the other end to the guide vane body, the slight deformation of the guide vane caused by the reflected airflow results in an angle change, allowing for real-time acquisition of the deflection angle change of the guide vane body. The temperature and humidity sensor is mounted on the guide vane, with its probe exposed to ensure direct contact with the environment. The groove is sealed with sealant. The vibration sensor is a miniature triaxial accelerometer with a measurement range of ±16g, a resolution of 3.9mg / LSB, and a sampling frequency of 1kHz. It can accurately collect vibration signals generated by rotor rotation. The vibration sensor is mounted on the connection between the guide vane and the rotor shield via a metal bracket. It is used to collect vibration signals from rotor vibration and airflow disturbance, providing interference characteristic data for subsequent data filtering.

[0041] The active airflow emission module includes a micro air pump, an airflow regulation submodule, an airflow transmission module, and a drive control circuit. The micro air pump is located inside the rotor protective cover. The airflow regulation submodule includes an airflow regulator, an airflow filter, and a flow regulating valve. One end of the airflow transmission module is connected to the airflow regulation submodule, and the other end is connected to the guide channel system. The drive control circuit is used to receive PWM signals from the data processing module and control the start / stop and output power of the micro air pump.

[0042] The data processing module is used to implement multi-sensor data filtering algorithms, airflow speed correction algorithms, and obstacle information calculation algorithms. The data processing module is used to collect, filter, and fuse sensor data, as well as calculate obstacle information and switch modes.

[0043] The multi-sensor data filtering algorithm includes Kalman filtering, wavelet transform filtering, and moving average filtering. The Kalman filtering algorithm is used for real-time filtering of pressure sensor data. The state equation of the Kalman filtering algorithm is: X(k)=A×X(k-1)+B×u(k)+w(k), where X(k) is the current pressure state, A is the state transition matrix with a value of 1, B is the control matrix with a value of 0, u(k) is the control quantity, and w(k) is the process noise, which follows a Gaussian distribution with a variance of 0.01. The observation equation of the Kalman filtering algorithm is: Z(k)=H×X(k)+v(k), where Z(k) is the sensor observation value, H is the observation matrix with a value of 1, and v(k) is the observation noise, which follows a Gaussian distribution with a variance of 0.005. The signal-to-noise ratio of the filtered pressure data is ≥40dB, which can effectively remove the interference of environmental wind and electronic noise.

[0044] The wavelet transform filtering algorithm is used to filter angular displacement sensor data, eliminating rotor vibration interference. The algorithm uses a db4 wavelet basis with a 3-layer decomposition layer. Characteristic frequencies of rotor vibration are collected by a vibration sensor, and wavelet coefficients within the frequency range of 20-50Hz are set as a threshold with a calibrated value of 0.001°. This effectively removes vibration interference components. The filtered angular displacement data has an error ≤0.005°, accurately reflecting the airflow disturbance and deflection of the guide vanes. The moving average filtering algorithm is used to filter temperature and humidity sensor data. This algorithm uses a 10-point moving average to eliminate the influence of instantaneous environmental fluctuations.

[0045] The airflow velocity correction algorithm corrects the airflow propagation velocity based on temperature and relative humidity, and the formula used for the correction is as follows: v is the corrected airflow velocity (m / s), T is the collected ambient temperature (°C), and RH is the collected relative humidity (%).

[0046] The obstacle information calculation algorithm, based on filtered sensor data, is used to accurately calculate the distance, orientation, and type of obstacles. In active obstacle avoidance mode, the time difference Δt = t2 - t1 is calculated by measuring the emission time t1 of the active airflow and the reception time t2 of the reflected airflow. Combined with the corrected airflow velocity v, the obstacle distance D (m) is obtained, i.e., the calculation formula for obstacle distance D is: D = (v × Δt) / 2, where the emission time t1 is the time when the data processing module sends the air pump start PWM signal, and the reception time t2 is the time when the pressure sensor at the end of the branch guide channel detects the signal change with a rising slope > 5 kPa / s. t1 and t2 are accurately captured through hardware interrupts, with a time measurement accuracy ≤ 1 μs. The obstacle information calculation algorithm uses the reception time difference of the reflected airflow from the four sets of guide vane sensing components and employs a weighted triangulation method to calculate the azimuth angle α and pitch angle β of the obstacle, where the range of the azimuth angle α is the horizontal direction. The range is 0° to 360°, and the pitch angle β is -90° to 90° in the vertical direction. The weighted triangulation method calculates the weight allocation using the peak signal value of the pressure sensor as the weight factor. The larger the peak signal value, the higher the weight. The weight coefficient = peak signal value / sum of 4 peak values, to ensure that the data from the guide vane, which is closer and has a stronger reflected signal, contributes more to the positioning result. The obstacle information calculation algorithm identifies obstacle types based on the signal change characteristics of the pressure sensor, establishing a type identification model to distinguish between rigid and flexible obstacles. The signal change characteristics include the rising edge slope, peak duration, and signal attenuation rate. For rigid obstacles, the signal rising edge slope is >10 kPa / s, the peak duration is >50 ms, and the signal attenuation rate is <0.5 kPa / ms. For flexible obstacles, the signal rising edge slope is <5 kPa / s, the peak duration is <20 ms, and the signal attenuation rate is >1.5 kPa / ms.

