Unmanned aerial vehicle landing control method and device, unmanned aerial vehicle take-off and landing platform and storage medium

Abstract

The invention relates to an unmanned aerial vehicle landing control method and device, an unmanned aerial vehicle take-off and landing platform and a storage medium, and the method comprises the steps: obtaining the positioning information of an unmanned aerial vehicle in real time in the landing process of the unmanned aerial vehicle; determining the relative position of the unmanned aerial vehicle and the unmanned aerial vehicle take-off and landing platform based on the positioning information; under the condition that the relative positions of the multiple unmanned aerial vehicles meet expected conditions, determining a target landing sequence of the unmanned aerial vehicles based on an improved banker's algorithm; controlling each unmanned aerial vehicle to land based on the target landing sequence, and adjusting a transition distance threshold value of each stage in the landing process based on the environment information and the positioning information of the currently landed target unmanned aerial vehicle in the landing process to obtain a target threshold value; and switching a control mode to control the target unmanned aerial vehicle to complete landing based on the target threshold and the relative position of the target unmanned aerial vehicle. Therefore, based on the real-time environment information and the positioning information of the target unmanned aerial vehicle, the transition distance threshold value of each stage is adjusted, so that the landing process is more suitable for the real-time environment.

Description

Technical Field

[0001] This application relates to the field of flow measurement technology, and in particular to a method, apparatus, take-off and landing platform and storage medium for unmanned aerial vehicle (UAV) landing control. Background Technology

[0002] With the booming development of the low-altitude economy, drones, due to their flexibility and maneuverability, are increasingly widely used in emergency rescue, personnel search and rescue, forest patrol, and unmanned cargo delivery. However, when performing mobile flight missions in complex terrain areas such as the wild, mountains, and waterways, drones often face numerous challenges: uneven ground in the take-off and landing area, signal interference in mountainous areas causing deviations in take-off and landing positions, and insufficient battery power. These issues can all lead to mission interruptions or even failure. Since fixed hangars require pre-installation, deployment, and power connection, they are difficult to adapt to mobile flight missions. Therefore, using portable helipads to assist drone take-off and landing has become a common solution.

[0003] However, in existing assisted take-off and landing methods based on portable helipads, these helipads, which are portable, foldable, and have adjustable support legs, often only serve as a platform for drone take-off and landing, failing to take into account the emergency energy supply needs during outdoor emergency flights, as well as the guidance function for safe and accurate landing of drones during nighttime flights and return flights.

[0004] Therefore, how to achieve precise control over the landing of drones is an urgent problem to be solved. Summary of the Invention

[0005] Therefore, it is necessary to provide a drone landing control method, device, drone take-off and landing platform, and storage medium to address the aforementioned technical problems.

[0006] In a first aspect, this application provides a drone landing control method, applied to a drone take-off and landing platform, the method comprising: During the descent of the drone, the drone's location information is acquired in real time; Based on the positioning information, the relative position of the UAV and the UAV take-off and landing platform is determined; Given that the relative positions of the multiple UAVs meet the expected conditions, the target landing order of each UAV is determined based on the improved Banker's Algorithm. Based on the target landing sequence, each UAV is controlled to land, and during the landing process, the transition distance threshold of each stage is adjusted based on the environmental information of the target UAV currently landing and the positioning information to obtain the target threshold. Based on the target threshold and the relative position of the target UAV, the control mode is switched to control the target UAV to complete the landing.

[0007] In one embodiment, the real-time acquisition of the drone's location information includes at least one of the following: The first positioning information of the UAV is obtained based on a visual sensor; The second positioning information of the UAV is obtained based on the position sensor; Point cloud data of the UAV is acquired using lidar.

[0008] In one embodiment, the real-time acquisition of the drone's location information further includes: Set the initial state vector of the UAV; wherein the initial state vector includes the coordinates, velocity and acceleration of the UAV; Based on the inertial navigation data acquired by the microelectromechanical system, the predicted state and covariance matrix corresponding to the initial state vector are determined by combining the kinematic model. The point cloud data is determined as the primary observation, and the first positioning information and / or the second positioning information are determined as auxiliary observations. The predicted state is fused and updated with the main observation and / or the auxiliary observation based on the extended Kalman filter to obtain the positioning information.

