Unmanned aerial vehicle landing control method and device, computer device and storage medium

Abstract

The application discloses a kind of unmanned plane landing control method, comprising: receiving unmanned plane feedback position acquisition data and wind acquisition data, according to position acquisition data, determine initial pose, and according to wind acquisition data, determine wind field data;According to wind field data, initial pose is adjusted and handled, obtain current pose, and current pose is sent to unmanned plane, to make unmanned plane according to control instruction initial pose is adjusted as current pose;According to current pose and predicted wind speed in wind field data, determine landing trajectory, and send landing trajectory to unmanned plane, to make unmanned plane start landing according to landing trajectory;Real-time monitoring relative information between unmanned plane and docking cabinet platform, when relative information meets preset docking condition, to make unmanned plane land to docking cabinet platform.The application improves the precision control of unmanned plane landing under the condition of crosswind, gust or wind direction mutation, effectively avoids the direction drift and landing failure caused by wind field.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) control technology, and in particular to a UAV landing control method, device, computer equipment, and storage medium. Background Technology

[0002] The rapid development of drone technology has led to the widespread application of drones in logistics and delivery. To achieve high-frequency, automated operation, more and more drones are adopting docking station platforms as ground support systems for their logistics transportation. However, in practical applications, the landing accuracy of drones is affected by various factors, especially in outdoor environments with high wind speeds, frequent changes in wind direction, or complex terrain, where landing deviation is particularly severe. Summary of the Invention

[0003] This invention provides a method, apparatus, computer equipment, and storage medium for controlling the landing of unmanned aerial vehicles (UAVs) to improve the problem of large landing deviations of UAVs in windy environments in the prior art.

[0004] A method for controlling the landing of an unmanned aerial vehicle (UAV), comprising: The system receives position data and wind data from the drone, determines the initial pose of the drone based on the position data, and determines the wind field data corresponding to the initial pose based on the wind data. The initial pose is adjusted based on the wind field data to obtain control commands, and the control commands are sent to the UAV so that the UAV adjusts the initial pose to the current pose according to the control commands. Based on the current pose and the predicted wind speed in the wind field data, a landing trajectory corresponding to the UAV is determined, and the landing trajectory is sent to the UAV so that the UAV can begin to land according to the landing trajectory. The relative information between the drone and the docking station platform is monitored in real time, and when the relative information meets the preset docking conditions, the drone is allowed to land on the docking station platform.

[0005] A drone landing control device, comprising: The data determination module is used to receive real-time position data and wind data from the UAV, determine the initial pose corresponding to the UAV based on the position data, and determine the wind field data corresponding to the initial pose based on the wind data. The pose adjustment module is used to adjust the initial pose according to the wind field data, obtain control commands, and send the control commands to the UAV so that the UAV adjusts the initial pose to the current pose according to the control commands. The landing trajectory determination is used to determine the landing trajectory corresponding to the UAV based on the current pose and the predicted wind speed in the wind field data, and send the landing trajectory to the UAV so that the UAV can start landing according to the landing trajectory; The drone landing module is used to monitor the relative information between the drone and the docking cabinet platform in real time, and to land the drone on the docking cabinet platform when the relative information meets the preset docking conditions.

[0006] A computer device includes a memory, a controller, and a computer program stored in the memory and executable on the controller, wherein the controller executes the computer program to implement the aforementioned UAV landing control method.

[0007] A computer-readable storage medium storing a computer program that, when executed by a controller, implements the aforementioned UAV landing control method.

[0008] This invention provides a drone landing control method, apparatus, computer equipment, and storage medium. Based on location and wind data, the initial attitude of the drone and wind field data are determined, thereby determining the current attitude of the drone under the wind field data. This allows for adjustment of the initial attitude to ensure the drone's attitude conforms to the wind field data during landing. Based on the current attitude and predicted wind speed, the drone's landing trajectory is determined, facilitating attitude adjustment within the wind field and enabling control to initiate landing. Real-time monitoring of relative information and preset docking conditions ensures the drone's relative information meets these conditions, improving precise control of drone landing under crosswinds, gusts, or sudden wind changes, effectively preventing directional drift and landing failure caused by wind. Attached Figure Description

[0009] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0010] Figure 1 This is a flowchart of a drone landing control method according to an embodiment of the present invention; Figure 2 This is a flowchart of step S20 of the UAV landing control method in one embodiment of the present invention; Figure 3This is a flowchart of step S30 of the UAV landing control method in one embodiment of the present invention; Figure 4 This is a schematic block diagram of a drone landing control device according to an embodiment of the present invention; Figure 5 This is a schematic diagram of a computer device according to an embodiment of the present invention. Detailed Implementation

[0011] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0012] This invention provides a method for controlling the landing of an unmanned aerial vehicle (UAV). In one embodiment, as follows: Figure 1 As shown, its technical solution mainly includes the following steps S10-S40: S10: Receive position data and wind data collected by the UAV; determine the initial pose corresponding to the UAV based on the position data; and determine the wind field data corresponding to the initial pose based on the wind data.