[0047] The flight control module is signal-connected to the data processing module and is used to execute a graded obstacle avoidance response strategy based on obstacle information, while also achieving fault-tolerant control.

[0048] In this embodiment, the flight control module executes graded obstacle avoidance actions based on the obstacle information output by the data processing module, and simultaneously achieves coordination between the flight mission and obstacle avoidance actions, including graded obstacle avoidance response and fault handling.

[0049] The communication interaction adopts the CAN bus protocol to communicate with the data processing module, while reserving a UART interface as a backup communication channel to ensure the real-time performance of obstacle avoidance commands and improve communication reliability. The data processing module transmits obstacle distance, azimuth angle, pitch angle, type, risk level, mode switching command, and fault alarm signal to the flight control module. The flight control module transmits UAV flight speed, flight attitude, GPS position, and flight mode to the data processing module.

[0050] The active obstacle avoidance mode corresponds to a tiered obstacle avoidance response, including a warning-level response, an obstacle avoidance-level response, and an emergency-level response. The warning-level response corresponds to an obstacle distance D > 5m, indicating a low risk level. Under the action command of the warning-level response, the UAV will fine-tune its flight direction, with a deflection angle ≤ 3°, maintaining its original flight speed. If in automatic operation mode, it will synchronously adjust its operational route to avoid obstacles in advance and simultaneously send "obstacle warning" information to the ground station, including the obstacle's location and type information, for operator monitoring. The obstacle avoidance-level response corresponds to an obstacle distance D ≤ 5m and ≤ 2m. Under the action command of the obstacle avoidance-level response, the UAV will reduce its flight speed to 60% of its original speed and perform differentiated flyaround maneuvers based on the obstacle's azimuth and type. If the obstacle type is a rigid obstacle, the flyaround radius is D + 1m, and the flyaround trajectory is horizontal. The smooth circular arc has a radius of curvature ≥ 2m. If the obstacle type is flexible, the detour radius is D + 0.5m, and the detour speed can be appropriately increased to 70% of the original speed. After the detour is completed, it automatically returns to the original operation route to ensure the continuity of the operation. The emergency response corresponds to an obstacle distance D < 2m. Under the action command of the emergency response, the UAV will immediately trigger the emergency braking command of the ESC. By adjusting the speed difference of the quadcopter, the UAV will hover within 0.5 seconds and simultaneously move 3m away from the obstacle at a speed of 2m / s to ensure escape from the danger zone. If a new obstacle is detected during the translation, the translation will stop and hover, and an "emergency obstacle avoidance request" will be sent to the ground station to wait for operator instructions. In special scenarios such as emergency rescue, it will automatically switch to manual mode and be controlled by the operator. In this embodiment, the detour or hovering operations under different obstacle avoidance response states are existing technologies and will not be described in detail here.

[0051] The fault handling includes sensor fault handling, air pump fault handling, communication fault handling, and power supply fault handling. Sensor fault handling is as follows: if a sensor's data is abnormal, exceeding the preset abnormal range by more than twice the normal range, the data from that sensor is automatically discarded, and interpolation calculations are performed using the corresponding sensor data from the other three groups of guide vanes to ensure the continuity of obstacle information calculation; if two or more sensors on the same guide vane fail, the guide vane assembly is marked as faulty, and only data from the other three groups of components is used. Air pump fault handling is as follows: if a micro air pump fails, the air pump is shut down. In active mode, only the other three sets of air pumps are used for detection, while the flight speed is reduced to 3 m / s. The communication failure handling is as follows: if the CAN bus communication is interrupted, the flight control module automatically switches to the backup UART communication channel; if the backup channel is also interrupted, the emergency obstacle avoidance mode is activated, the flight speed is reduced to 2 m / s, and the drone flies only in passive obstacle avoidance mode, while sending an alarm signal to the ground station. The power failure handling is as follows: if the power supply voltage is lower than 3.5V, the flight control module controls the drone to reduce its speed to 2 m / s, prioritizes the return-to-home mission, and ensures that the drone safely returns to the takeoff point.