[0009] In one embodiment, determining the relative position of the UAV and the UAV take-off and landing platform based on the positioning information includes: In the event of failure of the visual sensor and / or the position sensor, the point cloud data of the UAV is registered with a pre-stored 3D point cloud map to determine the spatial transformation matrix between the two. Based on the spatial transformation matrix, the point cloud data of the UAV is transformed into the target coordinate system corresponding to the three-dimensional point cloud map to obtain the first position; The relative position is determined based on the first position and the second position of the UAV take-off and landing platform in the target coordinate system.

[0010] In one embodiment, determining the target landing order of each drone based on an improved banker's algorithm, given that the relative positions of the plurality of drones meet the expected conditions, includes: Obtain the remaining battery power, mission urgency factor, and straight-line distance to the drone take-off and landing platform for each drone; Based on the remaining battery power, the mission urgency factor, and the straight-line distance, the overall priority of each UAV is determined; based on the number of idle platforms in the current UAV take-off and landing platform and the overall priority, the target landing order of the UAVs is determined.

[0011] In one embodiment, the step of adjusting the transition distance thresholds for each stage of the landing process based on the environmental information and positioning information of the target UAV during the landing process to obtain the target threshold includes: The environmental coefficient is determined based on the temperature, humidity, wind speed and / or wind direction in the environmental information. Based on the real-time load in the location information, determine the load coefficient; Based on the environmental coefficient and / or the load coefficient, the transition distance threshold is adjusted to obtain the target threshold.

[0012] In one embodiment, the method further includes: While controlling the target drone to complete its landing, obtain the current battery level of the target drone; If the current battery level is less than the charging threshold, the target drone will be charged. The remaining charging time is determined based on the current charging power of the drone take-off and landing platform and the current battery level. If the charging time is less than the target threshold, detect the environmental information of the target drone. When the target drone has finished charging and the environmental information meets the takeoff conditions, a departure command is sent to the target drone.

[0013] Secondly, this application also provides a drone landing control device, applied to a drone take-off and landing platform, the device comprising: The acquisition module is used to acquire the drone's location information in real time during the drone's landing process; The positioning module is used to determine the relative position of the UAV and the UAV take-off and landing platform based on the positioning information; the determination module is used to determine the target landing order of each UAV based on the improved Banker's Algorithm, provided that the relative positions of multiple UAVs meet the expected conditions. An adjustment module is used to control each UAV to land based on the target landing sequence, and to adjust the transition distance threshold of each stage of the landing process based on the environmental information and positioning information of the target UAV currently landing, so as to obtain the target threshold. The control module is used to switch control modes to control the target drone to complete the landing based on the target threshold and the relative position of the target drone.

[0014] Thirdly, this application also provides a drone take-off and landing platform, which includes a support platform, power generation components, a power supply system, and a control system; wherein, The support platform includes a platform body and adjustable legs; the platform body is used to support the UAV; the adjustable legs are used to adjust the height of the UAV take-off and landing platform; the adjustable legs are connected to the platform body by bolts. The power generation component includes a solar panel, a battery, and a control circuit; the solar panel is fixed above the support platform; the battery is fixed below the support platform. The control system includes an edge box and a light-assisted landing assembly; the light-assisted landing assembly is used to assist in guiding the drone to land; the light-assisted landing assembly is located above the drone take-off and landing platform; the edge box includes a processor and a memory; wherein the memory is used to store a computer program; the processor is configured to: when executing the computer program, implement the steps of the method described in any embodiment of this application.

[0015] Fourthly, this application also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program thereon, which, when executed by a processor, implements the steps of the methods described in any embodiment of this application.

[0016] The aforementioned UAV landing control method addresses two key issues. First, by acquiring the relative positions of the UAV and the landing platform in real time and determining the target landing sequence based on an improved banker's algorithm, it effectively solves the resource contention problem when multiple UAVs land simultaneously. This ensures that UAVs land in the optimal order, reducing the likelihood of collisions between UAVs in the air or on the ground. Second, during the landing process, the transition distance thresholds for each stage are adjusted based on the real-time environmental and positioning information of the target UAV. This makes the landing process more closely match the real-time environment, reducing landing failures caused by sudden environmental changes, meeting the requirements of mobile missions, and improving the reliability of field operations. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the structure of an unmanned aerial vehicle (UAV) take-off and landing platform according to an exemplary embodiment; Figure 2 This is a schematic diagram illustrating the working principle of an unmanned aerial vehicle (UAV) take-off and landing platform according to an exemplary embodiment; Figure 3 This is a flowchart illustrating a drone landing control method according to an exemplary embodiment; Figure 4 This is a structural block diagram of a drone landing control device according to an exemplary embodiment; Figure 5 This is an internal structural diagram of an edge box according to an exemplary embodiment. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0019] The terms "first," "second," and "third" used in the embodiments of this application are for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first," "second," or "third" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to such processes, methods, products, or apparatus.