[0013] Understandably, location data acquisition includes, but is not limited to, radar data, camera data, and data related to the UAV acquired by the inertial measurement unit, including but not limited to the UAV's angular velocity, acceleration data, docking station identification, and images acquired simultaneously by dual cameras. Wind data acquisition refers to the airspeed data acquired by the pitot tube in the UAV and the ground speed data acquired by the inertial navigation system. Initial pose refers to the pose of the UAV when it establishes communication with the docking station. Pose refers to the UAV's position in the air (i.e., its position in three-dimensional space, for example, its position relative to the docking station platform) and attitude (i.e., pitch angle: the angle at which an object tilts up or down; roll angle: the angle at which an object tilts left or right; yaw angle: the angle at which an object turns left or right). Wind field data refers to the collection of information describing the wind in an area at a certain distance above the docking station, including but not limited to wind speed and wind direction. A UAV is an aircraft that completes its flight mission through radio remote control equipment, onboard computer programs, or a combination of both.

[0014] Specifically, after the UAV enters the communication range of the docking station and establishes a communication connection, it sends a landing permission to the central coordinator. Upon receiving the landing permission, the UAV collects position and wind field information through its onboard equipment and sends it to the controller. This allows the controller to receive the position and wind data collected by the UAV. Then, based on the position data, the initial pose corresponding to the UAV is determined. This is achieved by using a Kalman filter algorithm to analyze the UAV pose obtained through a combination of vision-inertial navigation (inertial navigation, which uses inertial sensors (accelerometers and gyroscopes) to measure the acceleration and angular velocity of a moving body and calculate its position, velocity, and attitude), the precise distance between the UAV and the docking station platform obtained by millimeter-wave radar, the contour information of the docking station platform, and the stereoscopic positioning obtained by binocular vision. This analysis yields a real-time six-degree-of-freedom pose estimate of the UAV relative to the docking station platform, which is then determined as the initial pose. Simultaneously, based on wind data collection, the wind field data corresponding to the initial pose is determined. That is, the three-dimensional wind force acting on the UAV is estimated in real time using the pitot tube and barometer in the airborne equipment and identified as wind field data. This wind field data is then used as a feedforward signal input to the control system, allowing the controller to anticipate and respond to the impact of the wind force in advance, thus improving the timeliness of wind resistance response.

[0015] S20, the initial pose is adjusted based on the wind field data to obtain control commands, and the control commands are sent to the UAV so that the UAV adjusts the initial pose to the current pose according to the control commands.

[0016] Understandably, the current pose refers to the position and attitude of the drone that conforms to the wind speed and direction in the wind field data.

[0017] Specifically, the initial pose is adjusted based on wind field data. This is achieved by deploying an LQR (linear quadratic regulator) controller in the dynamic control module. Based on the real-time wind speed collected from the wind field data, a preset wind speed-gain matrix mapping table is used (each wind speed range corresponds to a pre-calculated LQR state feedback gain matrix K). Simultaneously, the state deviation between the desired state x and the real-time state X of the UAV is calculated using real-time pose (position, attitude) and wind speed data collected by sensors. Next, the matched preset gain matrix K is substituted into the LQR control law formula u = -K·(X - x) (where u is the control command, K is the state feedback gain matrix, the core adjustment is the control gain in the position and attitude dimensions of K, X is the real-time state vector of the UAV, and x is the desired state vector), to calculate the control command that matches the current wind speed. Finally, the control command is sent to the UAV's actuators, and the UAV adjusts from the initial pose to the current pose after executing the control command.

[0018] S30: Based on the current pose and the predicted wind speed in the wind field data, determine the landing trajectory corresponding to the UAV, and send the landing trajectory to the UAV so that the UAV can begin to land according to the landing trajectory.

