Disconnected unmanned aerial vehicle recovery system

Abstract

The invention relates to a lost unmanned aerial vehicle recovery system. The lost unmanned aerial vehicle recovery system comprises a search unmanned aerial vehicle group, a recovery unmanned aerial vehicle and a controller, wherein the search unmanned aerial vehicle group and the recovery unmanned aerial vehicle are both connected with the controller; the controller is used for generating a search path of the search unmanned aerial vehicle group according to a flight log of the lost unmanned aerial vehicle and environment data of the lost unmanned aerial vehicle after receiving a lost signal fed back by the lost unmanned aerial vehicle; the search unmanned aerial vehicle group is used for carrying out search according to the search path, determining the communication loss position of the communication loss unmanned aerial vehicle and sending the communication loss position to the controller; the controller is also used for determining a recovery path of the recovery unmanned aerial vehicle based on the received lost communication position; and the recovery unmanned aerial vehicle is used for recovering the lost unmanned aerial vehicle according to the recovery path. By adopting the method, the lost unmanned aerial vehicle can be quickly and accurately found.

Description

Technical Field

[0001] This application relates to the field of drone recovery technology, and in particular to a system for recovering lost drones. Background Technology

[0002] In complex environments or during long-distance flight missions, drones often lose contact with the ground control center due to reasons such as signal interruption, battery depletion, hardware failure, or environmental interference. Once lost, the drone may crash or remain stranded in inaccessible areas.

[0003] In related technologies, to recover a missing drone, searches can be conducted through manual search, autonomous return of a single drone, and ground base station-assisted positioning.

[0004] However, manual search is inefficient, and the autonomous return of a single drone is limited by communication recovery capabilities. The coverage of ground base station positioning is insufficient, making it impossible to quickly and accurately locate the lost drone. Summary of the Invention

[0005] Therefore, it is necessary to provide a lost drone recovery system that can quickly and accurately retrieve lost drones, addressing the aforementioned technical issues.

[0006] In a first aspect, this application provides a lost drone recovery system, which includes: a search drone swarm, a recovery drone, and a controller, wherein the search drone swarm and the recovery drone are both connected to the controller;

[0007] The controller is used to generate a search path for the search drone swarm based on the flight log of the missing drone and the environmental data of the missing drone after receiving the missing signal from the missing drone.

[0008] The search drone swarm is used to search according to the search path, determine the location where the missing drone went missing, and send the location to the controller.

[0009] The controller is also configured to determine the recovery path of the recovered drone based on the received location of the lost contact;

[0010] The recovery drone is used to recover the lost drone according to the recovery route.

[0011] In one embodiment, the controller is further configured to construct a three-dimensional search heatmap based on the flight logs of the missing UAV, meteorological data in the environmental data, and terrain elevation model; divide the three-dimensional search heatmap into grid areas to determine the search path of any one of the search UAVs in the search UAV swarm; and determine the recovery path of the recovery UAV based on the location of the missing UAV.

[0012] In one embodiment, each search drone in the search drone swarm includes a multispectral acquisition device and a search processor;

[0013] The multispectral acquisition device is used to acquire image data of the target area during the flight of the search drone;

[0014] The search processor is used to analyze the image data collected by each search drone to determine the location where the missing drone went missing.

[0015] In one embodiment, the multispectral acquisition device includes an infrared camera and a visible light camera.

[0016] The infrared camera is used to collect infrared radiation data of the target area;

[0017] The visible light camera is used to capture visible light images of the target area.

[0018] In one embodiment, the search processor is further configured to input the infrared radiation data of the target area and the visible light image into a preset location determination model, analyze them using a sliding window mechanism, obtain location information that matches the features of the missing drone, and perform error correction on the location information to obtain the missing location of the missing drone.

[0019] In one embodiment, the recovered drone includes an obstacle avoidance sensor, a recovery processor, and a device for capturing the lost drone;

[0020] The obstacle avoidance sensor is used to detect the distance between the obstacle and the obstacle avoidance sensor;

[0021] The recovery processor is used to adjust the flight attitude of the recovery drone according to the distance; and, when the distance between the lost drone and the obstacle avoidance sensor is less than a preset threshold, to adjust the lost drone grasping device according to the size and shape of the lost drone.

[0022] The missing drone retrieval device is used to retrieve and secure the missing drone.