[0052] Example 2.

[0053] like Figure 4-6 As shown, this invention provides an intelligent obstacle avoidance method for quadcopter UAVs based on guide vanes, comprising the following steps: S1. Initialize the UAV. The initialization operation includes benchmark calibration and preset angle change threshold and pressure change threshold in passive obstacle avoidance mode, and determine the original flight path and original flight speed. S2. When the UAV is working, it initially executes the passive obstacle avoidance mode. In the passive obstacle avoidance mode, the air pressure value in the airflow groove inside the airflow guide plate is detected by the airflow guide plate pressure sensor, and the airflow angle value affected by the airflow is determined by the angular displacement sensor. The airflow pressure change value and airflow angle change value are determined according to the airflow pressure value and the airflow angle value, respectively. By comparing the change in airflow pressure with a preset pressure change threshold and the change in airflow angle with a preset angle change threshold, it is determined whether the disturbance is caused by the rotor's own airflow; if so, no obstacle avoidance is performed. If not, the passive risk level is determined based on the peak value of the change in air pressure, the peak value of the change in air angle, and the rate of change, and different obstacle avoidance actions are determined based on the passive risk level. Simultaneously monitor the ambient light intensity level and the risk level of the flight area, and determine whether to switch to active obstacle avoidance mode; S3. After obstacle avoidance is completed, the UAV calculates the shortest return path based on the original route and the current position, and gradually restores the original flight speed.

[0054] Step S2, “determining the change in air pressure and the change in air angle based on the air pressure and the air angle respectively”, includes: filtering the air angle value using wavelet transform to remove the interference frequency part generated by rotor vibration. The interference frequency part generated by rotor vibration is obtained through a vibration sensor. The change in the deflection angle of the guide vane caused by airflow disturbance is extracted. The change in the deflection angle of the guide vane Δθ is the difference between the detected airflow angle value and the initial angle value of the guide vane. The airflow pressure value is filtered using Kalman filtering to remove environmental noise and extract the airflow pressure change ΔP, which is the difference between the detected airflow pressure value and the initial pressure value in the guide channel.

[0055] Step S2, "determining the passive perception risk level based on the peak value of the airflow pressure change, the peak value of the airflow angle change, and the rate of change, and determining different obstacle avoidance actions based on the passive perception risk level," includes: determining the passive perception risk level R1 based on R1=(ΔP / P0)×0.7+(Δθ / (θ0+0.1))×0.3. If R1 < preset first passive perception risk level value A1, it is determined to be low risk, and is identified as a distant obstacle or small interference object. Only the disturbance warning information is sent to the flight control unit. The flight control unit maintains the original flight attitude and speed, and the data processing unit continuously monitors the disturbance change trend. If the preset first passive perception risk level value A1 < R1 < the preset second passive perception risk level value A2, it is determined to be a medium risk and identified as a medium-distance obstacle. Based on the distribution of Δθ and ΔP of two or more sets of guide vanes, the location of the obstacle is determined. If the Δθ and ΔP of the guide vane on one side are significantly increased, the obstacle is determined to be located on that side, and the UAV will deflect by a preset angle (the preset angle is 3°) to maintain the original flight speed. If the preset second passive perception risk level value A2 < R1, it is determined to be a high risk and identified as a close-range obstacle. The drone is controlled to hover within a preset time and move a preset distance (preset distance is 3m) in the direction with the least disturbance. During the movement, airflow disturbance is continuously monitored. If the disturbance disappears, the drone returns to the original flight path. If the disturbance continues, the mode switch is triggered and the active obstacle avoidance mode is activated. In this embodiment, A1 is 0.1 and A2 is 0.3.

[0056] In this embodiment, the ambient light intensity level R2 = actual light intensity / maximum light intensity; the flight area risk level R3 = 1 - distance from high-risk area / safe distance; the distance from high-risk area is calculated in real time by the GPS positioning system as the straight-line distance between the UAV fuselage and the nearest high-risk area boundary. The nearest high-risk area boundary is also obtained by manual selection or identification by the positioning system, and the straight-line distance is calculated by the GPS positioning system.