[0019] Understandably, predicted wind speed refers to the wind speed at different times within the wind field where the drone is located. Landing trajectory refers to the spatial path of the drone from the start of its descent to the docking platform.

[0020] Specifically, based on the current pose and the predicted wind speed from the wind field data, the landing trajectory corresponding to the UAV is determined. In other words, the MPC controller, based on the UAV's dynamics model, solves an optimal control problem within a finite time domain in each control cycle, considering the current state and predicted wind speed, and generates the landing trajectory based on all optimal control methods. The UAV's landing trajectory is corrected in real time to ensure accurate landing on the docking station platform.

[0021] In one embodiment, the model predictive control module performs time-domain prediction on the UAV model, current pose, and predicted wind speed. Specifically, starting from the current pose and combining it with the predicted wind speed, the model predictive control module predicts the possible states of the UAV under different control commands over N future time steps (e.g., 10 steps, each 0.5 seconds, for a total of 5 seconds), thus corresponding to the time-domain states of each finite time domain. Next, the model predictive control module performs constrained optimization processing on the time-domain states corresponding to each finite time domain. That is, it predicts the time-domain state in each finite time domain and sorts all prediction results by time to obtain the optimal control sequence. Finally, the first control state in each finite time domain control sequence is sorted by time order to obtain the landing trajectory.

[0022] S40: Monitor the relative information between the drone and the docking station platform in real time, and when the relative information meets the preset docking conditions, allow the drone to land on the docking station platform.

[0023] Understandably, relative information includes, but is not limited to, the drone's horizontal relative position, roll and pitch angles, relative speed, and steady-state period. Preset docking conditions refer to the pre-defined conditions for the drone to land on the docking platform, including, but not limited to, position conditions corresponding to the horizontal relative position, attitude conditions corresponding to the roll and pitch angles, speed conditions corresponding to the relative speed, and a pre-defined number of control cycles corresponding to the steady-state period.

[0024] Specifically, the relative information between the drone and the docking station platform is monitored in real time, and preset docking conditions are obtained. The relative information is compared with the preset docking conditions to determine whether the relative information meets the preset docking conditions. When all information in the relative information meets the preset docking conditions, the relative information is determined to meet the preset docking conditions, allowing the drone to land on the docking station platform and send landing information to the docking station. After the drone makes contact with the docking station platform, the electromagnetic locking mechanism is instantly energized and locked.

[0025] In one embodiment, if one or more pieces of information in the relative information do not meet the preset docking conditions, the docking cabinet platform is re-identified and the drone position is adjusted until all information in the relative information meets the preset docking conditions, so that the drone can land on the docking cabinet platform.

[0026] The UAV landing control method in this embodiment of the invention determines the initial attitude of the UAV and the wind field data based on location and wind data, thereby determining the current attitude of the UAV under the wind field data and adjusting the initial attitude to ensure that the UAV's attitude conforms to the landing conditions under the wind field data. Based on the current attitude and predicted wind speed, the landing trajectory of the UAV is determined, facilitating the adjustment of the UAV's attitude in the wind field and ultimately controlling the UAV to begin landing. By real-time monitoring of relative information and preset docking conditions, the method ensures that the relative information of the UAV meets the preset docking conditions, thereby improving the precision control of UAV landing under crosswinds, gusts, or sudden wind changes, and effectively avoiding directional drift and landing failure caused by the wind field.

[0027] In one embodiment, step S10, namely determining the initial pose corresponding to the UAV based on the location data, includes: S101, the predicted pose of the UAV is determined based on the angular velocity data and acceleration data in the position acquisition data.

[0028] S102, determine the absolute pose of the UAV based on the image acquisition data in the location acquisition data.

[0029] S103, determine the initial pose corresponding to the UAV based on the predicted pose and the absolute pose.

[0030] In essence, angular velocity data refers to physical quantities describing the speed and direction of a UAV's rotation around a certain axis. Acceleration data refers to physical quantities describing the rate and direction of change of the UAV's velocity. Predicted pose refers to the position and attitude information of a UAV at a given moment, calculated using an algorithmic model based on its angular velocity and acceleration. Absolute pose refers to the position and attitude information of the UAV within a fixed, global reference coordinate system.

