Four-rotor unmanned aerial vehicle nest protection method and system based on blade angle regulation

Abstract

The present application relates to the technical field of rotorcraft, and proposes a quadrotor UAV nest protection method and system based on blade angle regulation, which comprises: performing folding angle optimization analysis on each blade of the UAV to obtain the optimal folding angle of each blade; performing deviation analysis on the actual azimuth angle information and the optimal folding angle to obtain the angle deviation amount corresponding to each blade in the UAV; dynamically correcting the preset flexible contact force control curve, and based on the correction result and the angle deviation amount, calculating the control parameter of the lever motion; decomposing the lever motion control parameter into a sub-control instruction sequence and synchronously sending it to the multi-axis motion controller of the external motor lever mechanism; based on the received take-off preparation instruction, performing blade unfolding reset control on the UAV in the folded state, and turning the blades to the target flight preparation angle, effectively improving the folding safety and unfolding precision of the quadrotor UAV.

Description

Technical Field

[0001] This invention relates to the field of rotorcraft technology, and in particular to a method and system for protecting the nest of a quadcopter unmanned aerial vehicle based on blade angle control. Background Technology

[0002] During the landing and takeoff deployment processes of a quadcopter drone's nest, the propeller angles need to be adjusted. Current technologies determine the propeller retraction angle in a relatively simple way, typically using a preset fixed angle or calculating based on individual propellers, without comprehensively considering the total rotational travel of all propellers and the dynamic clearance between adjacent propellers. This approach easily leads to interference or collisions between adjacent propellers during retraction, and the long total travel of the lever from the initial position to the retracted position increases the retraction time, resulting in low nest operation efficiency. Furthermore, existing methods lack effective utilization of the real-time contact force between the lever and the propellers in lever motion control, relying solely on angle deviation for position control. This makes it difficult to adapt to contact force fluctuations caused by changes in propeller attitude, easily leading to overshoot or undershoot, and even damage to the propeller surface.

[0003] In terms of multi-blade synchronous control, existing technologies typically send individual control parameters directly to the motor drivers of each axis, without decomposing the control parameters into ordered sub-command sequences corresponding to each rotor. This results in asynchronous turning actions of each blade in time, leading to poor consistency in the retraction process. Regarding blade deployment and reset, existing solutions often employ a unidirectional control strategy that is the opposite of the retraction process, lacking a mechanism to eliminate mechanical backlash. This causes a deviation between the deployed blade angle and the target flight preparation angle, affecting the consistency of blade attitude before takeoff. These issues mean that the retraction safety, deployment accuracy, and overall automation level of existing quadcopter UAV nest protection methods need improvement. Summary of the Invention

[0004] This invention provides a method and system for protecting the nest of a quadcopter unmanned aerial vehicle (UAV) based on blade angle control, in order to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides a method for protecting the nest of a quadcopter unmanned aerial vehicle based on blade angle control, comprising: S1: Based on the UAV propeller attitude dataset, with the total turning stroke minimized and the propeller blades maintaining a safe dynamic gap as constraints, the retraction angle of each UAV propeller blade is optimized to obtain the optimal retraction angle of each propeller blade. S2: Perform a deviation analysis between the actual azimuth angle information in the propeller attitude dataset and the optimal retraction angle to obtain the angle deviation amount corresponding to each propeller in the UAV. S3: The real-time contact force between the lever and the propeller in the UAV is introduced to dynamically correct the preset flexible contact force control curve, and the lever motion control parameters for driving the external motor lever mechanism of the UAV are calculated based on the correction result and the angle deviation. S4: Decompose the lever motion control parameters into a sequence of sub-control commands corresponding to each rotor of the UAV, and send them synchronously to the multi-axis motion controller of the external motor lever mechanism to perform a multi-blade synchronous rotation and retraction operation. S5: Based on the received takeoff preparation command, perform propeller deployment reset control on the UAV in the retracted state, turn the propellers to the target flight preparation angle, and obtain a takeoff permission signal.

[0006] In a preferred embodiment, the UAV-based propeller attitude dataset, constrained by minimizing the total turning stroke and maintaining a safe dynamic clearance between the propellers, performs a retraction angle optimization analysis on each propeller of the UAV to obtain the optimal retraction angle for each propeller, including: After the UAV lands on the nesting platform, the current spatial azimuth angle of the UAV and the initial gap between adjacent blades are collected to obtain the blade attitude dataset of the UAV. Based on the blade attitude dataset, the first constraint is that the sum of the total turning path lengths of all blades is minimized, and the second constraint is that the dynamic gap between any two adjacent blades is not less than the safety gap threshold. Multiple candidate retraction angle sets are constructed. The optimal convergence angle for each blade is obtained by performing a dual-objective optimal combination selection on the multiple candidate convergence angle sets.

[0007] In a preferred embodiment, the first constraint is that the sum of the total turning path lengths of all blades is minimized, and the second constraint is that the dynamic clearance between any two adjacent blades is not less than a safety clearance threshold. Multiple candidate convergence angle sets are constructed, including: For each blade, the current azimuth angle is used as the starting angle, and multiple candidate retraction angles are generated within a preset retraction angle range based on a fixed step size. The candidate retraction angles correspond to a turning endpoint position. The Cartesian product of multiple candidate convergence angles corresponding to each propeller blade is combined to obtain the candidate convergence combination of the UAV, wherein each propeller blade in the candidate convergence combination corresponds to a candidate convergence angle; Sum the rotation strokes of all blades in each candidate folding combination to obtain the total rotation path length corresponding to each candidate folding combination. Based on the candidate folding combinations, the dynamic gap change curve of any two adjacent blades during the rotation process is monitored in real time. Based on each dynamic gap change curve and a preset safety threshold, the candidate folding combinations are screened to obtain feasible combinations that satisfy the second constraint condition. The feasible combination with the smallest total turning path is taken as the set of multiple candidate folding angles for the UAV.

[0008] In a preferred embodiment, the step of performing deviation analysis on the actual azimuth angle information in the propeller attitude dataset and the optimal convergence angle to obtain the angle deviation for each propeller in the UAV includes: Based on the blade attitude dataset and the optimal retraction angle, the actual azimuth angle information of each blade is compared with the target retraction angle to obtain the initial deviation value of each blade. Based on the sign of the initial deviation value, the turning direction of each blade is determined, and the absolute value of the initial deviation value is used as the turning stroke of each blade. The rotation direction and rotation stroke of each blade are associated and stored to obtain the angular deviation of each blade, which includes the direction identifier and stroke amplitude.