[0057] Step S2, “determining whether to switch to active obstacle avoidance mode” includes: if the passive obstacle avoidance risk level R1 ≥ the first preset threshold A31 or the ambient light intensity level R2 ≤ the second preset threshold A32 or the flight area risk level R3 ≥ the third preset threshold A33”, immediately send a mode switching command to switch to active obstacle avoidance mode. If the passive obstacle avoidance risk level R1 is less than the first preset threshold A31 for a continuous preset time, and the ambient light intensity level R2 is greater than the second preset threshold A32 and the flight area risk level R3 is less than the third preset threshold A33, the passive obstacle avoidance mode is maintained. In this embodiment, A31=0.1, A32=0.5, and A33=0.2.

[0058] Step S2 further includes: the active obstacle avoidance mode includes: S211 the active airflow emission module emits stable airflow in sequence, and the pressure sensor and angular displacement sensor collect the reflected airflow signal; S212 calculates the airflow propagation speed using an airflow speed correction algorithm and calculates the obstacle distance by combining the time difference between transmission and reception. In this embodiment, calculating the obstacle distance by combining the time difference between transmission and reception includes: when the pressure sensor detects a sudden change in the airflow pressure signal, determining the acquisition and reception time t2, and calculating the time difference Δt = t2 - t1; t1 is the start time when the pressure sensor detects the airflow pressure value; based on the airflow speed v determined by the airflow speed correction algorithm and the time difference Δt, the obstacle distance is calculated using the formula D = (v × Δt) / 2. If two or more sets of guide vanes detect the same obstacle, the average obstacle distance calculated by the two or more sets of guide vanes is taken as the final obstacle distance.

[0059] The airflow velocity correction algorithm corrects the airflow propagation velocity based on temperature and relative humidity, and the formula used for the correction is as follows: v is the corrected airflow velocity (m / s), T is the collected ambient temperature (°C), and RH is the collected relative humidity (%).

[0060] S213 calculates the azimuth and pitch angles using a weighted triangulation method. The weighted triangulation method uses the signal peak values ​​of each guide vane pressure sensor as weighting factors to calculate the azimuth α and pitch angle β of the obstacle. S214 Identifies obstacle types based on the characteristics of reflected airflow signals; In this embodiment, the captured reflected airflow signals are subjected to feature analysis to extract three core feature parameters: rise slope, peak duration, and signal attenuation rate, and the obstacle type is determined based on the core feature parameters. Type recognition: The extracted signal feature parameters are compared with preset thresholds to distinguish between rigid and flexible obstacles. Rigid obstacles: rising edge slope > 10 kPa / s, peak duration > 50 ms, signal attenuation rate < 0.5 kPa / ms; Flexible obstacles: rising edge slope < 5 kPa / s, peak duration < 20 ms, signal attenuation rate > 1.5 kPa / ms.

[0061] S215 performs graded obstacle avoidance response based on obstacle information; after active obstacle avoidance is completed, it continuously monitors environmental parameters to determine whether to switch back to passive obstacle avoidance mode.

[0062] The active obstacle avoidance response level is determined by comparing the distance to the obstacle with the preset distance.

[0063] Warning-level response (D>5m, low risk): Fine-tune flight direction (deflection angle≤3°), maintain original flight speed, update operational route synchronously, and avoid obstacles in advance; send warning information to ground station, including detailed obstacle parameters; Obstacle avoidance level response (2m≤D≤5m, medium risk): Reduce flight speed to 60% of original speed and perform differentiated flight maneuvers: Rigid obstacles: The flight radius is D+1m, and the flight trajectory is a smooth circular arc (radius of curvature ≥2m) to avoid collisions; Flexible obstacles: The detour radius is D+0.5m, and the detour speed is increased to 70% of the original speed, improving work efficiency.

[0064] Emergency response (D < 2m, high risk): Immediately trigger ESC emergency braking, achieve hovering within 0.5 seconds (hovering error ≤ 0.3m); move 3m away from the obstacle at a speed of 2m / s; if a new obstacle is detected during the movement, immediately hover and send an emergency request to the ground station, awaiting operator instructions; automatically switch to manual mode in emergency rescue scenarios.