[0031] Specifically, after receiving position acquisition data, the inertial measurement unit (IMU) in the airborne equipment outputs angular velocity and acceleration data at a high frequency of 500Hz. The IMU then uses an inertial navigation algorithm to predict the predicted pose (position and attitude) of the UAV in real time, and inputs this predicted pose into an extended Kalman filter (EPF). Simultaneously, the onboard vision system acquires image data at a frequency of 20Hz, identifies the docking station markers, and extracts their 2D image coordinates. Combined with the known 3D world coordinates of the markers, the PnP algorithm is used to calculate the absolute pose of the UAV relative to the docking station. This visually measured pose is then input into the EPF as the observation value. The EPF then fuses the predicted and absolute poses. Specifically, the EPF uses the predicted pose as a priori estimate and the absolute pose as the observation value, calculating the residuals to complete the data fusion. This process corrects the pose estimate and iteratively updates the zero bias of the IMU. When the change in the zero-bias estimate is continuously less than a set threshold, and the trace of the pose estimation covariance matrix is ​​stable in the minimum range, the alignment process is considered to have converged and the final output is the calibrated accurate initial pose.

[0032] In one embodiment, the intrinsic parameters (including distortion parameters) and extrinsic parameters (including the relative pose between the two cameras) of the dual-camera system are calibrated. Simultaneously, the world coordinate system coordinates of the docking station markers are pre-calibrated and fixed. After the UAV arrives within the visual recognition range of the docking station, the two cameras synchronously acquire images containing the markers. First, stereo correction is performed based on the calibrated intrinsic and extrinsic parameters (eliminating lens distortion and aligning non-coplanar rows to coplanar rows) to eliminate image distortion and pose deviation between the two cameras. Then, feature points of the docking station markers are extracted from the left and right images. Corresponding point pairs of the markers in the left and right images are established using a stereo matching algorithm. Based on the principle of binocular triangulation, the three-dimensional coordinates of the markers in the camera coordinate system are calculated using the relative pose of the two cameras. Next, the known world coordinates of the markers and the calculated three-dimensional coordinates of the camera coordinate system are input into the PnP algorithm to solve for the rigid body transformation matrix (including rotation matrix and translation vector) from the camera coordinate system to the docking station's world coordinate system. Finally, the initial pose of the UAV relative to the docking station is output.

[0033] In this embodiment, angular velocity and acceleration data are used to estimate the UAV's pose and determine the predicted pose. Image acquisition data is used to calculate the UAV's absolute pose, thereby enabling the fusion of high-frequency and low-frequency poses and improving the accuracy of the initial position.

[0034] In one embodiment, step S20, which involves adjusting the initial pose based on the wind field data to obtain control commands corresponding to the UAV, includes: S201, based on the predicted wind speed in the wind field data, a gain scheduling strategy is adopted to determine the preset gain matrix corresponding to the UAV.

[0035] S202, adjust the gain of the initial pose according to the preset gain matrix to obtain the control command corresponding to the UAV.

[0036] In essence, a preset gain matrix refers to a matrix parameter pre-set in a control system or filtering algorithm to adjust the mapping relationship between input and output. A state feedback matrix, in a state feedback control system, refers to a matrix parameter used to weight the system's state variables to generate the control input.

[0037] Specifically, a gain scheduling strategy is adopted. Based on the predicted wind speed in the wind field data, a corresponding preset gain matrix is ​​matched and determined from the gain matrix library. This preset gain matrix has been pre-optimized for different wind speed ranges, including gain coefficients for position control and attitude control. Then, the real-time and desired pose states of the UAV are collected, and the state deviation between the real-time and desired pose states is calculated. The state deviation and the matched preset gain matrix are substituted into the LQR control law, and the control commands corresponding to the UAV are calculated through closed-loop feedback control. Furthermore, the weight matrices Q (state deviation weight) and R (control cost weight) of the LQR controller can be adjusted according to the wind speed, and the preset gain matrix is ​​then solved online.

[0038] In this embodiment, the preset gain matrix is ​​determined by the predicted wind speed in the wind field data, thereby determining the state feedback matrix, which in turn enables the adjustment of the pose and improves the precise control of the UAV landing under crosswind, gust or sudden wind direction conditions.

[0039] In one embodiment, step S30, namely determining the landing trajectory corresponding to the UAV based on the current pose and the predicted wind speed in the wind field data, includes: S301, the model prediction control module performs time-domain prediction on the current pose and the predicted wind speed to obtain the time-domain state corresponding to each finite time domain.