[0009] In a preferred embodiment, the step of dynamically correcting a preset flexible contact force control curve by introducing the real-time contact force between the lever and the propeller in the UAV includes: The contact force signal between the lever and the propeller is collected in real time by a force sensing element installed on the lever in the UAV, and the contact force signal is converted into an analog voltage. By comparing and analyzing the simulated voltage with the expected voltage value in the flexible contact force control curve point by point, the dynamic correction coefficient of the flexible contact force control curve is obtained. Based on the dynamic correction coefficient, the current output value of the flexible contact force control curve is adjusted in real time to obtain the corrected force control reference value.

[0010] In a preferred embodiment, the step of calculating the lever motion control parameters for driving the external motor lever mechanism of the UAV based on the correction result and the angle deviation includes: The force control reference value includes the position control base value, which converts the turning direction and stroke amplitude in the angle deviation into the position control offset. The position control base value and the position control offset are superimposed to obtain the position control component of each blade; The position control component and the force control compensation component in the force control reference value are combined and packaged to obtain the lever motion control parameters of the UAV external motor lever mechanism.

[0011] In a preferred embodiment, the step of combining the position control component and the force control compensation component to obtain the lever motion control parameters of the UAV external motor lever mechanism includes: Based on the position control component and the force control compensation component, the optimized value of the position control component is calculated using the position optimization formula, which is specifically expressed as follows: ; In the formula, This represents the optimized value of the position control component. This represents the angle deviation value in the stated angle deviation amount. This represents the corrected force control reference value. This represents the preset characteristic force constant. Represents an exponential function with the natural constant as its base; The optimized value of the position control component and the force control compensation component are packaged together to obtain the lever motion control parameters of the UAV external motor lever mechanism.

[0012] In a preferred embodiment, the step of decomposing the lever motion control parameters into a sequence of sub-control commands corresponding to each rotor of the UAV, and synchronously sending them to the multi-axis motion controller of the external motor lever mechanism to execute the multi-blade synchronous turning and retraction operation includes: Based on the motor shaft identifier corresponding to each rotor in the UAV, the lever motion control parameters are converted into multiple sub-control commands, each sub-control command corresponding to the lever motion of one rotor; Each sub-control instruction is arranged in timestamp order to obtain a sub-control instruction sequence corresponding to each rotor. The sub-control instruction sequence includes the current position instruction, the target position instruction, and the motion speed gear indicator. Multiple sub-control command sequences are simultaneously sent to the multi-axis motion controller of the external motor lever structure, triggering the multi-axis motion controller to start each axis motor at the same time. Based on the sub-control command sequence, the multi-axis motion controller drives the corresponding lever to perform synchronous turning actions until each blade reaches the target retraction angle.

[0013] In a preferred embodiment, the step of performing propeller deployment reset control on the UAV in the retracted state based on the received takeoff preparation command, turning the propellers to the target flight preparation angle, and obtaining a takeoff permission signal includes: The takeoff preparation command includes the target flight preparation angle corresponding to each propeller blade; The retracted state angle corresponding to each propeller blade is compared with the target flight preparation angle to obtain the deployment and reset deviation of each propeller blade; The deployment reset deviation is mapped and converted to obtain the deployment control parameters corresponding to each blade, and the deployment control parameters are sent to the multi-axis motion controller. The multi-axis motion controller drives the lever to turn each propeller from the retracted state angle to the target flight preparation angle. After each propeller reaches the target flight preparation angle, a takeoff permission signal is generated and output.

[0014] To address the aforementioned problems, this invention also provides a quadcopter UAV nest protection system based on blade angle control. The system includes an angle optimization module, a deviation analysis module, a force control correction module, a synchronization control module, and a reset control module, wherein: The angle optimization module is used to perform retraction angle optimization analysis on each blade of the UAV based on the UAV's blade attitude dataset, with the constraints of minimizing the total turning stroke and maintaining a safe dynamic gap between the blades, to obtain the optimal retraction angle of each blade. The deviation analysis module is used to perform deviation analysis on the actual azimuth angle information in the propeller attitude dataset and the optimal retraction angle to obtain the angle deviation amount corresponding to each propeller in the UAV. The force control correction module is used to introduce the real-time contact force between the lever and the propeller in the UAV to dynamically correct the preset flexible contact force control curve, and calculate the lever motion control parameters of the lever mechanism that drives the external motor of the UAV based on the correction result and the angle deviation. The synchronization control module is used to decompose the lever motion control parameters into a sequence of sub-control commands corresponding to each rotor of the UAV, and send them synchronously to the multi-axis motion controller of the external motor lever mechanism to perform multi-blade synchronous turning and retraction operation. The reset control module is used to perform propeller deployment reset control on the UAV in the retracted state based on the received takeoff preparation command, turn the propellers to the target flight preparation angle, and obtain a takeoff permission signal.

[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention optimizes the retraction angle of each blade by minimizing the total turning stroke and maintaining a safe dynamic clearance between blades. This minimizes the total turning path length of all blades during the retraction process, while ensuring that the dynamic clearance between any adjacent blades is never lower than the safety threshold. This effectively avoids collisions or jamming between blades, significantly improving the safety and reliability of the retraction action. Furthermore, due to the minimum total turning stroke, the execution time of the retraction action is significantly shortened, improving the overall operating efficiency of the machine nest.

[0016] 2. This invention dynamically corrects the preset flexible contact force control curve by introducing the real-time contact force between the lever and the blade, and combines the correction result with the angle deviation to generate lever motion control parameters. At the same time, the invention adopts a staged speed adjustment and reverse micro-motion verification mechanism in the deployment and reset control, which enables the lever to adapt to changes in contact force in real time during the retraction and deployment process, eliminates the influence of mechanical return clearance on angle positioning, thereby significantly improving the positioning accuracy of blade rotation and the blade surface protection performance, ensuring the consistency of blade attitude before takeoff and flight safety. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating a method for protecting the nest of a quadrotor UAV based on blade angle control, according to an embodiment of the present invention. Figure 2 A functional block diagram of a quadcopter UAV nest protection system based on blade angle control provided in an embodiment of the present invention; The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0018] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0019] This application provides a method for protecting the nests of quadrotor drones based on propeller angle control. The execution entity of this method includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application: a server, a terminal, etc. In other words, the method for protecting the nests of quadrotor drones based on propeller angle control can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster. The server can be an independent server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDN), and big data and artificial intelligence platforms.