[0023] In one embodiment, the recovered drone also includes a pressure sensor;

[0024] The pressure sensor is used to collect the clamping pressure of the missing drone during the process of the missing drone capture device;

[0025] The recovery processor is used to adjust the gripping device for the lost drone; the adjusted clamping pressure meets preset conditions.

[0026] In one embodiment, the recovery processor is further configured to detect the attitude data of the recovery drone holding the lost drone; if the attitude data is within a preset safety threshold range, determine that the stability verification has passed; if the attitude data is not within the preset safety threshold range, determine that the stability verification has failed.

[0027] In one embodiment, the missing drone includes a communication status monitor, a flight parameter acquisition unit, and a missing drone processor;

[0028] The communication status monitor is used to continuously detect the communication link status between the lost drone and the control center before it lost contact;

[0029] The flight parameter acquisition device is used to collect flight parameter information of the missing drone before it lost contact;

[0030] The disconnection processor is used to send a disconnection signal to the controller when the communication link is interrupted and / or the flight parameter information is abnormal.

[0031] In one embodiment, the disconnection processor is further configured to send a disconnection signal to the controller when the communication link is in a communication interruption state and the battery level in the flight parameter information is within the normal power range; and not to send a disconnection signal to the controller when the communication link is in a communication interruption state and the battery level in the flight parameter information is not within the normal power range.

[0032] The aforementioned missing drone recovery system includes: a search drone swarm, a recovery drone, and a controller. The search drone swarm and the recovery drone are both connected to the controller. The controller, upon receiving a loss signal from a missing drone, generates a search path for the search drone swarm based on the missing drone's flight log and environmental data. The search drone swarm searches according to the search path to determine the missing drone's location and sends this location to the controller. The controller also determines a recovery path for the recovery drone based on the received location. The recovery drone retrieves the missing drone according to the recovery path. During the recovery process, the coordinated operation of the search drone swarm, recovery drone, and controller ensures that the search and recovery process is unaffected by the environment. Therefore, regardless of the complexity of the environment, the controller can quickly and accurately locate the missing drone by controlling a distributed drone swarm for parallel searching. Then, by controlling the recovery drone to target and retrieve the missing drone based on its location, the missing drone can be found quickly and accurately. Attached Figure Description

[0033] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0034] Figure 1 This is a first environmental diagram of a lost drone recovery system in one embodiment;

[0035] Figure 2 This is a second environmental diagram of a lost drone recovery system in one embodiment;

[0036] Figure 3 This is a third environment diagram of a lost drone recovery system in one embodiment;

[0037] Figure 4 This is a fourth environmental diagram of a lost drone recovery system in one embodiment. Detailed Implementation

[0038] 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.

[0039] Before providing a detailed description of the embodiments of this application, a detailed description of the background technology of this application will be given first.

[0040] With the rapid development of drone technology, drones have been widely used in military reconnaissance, logistics delivery, environmental monitoring, and disaster relief. However, in complex environments (such as densely populated urban areas, mountainous regions, and areas with electromagnetic interference) or during long-distance flight missions, drones often lose contact with the ground control center due to signal interruption, battery depletion, hardware failure, or environmental interference. Once lost, the drone may crash or remain stranded in inaccessible areas, leading to equipment loss, data leakage, or even safety incidents.

[0041] In related technologies, the recovery of missing drones mainly relies on three methods: manual search, autonomous return of a single drone, and ground base station-assisted positioning. Manual search involves ground personnel searching visually or with handheld devices based on the last known location. This method is inefficient and limited by terrain and weather conditions, making it highly impractical in complex environments. Autonomous return of a single drone relies on a pre-set return procedure or an emergency backup communication link, which cannot be executed if communication is completely interrupted or positioning fails. Ground base station-assisted positioning enhances signal coverage or deploys multiple base stations for triangulation, but its coverage is limited and it struggles to cope with signal obstruction in dynamic environments. For example, in densely populated urban areas, missing drones may be obstructed by tall buildings, making it difficult for related technologies to quickly locate the target position. Furthermore, the recovery process faces interference from dynamic obstacles, increasing the risk of losing the drone.

[0042] To address the aforementioned problems, this application provides a lost drone recovery system. This system utilizes a distributed drone swarm for searching and then uses recovery drones to retrieve the lost drone, enabling rapid and accurate recovery. The technical solution of this application will be described in detail below.