[0040] S302, the model predictive control module performs constrained optimization processing on the time domain states corresponding to each of the finite time domains to obtain the optimal control sequence, and determines the landing trajectory based on the optimal control sequence.

[0041] In essence, Model Predictive Control (MPC) is an advanced closed-loop optimization control strategy based on models. Finite-time domain refers to solving an optimization problem within a fixed, finite-length time interval; the length of this time interval is called the time domain length. Time-domain state refers to the state of the UAV under different commands in different finite-time domains. Optimal control sequence refers to a set of control sequences obtained by solving a constrained finite-time domain optimization problem that minimizes a preset cost function and satisfies all input / state / output constraints.

[0042] Specifically, the entire UAV landing process is discretized into several consecutive time steps, each corresponding to a set of three-dimensional spatial coordinates. The coordinates of all time steps are arranged sequentially to form a three-dimensional position coordinate sequence. The curves showing the change of these three-dimensional spatial coordinates over time exhibit the trajectory characteristics of "uniform descent from initial altitude, followed by deceleration and buffering above the docking station platform." Simultaneously, the MPC module, using real-time position error and target speed requirements as constraints, performs roll optimization in conjunction with the UAV dynamics model. It plans a set of roll, pitch, and yaw attitude angles for each time step, and the attitude angles of all time steps form an attitude angle sequence, ensuring that the changes in each angle over time are continuous and smooth. While planning the attitude angles, the MPC module simultaneously generates linear velocity and angular velocity curves: the horizontal linear velocity gradually approaches 0 to ensure the UAV accurately aligns with the docking station platform; the vertical linear velocity is divided into two stages: a uniform descent stage to meet the requirements of rapid landing, and a deceleration and buffering stage to reduce landing impact; the angular velocity curve is strictly matched with the rate of change of the attitude angles to ensure the smoothness of attitude adjustments. Finally, the MPC module converts the planned position, attitude, and velocity information into a sequence of control commands for each time step. This sequence of commands is defined as the UAV's landing trajectory. This sequence of control commands is then sent to the UAV's flight control system, driving the UAV to complete the landing process along the planned trajectory.

[0043] In this embodiment, the model prediction control module enables the division of the finite time domain and the determination of the time domain state, thereby achieving constrained optimization of the time domain state, determining the optimal control sequence, and improving the precise control of UAV landing under crosswind, gust, or sudden wind direction conditions.

[0044] In one embodiment, after step S30, i.e., after sending the landing trajectory to the drone so that the drone can begin landing according to the landing trajectory, and before the drone lands on the docking station platform, the method further includes: S303, receives real-time position data and real-time wind data corresponding to each sampling period from the UAV, and determines the real-time pose corresponding to the real-time position data and the real-time wind field data corresponding to the real-time wind data based on the real-time position data and the real-time wind data.

[0045] S304, Based on the real-time pose and the real-time wind field data, the landing trajectory of the UAV is corrected in real time to obtain an updated landing trajectory.

[0046] In essence, real-time position data refers to radar and camera data collected at different sampling periods. Real-time wind data refers to airspeed data collected by the pitot tube and ground speed data collected by the inertial navigation system at different sampling periods. Real-time pose refers to the UAV's position and attitude at different sampling periods. Real-time wind field data refers to wind speed, direction, and other related information at different sampling periods. Updated landing trajectory refers to the optimal spatial path for landing on the docking station platform in each sampling period.

[0047] Specifically, after the UAV begins its descent according to its landing trajectory, the system receives real-time position and wind data corresponding to each sampling period from the UAV. Then, based on the real-time position and wind data, the system determines the real-time pose corresponding to the position data and the real-time wind field data corresponding to the wind data. Specifically, using a Kalman filter algorithm, the system analyzes the UAV pose obtained through a vision-inertial navigation system, the precise distance between the UAV and the docking station platform obtained through millimeter-wave radar, the contour information of the docking station platform, and the stereoscopic positioning obtained through binocular vision. This yields a real-time six-degree-of-freedom pose estimate of the UAV relative to the docking station platform, which is then determined as the real-time pose. Simultaneously, real-time wind data collected by the onboard equipment, including the pitot tube and barometer, is used to estimate the three-dimensional wind force acting on the UAV and determine this as the real-time wind field data.