[0020] Reference Figure 1 The diagram shown is a flowchart illustrating a quadcopter UAV nest protection method based on blade angle control according to an embodiment of the present invention. In this embodiment, the quadcopter UAV nest protection method based on blade angle control includes: S1: Based on the UAV propeller attitude dataset, with the total turning stroke minimized and the propeller blades maintaining a safe dynamic gap as constraints, the retraction angle of each UAV propeller blade is optimized to obtain the optimal retraction angle of each propeller blade. In this embodiment of the invention, the propeller attitude dataset based on the UAV, with the constraints of minimizing the total turning stroke and maintaining a safe dynamic clearance between the propellers, performs a convergence angle optimization analysis on each propeller of the UAV to obtain the optimal convergence angle of each propeller, including: After the UAV lands on the nesting platform, the current spatial azimuth angle of the UAV and the initial gap between adjacent blades are collected to obtain the blade attitude dataset of the UAV. Based on the blade attitude dataset, the first constraint is that the sum of the total turning path lengths of all blades is minimized, and the second constraint is that the dynamic gap between any two adjacent blades is not less than the safety gap threshold. Multiple candidate retraction angle sets are constructed. The optimal convergence angle for each blade is obtained by performing a dual-objective optimal combination selection on the multiple candidate convergence angle sets.

[0021] The first constraint is that the sum of the total turning path lengths of all blades is minimized, and the second constraint is that the dynamic clearance between any two adjacent blades is not less than a safety clearance threshold. Multiple candidate convergence angle sets are constructed, including: For each blade, the current azimuth angle is used as the starting angle, and multiple candidate retraction angles are generated within a preset retraction angle range based on a fixed step size. The candidate retraction angles correspond to a turning endpoint position. The Cartesian product of multiple candidate convergence angles corresponding to each propeller blade is combined to obtain the candidate convergence combination of the UAV, wherein each propeller blade in the candidate convergence combination corresponds to a candidate convergence angle; Sum the rotation strokes of all blades in each candidate folding combination to obtain the total rotation path length corresponding to each candidate folding combination. Based on the candidate folding combinations, the dynamic gap change curve of any two adjacent blades during the rotation process is monitored in real time. Based on each dynamic gap change curve and a preset safety threshold, the candidate folding combinations are screened to obtain feasible combinations that satisfy the second constraint condition. The feasible combination with the smallest total turning path is taken as the set of multiple candidate folding angles for the UAV.

[0022] It should be noted that after the UAV lands on the nest parking platform, the vision sensors built into the nest sequentially collect the current spatial azimuth angle of each propeller and the initial gap between adjacent propellers. The collected raw data is then processed by time-series smoothing filtering to remove outliers caused by vibration or noise, generating the UAV's propeller attitude dataset.

[0023] For each blade, the current azimuth angle of the blade is determined as the starting angle of rotation, and multiple candidate retraction angles are generated sequentially within the preset retraction angle range according to a fixed angle step size. Each candidate retraction angle represents a rotation endpoint position of the blade.

[0024] Furthermore, the preset retraction angle range refers to a continuous angle interval determined by the mechanical limiting angle of the blade root hinge, the accommodating boundary angle of the nest retraction compartment, and the maximum safe angle at which adjacent blades do not physically interfere, when each blade retracts towards the center of the nest from its initial azimuth angle corresponding to its current flight attitude. The lower limit of this interval is the starting angle at which the blade begins to retract, and the upper limit is the extreme angle at which the blade will not touch other blades or nest structures when it retracts close to the fuselage or nest floor.

[0025] The multiple candidate retraction angles corresponding to each propeller blade are combined by Cartesian product. That is, an angle is selected from the candidate retraction angles of each propeller blade and combined into an angle combination to generate the candidate retraction combination of the UAV. In each candidate retraction combination, each propeller blade corresponds to a selected candidate retraction angle.

[0026] For each candidate folding combination, calculate the unidirectional turning path length required for all blades in the combination to turn from their respective starting angles to their respective candidate folding angles, and add these unidirectional turning path lengths together to obtain the total turning path length corresponding to the candidate folding combination.

[0027] For each candidate retraction combination, the process of all blades synchronously rotating from their respective starting angles to their respective candidate retraction angles is simulated. During the entire rotation process, the dynamic gap change between any two adjacent blades is monitored in real time, and the gap value at each moment is recorded to form each dynamic gap change curve.

[0028] The minimum value on each dynamic clearance variation curve is compared with the preset safety clearance threshold. If the minimum value is lower than the safety clearance threshold, the candidate folding combination is eliminated. If the minimum value on the dynamic clearance variation curves of all adjacent blades is not lower than the safety clearance threshold, the candidate folding combination is retained as a feasible combination that satisfies the second constraint condition.

[0029] Furthermore, the preset safety clearance threshold refers to the minimum permissible clearance value set to prevent adjacent blades from contacting or colliding during rotation. This threshold is determined in advance based on the specific model parameters of the UAV, including blade length, width, material elastic modulus, maximum permissible elastic deformation, and dynamic offset margin under nacelle vibration conditions. Specifically, a critical clearance value is calculated by measuring the centrifugal deformation of the blades at maximum rotational speed and their natural sag under static conditions, and adding a safety redundancy factor. When the real-time dynamic clearance between any two adjacent blades is less than this critical value, a collision risk is considered to exist. This threshold is typically selected within the range of one to five percent of the blade length.

[0030] Among all feasible combinations that satisfy the second constraint, the total turning path length of each combination is compared, and the feasible combination with the smallest total turning path length is selected. The candidate retraction angle corresponding to each blade in this combination is determined as the optimal retraction angle of that blade, thus obtaining the optimal retraction angle of each blade.

[0031] By collecting UAV propeller attitude datasets and using the dual constraints of minimizing the total turning stroke and maintaining a safe dynamic clearance between propellers, the retraction angle of each propeller was optimized. This minimizes the total turning path length during synchronous retraction, significantly reducing the travel and execution time of the lever mechanism and improving the efficiency of the drone's retraction operation. Simultaneously, by ensuring that the dynamic clearance between any two adjacent propellers remains above the safe clearance threshold throughout the turning stroke, collisions or jamming between propellers are effectively avoided, improving the safety and reliability of the retraction action and providing an accurate angle reference for subsequent precise deviation analysis and force control correction.