[0043] In one exemplary embodiment, such as Figure 1 As shown, a lost drone recovery system is provided, which includes: a search drone swarm, a recovery drone, and a controller, wherein the search drone swarm and the recovery drone are all connected to the controller;

[0044] The controller is used to generate a search path for the search drone swarm based on the flight log of the missing drone and the environmental data of the missing drone after receiving the missing signal from the missing drone.

[0045] The search drone swarm is used to search according to the search path, determine the location where the missing drone went missing, and send the location to the controller.

[0046] The controller is also configured to determine the recovery path of the recovered drone based on the received location of the lost contact;

[0047] The recovery drone is used to recover the lost drone according to the recovery route.

[0048] A search drone swarm refers to a group of multiple drones with autonomous navigation capabilities, primarily used to improve area coverage efficiency through parallel searches. For example, a quadcopter drone equipped with a Differential Positioning System (GPS) and a multispectral camera can be used. A search drone swarm can include at least three drones with different sensor modules: the first search drone is equipped with an infrared thermal imager and lidar; the second search drone is equipped with a high-resolution optical camera and a gas sensor; and the third search drone integrates a BeiDou positioning module and an acoustic positioning device.

[0049] Recoverable drones refer to drone equipment specifically designed for physical capture and transport, primarily used to safely retrieve lost drones after locating them and return them to their origin. For example, a recoverable drone may employ a quadcopter with redundant power design, featuring an electromagnetic adsorption recovery claw and a foldable buffer net on the underside of the fuselage. Pressure sensors are embedded in the edge of the net, and visual navigation cameras are equipped on both sides of the fuselage, providing a 360° field of view covering the recovery claw's operating area.

[0050] The controller can be integrated into a computer device. The controller is connected to the missing drone, the search drone swarm, and the recovery drone. It is mainly used to receive the missing drone's signal, control the search drone swarm to search, and control the recovery drone to recover the missing drone.

[0051] A missing drone can be any type of drone. If the drone is not actually out of contact and is in flight, it will determine if there are any anomalies in the flight process. When an anomaly is detected, it will generate a loss-of-contact signal and remotely send it to the controller. This signal may carry information such as the drone's last known location before losing contact, its flight log, and environmental data collected during the flight. The flight log may contain flight data from a period prior to losing contact (about half an hour before), such as latitude, longitude, altitude, speed, and heading angle. Environmental data may include meteorological data and terrain elevation models.

[0052] After receiving the missing drone's signal, the controller can analyze the signal to obtain the drone's flight log and environmental data. Using a path generation algorithm, a grid-like search area is established starting from the predicted location. The weight of each grid node is determined by terrain complexity, signal obstruction probability, and historical missing drone data. Path planning prioritizes covering high-weight areas, resulting in a search path for the entire drone swarm, which includes the search path of each individual drone. For example, the path generation algorithm could be an ant colony algorithm, a breadth-first search algorithm, or a depth-first search algorithm, etc.

[0053] And / or, the flight logs of the missing drone, the environmental data of the missing drone, and relevant information of the search drone swarm can be input into a preset search path division model. Through the analysis of the search path division model, the search path of the search drone swarm can be determined.

[0054] After acquiring the search path of the search drone swarm, the controller sends the search path of each search drone within the swarm to the corresponding search drone. Each search drone, upon receiving its search path, executes the search task according to that path. When any search drone locates the missing drone according to its search path, it reports the missing drone's location to the controller.

[0055] After receiving the location of the missing drone, the controller plans a retrieval path based on the location of the recoverable drone and the location of the missing drone. It also determines the capture parameters based on the size and shape of the missing drone. The controller then sends the retrieval path and capture parameters to the recoverable drone. Following the retrieval path, the recoverable drone flies to the location of the missing drone, adjusts its position according to the capture parameters, and captures the missing drone. After capture, it carries the missing drone to a preset location.

[0056] The aforementioned missing drone recovery system includes: a search drone swarm, recovery drones, and a controller. Both the search drone swarm and the recovery drones are connected to the controller. The controller, upon receiving a loss signal from a missing drone, generates a search path for the search drone swarm based on the missing drone's flight log and environmental data. The search drone swarm searches according to the search path to determine the missing drone's location and sends this location to the controller. The controller also determines a recovery path for the recovery drones based on the received location. The recovery drones retrieve the missing drones according to the recovery path. During the recovery process, the coordinated operation of the search drone swarm, recovery drones, and controller ensures that the search and recovery process is unaffected by the environment. Therefore, regardless of the complexity of the environment, the controller can quickly and accurately locate the missing drone by controlling a distributed drone swarm for parallel searching. The recovery drones then selectively retrieve the missing drones based on their location, thus enabling rapid and accurate recovery.