[0020] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a mutually exclusive, independent, or alternative embodiment. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0021] In some embodiments, the UAV landing control method provided in this application can be applied to a UAV take-off and landing platform, which includes a support platform, a power generation component, a power supply system, and a control system; wherein, the support platform includes a platform body and adjustable legs; the platform body is used to support the UAV; the adjustable legs are used to adjust the height of the UAV take-off and landing platform; the adjustable legs are connected to the platform body by bolts. The power generation component includes a solar panel, a battery, and a control circuit; the solar panel is fixed above the support platform; the battery is fixed below the support platform. The control system includes an edge box and a light-assisted landing assembly; the light-assisted landing assembly is used to assist in guiding the drone to land; the light-assisted landing assembly is located above the drone take-off and landing platform; the edge box includes a processor and a memory; wherein the memory is used to store a computer program; the processor is configured to: when executing the computer program, implement the steps of the method described in any embodiment of this application.

[0022] For example, such as Figure 2 As shown, Figure 2 This is a schematic diagram of a drone take-off and landing platform. The drone take-off and landing platform includes landing pad feet 11, landing pad corner protectors 12, a level 13, LED landing indicator lights 14, solar cells 15, an aluminum alloy frame 16, an AC power output 17, a system switch 18, an indicator light switch 19, and a digital electronic charging port 21. Among them, the landing pad feet 11 are adjustable feet used to adjust the platform height; the level 13 is used to adjust the platform level; the LED landing indicator lights 14 are used to coordinate control and guide the drone landing, enhancing nighttime landing safety; the AC power output 17 can provide 220V AC power; and the digital electronic charging port 21 can integrate multiple interfaces such as USB, Type-C, and QC3.0.

[0023] For example, such as Figure 2 As shown, Figure 2 This is a schematic diagram illustrating the working principle of a drone take-off and landing platform.

[0024] In some embodiments, such as Figure 3 As shown, a drone landing control method is provided, applied to a drone take-off and landing platform. The method includes the following steps: S101: During the landing process of the drone, the drone's positioning information is acquired in real time.

[0025] In some embodiments, due to unfavorable real-time environmental conditions (such as excessive wind speed or low visibility), insufficient current battery power of the drone, or other possible reasons, it is necessary to control the drone to land on the drone take-off and landing platform; during the landing process, the drone's positioning information is obtained in real time.

[0026] In some embodiments, acquiring the drone's location information in real time includes one of the following: The first positioning information of the UAV is obtained based on a visual sensor; The second positioning information of the UAV is obtained based on the position sensor; Point cloud data of the UAV is acquired using lidar.

[0027] In this application embodiment, the visual sensor may include, but is not limited to, a monocular visual sensor, a binocular visual sensor, and an RGB-D camera.

[0028] In this application embodiment, the position sensor may include, but is not limited to, Global Positioning System (GPS), Beidou Navigation Satellite System (BDS), Real-Time Kinematic (RTK) technology, and Inertial Measurement Unit (IMU).

[0029] In this embodiment of the application, the lidar can calculate the distance using laser time-of-flight (ToF) or phase difference, and output point cloud data (including three-dimensional coordinates).

[0030] In this way, even if the visual sensor and / or position sensor fails, the point cloud data of the UAV can still be obtained through LiDAR, which enhances the anti-interference capability, thereby avoiding positioning interruption and ensuring the continuity of UAV operation tasks.

[0031] S102, Based on the positioning information, determine the relative position of the UAV and the UAV take-off and landing platform.

[0032] In one embodiment, the control system in the UAV take-off and landing platform can transform the coordinates of the UAV and the UAV take-off and landing platform into the same coordinate system based on at least one of the first positioning information, the second positioning information, or point cloud data, thereby determining the relative position of the UAV and the UAV take-off and landing platform.

[0033] In some embodiments, determining the relative position of the UAV and the UAV take-off and landing platform based on the positioning information includes: In the event of failure of the visual sensor and / or the position sensor, the point cloud data of the UAV is registered with a pre-stored 3D point cloud map to determine the spatial transformation matrix between the two. Based on the spatial transformation matrix, the point cloud data of the UAV is transformed into the target coordinate system corresponding to the three-dimensional point cloud map to obtain the first position; The relative position is determined based on the first position and the second position of the UAV take-off and landing platform in the target coordinate system.

[0034] In this embodiment, the spatial transformation matrix may include translation and rotation angle.