[0032] S2: Perform a deviation analysis between the actual azimuth angle information in the propeller attitude dataset and the optimal retraction angle to obtain the angle deviation amount corresponding to each propeller in the UAV. In this embodiment of the invention, the step of performing deviation analysis between the actual azimuth angle information in the propeller attitude dataset and the optimal convergence angle to obtain the angle deviation for each propeller in the UAV includes: Based on the blade attitude dataset and the optimal retraction angle, the actual azimuth angle information of each blade is compared with the target retraction angle to obtain the initial deviation value of each blade. Based on the sign of the initial deviation value, the turning direction of each blade is determined, and the absolute value of the initial deviation value is used as the turning stroke of each blade. The rotation direction and rotation stroke of each blade are associated and stored to obtain the angular deviation of each blade, which includes the direction identifier and stroke amplitude.

[0033] It needs to be explained in detail that the actual azimuth angle information of each blade is read from the blade attitude dataset, and the target azimuth angle corresponding to each blade is read from the obtained optimal azimuth angle. The actual azimuth angle and the target azimuth angle are numerically compared, that is, the target azimuth angle is subtracted from the actual azimuth angle to obtain the initial deviation value of each blade.

[0034] The direction of rotation of each blade is determined by the sign of its initial deviation value: if the initial deviation value is positive, it is determined that it needs to be rotated clockwise; if the initial deviation value is negative, it is determined that it needs to be rotated counterclockwise. At the same time, the absolute value of the initial deviation value is taken as the rotation stroke of the blade.

[0035] The rotation direction and rotation stroke of each blade are associated and stored to generate a data structure containing a direction identifier and a stroke amplitude. The direction identifier records clockwise or counterclockwise rotation, and the stroke amplitude records the absolute value of the rotation angle. This data structure is the angular deviation corresponding to each blade.

[0036] By accurately comparing the actual azimuth angle information in the blade attitude dataset with the optimal retraction angle, the initial deviation value of each blade is obtained. The turning direction is determined based on the positive or negative sign of the deviation value, and the turning stroke is determined based on the absolute value. This generates an angle deviation quantity containing a direction indicator and a stroke amplitude, providing clear and accurate control input parameters for subsequent lever motion control. This avoids fuzzy direction judgment and stroke estimation, effectively improving the accuracy and response speed of angle adjustment during blade retraction. At the same time, it ensures that the turning action of each blade matches its own deviation characteristics, improving the consistency and reliability of the overall retraction action.

[0037] S3: The real-time contact force between the lever and the propeller in the UAV is introduced to dynamically correct the preset flexible contact force control curve, and the lever motion control parameters for driving the external motor lever mechanism of the UAV are calculated based on the correction result and the angle deviation. In this embodiment of the invention, the step of dynamically correcting a preset flexible contact force control curve by introducing the real-time contact force between the lever and the propeller in the UAV includes: The contact force signal between the lever and the propeller is collected in real time by a force sensing element installed on the lever in the UAV, and the contact force signal is converted into an analog voltage. By comparing and analyzing the simulated voltage with the expected voltage value in the flexible contact force control curve point by point, the dynamic correction coefficient of the flexible contact force control curve is obtained. Based on the dynamic correction coefficient, the current output value of the flexible contact force control curve is adjusted in real time to obtain the corrected force control reference value.

[0038] The calculation of the lever motion control parameters for driving the external motor lever mechanism of the UAV based on the correction result and the angle deviation includes: The force control reference value includes the position control base value, which converts the turning direction and stroke amplitude in the angle deviation into the position control offset. The position control base value and the position control offset are superimposed to obtain the position control component of each blade; The position control component and the force control compensation component in the force control reference value are combined and packaged to obtain the lever motion control parameters of the UAV external motor lever mechanism.

[0039] The combination and packaging of the position control component and the force control compensation component to obtain the lever motion control parameters of the UAV external motor lever mechanism includes: Based on the position control component and the force control compensation component, the optimized value of the position control component is calculated using the position optimization formula, which is specifically expressed as follows: ; In the formula, This represents the optimized value of the position control component. This represents the angle deviation value in the stated angle deviation amount. This represents the corrected force control reference value. This represents the preset characteristic force constant. Represents an exponential function with the natural constant as its base; The optimized value of the position control component and the force control compensation component are packaged together to obtain the lever motion control parameters of the UAV external motor lever mechanism.

[0040] It should be specifically explained that the contact force signal between the lever and the propeller is collected in real time by a force sensing element installed on the lever in the drone, and the contact force signal is converted into an analog voltage output.

[0041] The simulated voltage is compared point by point with the expected voltage value at the corresponding point in the preset flexible contact force control curve. A correction ratio is determined based on the difference at each comparison point, and this correction ratio is used as the dynamic correction coefficient of the flexible contact force control curve.

[0042] Furthermore, the preset flexible contact force control curve refers to a data curve pre-stored in the engine controller that characterizes the relationship between the desired contact force between the lever and the blade and the lever's travel distance. This curve has the lever's travel position on the horizontal axis and the voltage value corresponding to the desired contact force on the vertical axis, presenting a smooth shape that gradually changes from the starting point to the ending point.

[0043] The curve is constructed based on the mechanical characteristics of the contact process between the lever and the propeller: in the initial stage of the turn, the propeller is stationary and requires a small contact force to overcome static friction, so the slope of the initial segment of the curve is relatively gentle; in the middle stage of the turn, the propeller begins to rotate at a constant speed, requiring a constant contact force to prevent jitter, so the middle segment of the curve remains horizontal; near the end of the turn, the propeller is about to reach the target convergence angle, requiring a gradual reduction in contact force to avoid overshoot, so the slope of the final segment of the curve gradually decreases to zero. This curve was obtained through experimental calibration: a force sensor was installed on the lever mechanism, and the lever was driven at low speed to turn the propeller, recording the actual contact force at different stroke positions. This data was then smoothed and filtered to form a standard curve, which serves as a reference curve for subsequent dynamic correction. This curve is not dependent on a specific UAV model and can be pre-adjusted and stored according to parameters such as the inertia and elastic modulus of different propellers.