[0057] The following is a detailed description of the specific content of the controller. The controller is also used to construct a three-dimensional search heat map based on the flight log of the missing UAV, the meteorological data in the environmental data, and the terrain elevation model; and to divide the three-dimensional search heat map into grid areas to determine the search path of any one of the search UAVs in the search UAV swarm; and to determine the recovery path of the recovery UAV based on the location of the missing UAV.

[0058] Among these, the meteorological data in the environmental data can be real-time monitored data, such as wind speed, wind direction, air pressure, and precipitation. The terrain elevation model can be a three-dimensional terrain model generated according to a preset scale, including ground vegetation height and building outlines.

[0059] In this embodiment, the controller can perform a three-dimensional model of the missing UAV's flight process based on the UAV's flight log, meteorological data from the environmental data, and terrain elevation model, to obtain a three-dimensional search heatmap that matches the flight process. This three-dimensional search heatmap corresponds to the flight environment of the missing UAV.

[0060] Next, the controller can divide the 3D search heatmap into grid regions. Based on the divided grid regions and the number of search drones, it determines the flight path of each search drone during the search process, i.e., the search path for the missing drone. It should be noted that the controller can also dynamically adjust the number of search drones in the swarm based on the complexity of the 3D search heatmap.

[0061] Furthermore, once the location of the missing drone is determined by searching the drone swarm, the controller can also determine the flight path of the recovery drone during the recovery process, i.e., the recovery path of the missing drone, based on the location of the missing drone.

[0062] The controller in the aforementioned missing drone recovery system is also used to construct a three-dimensional search heatmap based on the missing drone's flight logs, meteorological data from the environmental data, and a terrain elevation model; to divide the three-dimensional search heatmap into grid areas to determine the search path of any one of the search drones in the search drone swarm; and to determine the recovery path of the recovery drone based on its location where it went missing. By integrating the flight logs, meteorological data, and terrain elevation model, a three-dimensional search heatmap adapted to the environment of the missing drone can be constructed. Dividing the three-dimensional search heatmap into grid areas allows for accurate analysis of the search path of any one of the search drones in the swarm. By integrating the missing drone's flight logs, meteorological data, and terrain elevation model to construct a three-dimensional search heatmap, the possible locations of the missing drone can be accurately predicted. Flight log analysis reveals its historical movement trends, meteorological data simulates airflow effects, and the terrain elevation model avoids dangerous terrain; the combination of these three elements makes the search heatmap more closely resemble the actual scenario. Based on this grid division to determine the search path, the search drone swarm can quickly focus on high-probability areas, significantly shorten the search time, and improve the search efficiency for missing drones.

[0063] The following example illustrates the specific details of the search process involving a swarm of search drones. Figure 2 As shown, each of the search drones in the aforementioned search drone swarm includes a multispectral acquisition device and a search processor;

[0064] The multispectral acquisition device is used to acquire image data of the target area during the flight of the search drone;

[0065] The search processor is used to analyze the image data collected by each search drone to determine the location where the missing drone went missing.

[0066] A multispectral acquisition device refers to a device capable of simultaneously acquiring information about a target area across multiple spectral bands. In one embodiment, the multispectral acquisition device includes an infrared camera and a visible light camera.

[0067] The infrared camera is used to collect infrared radiation data of the target area;

[0068] The visible light camera is used to capture visible light images of the target area.

[0069] In the embodiments of this application, for any search drone, the search drone can collect infrared radiation data and visible light images of the target area in real time while flying along the search path.

[0070] After receiving image data from the multispectral acquisition device, the search processor can first use image denoising algorithms to denoise the image data, avoiding noise interference in the subsequent determination of the missing drone's location. Since different spectra have varying penetration capabilities for obstacles, the search processor can input infrared radiation data and visible light images into a preset location determination model. A sliding window mechanism is used to score the confidence level of each small region within the target area. Based on the confidence level scores, small regions matching the characteristic features of the missing drone are identified, and the location of these small regions is taken as the missing drone's location. Alternatively, the search processor can also use target recognition algorithms to analyze the acquired image data to determine the missing drone's location.