[0035] In one embodiment, the lidar emits laser pulses at a predetermined frequency to generate three-dimensional point cloud data of the UAV. Each point in the point cloud data contains information such as three-dimensional coordinates and reflection intensity. Noise points are removed by a filtering algorithm (such as voxel filtering), and valid points are retained. The filtered point cloud data is registered with a pre-stored three-dimensional point cloud map (high-precision terrain map), and the spatial transformation matrix between the two is calculated using the Iterative Closest Points (ICP) algorithm. The real-time point cloud coordinates of the UAV are transformed to the target coordinate system using the spatial transformation matrix to obtain the first position of the UAV. By calculating the second position of the UAV take-off and landing platform in the target coordinate system with the first position, the relative position between the UAV and the UAV take-off and landing platform can be determined.

[0036] S103, if the relative positions of the multiple UAVs meet the expected conditions, determine the target landing order of each UAV based on the improved Banker's Algorithm.

[0037] In this embodiment of the application, the expected condition is that the real-time distance between the drone and the drone take-off and landing platform reaches a distance threshold.

[0038] In this embodiment, the Banker's Algorithm is a resource allocation and scheduling algorithm used to avoid deadlock. The improved Banker's Algorithm further optimizes the resource allocation process by setting priorities for drones and prioritizing the landing needs of high-priority drones.

[0039] In some embodiments, when multiple drones land at a target altitude, i.e., the real-time distance between the drone and the drone landing platform is a distance threshold, the target landing order of each drone can be determined by dynamically sorting them according to the drone's flight status (such as remaining battery power and attitude information), the urgency of the drone's mission, and the number of drones that the drone landing platform can carry.

[0040] S104, based on the target landing sequence, control each UAV to land, and during the landing process, based on the environmental information of the target UAV currently landing and the positioning information, adjust the transition distance threshold of each stage of the landing process to obtain the target threshold.

[0041] In this embodiment of the application, environmental information may include, but is not limited to, one of wind speed, wind direction, temperature, humidity, lighting conditions, and terrain undulation.

[0042] The embodiments in this application are summarized, and the stages may include, but are not limited to, long-range stages, medium-range stages, and close-range stages.

[0043] In one embodiment, during the process of controlling the landing of a drone based on the target landing sequence, the transition distance threshold for each stage is dynamically adjusted based on the environmental and positioning information of the landing target drone. For example, in a mountainous environment with strong winds, due to the high wind speed and undulating terrain, the transition distance threshold from the mid-range stage to the close-range stage can be increased to allow for more buffer time.

[0044] S105, based on the target threshold and the relative position of the target drone, switch the control mode to control the target drone to complete the landing.

[0045] In one embodiment, the UAV take-off and landing platform can determine the current stage of the target UAV based on a target threshold and relative position; and switch control modes to control the landing of the target UAV according to the stage. For example, at long distances, GPS / RTK can be used for positioning and the landing speed decreases slowly; at medium distances, visual-assisted positioning can be further combined and the landing speed decreases more rapidly; at close distances, LiDAR can be switched for positioning to avoid landing obstacles caused by uneven ground, and the landing speed decrease trend can be dynamically adjusted according to the target UAV's current landing speed and relative position.

[0046] The aforementioned UAV landing control method addresses two key issues. First, by acquiring the relative positions of the UAV and the landing platform in real time and determining the target landing sequence based on an improved banker's algorithm, it effectively solves the resource contention problem when multiple UAVs land simultaneously. This ensures that UAVs land in the optimal order, reducing the likelihood of collisions between UAVs in the air or on the ground. Second, during the landing process, the transition distance thresholds for each stage are adjusted based on the real-time environmental and positioning information of the target UAV. This makes the landing process more closely match the real-time environment, reducing landing failures caused by sudden environmental changes, meeting the requirements of mobile missions, and improving the reliability of field operations.

[0047] In some embodiments, the real-time acquisition of the drone's location information further includes: Set the initial state vector of the UAV; wherein the initial state vector includes the coordinates, velocity and acceleration of the UAV; Based on the inertial navigation data acquired by the microelectromechanical system, the predicted state and covariance matrix corresponding to the initial state vector are determined by combining the kinematic model. The point cloud data is determined as the primary observation, and the first positioning information and / or the second positioning information are determined as auxiliary observations. The predicted state is fused and updated with the main observation and / or the auxiliary observation based on the extended Kalman filter to obtain the positioning information.