[0044] The current output value of the flexible contact force control curve is adjusted in real time using this dynamic correction coefficient. That is, the current output value is multiplied by the dynamic correction coefficient to obtain the corrected force control reference value.

[0045] The position control base value is extracted from the corrected force control reference value. At the same time, the turning direction and stroke amplitude in the angle deviation are converted into a position control offset. The magnitude of the offset is equal to the stroke amplitude, and the direction is consistent with the turning direction.

[0046] The position control base value and the position control offset are algebraically superimposed, that is, when the turning direction is the same as the position control base value direction, the sum is taken, and when they are opposite, the difference is taken to obtain the position control component of each blade.

[0047] Extract the force control compensation component from the corrected force control reference value, and treat the force control compensation component and the position control component as two data items to be packaged.

[0048] The angle deviation value in the angle deviation quantity is used as input. At the same time, the corrected force control reference value is compared with a preset characteristic force constant. First, the ratio of the corrected force control reference value to the characteristic force constant is calculated. Then, the ratio is incremented by one and multiplied by the angle deviation value. Finally, the product result is multiplied by the exponential function value with the natural constant as the base and the negative ratio as the exponent to obtain the optimized value of the position control component.

[0049] Furthermore, This represents the angle deviation value within the aforementioned angle deviation amount. The magnitude of this value directly reflects the degree of deviation between the current blade attitude and the target convergence angle. As a baseline factor for the entire product, the larger its value, the larger the base value of the optimized value of the position control component, meaning a larger position adjustment is needed to correct a larger angular deviation; conversely, a smaller value indicates a larger baseline. The smaller the value, the smaller the base value of the optimized position control component. This parameter ensures that the basic adjustment range of position control is proportional to the angle deviation, satisfying the control logic of large adjustment for large deviations and small adjustment for small deviations.

[0050] The corrected force control reference value reflects the current real-time contact force between the lever and the blade. Simultaneously appearing in linear factors and exponential factors In the middle. When When the values ​​are small, the linear factor approaches 1, and the exponential factor approaches 1. near That is, when the contact force is slight, the position command is basically output according to the angular deviation; when As the linear factor increases, it amplifies the position adjustment amount, helping to overcome the increased static friction or elastic resistance; while the exponential factor increases with... The increase exhibits an exponential decay, thus exerting a suppressive effect. The product of the two forms a unimodal characteristic of first increasing and then decreasing, making... exist It reaches its maximum value when it is at a moderate value, and... When the contact force is too large, it decays rapidly, thus preventing the position command from being increased further when the contact force is too large, which could lead to overload.

[0051] The characteristic force constant is a predefined calibration parameter. This parameter is obtained through experimental calibration: during the factory calibration phase of the lever mechanism, the lever is driven to rotate the blade at an extremely low speed, and the peak contact force at the instant the blade starts rotating from rest and the average contact force during normal rotation are recorded. The intermediate value between the two is taken as the characteristic force constant and is pre-stored in the machine controller. Its function is to adjust the force control reference value. Ratios normalized to dimensionless This makes the exponential and linear terms in the formula no longer dependent on specific units of force. The value of determines the degree of sensitivity to contact force in the formula: When the value is small, the same This will result in a larger ratio, making the exponential decay more severe and the linear amplification more significant, meaning the system is more sensitive to changes in contact force. When the value is large, the ratio is small, the formula behavior is closer to linear, and the system is not sensitive to changes in contact force. Once calibrated, it remains constant throughout the control process.

[0052] Exponential function With the natural constant as the base and the negative normalization force ratio as the exponent, this function... The value increases and then monotonically decreases, with the rate of decrease gradually slowing down. In the formula, this exponential factor and the linear factor... Multiplication, joint regulation The amplification factor. The exponential factor provides non-linear decay characteristics, so that when the contact force is too large, even if the linear factor increases, the overall product will still decrease rapidly, thereby forcibly reducing the optimized value of the position control component and realizing the overload protection function.

[0053] When the contact force is very small, the optimal value is... Approximately equal to the angular deviation value Position adjustment is performed according to normal deviation. As the contact force gradually increases to near the characteristic force constant, the linear factor increases to approximately 2, the exponential factor decreases to approximately 0.368, and the product is approximately 0.736. Slightly smaller This indicates that under moderate contact force, the position adjustment is slightly reduced to avoid impact. When the contact force is much greater than the characteristic force constant, the linearity factor is approximately... The exponential factor is approximately equal to Because exponential decay occurs much faster than linear growth, When the position adjustment is suppressed to near zero, the lever stops turning further to prevent overload damage. This formula achieves intelligent optimization by compensating for angular deviations as much as possible within the safe contact force range and automatically limiting the position when the contact force is too large.

[0054] The optimized values ​​of the position control component and the force control compensation component are combined and packaged according to a preset data format to generate the lever motion control parameters for driving the external motor lever mechanism of the UAV.

[0055] By introducing real-time contact force between the lever and the blade to dynamically correct the preset flexible contact force control curve, the lever can adapt to contact force fluctuations caused by changes in blade attitude during the turning process, effectively avoiding overshoot or undershoot due to sudden changes in contact force. Simultaneously, based on the corrected force control reference value and angle deviation, the optimized value of the position control component is calculated using a position optimization formula. This formula, through the product coupling of linear and exponential factors, provides sufficient position adjustment to overcome resistance when the contact force is moderate, and automatically attenuates the position command to prevent overload when the contact force is excessive. This significantly improves the compliance and safety of the lever motion control and reduces the risk of blade surface damage. Furthermore, the optimized value of the position control component and the force control compensation component are packaged to generate lever motion control parameters, achieving force-position coordinated control and improving the smoothness and accuracy of angle adjustment during the retraction process.

[0056] S4: Decompose the lever motion control parameters into a sequence of sub-control commands corresponding to each rotor of the UAV, and send them synchronously to the multi-axis motion controller of the external motor lever mechanism to perform a multi-blade synchronous rotation and retraction operation. In this embodiment of the invention, the step of decomposing the lever motion control parameters into a sequence of sub-control commands corresponding to each rotor of the UAV, and synchronously sending them to the multi-axis motion controller of the external motor lever mechanism to execute the multi-blade synchronous turning and retraction operation includes: Based on the motor shaft identifier corresponding to each rotor in the UAV, the lever motion control parameters are converted into multiple sub-control commands, each sub-control command corresponding to the lever motion of one rotor; Each sub-control instruction is arranged in timestamp order to obtain a sub-control instruction sequence corresponding to each rotor. The sub-control instruction sequence includes the current position instruction, the target position instruction, and the motion speed gear indicator. Multiple sub-control command sequences are simultaneously sent to the multi-axis motion controller of the external motor lever structure, triggering the multi-axis motion controller to start each axis motor at the same time. Based on the sub-control command sequence, the multi-axis motion controller drives the corresponding lever to perform synchronous turning actions until each blade reaches the target retraction angle.