[0065] In another embodiment, the method further includes: fault detection and fault tolerance: monitoring the movement of the UAV throughout the process, automatically removing data from a single pressure sensor or angular displacement sensor when the data is abnormal, and using linear interpolation to calculate the airflow pressure or airflow angle value using data from pressure sensors or angular displacement sensors corresponding to other guide vanes. By adding forward detection of the UAV's operation during the entire process, and determining the final value through linear interpolation when a sensor fails, the method eliminates the need to replace the sensor before measurement, ensuring data accuracy and making measurement more convenient.

[0066] In this embodiment, after the drone is powered on, the power management module works first to power the data processing module. After the data processing module starts, it executes the initialization program and performs self-tests on each module in sequence. The initialization operation is completed within 0-60 seconds after the drone starts, including hardware self-test, reference value calibration and parameter configuration. The hardware self-test is completed within 0-5 seconds, including sensor self-test, active airflow emission module self-test, communication self-test, and power supply self-test. The sensor self-test sends calibration commands to all pressure sensors, angular displacement sensors, temperature and humidity sensors, and vibration sensors. If the reference value returned by the sensor is within the preset normal range, the sensor is considered normal; otherwise, it is marked as a faulty sensor. The preset normal range is: pressure: 0±0.1kPa, angle: 0±0.01°, temperature: -20℃~60℃, humidity: 0%RH~100%RH. The active airflow emission module self-test sends 500ms PWM signals with a 50% duty cycle to each of the four miniature air pumps. The system detects the operating current of the air pump using a current sensor. If the current is within the range of 80-120mA and the pressure sensor detects an increase in airflow pressure (≥0.2kPa), the air pump is considered normal; otherwise, it is marked as a faulty air pump. The communication self-test sends a test signal to the flight control module via the data processing module. If a feedback signal is received within 100ms, communication is considered normal; otherwise, it switches to the backup communication channel for retesting. The power self-test detects the input voltage, output voltage, and output current via the power management module. If all are within the preset range and there are no short circuit or overcurrent alarms, the power system is considered normal; otherwise, a power fault alarm signal is sent, prohibiting the drone from taking off.

[0067] The benchmark calibration is completed within 5-30 seconds. By collecting sensor data in an interference-free environment, a system working benchmark is established to eliminate initial sensor errors, including static benchmark calibration and dynamic benchmark calibration. The static reference calibration includes a pressure reference value P0, an angle reference value θ0, a temperature and humidity reference value, and a vibration reference value. The pressure reference value P0 is taken as the static average value of the pressure sensors at the inlet end of the main flow channel of the four sets of guide vanes, which is recorded and stored in the buffer of the data processing module. The angle reference value θ0 is taken as the static deflection angle average of the four sets of angular displacement sensors, with a default calibration of 0°. If there is a deviation, it is eliminated by software compensation. The temperature and humidity reference value records the current ambient temperature and humidity data as the initial reference value for the subsequent airflow velocity correction algorithm. The vibration reference value collects the vibration signal when the rotor is not running as the interference reference threshold for the subsequent filtering algorithm. The fault threshold configuration is achieved by setting abnormal sensor data thresholds, air pump fault current thresholds, and low power supply voltage thresholds. Once completed, an "initialization complete" signal is sent to the flight control module, allowing the UAV to perform flight missions. The fault thresholds are defined as sensor data exceeding the normal range by 2 times, air pump fault current <80mA or >120mA, and power supply voltage of 3.5V.

[0068] To verify the technical effectiveness of this invention, a mainstream consumer-grade quadcopter drone with a takeoff weight of 800g and a flight time of 30 minutes was selected and modified. The obstacle avoidance system of this invention was then installed, and comparative experiments were conducted in different scenarios. The experimental results are as follows: 1. Environmental adaptability test Experimental scenarios: Dark environment with light intensity <5 lux, smoky environment with visibility <5m, narrow warehouse passage with a width of 70cm, and strong wind environment with a wind speed of 5m / s; Comparison scheme: A product integrating existing visual, ultrasonic, and lidar obstacle avoidance systems; Experimental index: Obstacle avoidance success rate. The test results of this invention are shown in (Table 1).

[0069] Table 1 Experimental conclusion: The obstacle avoidance success rate of the system of this invention is better than that of existing comparative solutions in various complex environments. It has significant advantages, especially in visual failure scenarios such as darkness and smoke. It can also be adapted to operation in narrow spaces, and its environmental adaptability reaches industrial-grade standards.