[0048] Next, based on the real-time wind speed in the real-time wind field data, a preset gain matrix corresponding to the UAV is determined, and a state feedback matrix is ​​determined based on the preset gain matrix. Then, the real-time pose is adjusted using the state feedback matrix to obtain the target control command corresponding to the UAV, and the UAV is adjusted to the target pose using the target control command. Then, the model prediction control module performs time-domain prediction on the target pose and real-time wind speed to obtain the real-time time-domain state corresponding to each finite time domain, and performs constrained optimization processing on the time-domain state corresponding to each real-time finite time domain to obtain the real-time control sequence. The updated landing trajectory is determined based on all real-time control sequences, that is, the first step of the control in all real-time control sequences is sorted in time sequence to obtain the updated landing trajectory.

[0049] In this embodiment, real-time position data and real-time wind data are received for each sampling period, enabling the calculation and determination of real-time pose and wind field data in different sampling periods. Real-time pose and wind field data are used to correct the landing trajectory in real time, thereby enabling the calculation of updated landing trajectories and achieving precise control of the UAV landing, effectively avoiding directional drift and landing failure caused by wind.

[0050] In one embodiment, after step S40, i.e. after real-time monitoring of the relative information between the drone and the docking station platform, and before the drone lands on the docking station platform, the method further includes: S50, when the horizontal relative position in the relative information is within the preset tolerance range, the servo motor drives the docking cabinet platform to move until the horizontal relative position is within the preset tolerance range, and then it is confirmed that the relative information meets the preset docking conditions.

[0051] Understandably, the preset tolerance range refers to the pre-set deviation threshold between the drone and the docking station platform in the horizontal plane, for example, ±5 cm. The horizontal relative position refers to the relative position of the drone and the docking station platform in the horizontal plane.

[0052] Specifically, after real-time monitoring of the relative information between the drone and the docking station platform, preset docking conditions are obtained. The horizontal relative position in the relative information is compared with the preset tolerance range in the preset docking conditions to determine whether to drive the docking station platform to move. When the horizontal relative position in the relative information is within the preset tolerance range, the horizontal deviation between the drone and the docking station platform is calculated based on the horizontal relative position, and this horizontal deviation is sent to the servo motor of the docking station. The servo motor drives the docking station platform to move, eliminating the horizontal deviation and thus confirming that the relative information meets the preset docking conditions.

[0053] In one embodiment, when the horizontal relative position in the relative information exceeds a preset tolerance range, the position of the UAV is adjusted so that the relative information of the UAV after the position adjustment meets the preset docking conditions.

[0054] In this embodiment, by combining a preset tolerance range with servo fine-tuning of the docking cabinet, the drone and the docking cabinet are controlled as a collaborative system, which reduces the drone's dependence on the accuracy of absolute positioning and improves the overall fault tolerance and docking success rate.

[0055] In one embodiment, the UAV landing control method further includes: S60, when an anomaly is detected, sends a go-around command to the drone, so that the drone can perform a preset safety operation corresponding to the go-around command.

[0056] Understandably, a go-around command refers to an instruction for a drone to abort its descent and restart its climb during the landing process. A preset safe operation refers to a pre-defined safe go-around procedure, such as stopping the descent and restarting the climb. The anomaly can be a pre-defined abnormal situation, such as an out-of-range attitude condition, meaning the drone's position or attitude parameters exceed a pre-defined safety threshold range, or the predicted wind speed in the wind field data exceeds a preset wind speed threshold.

[0057] Specifically, the entire landing process of the UAV is monitored in real time. When an anomaly is detected, a go-around command is obtained and sent to the UAV, causing it to perform a preset safety operation corresponding to the go-around command, i.e., stop landing and resume ascent. In one embodiment, when the UAV's attitude is detected to be out of limits, a go-around command is sent to the UAV to stop landing, and the UAV resumes ascent according to the go-around command. In another embodiment, when the predicted wind speed in the wind field data is detected to exceed a preset wind speed threshold, a go-around command is sent to the UAV to stop landing, and the UAV resumes ascent according to the go-around command.

[0058] In another embodiment, if the anomaly disappears within a preset time range, the connection with the docking station is re-established, and the landing path is re-planned based on the drone's current location, causing the drone to land on the docking station platform. For example, if the wind speed decreases and the real-time wind speed in the wind field is less than a preset wind speed threshold, the drone's position and attitude are determined, the connection with the docking station is re-established, and the landing path is re-planned. If the anomaly does not disappear within the preset time range, an available docking station platform near the drone's location is identified, and the drone lands on that platform.