[0057] It should be specifically noted that, based on the motor shaft identifier corresponding to each rotor in the drone, the lever motion control parameters are converted into multiple independent sub-control commands. Each sub-control command contains the position and speed information corresponding to that rotor, and each sub-control command uniquely corresponds to the lever motion of one rotor.

[0058] Furthermore, acquiring sub-control commands requires extracting the position control component and force control compensation component associated with the motor shaft from the lever motion control parameters based on the motor shaft identifier corresponding to each rotor in the UAV. These components are then reorganized according to the lever motion requirements of the rotor to generate an independent sub-control command containing the target position information, current position information, and motion speed gear identifier of the rotor. The above extraction and organization operations are repeated for each rotor until all rotors have obtained the corresponding sub-control commands.

[0059] Each sub-control command is arranged in chronological order according to its timestamp, generating a sub-control command sequence corresponding to each rotor. Each sub-control command sequence stores the current position command, the target position command, and the motion speed gear identifier in sequence.

[0060] Multiple sub-control command sequences are synchronously sent to the multi-axis motion controller of the external motor lever mechanism via a communication bus. After receiving all the sequences, the multi-axis motion controller simultaneously starts the drive output of each axis motor.

[0061] The multi-axis motion controller, based on the sub-control command sequence received by each axis, drives the corresponding lever to perform synchronous turning actions according to the current position command, target position command, and motion speed gear indicator in the sequence, until each blade reaches its respective target retraction angle.

[0062] By breaking down the lever motion control parameters into independent sub-control commands according to the motor shaft identifiers corresponding to each rotor, and adding a timestamp to each sub-control command to generate a sequence of sub-control commands, the lever motion of each rotor has clear information on its current position, target position, and speed level. Multiple sub-control command sequences are synchronously sent to the multi-axis motion controller via a communication bus, triggering the controller to simultaneously start the motors on each axis, achieving synchronized rotation and retraction of all blades. This process effectively avoids inconsistent blade movements caused by asynchronous or conflicting command transmissions, significantly improving the time synchronization and motion smoothness of the multi-blade retraction action. This ensures that the dynamic gap between the blades remains under control during retraction, further enhancing the overall reliability and consistency of the retraction operation.

[0063] S5: Based on the received takeoff preparation command, perform propeller deployment reset control on the UAV in the retracted state, turn the propellers to the target flight preparation angle, and obtain a takeoff permission signal.

[0064] In this embodiment of the invention, the step of performing propeller deployment reset control on the UAV in the retracted state based on the received takeoff preparation command, turning the propellers to the target flight preparation angle, and obtaining a takeoff permission signal includes: The takeoff preparation command includes the target flight preparation angle corresponding to each propeller blade; The retracted state angle corresponding to each propeller blade is compared with the target flight preparation angle to obtain the deployment and reset deviation of each propeller blade; The deployment reset deviation is mapped and converted to obtain the deployment control parameters corresponding to each blade, and the deployment control parameters are sent to the multi-axis motion controller. The multi-axis motion controller drives the lever to turn each propeller from the retracted state angle to the target flight preparation angle. After each propeller reaches the target flight preparation angle, a takeoff permission signal is generated and output.

[0065] It needs to be specifically explained that the takeoff preparation command sent by the ground station or the nest controller is received, and the target flight preparation angle corresponding to each propeller blade is parsed from the takeoff preparation command.

[0066] Read the current retracted angle of each propeller blade, compare the difference between the retracted angle and the target flight preparation angle, and subtract the target flight preparation angle from the retracted angle to obtain the deployment and reset deviation of each propeller blade.

[0067] Based on the magnitude and direction of the deployment and reset deviation of each blade, the deviation is mapped and converted into deployment control parameters required to drive the lever. These deployment control parameters include the turning direction indicator, the total turning stroke value, and the boundary threshold between the coarse adjustment margin and the fine adjustment margin required for segmented control. The deployment control parameters are then sent to the multi-axis motion controller.

[0068] After receiving the deployment control parameters, the multi-axis motion controller divides the deployment and reset deviation of each blade into coarse adjustment stage margin and fine adjustment stage margin. The coarse adjustment stage margin corresponds to the main part of the deviation, while the fine adjustment stage margin corresponds to a small range of deviation close to the target angle.

[0069] During the coarse adjustment phase, the multi-axis motion controller drives the lever to rotate the propeller rapidly at the first speed setting. At the same time, it reads the current angle of the propeller at fixed sampling intervals and compares the current angle with the target flight preparation angle until the current angle enters the starting threshold of the fine adjustment phase margin.

[0070] During the fine-tuning phase, the multi-axis motion controller switches to the second speed gear to drive the lever to rotate the propeller slowly and compares the current angle with the target flight preparation angle in real time. When the difference between the current angle and the target angle is less than the preset dead zone, the rotation stops.

[0071] After the rotation stops, the multi-axis motion controller triggers a reverse micro-motion verification action, that is, drives the lever to rotate a small step in the opposite direction and then returns to the stop position to eliminate the mechanical backlash between the lever and the blade.

[0072] After the verification action is completed, the multi-axis motion controller reads the final stationary angle of the propeller blade and compares the final stationary angle with the target flight preparation angle. If the deviation between the two is within the allowable range, a positioning confirmation signal for the propeller blade is generated.

[0073] Furthermore, the allowable range refers to the maximum permissible absolute value of the angular difference between the final stationary angle of the propeller blades and the target flight preparation angle. This threshold is jointly calibrated based on the flight control accuracy requirements of the UAV, the aerodynamic symmetry tolerance of the propeller blades, and the statistical characteristics of the mechanical return clearance. The value of this threshold is determined by the UAV's rotor layout, propeller geometry parameters, and the angle tracking accuracy of the flight controller. It is obtained through ground calibration tests conducted before the UAV leaves the factory: taking off at different deviation angles, measuring flight attitude stability and control response, and taking the maximum permissible deviation that does not cause significant attitude shift as the dead zone threshold.