[0070] 2. Positioning accuracy experiment Experimental scenario: An open indoor area was set up with rigid and flexible obstacles at different distances, including 0.5m, 2m, 5m, and 10m. In this experiment, the rigid obstacle was a wall, and the flexible obstacle was fabric. Experimental indicators: distance measurement error, azimuth measurement error, and type recognition accuracy. The test results of this invention are shown in (Table 2).

[0071] Table 2 Experimental conclusion: Within a detection range of 0.5-10m, the distance measurement error of the system of this invention is ≤±3cm, the azimuth measurement error is ≤±2°, and the type recognition accuracy is ≥94%, which fully meets the requirements for precise obstacle avoidance at close range.

[0072] 3. Experiment on the impact of lightweight design on battery life Experimental indicators: system weight, UAV flight time changes; Comparison schemes: no obstacle avoidance system, system equipped with the present invention, system equipped with a 16-line lidar obstacle avoidance system. The test results of the present invention are shown in (Table 3).

[0073] Table 3 Experimental conclusion: The total weight of the system of this invention is only 18g, which is much lower than that of the lidar system; the impact rate on the flight time of the UAV is only 8%, which is significantly better than the 30% of the lidar system, and can meet the requirements of long-term operation.

[0074] 4. Fault Tolerance Experiment Experimental scenarios: simulating single sensor failure, single air pump failure, and communication interruption failure; experimental indicators: fault identification time, fault tolerance success rate, and drone safety rate. The test results of this invention are shown in (Table 4).

[0075] Table 4 Experimental conclusion: The system of the present invention can identify various faults in a very short time, with a fault tolerance success rate of ≥98%, ensuring the flight safety of UAVs in fault conditions.

[0076] After the configuration settings in Embodiment 1 are completed, plant protection operations are carried out in wheat planting fields. The wheat planting fields contain obstacles such as crop leaves, utility poles, and irrigation pipes, which are of reference value. The system defaults to passive obstacle avoidance mode. When airflow disturbance (R1≥0.1) is detected, it switches to active obstacle avoidance mode, accurately identifies the type of obstacle and performs a fly-around maneuver, with an obstacle avoidance success rate of 96% and no significant impact on operation efficiency.

[0077] This invention adapts the size of the guide vane sensing component to the rotor shield of a small warehouse inspection drone, specifically setting it to a takeoff weight of 800g. The remaining structure is configured as described in Example 1. Testing was conducted in warehouse areas and large warehouses, covering conditions such as dense shelving, narrow aisles, and uneven lighting. The system can accurately identify obstacles such as shelves, columns, and cables, achieving a 99% obstacle avoidance success rate. In dark warehouse areas with less than 5 lux of light, the system switches to active obstacle avoidance mode, achieving a positioning accuracy of ±2.5cm and an azimuth error of ±1.8°, enabling it to complete normal inspection tasks. This demonstrates significant advantages over existing visual obstacle avoidance systems.

[0078] The working principle of this invention is as follows: An initialization operation ensures the system's baseline state and guarantees operational reliability. The UAV defaults to passive obstacle avoidance mode, enabling it to sense airflow under the rotor in scenarios with no obvious obstacles or favorable environmental conditions. By analyzing the pressure of reflected airflow from nearby obstacles and the angle changes caused by the deformation of the guide vanes, the system first compares these values ​​with preset pressure and angle change thresholds to determine if the disturbance is due to the rotor's own airflow. If rotor-related airflow disturbances are ruled out, the system then determines the passive risk level based on the peak values ​​of the airflow pressure and angle changes, as well as their rates of change, and performs obstacle avoidance operations in passive obstacle avoidance mode. Simultaneously, in passive obstacle avoidance mode, the system continuously monitors light levels and flight area levels. When both light levels and flight area levels are high, the system activates active obstacle avoidance mode for active obstacle avoidance. This allows for switching between passive and active obstacle avoidance modes based on different environmental conditions. In passive obstacle avoidance mode, the distance to the obstacle is determined by the reflected airflow on the guide vanes, thus enabling the adoption of different obstacle avoidance methods. By combining pressure and angle changes, the position of the obstacle is comprehensively determined from both pressure and guide vane deformation perspectives, thereby improving detection accuracy.