[0071] It should be noted that, since each search drone independently acquires images and determines the location of the lost contact, and multiple search drones are acquiring images and determining the location of the lost contact in real time, once any one search drone has determined the location of the lost drone, all search drones can cease their search and return according to the search path.

[0072] Each of the aforementioned search drones in the swarm includes a multispectral acquisition device and a search processor. The multispectral acquisition device is used to acquire image data of the target area during the flight of the search drone. The search processor is used to analyze the image data acquired by each search drone to determine the location where the missing drone went missing. During the flight of the search drone, the multispectral acquisition device can utilize different wavelengths to determine the differences in the reflectivity of different objects, clearly capturing image data that may be related to the missing drone. Further analysis of the image data can then accurately determine the location where the missing drone went missing.

[0073] In scenarios with vegetation obstruction and / or drastic changes in lighting, the search processor can also correct the acquired location of the missing drone using multispectral data and use the corrected location as the missing drone's location. In an exemplary embodiment, the search processor is further configured to input the infrared radiation data of the target area and the visible light image into a preset location determination model, analyze them using a sliding window mechanism, obtain location information matching the features of the missing drone, and correct the location information for errors to obtain the missing drone's location.

[0074] The pre-set location determination model is trained on a large amount of data, including drone features and various environmental scenarios. Taking the convolutional neural network model as an example, it internally consists of multiple convolutional layers, pooling layers, and fully connected layers. During the model training phase, infrared and visible light image samples of the missing drone from different angles and under different environments are used for learning, enabling the location determination model to deeply understand the characteristics of the missing drone in different wavelengths.

[0075] In this embodiment, the search process can use the infrared radiation data and the visible light image of the target area as input signals to a preset location determination model. A fixed-size window slides across the infrared radiation data and the visible light image at a certain step size. Each time the window moves, the location determination model analyzes the data within it. After the location determination model analyzes the target area data through the sliding window, it finally selects the area with the highest matching degree with the characteristics of the missing UAV and obtains the corresponding location information.

[0076] Due to obstructions and / or sudden changes in ambient light, the initial location information obtained for the missing drone often contains some errors, thus requiring error correction. The search processor can correct the location information based on image data acquired by the multispectral acquisition device to obtain the missing drone's location.

[0077] The search processor is further configured to input the infrared radiation data and visible light image of the target area into a preset location determination model, analyze them using a sliding window mechanism, and obtain location information matching the features of the missing drone; then, it performs error correction on the location information to obtain the missing location of the drone. The search processor inputs the infrared radiation data and visible light image of the target area into the preset location determination model and analyzes them using a sliding window mechanism. By performing error correction on the obtained location information, it can avoid the influence of obstruction of the missing drone and / or sudden changes in ambient light on the location information, thus accurately obtaining the missing location of the drone.

[0078] The above embodiments are all introductions to searching for drone swarms. The following embodiment will describe the specific details of drone recovery, such as... Figure 3 As shown, the recovered drone includes an obstacle avoidance sensor, a recovery processor, and a device for capturing the lost drone;

[0079] The obstacle avoidance sensor is used to detect the distance between the obstacle and the obstacle avoidance sensor;

[0080] The recovery processor is used to adjust the flight attitude of the recovery drone according to the distance; and, when the distance between the lost drone and the obstacle avoidance sensor is less than a preset threshold, to adjust the lost drone grasping device according to the size and shape of the lost drone.

[0081] The missing drone capture device is used to capture and secure the missing drone.

[0082] Obstacle avoidance sensors are used to detect the distance between the recovered drone and obstacles; for example, obstacle avoidance sensors can be lidar, millimeter-wave radar, etc. The device for capturing the lost drone can be a multi-degree-of-freedom robotic arm combined with a variable gripper.

[0083] In this embodiment, during the flight of the recovery drone along the recovery path, the obstacle avoidance sensor configured on the recovery drone can obtain the distance between the obstacle and the recovery drone in real time and transmit the distance to the recovery processor. After receiving the distance, the recovery processor determines whether the recovery drone may collide with the obstacle. If a collision is possible, the recovery processor needs to output an attitude adjustment command in a timely manner to adjust the attitude of the recovery drone during flight to avoid collision with the obstacle.