[0074] Once all propeller blades have generated a positioning confirmation signal, the multi-axis motion controller aggregates all positioning confirmation signals, generates a takeoff permission signal, and sends the takeoff permission signal to the UAV or nest controller.

[0075] By analyzing the target flight preparation angle of each propeller blade from the takeoff preparation command and comparing it with the current folded angle, the deployment and reset deviation is obtained. This deviation is then mapped and converted into deployment control parameters that include coarse and fine adjustment margins, enabling phased speed adjustment during the deployment and reset process. In the coarse adjustment phase, the propeller is rapidly adjusted to approach the target angle at the first speed setting. In the fine adjustment phase, the propeller is switched to the second speed setting for a slower approach, effectively balancing deployment speed and positioning accuracy. After the adjustment stops, a reverse micro-motion verification action is triggered to actively eliminate the mechanical backlash between the lever and the propeller blades. Position confirmation is determined by reading the allowable deviation range between the final stationary angle and the target angle, ensuring that the final deployment angle of each propeller blade is precisely consistent. This method significantly improves the angle positioning accuracy of deployment and reset, eliminates the problem of inconsistent propeller attitude caused by mechanical backlash, and provides reliable assurance for the propeller state before UAV takeoff, thereby improving flight safety and the reliability of automated nest operations.

[0076] like Figure 2The diagram shown is a functional block diagram of a quadcopter UAV nest protection system based on blade angle control provided in an embodiment of the present invention.

[0077] The quadcopter drone nest protection system 100 based on blade angle control described in this invention can be installed in an electronic device. Depending on the functions implemented, the quadcopter drone nest protection system 100 may include an angle optimization module 101, a deviation analysis module 102, a force control correction module 103, a synchronization control module 104, and a reset control module 105. The module described in this invention can also be called a unit, referring to a series of computer program segments that can be executed by the processor of an electronic device and perform a fixed function, stored in the memory of the electronic device.

[0078] In this embodiment, the functions of each module / unit are as follows: The angle optimization module 101 is used to perform retraction angle optimization analysis on each blade of the UAV based on the blade attitude dataset of the UAV, with the constraints of minimizing the total turning stroke and maintaining a safe dynamic gap between the blades, to obtain the optimal retraction angle of each blade. The deviation analysis module 102 is used to perform deviation analysis on the actual azimuth angle information in the propeller attitude dataset and the optimal retraction angle to obtain the angle deviation amount corresponding to each propeller in the UAV. The force control correction module 103 is used to introduce the real-time contact force between the lever and the propeller in the UAV to dynamically correct the preset flexible contact force control curve, and calculate the lever motion control parameters of the lever mechanism that drives the external motor of the UAV based on the correction result and the angle deviation. The synchronization control module 104 is used to decompose the lever motion control parameters into a sequence of sub-control commands corresponding to each rotor of the UAV, and synchronously send them to the multi-axis motion controller of the external motor lever mechanism to perform multi-blade synchronous turning and retraction operation. The reset control module 105 is used to perform propeller deployment reset control on the UAV in the retracted state based on the received takeoff preparation command, turn the propellers to the target flight preparation angle, and obtain a takeoff permission signal.

[0079] In the several embodiments provided by this invention, it should be understood that the disclosed methods and systems can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0080] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0081] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0082] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0083] The embodiments of this application can acquire and process relevant data based on an artificial intelligence technology. Artificial intelligence is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

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

Claims

1. A method for protecting the nest of a quadrotor UAV based on blade angle control, characterized in that, The method includes: S1: Based on the UAV propeller attitude dataset, with the total turning stroke minimized and the propeller blades maintaining a safe dynamic gap as constraints, the retraction angle of each UAV propeller blade is optimized to obtain the optimal retraction angle of each propeller blade. S2: Perform a deviation analysis between the actual azimuth angle information in the propeller attitude dataset and the optimal retraction angle to obtain the angle deviation amount corresponding to each propeller in the UAV. S3: The real-time contact force between the lever and the propeller in the UAV is introduced to dynamically correct the preset flexible contact force control curve, and the lever motion control parameters for driving the external motor lever mechanism of the UAV are calculated based on the correction result and the angle deviation. S4: Decompose the lever motion control parameters into a sequence of sub-control commands corresponding to each rotor of the UAV, and send them synchronously to the multi-axis motion controller of the external motor lever mechanism to perform a multi-blade synchronous rotation and retraction operation. S5: Based on the received takeoff preparation command, perform propeller deployment reset control on the UAV in the retracted state, turn the propellers to the target flight preparation angle, and obtain a takeoff permission signal.

2. The method for protecting the nest of a quadcopter UAV based on blade angle control as described in claim 1, characterized in that, The UAV-based propeller attitude dataset, constrained by minimizing the total turning stroke and maintaining a safe dynamic clearance between the propellers, performs a convergence angle optimization analysis on each propeller blade to obtain the optimal convergence angle for each propeller blade, including: After the UAV lands on the nesting platform, the current spatial azimuth angle of the UAV and the initial gap between adjacent blades are collected to obtain the blade attitude dataset of the UAV. Based on the blade attitude dataset, the first constraint is that the sum of the total turning path lengths of all blades is minimized, and the second constraint is that the dynamic gap between any two adjacent blades is not less than the safety gap threshold. Multiple candidate retraction angle sets are constructed. The optimal convergence angle for each blade is obtained by performing a dual-objective optimal combination selection on the multiple candidate convergence angle sets.

3. The method for protecting the nest of a quadrotor UAV based on blade angle control as described in claim 2, characterized in that, The first constraint is that the sum of the total turning path lengths of all blades is minimized, and the second constraint is that the dynamic clearance between any two adjacent blades is not less than a safety clearance threshold. Multiple candidate convergence angle sets are constructed, including: For each blade, the current azimuth angle is used as the starting angle, and multiple candidate retraction angles are generated within a preset retraction angle range based on a fixed step size. The candidate retraction angles correspond to a turning endpoint position. The Cartesian product of multiple candidate convergence angles corresponding to each propeller blade is combined to obtain the candidate convergence combination of the UAV, wherein each propeller blade in the candidate convergence combination corresponds to a candidate convergence angle; Sum the rotation strokes of all blades in each candidate folding combination to obtain the total rotation path length corresponding to each candidate folding combination. Based on the candidate folding combinations, the dynamic gap change curve of any two adjacent blades during the rotation process is monitored in real time. Based on each dynamic gap change curve and a preset safety threshold, the candidate folding combinations are screened to obtain feasible combinations that satisfy the second constraint condition. The feasible combination with the smallest total turning path is taken as the set of multiple candidate folding angles for the UAV.