Claims

1. A method for intelligent obstacle avoidance of a quadcopter UAV based on a guide vane, wherein a guide vane is provided on the rotor of the UAV, a guide groove is provided inside the guide vane, the guide groove is connected to an active airflow emission module, and a pressure sensor and an angular displacement sensor are provided on the guide vane, comprising the following steps: S1. Initialize the UAV. The initialization operation includes benchmark calibration and preset angle change threshold and pressure change threshold in passive obstacle avoidance mode, and determine the original flight path and original flight speed. S2. When the UAV is working, it initially executes the passive obstacle avoidance mode. In the passive obstacle avoidance mode, the air pressure value in the airflow groove inside the airflow guide plate is detected by the airflow guide plate pressure sensor, and the airflow angle value affected by the airflow is determined by the angular displacement sensor. The airflow pressure change value and airflow angle change value are determined according to the airflow pressure value and airflow angle value, respectively. The airflow pressure change value is compared with the preset pressure change threshold and the airflow angle change value is compared with the preset angle change threshold to determine whether it is a disturbance of the rotor's own airflow. If not, the passive perception risk level is determined according to the peak value of the airflow pressure change value, the peak value of the airflow angle change value and the change rate, and different obstacle avoidance actions are determined according to the passive perception risk level. At the same time, the ambient light intensity level and the risk level of the flight area are monitored, and it is determined whether to switch to the active obstacle avoidance mode. S3. After obstacle avoidance is completed, the UAV calculates the shortest return path based on the original route and the current position, and gradually restores the original flight speed.

2. The intelligent obstacle avoidance method for a quadcopter UAV based on a guide vane according to claim 1, characterized in that: Step S2, "determine the change value of air pressure and the change value of air angle according to the air pressure value and the air angle value respectively", includes: filtering the air angle value by wavelet transform filtering to remove the interference frequency part caused by rotor vibration, and extracting the change amount Δθ of the deflection angle of the guide vane caused by air disturbance. The airflow pressure value is filtered using Kalman filtering to remove environmental noise and extract the airflow pressure change ΔP.

3. The intelligent obstacle avoidance method for a quadcopter UAV based on a guide vane according to claim 1, characterized in that: Step S2, "determining the passive perception risk level based on the peak value of the airflow pressure change, the peak value of the airflow angle change, and the rate of change, and determining different obstacle avoidance actions based on the passive perception risk level," includes: determining the passive perception risk level R1 based on R1=(ΔP / P0)×0.7+(Δθ / (θ0+0.1))×0.

3. If R1 < the preset first passive perception risk level value, it is determined to be low risk, and is identified as a distant obstacle or a small interference object. Only the disturbance warning information is sent to the flight control unit. The flight control unit maintains the original flight attitude and speed, and the data processing unit continuously monitors the disturbance change trend. If the preset first passive perception risk level value < R1 < the preset second passive perception risk level value, it is determined to be a medium risk and identified as a medium-distance obstacle. Based on the distribution of Δθ and ΔP of two or more sets of guide vanes, the location of the obstacle is determined, and the UAV is deflected by a preset angle to maintain its original flight speed. If the preset second passive perception risk level value is <R1, it is determined to be a high risk and identified as a close-range obstacle. The drone is controlled to hover within a preset time and move a preset distance in the direction with the least disturbance. During the movement, airflow disturbance is continuously monitored. If the disturbance disappears, the drone returns to the original flight path. If the disturbance continues, the mode switch is triggered and the active obstacle avoidance mode is activated.

4. The intelligent obstacle avoidance method for a quadcopter UAV based on a guide vane according to claim 1, characterized in that: Step S2, "determine whether to switch to active obstacle avoidance mode", includes: if the passive obstacle avoidance risk level R1 ≥ the first preset threshold or the ambient light intensity level R2 ≤ the second preset threshold or the flight area risk level R3 ≥ the third preset threshold, immediately send a mode switching command to switch to active obstacle avoidance mode. If the passive obstacle avoidance risk level R1 is less than the first preset threshold for a continuous preset time, and the ambient light intensity level R2 is greater than the second preset threshold and the flight area risk level R3 is less than the third preset threshold, the passive obstacle avoidance mode will be maintained.

5. The intelligent obstacle avoidance method for a quadcopter UAV based on a guide vane according to claim 1, characterized in that: Step S2 further includes: the active obstacle avoidance mode includes: emitting stable airflow in a time sequence through an active airflow emission module; collecting reflected airflow signals through pressure sensors and angular displacement sensors; calculating the airflow propagation speed through an airflow speed correction algorithm; calculating the obstacle distance by combining the time difference between transmission and reception; calculating the azimuth and pitch angles through a weighted triangulation method; identifying the obstacle type based on the characteristics of the reflected airflow signals; executing a graded obstacle avoidance response based on the obstacle information; and continuously monitoring environmental parameters after active obstacle avoidance to determine whether to switch back to passive obstacle avoidance mode.