[0048] In this embodiment, the Extended Kalman Filter (EKF) is a state estimation method for nonlinear systems, employing a two-stage architecture of prediction and update to iteratively optimize the estimated value.

[0049] In this application embodiment, the micro-electro-mechanical system (MEMS) may include, but is not limited to, accelerometers, gyroscopes, IMUs, communication interfaces, and signal processing circuits.

[0050] In some embodiments, to address the failure of some sensors, multi-source data fusion can be achieved by dynamically adjusting the corresponding weights of the sensors and combining this with EKF. The initial state vector of the UAV is set as X = [X, Y, Z, v]. x ,v y ,v z ,a x ,a y ,a z ,] T Where X, Y, and Z indicate the coordinates of the UAV; v x v y v z Indicates the speed of the drone; a x a y a z It indicates the acceleration of the UAV; it can predict the state based on MEMS inertial navigation data (including secondary positioning information) and calculate the predicted state through a kinematic model. The covariance matrix P k | k-1 Point cloud data acquired by lidar is used as the primary observation value. Due to the strong anti-interference capability and high data reliability of lidar, it can be assigned a higher weight. If the visual sensor is functioning correctly, the first positioning information acquired by the visual sensor is used as the auxiliary observation value, assigned a lower weight. The Kalman gain K is calculated using the observation equation. k The predicted state is fused with the main observation and / or auxiliary observation to obtain the positioning information. For example, Among them, Z k The indicator observation vector is determined based on the main observation and / or auxiliary observation and their corresponding weights; H indicates the observation matrix.

[0051] In this embodiment, by using the first positioning information and / or the second positioning information as auxiliary observations, a global coordinate reference is provided, which complements the local high-precision positioning of the point cloud. Furthermore, the EKF fusion logic can dynamically allocate weights based on the reliability of the observations (e.g., prioritizing the trust of the point cloud when it is valid, and automatically increasing the weight of the auxiliary observations when the point cloud fails), enabling the system to maintain positioning continuity in various complex environments, enhancing environmental adaptability and anti-interference capabilities, improving positioning accuracy, and reducing the impact of some sensor failures.

[0052] In some embodiments, determining the target landing order of each drone based on an improved banker's algorithm, when the relative positions of the plurality of drones meet the expected conditions, includes: Obtain the remaining battery power, mission urgency factor, and straight-line distance to the drone take-off and landing platform for each drone; Based on the remaining battery power, the mission urgency factor, and the straight-line distance, the overall priority of each drone is determined; The target landing sequence of the UAV is determined based on the number of idle platforms in the current UAV take-off and landing platform and the overall priority.

[0053] For example, one way to determine the overall priority of drones is as follows: Among them, P i Indicates the overall priority of the i-th UAV; d i The value r indicates the straight-line distance from the i-th UAV to the UAV take-off and landing platform. i Indicates the remaining battery power of the i-th drone; k i The urgency coefficient of the i-th drone is indicated; w1, w2, and w3 indicate weight coefficients, which can be dynamically adjusted according to the scenario.

[0054] In one embodiment, the number of idle platforms and the set of drones waiting to land in the drone take-off and landing platform are determined. The drones in the set are sorted according to a comprehensive priority. Based on the sorted drone queue, the following checks are performed sequentially: for example, if the drone queue includes drones 1 to n, for drone i, it can be determined whether drone i+1 in the queue has enough remaining battery power to wait for drone i to land, charge, and take off; where n and i are positive integers. If drone i does not meet the above rules, a queue adjustment is triggered (e.g., moving it to the end of the queue), and the comprehensive priority of drone i is recalculated; this process continues until the landing order of all drones in the set has been determined, resulting in the target landing order.

[0055] In this embodiment, by using the remaining battery power as the core reference factor for priority, drones with critical battery power can be guaranteed to enter first, reducing safety accidents caused by battery depletion; the introduction of a mission urgency factor allows drones with critical missions to obtain higher priority, ensuring mission timeliness; and it can also balance efficiency and resources, reducing platform congestion or platform idleness.

[0056] In some embodiments, adjusting the transition distance thresholds for each stage of the landing process based on the environmental information and positioning information of the target UAV during the landing process to obtain the target threshold includes: The environmental coefficient is determined based on the temperature, humidity, wind speed and / or wind direction in the environmental information. Based on the real-time load in the location information, determine the load coefficient; Based on the environmental coefficient and / or the load coefficient, the transition distance threshold is adjusted to obtain the target threshold.