[0059] In this embodiment, when an anomaly is detected, the drone is controlled to take off again via a go-around command, which avoids the drone failing to land accurately on the docking station platform due to the anomaly. This improves the precision control of drone landing under crosswind, gust, or sudden wind direction conditions, and effectively avoids directional drift and landing failure caused by wind field.

[0060] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0061] In one embodiment, a drone landing control device is provided, which corresponds one-to-one with the drone landing control method described in the above embodiments. For example... Figure 4 As shown, the UAV landing control device includes a data determination module 10, an attitude adjustment module 20, a landing trajectory determination module 30, and a UAV landing module 40. Detailed descriptions of each functional module are as follows: The data determination module 10 is used to receive real-time position data and wind data collected by the UAV, determine the initial pose corresponding to the UAV based on the position data, and determine the wind field data corresponding to the initial pose based on the wind data. The pose adjustment module 20 is used to adjust the initial pose according to the wind field data, obtain control commands, and send the control commands to the UAV so that the UAV adjusts the initial pose to the current pose according to the control commands. The landing trajectory determination 30 is used to determine the landing trajectory corresponding to the UAV based on the current pose and the predicted wind speed in the wind field data, and send the landing trajectory to the UAV so that the UAV can start landing according to the landing trajectory; The drone landing module 40 is used to monitor the relative information between the drone and the docking cabinet platform in real time, and to land the drone on the docking cabinet platform when the relative information meets the preset docking conditions.

[0062] In one embodiment, the data determination module 10 includes: The pose prediction unit is used to determine the predicted pose of the UAV based on the angular velocity data and acceleration data in the position acquisition data. An absolute pose unit is used to determine the absolute pose of the UAV based on the image acquisition data in the position acquisition data. An initial pose unit is used to determine the initial pose corresponding to the UAV based on the predicted pose and the absolute pose.

[0063] In one embodiment, the pose adjustment module 20 includes: The matrix determination unit is used to determine a preset gain matrix corresponding to the UAV based on the predicted wind speed in the wind field data and by adopting a gain scheduling strategy. The gain adjustment unit is used to adjust the gain of the initial pose according to the preset gain matrix to obtain control commands corresponding to the UAV.

[0064] In one embodiment, the landing trajectory determination 30 includes: The time-domain state unit is used to perform time-domain prediction on the current pose and the predicted wind speed through the model prediction control module to obtain the time-domain state corresponding to each finite time domain. The landing trajectory unit is used to perform constrained optimization processing on the time-domain states corresponding to each of the finite time domains through the model prediction control module to obtain the optimal control sequence, and to determine the landing trajectory based on the optimal control sequence.

[0065] In one embodiment, the device further includes: The real-time data module is used to receive real-time position data and real-time wind data corresponding to each sampling period from the UAV, and to determine the real-time pose corresponding to the real-time position data and the real-time wind field data corresponding to the real-time wind data based on the real-time position data and the real-time wind data. The real-time correction module is used to correct the landing trajectory of the UAV in real time based on the real-time pose and the real-time wind field data, so as to obtain an updated landing trajectory.

[0066] In one embodiment, the device further includes: The dynamic tolerance module is used to drive the docking cabinet platform to move by a servo motor when the horizontal relative position in the relative information is within a preset tolerance range, until the horizontal relative position is within the preset tolerance range, thus confirming that the relative information meets the preset docking conditions.

[0067] In one embodiment, the device further includes: The drone go-around module is used to send a go-around command to the drone when an anomaly is detected, so that the drone can perform a preset safety operation corresponding to the go-around command.

[0068] Specific limitations regarding the drone landing control device can be found in the limitations of the drone landing control method described above, and will not be repeated here. Each module in the aforementioned drone landing control device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in hardware within or independently of the controller in the computer device, or stored in software in the memory of the computer device, so that the controller can call and execute the corresponding operations of each module.

[0069] In one embodiment, a computer device is provided, comprising a controller, a memory, a network interface, and a database connected via a system bus. The controller provides computing and control capabilities. The memory includes a readable storage medium and internal memory. The readable storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the readable storage medium. The network interface is used to communicate with external terminals via a network connection. When executed by the controller, the computer program implements a drone landing control method.

[0070] In one embodiment, a computer device is provided, including a memory, a controller, and a computer program stored in the memory and executable on the controller, wherein the controller executes the computer program to implement the above-described UAV landing control method.