[0084] When the recovery drone flies along the recovery path to the vicinity of the missing drone, and the distance detected by the obstacle avoidance sensor is less than a preset threshold, it indicates that the recovery drone has moved to the location where the missing drone went missing. At this point, the recovery drone can begin to grasp the missing drone. The recovery processor can adjust the grasping device of the missing drone according to its size and shape, so that the variable gripper of the grasping device can be adapted to grasp the missing drone. In this way, the grasping device can hold the missing drone more firmly, ensuring that the missing drone will not fall off during the return flight with the recovery drone.

[0085] The aforementioned drone recovery system includes an obstacle avoidance sensor, a recovery processor, and a missing drone grasping device. The obstacle avoidance sensor detects the distance between itself and an obstacle. The recovery processor adjusts the drone's flight attitude based on the distance. Furthermore, when the distance between the missing drone and the obstacle avoidance sensor is less than a preset threshold, the drone grasping device adjusts according to the drone's size and shape. The drone grasping device grasps and secures the missing drone. The obstacle avoidance sensor on the drone can detect the distance between itself and obstacles in real time, and the recovery processor dynamically adjusts its flight attitude based on this distance, effectively avoiding obstacles in the flight path. When the distance between the missing drone and the obstacle avoidance sensor is less than a preset threshold, the recovery processor adjusts the grasping device according to the drone's size and shape, achieving adaptive grasping and ensuring the reliability of the recovery operation.

[0086] To further ensure the reliability of the drone recovery process, the grasping process of the missing drone retrieval device needs to be adjusted to ensure that the missing drone does not fall during flight. Therefore, in one embodiment, see [reference needed]. Figure 3 As shown, the recovered drone also includes a pressure sensor;

[0087] The pressure sensor is used to collect the clamping pressure of the missing drone during the process of the missing drone capture device;

[0088] The recovery processor is used to adjust the gripping device for the lost drone; the adjusted clamping pressure meets preset conditions.

[0089] Pressure sensors can be installed on the missing drone grasping device to detect the gripping pressure in real time during the grasping process. Since the grippers of the missing drone grasp it from different positions, a pressure sensor can be installed on each gripper to collect the gripping pressure at different positions on the missing drone in real time.

[0090] In this embodiment, when the missing drone retrieval device retrieves the missing drone, a pressure sensor collects the clamping pressure in real time, converts the pressure signal into an electrical signal, and transmits it to the retrieval processor via an internal data bus. The retrieval processor can be a chip with multi-core processing capabilities.

[0091] After receiving the clamping pressure from the pressure sensor, the recovery processor needs to adjust the clamping parameters of the missing drone grasping device based on the clamping pressure until the clamping pressure meets a preset condition. Assuming the preset condition is that the clamping pressure is within a preset pressure range, the recovery processor can compare the current clamping pressure with the preset pressure range. If the current clamping pressure is less than the preset lower limit, the recovery processor controls the missing drone grasping device to increase the clamping force; if the current clamping pressure is greater than the preset upper limit, the recovery processor controls the missing drone grasping device to decrease the clamping force.

[0092] The aforementioned drone retrieval device also includes a pressure sensor; the pressure sensor is used to collect the clamping pressure of the drone-grabbing device during the process of retrieval; the retrieval processor is used to adjust the drone-grabbing device; the adjusted clamping pressure meets preset conditions. During the retrieval of the drone, the clamping pressure is collected in real time by the pressure sensor, and the clamping force is adjusted promptly based on the clamping pressure, ensuring that the retrieval device can firmly hold the drone while preventing damage to the drone due to excessive clamping force.

[0093] In one embodiment, the recovery processor is further configured to detect the attitude data of the recovery drone holding the lost drone; if the attitude data is within a preset safety threshold range, determine that the stability verification has passed; if the attitude data is not within the preset safety threshold range, determine that the stability verification has failed.

[0094] In this embodiment, after adjusting the clamping pressure between the recoverable drone and the lost drone, a gyroscope on the recovery processor detects the angular offset between them, and an accelerometer detects the vibration amplitude. Both the angular offset and the vibration amplitude are used as attitude data. If the values ​​are within a preset safety threshold range for three consecutive detection cycles, the stability verification is considered successful. The recoverable drone then initiates a return-to-home procedure, carrying the lost drone back to a preset location, which can be the ground recovery point of the lost drone.