[0057] In this embodiment, the transition distance threshold can indicate the triggering condition for moving from the current stage to the next stage. For example, a distance of less than 100m from the UAV take-off and landing platform can be understood as the transition distance threshold for moving from the mid-range stage to the close-range stage.

[0058] In one embodiment, the UAV take-off and landing platform can determine corresponding environmental coefficients based on the degree of influence of various environmental information on the UAV; for example, if the environmental information is wind speed, the higher the wind speed, the worse the aerodynamic stability of the UAV, requiring a longer positioning duration; one way to determine the wind speed influence coefficient is: k v =1+0.1×max(0,v-3); where v indicates wind speed.

[0059] In one embodiment, the positioning information may include the real-time coordinates and real-time payload of the UAV; based on the real-time coordinates, the current stage of the target UAV can be determined; the greater the payload, the greater the UAV's inertia and the slower its braking response, requiring an increased safety distance threshold. Based on the real-time payload, the transition distance threshold for the current stage can be adjusted. One method for determining the payload coefficient is: k m =1+0.5×max(0,m-50%); where m indicates the load, and m=100% is the full load.

[0060] In one embodiment, the stronger the environmental interference, the more lenient the transition distance threshold, allowing the UAV to enter the next stage in a more stable state. For the transition distance threshold of each stage, it can be dynamically adjusted based on the product of the initial threshold and the environmental coefficient / load coefficient to obtain the updated target threshold for each stage.

[0061] In this embodiment, the transition conditions of each stage can be dynamically "stretched" or "compressed" according to the real-time environment. For example, when the wind speed is high, the effective distance of satellite positioning can be extended, and when the load is heavy, the speed threshold can be reduced to reduce the inertial effect. Ultimately, this ensures that the UAV can always complete the stage switching in a relatively stable state in complex environments, and achieve a smooth transition in the landing process.

[0062] In some embodiments, the method further includes: While controlling the target drone to complete its landing, obtain the current battery level of the target drone; If the current battery level is less than the charging threshold, the target drone will be charged. The remaining charging time is determined based on the current charging power of the drone take-off and landing platform and the current battery level. If the charging time is less than the target threshold, detect the environmental information of the target drone. When the target drone has finished charging and the environmental information meets the takeoff conditions, a departure command is sent to the target drone.

[0063] In one embodiment, after the drone completes landing, the drone landing platform monitors the drone's current battery level in real time. When the current battery level is detected to be lower than a preset charging threshold, the platform automatically provides wireless charging for the drone or prompts staff to connect the drone to a charging device. During the charging process, the drone landing platform dynamically calculates the remaining charging time based on the current charging power and the drone's real-time battery level. When the remaining charging time is less than a target threshold (e.g., 1 minute), the platform simultaneously initiates the detection of the drone's environmental information (including surrounding obstacles, weather conditions, airspace interference, etc.). After the drone is fully charged, the platform immediately reminds staff to disconnect the charging cable. Once it is confirmed that the charging cable has been removed and the environmental information meets the takeoff safety conditions (e.g., no obstacles, wind speed ≤ 5 m / s), the platform issues a departure command to the drone. After receiving the command, the drone first activates an audible and visual warning, and then performs the takeoff operation. During takeoff, the platform continuously tracks the drone's position in real time to ensure its safe departure.

[0064] In this embodiment, the platform automatically monitors the battery level and triggers charging reminders, reducing the occurrence of drones being stranded due to low battery levels caused by human forgetting to charge. Furthermore, it can dynamically calculate the remaining charging time and trigger environmental detection in advance when the charging is nearing completion, achieving a seamless connection between the "charging-departure" process. This reduces time wasted due to human waiting or process disconnection, and also standardizes operations, reducing the randomness of human judgment.

[0065] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0066] Based on the same inventive concept, this application also provides a drone landing control device for implementing the drone landing control method described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations in one or more drone landing control device embodiments provided below can be found in the limitations of the drone landing control method described above, and will not be repeated here.

[0067] In one embodiment, such as Figure 4 As shown, the device, applied to a drone take-off and landing platform, includes: The acquisition module 10 is used to acquire the location information of the drone in real time during the landing process of the drone; The positioning module 20 is used to determine the relative position of the UAV and the UAV take-off and landing platform based on the positioning information; the determination module 30 is used to determine the target landing order of each UAV based on the improved Banker's Algorithm when the relative positions of multiple UAVs meet the expected conditions. The adjustment module 40 is used to control each UAV to land based on the target landing sequence, and to adjust the transition distance threshold of each stage of the landing process based on the environmental information and the positioning information of the target UAV currently landing, so as to obtain the target threshold. The control module 50 is used to switch control modes to control the target drone to complete the landing based on the target threshold and the relative position of the target drone.