6. The intelligent obstacle avoidance method for a quadcopter UAV based on a guide vane according to claim 1, characterized in that: Ambient light intensity level R2 = actual light intensity / maximum light intensity; Flight area risk level R3 = 1 - distance from high-risk area / safe distance; The distance from high-risk area is calculated in real time using the GPS positioning system as a straight-line distance between the drone fuselage and the nearest high-risk area boundary.

7. The intelligent obstacle avoidance method for a quadcopter UAV based on a guide vane according to claim 5, characterized in that: In active obstacle avoidance mode, calculating obstacle distance by combining the time difference between transmission and reception includes: when the pressure sensor detects a sudden change in the airflow pressure signal, determining the acquisition and reception time t2, and calculating the time difference Δt = t2 - t1; where t1 is the start time when the pressure sensor detects the airflow pressure value. In the active obstacle avoidance mode, the obstacle type identification based on the characteristics of reflected airflow signals includes: performing feature analysis on the captured reflected airflow signals, extracting three core feature parameters: rise slope, peak duration, and signal attenuation rate, and determining the obstacle type based on the core feature parameters; In the active obstacle avoidance mode, the obstacle distance is calculated by combining the time difference between transmission and reception. This includes: calculating the obstacle distance using the formula D=(v×Δt) / 2 based on the airflow velocity v determined by the airflow velocity correction algorithm and the time difference Δt; if two or more sets of guide vanes detect the same obstacle, the average obstacle distance calculated by the two or more sets of guide vanes is taken as the final obstacle distance; using the weighted triangulation method, the azimuth angle α and pitch angle β of the obstacle are calculated using the signal peak value of the pressure sensor of each guide vane as the weighting factor; and determining the active obstacle avoidance response level by comparing the obstacle distance with the preset distance.

8. The intelligent obstacle avoidance method for a quadcopter UAV based on a guide vane according to claim 5, characterized in that: The airflow velocity correction algorithm corrects the airflow propagation velocity based on temperature and relative humidity, and the formula used for the correction is as follows: v is the corrected airflow velocity, T is the collected ambient temperature, and RH is the collected relative humidity.

9. A quadrotor drone intelligent obstacle avoidance system based on a guide vane, used to implement the quadrotor drone intelligent obstacle avoidance method based on a guide vane as described in any one of claims 1-8, comprising a drone body, the drone body including a rotor protective cover, characterized in that: It also includes a guide vane sensing module, an active airflow emission module, a data processing module, and a flight control module. The guide vane sensing module includes a guide vane, a guide channel system disposed within the guide vane, a pressure sensor, and an angular displacement sensor. The guide vane is connected to the rotor shield. The guide channel system includes a main flow channel extending along the length of the guide vane body and branch flow channels located on both sides of the main flow channel. The inlet of the main flow channel is connected to the active airflow emission module. A pressure sensor is disposed at the outlet end of the branch flow channels. An angular displacement sensor is disposed at the connection between the guide vane and the rotor shield. The data processing module includes the UAV initialization operation and passive obstacle avoidance mode. It detects the airflow pressure value in the guide groove of the guide vane through the guide vane pressure sensor, determines the airflow angle value affected by the airflow through the angular displacement sensor, determines the airflow pressure change value and airflow angle change value according to the airflow pressure value and airflow angle value respectively, and determines whether the airflow pressure change value is due to the rotor's own airflow disturbance by comparing the airflow pressure change value with the preset pressure change threshold and the airflow angle change value with the preset angle change threshold. If not, the passive perception risk level is determined according to the peak value of the airflow pressure change value, the peak value of the airflow angle change value and the change rate. The flight control module is signal-connected to the data processing module and is used to determine different obstacle avoidance actions based on the passively perceived risk level. At the same time, it executes graded obstacle avoidance responses based on obstacle information and achieves fault-tolerant control.

10. The intelligent obstacle avoidance system for a quadcopter UAV based on a guide vane according to claim 9, characterized in that: It also includes a temperature and humidity sensor and a vibration sensor; the temperature and humidity sensor is set on a preset groove on the surface of the guide vane body, and a sealant is provided around the preset groove; the vibration sensor is set at the connection between the guide vane and the rotor guard by a fastener.

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