[0095] The aforementioned recovery processor is also used to detect the attitude data of the recovery drone holding the lost drone; if the attitude data is within a preset safety threshold range, the stability verification is determined to be successful; if the attitude data is not within the preset safety threshold range, the stability verification is determined to be unsuccessful. Verifying the attitude data of the recovery drone holding the lost drone through a stability verification mechanism before the recovery drone returns can effectively reduce the risk of secondary falls due to unstable connections during the recovery process.

[0096] The following example illustrates the process of a missing drone before it lost contact. Figure 4 As shown, the missing drone includes a communication status monitor, a flight parameter acquisition device, and a loss-of-connection processor;

[0097] The communication status monitor is used to continuously detect the communication link status between the lost drone and the control center before it lost contact;

[0098] The flight parameter acquisition device is used to collect flight parameter information of the missing drone before it lost contact;

[0099] The disconnection processor is used to send a disconnection signal to the controller when the communication link is interrupted and / or the flight parameter information is abnormal.

[0100] The flight parameter information can include the missing drone's flight altitude, speed, battery level, and location.

[0101] Before losing contact, the missing drone maintained communication with the control center. Once contact is lost, communication between the two is interrupted. A communication status monitor can use a wireless radio frequency signal receiver to monitor the communication link status between the missing drone and the recovering drone in real time, providing real-time data for determining whether contact has been lost. The communication link status is then sent to a contact loss processor. Once the contact loss processor determines that the communication link status is interrupted, it immediately sends a contact loss signal to the controller. For example, if the signal strength is below -90dBm for three consecutive detection cycles, the communication link status is determined to be interrupted.

[0102] Meanwhile, the flight parameter acquisition unit also needs to collect flight parameter information of the lost drone in real time before it lost contact and send this information to the loss-of-contact processor. Once the loss-of-contact processor determines that the flight parameter information is abnormal, it immediately sends a loss-of-contact signal to the controller. It should be noted that the flight parameter acquisition unit includes a multi-source sensor group, such as an inertial measurement unit, battery voltage detection circuit, etc. Different sensors can collect different flight parameter information. For example, the flight parameter acquisition unit can collect the drone's flight altitude, speed, battery voltage, and latitude and longitude coordinates once per second. When the battery voltage is lower than 3.3V or the flight speed deviates from the set value by ±5m / s for 30 consecutive seconds, it is determined that there is an abnormality in the flight parameters.

[0103] It should also be noted that the controller can perform analysis based on two dimensions: communication status and flight parameters. That is, when the communication link is interrupted and the flight parameter information is abnormal, the drone is confirmed to be in a disconnected state. For example, if the communication interruption lasts for 120 seconds or the cumulative abnormal flight parameter information reaches the level three warning, a disconnection signal is generated and the transmission protocol is initiated.

[0104] The aforementioned missing drone includes a communication status monitor, a flight parameter collector, and a disconnection processor. The communication status monitor continuously monitors the communication link status between the drone and the control center before the drone lost contact. The flight parameter collector collects flight parameter information from the drone before it lost contact. The disconnection processor sends a disconnection signal to the controller when the communication link is interrupted and / or the flight parameter information is abnormal. By using the communication status monitor and flight parameter collector to collect relevant information about the drone before it lost contact, and by accurately determining whether the drone is disconnected from multiple dimensions such as communication link status and flight parameter information, a disconnection signal can be promptly reported to the controller.

[0105] Based on the above embodiments, in order to more accurately determine whether the drone is in a lost-connection state, it is also necessary to continue to determine the drone's battery level in the event of a communication interruption. Therefore, in one embodiment, the aforementioned lost-connection processor is further configured to send a lost-connection signal to the controller when the communication link is interrupted and the battery level in the flight parameter information is within the normal range; and when the communication link is interrupted and the battery level in the flight parameter information is not within the normal range, it is not necessary to send a lost-connection signal to the controller.

[0106] When the disconnection processor determines that the communication link is interrupted, it could be due to either insufficient battery power or the drone being in a disconnected state. In this case, the disconnection processor can check if the battery level in the collected flight parameters is within the normal range. If it is, the communication interruption is not due to insufficient battery power, and a disconnection signal needs to be sent to the controller. If it is not, the communication interruption is due to insufficient battery power, and a disconnection signal needs to be sent to the controller.