[0068] In one embodiment, the acquisition module 10 is configured to perform at least one of the following steps: The first positioning information of the UAV is obtained based on a visual sensor; The second positioning information of the UAV is obtained based on the position sensor; Point cloud data of the UAV is acquired using lidar.

[0069] In one embodiment, the acquisition module 10 is configured to perform the following steps: Set the initial state vector of the UAV; wherein the initial state vector includes the coordinates, velocity and acceleration of the UAV; Based on the inertial navigation data acquired by the microelectromechanical system, the predicted state and covariance matrix corresponding to the initial state vector are determined by combining the kinematic model. The point cloud data is determined as the primary observation, and the first positioning information and / or the second positioning information are determined as auxiliary observations. The predicted state is fused and updated with the main observation and / or the auxiliary observation based on the extended Kalman filter to obtain the positioning information.

[0070] In one embodiment, the positioning module 20 is configured to perform the following steps: In the event of failure of the visual sensor and / or the position sensor, the point cloud data of the UAV is registered with a pre-stored 3D point cloud map to determine the spatial transformation matrix between the two. Based on the spatial transformation matrix, the point cloud data of the UAV is transformed into the target coordinate system corresponding to the three-dimensional point cloud map to obtain the first position; The relative position is determined based on the first position and the second position of the UAV take-off and landing platform in the target coordinate system.

[0071] In one embodiment, the determining module 30 is configured to perform the following steps: Obtain the remaining battery power, mission urgency factor, and straight-line distance to the drone take-off and landing platform for each drone; Based on the remaining battery power, the mission urgency factor, and the straight-line distance, the overall priority of each drone is determined; The target landing sequence of the UAV is determined based on the number of idle platforms in the current UAV take-off and landing platform and the overall priority.

[0072] In one embodiment, the adjustment module 40 is configured to perform the following steps: The environmental coefficient is determined based on the temperature, humidity, wind speed and / or wind direction in the environmental information. Based on the real-time load in the location information, determine the load coefficient; Based on the environmental coefficient and / or the load coefficient, the transition distance threshold is adjusted to obtain the target threshold.

[0073] In one embodiment, the apparatus further includes: The acquisition module 10 is used to acquire the current battery level of the target drone when the target drone is controlled to complete the landing. A charging module is used to charge the target drone when the current power level is less than a charging threshold. The prediction module is used to determine the remaining charging time based on the current charging power of the UAV take-off and landing platform and the current battery level. The detection module is used to detect the environmental information of the target drone when the charging time is less than the target threshold. The control module 50 is used to send a departure command to the target drone when the target drone has finished charging and the environmental information meets the takeoff conditions.

[0074] Each module in the aforementioned UAV landing control device can be implemented entirely or partially through software, hardware, or a combination thereof. Each module can be embedded in the processor of the electronic device in hardware form or independent of the processor, or it can be stored in the memory of the electronic device in software form, so that the processor can call and execute the corresponding operations of each module.

[0075] In one embodiment, an edge box in a drone take-off and landing platform is provided, the internal structure of which can be shown in the following diagram. Figure 5 As shown, the edge box includes a processor, memory, communication interface, and input device connected via a method bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores operating methods and computer programs. The internal memory provides an environment for the operation of the operating methods and computer programs stored in the non-volatile storage medium. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it implements a drone landing control method.

[0076] Those skilled in the art will understand that Figure 4 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the control system to which the present application is applied. A specific control system may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0077] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps in the above method embodiments.

[0078] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps performed by the processor of the control system of any of the above.

[0079] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0080] 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 a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, compilable logic units, quantum computing-based drone landing control logic units, etc., and are not limited to these.

[0081] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0082] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for controlling the landing of an unmanned aerial vehicle (UAV), characterized in that, Applied to unmanned aerial vehicle (UAV) take-off and landing platforms, the method includes: During the descent of the drone, the drone's location information is acquired in real time; Based on the positioning information, the relative position of the UAV and the UAV take-off and landing platform is determined; Given that the relative positions of the multiple UAVs meet the expected conditions, the target landing order of each UAV is determined based on the improved Banker's Algorithm. Based on the target landing sequence, each UAV is controlled to land, and during the landing process, the transition distance threshold of each stage is adjusted based on the environmental information of the target UAV currently landing and the positioning information to obtain the target threshold. Based on the target threshold and the relative position of the target UAV, the control mode is switched to control the target UAV to complete the landing.