4. The method for protecting the nest of a quadcopter UAV based on blade angle control as described in claim 1, characterized in that, The deviation analysis of the actual azimuth angle information in the propeller attitude dataset and the optimal convergence angle is performed to obtain the angle deviation for each propeller in the UAV, including: Based on the blade attitude dataset and the optimal retraction angle, the actual azimuth angle information of each blade is compared with the target retraction angle to obtain the initial deviation value of each blade. Based on the sign of the initial deviation value, the turning direction of each blade is determined, and the absolute value of the initial deviation value is used as the turning stroke of each blade. The rotation direction and rotation stroke of each blade are associated and stored to obtain the angular deviation of each blade, which includes the direction identifier and stroke amplitude.

5. The method for protecting the nest of a quadcopter UAV based on blade angle control as described in claim 1, characterized in that, The method of dynamically correcting the preset flexible contact force control curve by introducing the real-time contact force between the lever and the propeller in the UAV includes: The contact force signal between the lever and the propeller is collected in real time by a force sensing element installed on the lever in the UAV, and the contact force signal is converted into an analog voltage. By comparing and analyzing the simulated voltage with the expected voltage value in the flexible contact force control curve point by point, the dynamic correction coefficient of the flexible contact force control curve is obtained. Based on the dynamic correction coefficient, the current output value of the flexible contact force control curve is adjusted in real time to obtain the corrected force control reference value.

6. The method for protecting the nest of a quadcopter UAV based on blade angle control as described in claim 5, characterized in that, The calculation of the lever motion control parameters for driving the external motor lever mechanism of the UAV based on the correction result and the angle deviation includes: The force control reference value includes the position control base value, which converts the turning direction and stroke amplitude in the angle deviation into the position control offset. The position control base value and the position control offset are superimposed to obtain the position control component of each blade; The position control component and the force control compensation component in the force control reference value are combined and packaged to obtain the lever motion control parameters of the UAV external motor lever mechanism.

7. The method for protecting the nest of a quadrotor UAV based on blade angle control as described in claim 6, characterized in that, The combination and packaging of the position control component and the force control compensation component to obtain the lever motion control parameters of the UAV external motor lever mechanism includes: Based on the position control component and the force control compensation component, the optimized value of the position control component is calculated using the position optimization formula, which is specifically expressed as follows: ； In the formula, This represents the optimized value of the position control component. This represents the angle deviation value in the stated angle deviation amount. This represents the corrected force control reference value. This represents the preset characteristic force constant. Represents an exponential function with the natural constant as its base; The optimized value of the position control component and the force control compensation component are packaged together to obtain the lever motion control parameters of the UAV external motor lever mechanism.

8. The method for protecting the nest of a quadcopter UAV based on blade angle control as described in claim 1, characterized in that, The process of decomposing the lever motion control parameters into a sequence of sub-control commands corresponding to each rotor of the UAV, and synchronously sending them to the multi-axis motion controller of the external motor lever mechanism to execute the multi-blade synchronous turning and retraction operation includes: Based on the motor shaft identifier corresponding to each rotor in the UAV, the lever motion control parameters are converted into multiple sub-control commands, each sub-control command corresponding to the lever motion of one rotor; Each sub-control instruction is arranged in timestamp order to obtain a sub-control instruction sequence corresponding to each rotor. The sub-control instruction sequence includes the current position instruction, the target position instruction, and the motion speed gear indicator. Multiple sub-control command sequences are simultaneously sent to the multi-axis motion controller of the external motor lever structure, triggering the multi-axis motion controller to start each axis motor at the same time. Based on the sub-control command sequence, the multi-axis motion controller drives the corresponding lever to perform synchronous turning actions until each blade reaches the target retraction angle.

9. The method for protecting the nest of a quadrotor UAV based on blade angle control as described in claim 1, characterized in that, Based on the received takeoff preparation command, the process of performing propeller deployment reset control on the UAV in the retracted state, turning the propellers to the target flight preparation angle, and obtaining a takeoff permission signal includes: The takeoff preparation command includes the target flight preparation angle corresponding to each propeller blade; The retracted state angle corresponding to each propeller blade is compared with the target flight preparation angle to obtain the deployment and reset deviation of each propeller blade; The deployment reset deviation is mapped and converted to obtain the deployment control parameters corresponding to each blade, and the deployment control parameters are sent to the multi-axis motion controller. The multi-axis motion controller drives the lever to turn each propeller from the retracted state angle to the target flight preparation angle. After each propeller reaches the target flight preparation angle, a takeoff permission signal is generated and output.

10. A quadcopter UAV nest protection system based on blade angle control, characterized in that, To implement the quadrotor UAV nest protection method based on blade angle control as described in claim 1, the system includes an angle optimization module, a deviation analysis module, a force control correction module, a synchronization control module, and a reset control module, wherein: The angle optimization module is used to perform retraction angle optimization analysis on each blade of the UAV based on the UAV's blade attitude dataset, with the constraints of minimizing the total turning stroke and maintaining a safe dynamic gap between the blades, to obtain the optimal retraction angle of each blade. The deviation analysis module is used to perform deviation analysis on the actual azimuth angle information in the propeller attitude dataset and the optimal retraction angle to obtain the angle deviation amount corresponding to each propeller in the UAV. The force control correction module is used to introduce the real-time contact force between the lever and the propeller in the UAV to dynamically correct the preset flexible contact force control curve, and calculate the lever motion control parameters of the lever mechanism that drives the external motor of the UAV based on the correction result and the angle deviation. The synchronization control module is used to decompose the lever motion control parameters into a sequence of sub-control commands corresponding to each rotor of the UAV, and send them synchronously to the multi-axis motion controller of the external motor lever mechanism to perform multi-blade synchronous turning and retraction operation. The reset control module is used to perform propeller deployment reset control on the UAV in the retracted state based on the received takeoff preparation command, turn the propellers to the target flight preparation angle, and obtain a takeoff permission signal.

Images