[0107] The aforementioned disconnection processor is further configured to send a disconnection signal to the controller when the communication link is interrupted and the battery level in the flight parameter information is within the normal range; and not to send a disconnection signal to the controller when the communication link is interrupted and the battery level in the flight parameter information is not within the normal range. In the event of a communication interruption, further verification of the battery level can determine whether the drone is disconnected, avoiding the false triggering of a disconnection signal due to low battery power.

[0108] 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 and / or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0109] 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. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, database, or other media used in the embodiments provided in this application can include at least one of non-volatile memory 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, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0110] 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 application.

[0111] 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 system for recovering a lost drone, characterized in that, The lost drone recovery system includes: a search drone swarm, a recovery drone, and a controller, wherein the search drone swarm and the recovery drone are both connected to the controller; The controller is used to generate a search path for the search drone swarm based on the flight log of the missing drone and the environmental data of the missing drone after receiving the missing signal from the missing drone. The search drone swarm is used to search according to the search path, determine the location where the missing drone went missing, and send the location to the controller. The controller is also configured to determine the recovery path of the recovered drone based on the received location of the lost contact; The recovery drone is used to recover the lost drone according to the recovery route.

2. The system according to claim 1, characterized in that, The controller is further configured to construct a three-dimensional search heat map based on the flight logs of the missing UAV, meteorological data in the environmental data, and terrain elevation model; divide the three-dimensional search heat map into grid areas to determine the search path of any one of the search UAVs in the search UAV swarm; and determine the recovery path of the recovery UAV based on the location of the missing UAV.

3. The system according to claim 1 or 2, characterized in that, Each of the search drones in the search drone swarm includes a multispectral acquisition device and a search processor; The multispectral acquisition device is used to acquire image data of the target area during the flight of the search drone; The search processor is used to analyze the image data collected by each search drone to determine the location where the missing drone went missing.

4. The system according to claim 3, characterized in that, The multispectral acquisition device includes an infrared camera and a visible light camera. The infrared camera is used to collect infrared radiation data of the target area; The visible light camera is used to capture visible light images of the target area.

5. The system according to claim 4, characterized in that, The search processor is further configured to input the infrared radiation data of the target area and the visible light image into a preset location determination model, analyze them using a sliding window mechanism, obtain location information that matches the features of the missing drone, and correct the location information for errors to obtain the location where the missing drone went missing.

6. The system according to claim 1 or 2, characterized in that, The recovered drone includes obstacle avoidance sensors, a recovery processor, and a device for capturing lost drones. The obstacle avoidance sensor is used to detect the distance between the obstacle and the obstacle avoidance sensor; The recovery processor is used to adjust the flight attitude of the recovery drone according to the distance; and, when the distance between the lost drone and the obstacle avoidance sensor is less than a preset threshold, to adjust the lost drone grasping device according to the size and shape of the lost drone. The missing drone retrieval device is used to retrieve and secure the missing drone.

7. The system according to claim 6, characterized in that, The recovery drone also includes a pressure sensor; The pressure sensor is used to collect the clamping pressure of the missing drone during the process of the missing drone capture device; The recovery processor is used to adjust the gripping device for the lost drone; the adjusted clamping pressure meets preset conditions.

8. The system according to claim 6, characterized in that, The recovery processor is also used to detect the attitude data of the recovery drone holding the lost drone; if the attitude data is within a preset safety threshold range, it determines that the stability verification has passed. If the attitude data is not within the preset safety threshold range, the stability verification is determined to have failed.

9. The system according to claim 1 or 2, characterized in that, The missing drone includes a communication status monitor, a flight parameter acquisition device, and a missing processor; The communication status monitor is used to continuously detect the communication link status between the lost drone and the control center before it lost contact; The flight parameter acquisition device is used to collect flight parameter information of the missing drone before it lost contact; The disconnection processor is used to send a disconnection signal to the controller when the communication link is interrupted and / or the flight parameter information is abnormal.

10. The system according to claim 9, characterized in that, The disconnection processor is further configured to send a disconnection signal to the controller when the communication link is interrupted and the battery level in the flight parameter information is within the normal range; and not to send a disconnection signal to the controller when the communication link is interrupted and the battery level in the flight parameter information is not within the normal range.

Images