2. The method according to claim 1, characterized in that, The real-time acquisition of the drone's location information includes at least one of the following: The first positioning information of the UAV is obtained based on a visual sensor; The second positioning information of the UAV is obtained based on the position sensor; Point cloud data of the UAV is acquired using LiDAR.

3. The method according to claim 2, characterized in that, The real-time acquisition of the drone's location information also includes: Set the initial state vector of the UAV; wherein the initial state vector includes the coordinates, velocity and acceleration of the UAV; Based on the inertial navigation data acquired by the microelectromechanical system, the predicted state and covariance matrix corresponding to the initial state vector are determined by combining the kinematic model. The point cloud data is determined as the primary observation, and the first positioning information and / or the second positioning information are determined as auxiliary observations. The predicted state is fused and updated with the main observation and / or the auxiliary observation based on the extended Kalman filter to obtain the positioning information.

4. The method according to claim 2, characterized in that, Determining the relative position of the UAV and the UAV take-off and landing platform based on the positioning information includes: In the event of failure of the visual sensor and / or the position sensor, the point cloud data of the UAV is registered with a pre-stored 3D point cloud map to determine the spatial transformation matrix between the two. Based on the spatial transformation matrix, the point cloud data of the UAV is transformed into the target coordinate system corresponding to the three-dimensional point cloud map to obtain the first position; The relative position is determined based on the first position and the second position of the UAV take-off and landing platform in the target coordinate system.

5. The method according to claim 1, characterized in that, When the relative positions of the multiple drones meet the expected conditions, the target landing order of each drone is determined based on the improved Banker's Algorithm, including: Obtain the remaining battery power, mission urgency factor, and straight-line distance to the drone take-off and landing platform for each drone; Based on the remaining battery power, the mission urgency factor, and the straight-line distance, the overall priority of each drone is determined; The target landing sequence of the UAV is determined based on the number of idle platforms in the current UAV take-off and landing platform and the overall priority.

6. The method according to claim 3, characterized in that, The step of adjusting the transition distance thresholds for each stage of the landing process based on the environmental information and positioning information of the target UAV during the landing process to obtain the target threshold includes: The environmental coefficient is determined based on the temperature, humidity, wind speed and / or wind direction in the environmental information. Based on the real-time load in the location information, determine the load coefficient; Based on the environmental coefficient and / or the load coefficient, the transition distance threshold is adjusted to obtain the target threshold.

7. The method according to claim 1, characterized in that, The method further includes: While controlling the target drone to complete its landing, obtain the current battery level of the target drone; If the current battery level is less than the charging threshold, the target drone will be charged. The remaining charging time is determined based on the current charging power of the drone take-off and landing platform and the current battery level. If the charging time is less than the target threshold, detect the environmental information of the target drone. When the target drone has finished charging and the environmental information meets the takeoff conditions, a departure command is sent to the target drone.

8. A drone landing control device, characterized in that, The device, applied to unmanned aerial vehicle (UAV) take-off and landing platforms, includes: The acquisition module is used to acquire the drone's location information in real time during the drone's landing process; A positioning module is used to determine the relative position of the UAV and the UAV take-off and landing platform based on the positioning information; A determination module is used to determine the target landing order of each UAV based on an improved banker algorithm, provided that the relative positions of the multiple UAVs meet the expected conditions. An adjustment module is used to control each UAV to land based on the target landing sequence, and to adjust the transition distance threshold of each stage of the landing process based on the environmental information and positioning information of the target UAV currently landing, so as to obtain the target threshold. The control module is used to switch control modes to control the target drone to complete the landing based on the target threshold and the relative position of the target drone.

9. A drone take-off and landing platform, characterized in that, The UAV take-off and landing platform includes a support platform, power generation components, a power supply system, and a control system; wherein... The support platform includes a platform body and adjustable legs; the platform body is used to support the UAV; the adjustable legs are used to adjust the height of the UAV take-off and landing platform; the adjustable legs are connected to the platform body by bolts. The power generation component includes a solar panel, a battery, and a control circuit; the solar panel is fixed above the support platform; the battery is fixed below the support platform. The control system includes an edge box and a light-assisted landing assembly; the light-assisted landing assembly is used to assist in guiding the drone to land; the light-assisted landing assembly is located above the drone take-off and landing platform; the edge box includes a processor and a memory; wherein the memory is used to store a computer program; the processor is configured to, when executing the computer program, implement the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it performs the steps of the method according to any one of claims 1 to 7.

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