[0071] In one embodiment, a computer-readable storage medium is provided, the computer-readable storage medium storing a computer program that, when executed by a controller, implements the above-described UAV landing control method.

[0072] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by instructing related hardware with computer-readable instructions. These computer-readable instructions can be stored in a non-volatile readable storage medium or a volatile readable storage medium. When executed, these computer-readable instructions can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0073] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0074] The above-described 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 the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for controlling the landing of an unmanned aerial vehicle (UAV), characterized in that, include: The system receives position data and wind data from the drone, determines the initial pose of the drone based on the position data, and determines the wind field data corresponding to the initial pose based on the wind data. The initial pose is adjusted based on the wind field data to obtain control commands, and the control commands are sent to the UAV so that the UAV adjusts the initial pose to the current pose according to the control commands. Based on the current pose and the predicted wind speed in the wind field data, a landing trajectory corresponding to the UAV is determined, and the landing trajectory is sent to the UAV so that the UAV can begin to land according to the landing trajectory. The relative information between the drone and the docking station platform is monitored in real time, and when the relative information meets the preset docking conditions, the drone is allowed to land on the docking station platform.

2. The UAV landing control method as described in claim 1, characterized in that, The step of determining the initial pose corresponding to the UAV based on the location data includes: The predicted pose of the UAV is determined based on the angular velocity and acceleration data collected from the location data. The absolute pose of the UAV is determined based on the image acquisition data in the location acquisition data. Based on the predicted pose and the absolute pose, the initial pose corresponding to the UAV is determined.

3. The UAV landing control method as described in claim 1, characterized in that, The step of adjusting the initial pose based on the wind field data to obtain control commands includes: Based on the predicted wind speed in the wind field data, a gain scheduling strategy is adopted to determine the preset gain matrix corresponding to the UAV. The initial pose is adjusted according to the preset gain matrix to obtain the control command corresponding to the UAV.

4. The UAV landing control method as described in claim 1, characterized in that, The step of determining the landing trajectory corresponding to the UAV based on the current pose and the predicted wind speed in the wind field data includes: The model prediction control module performs time-domain prediction on the current pose and the predicted wind speed to obtain the time-domain state corresponding to each finite time domain. The model predictive control module performs constrained optimization on the time-domain states corresponding to each of the finite time domains to obtain the optimal control sequence, and determines the landing trajectory based on the optimal control sequence.

5. The UAV landing control method as described in claim 1, characterized in that, After sending the landing trajectory to the drone so that the drone can begin landing according to the landing trajectory, the method further includes: Receive real-time location data and real-time wind data corresponding to each sampling period from the UAV, and determine the real-time pose corresponding to the real-time location data and the real-time wind field data corresponding to the real-time wind data based on the real-time location data and the real-time wind data. Based on the real-time pose and the real-time wind field data, the landing trajectory of the UAV is corrected in real time to obtain an updated landing trajectory.

6. The UAV landing control method as described in claim 1, characterized in that, After real-time monitoring of the relative information between the drone and the docking station platform, the following is also included: When the horizontal relative position in the relative information is within the preset tolerance range, the servo motor drives the docking cabinet platform to move until the horizontal relative position is within the preset tolerance range, confirming that the relative information meets the preset docking conditions.

7. The UAV landing control method as described in claim 1, characterized in that, The method includes: When an anomaly is detected, a go-around command is sent to the drone, instructing the drone to perform a preset safety operation corresponding to the go-around command.

8. A drone landing control device, characterized in that, include: The data determination module is used to receive real-time position data and wind data from the UAV, determine the initial pose corresponding to the UAV based on the position data, and determine the wind field data corresponding to the initial pose based on the wind data. The pose adjustment module is used to adjust the initial pose according to the wind field data, obtain control commands, and send the control commands to the UAV so that the UAV adjusts the initial pose to the current pose according to the control commands. The landing trajectory determination is used to determine the landing trajectory corresponding to the UAV based on the current pose and the predicted wind speed in the wind field data, and send the landing trajectory to the UAV so that the UAV can start landing according to the landing trajectory; The drone landing module is used to monitor the relative information between the drone and the docking cabinet platform in real time, and to land the drone on the docking cabinet platform when the relative information meets the preset docking conditions.

9. A computer device, comprising a memory, a controller, and a computer program stored in the memory and executable on the controller, characterized in that, When the controller executes the computer program, it implements the UAV landing control method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the controller, it implements the UAV landing control method as described in any one of claims 1 to 7.

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