Unmanned aerial vehicle control method, electronic device, and storage medium

Abstract

An unmanned aerial vehicle control method, an electronic device, and a storage medium. The control method comprises: performing detection on a space on a flight path of an unmanned aerial vehicle, so as to obtain information of an object in the space, wherein the unmanned aerial vehicle has the capability of detecting an object in the space beyond a first distance, and the first distance is set on the basis of the shape and size of an obstacle, a relative velocity between the obstacle and the unmanned aerial vehicle, etc. (S901); identifying the object in the space, so as to obtain attribute information of the object in the space (S902); on the basis of the attribute information of the object in the space, determining whether the unmanned aerial vehicle needs to perform obstacle avoidance (S903); when it is determined that the unmanned aerial vehicle needs to perform obstacle avoidance, controlling the unmanned aerial vehicle to perform obstacle avoidance (S904); and when it is determined that the unmanned aerial vehicle does not need to perform obstacle avoidance, controlling the unmanned aerial vehicle to maintain the flight path (S905).

Description

UAV control method, electronic device and storage medium Technical Field

[0001] The present disclosure relates to the technical field of drones, and in particular to a drone control method, electronic equipment, and storage medium. Background Art

[0002] A drone is an unmanned aircraft controlled by a radio remote control and its own programmable controller. As drone technology matures, it's increasingly being used in delivery scenarios and aerial photography. Summary of the Invention

[0003] The embodiments of the present disclosure provide a control method, electronic device, and storage medium for a drone, which can be used to solve problems in related technologies. The technical solution includes the following contents.

[0004] On the one hand, an embodiment of the present disclosure provides a method for controlling a drone, the method comprising: detecting a space on a flight path of the drone to obtain information about objects in the space, wherein the drone has the ability to detect objects in the space beyond a first distance, and the first distance is set according to at least one of the shape, size, and relative speed of the obstacle to the drone; identifying objects in the space to obtain attribute information of the objects in the space, the attribute information comprising at least one of the shape, size, relative speed relationship with the drone, and relative position relationship of the objects in the space to the drone; determining whether the drone needs to avoid obstacles based on the attribute information of the objects in the space; if it is determined that the drone needs to avoid obstacles, controlling the drone to avoid obstacles; if it is determined that the drone does not need to avoid obstacles, controlling the drone to maintain the flight path.

[0005] In a feasible implementation, after controlling the drone to avoid obstacles, it also includes: obtaining attribute information of objects in the space after obstacle avoidance; determining whether the drone needs to avoid obstacles based on the attribute information of objects in the space after obstacle avoidance; and controlling the drone to avoid obstacles if it is determined that the drone needs to avoid obstacles.

[0006] In a feasible implementation, when it is determined that the drone does not need to avoid obstacles, the method further includes: planning a path to return the drone to the flight path, wherein the planned path satisfies that the distance between the drone and the object in the space is not less than the second distance.

[0007] In a feasible implementation, the second distance is determined by the relative speed between the drone and the object in space.

[0008] In a feasible implementation, before controlling the drone to avoid obstacles, it also includes: determining an obstacle avoidance strategy based on the type or size of the drone and the attribute information of objects in the space; correspondingly, controlling the drone to avoid obstacles includes: controlling the drone to avoid obstacles according to the obstacle avoidance strategy.

[0009] In a feasible implementation, the control method is used for a drone onboard system, and the drone onboard system is communicatively connected to a remote server or a drone control terminal.

[0010] In a feasible implementation, objects in space are identified to obtain attribute information of the objects in space, including: sending information about the objects in space to a remote server or a drone operation terminal; receiving identification information of the objects in space from the remote server or the drone operation terminal to obtain attribute information of the objects in space.

[0011] On the other hand, an embodiment of the present disclosure provides a control device for a drone, the device comprising: a perception module for detecting the space on the drone's flight path to obtain information about objects in the space, wherein the drone has the ability to detect objects in the space beyond a first distance, and the first distance is set according to at least one of the shape, size, and relative speed of the obstacle to the drone; an identification module for identifying objects in the space to obtain attribute information of the objects in the space, the attribute information including at least one of the shape, size, relative speed relationship with the drone, and relative position relationship of the objects in the space to the drone; a processing module for determining whether the drone needs to avoid obstacles based on the attribute information of the objects in the space; a control module for controlling the drone to avoid obstacles if it is determined that the drone needs to avoid obstacles; and controlling the drone to maintain the flight path if it is determined that the drone does not need to avoid obstacles.

[0012] In a feasible implementation, the processing module is also used to: obtain attribute information of objects in the space after obstacle avoidance; determine whether the drone needs to avoid obstacles based on the attribute information of objects in the space after obstacle avoidance; correspondingly, the control module is specifically used to control the drone to avoid obstacles when it is determined that the drone needs to avoid obstacles.

[0013] In a feasible embodiment, when it is determined that the drone does not need to avoid obstacles, the processing module is further used to: plan a path to make the drone return to the flight path, wherein the planned path satisfies that the distance between the drone and the object in the space is not less than the second distance.

[0014] In a feasible implementation, the second distance is determined by the relative speed between the drone and the object in space.

[0015] In a feasible implementation, before controlling the drone to avoid obstacles, the processing module is also used to: determine an obstacle avoidance strategy based on the type or size of the drone and the attribute information of objects in the space; correspondingly, the control module is specifically used to: control the drone to avoid obstacles according to the obstacle avoidance strategy.

[0016] In a feasible implementation, the drone control device is coupled to the drone onboard system, and the drone onboard system is communicatively connected to a remote server or a drone control terminal.

[0017] In a feasible embodiment, the drone control device also includes a communication module, which is used to: send information about objects in the space to a remote server or a drone operation terminal; and receive identification information about the objects in the space from the remote server or the drone operation terminal to obtain attribute information of the objects in the space.

[0018] In a feasible embodiment, the identification information of objects in the space exists in a remote server or a drone operation terminal, and the communication module is also used to: receive a first instruction or a second instruction from the remote server or the drone operation terminal, the first instruction is used to instruct the drone to avoid obstacles, and the second instruction is used to instruct the drone to avoid obstacles according to the obstacle avoidance strategy determined by the remote server or the drone operation terminal.

[0019] On the other hand, an embodiment of the present disclosure provides an electronic device, comprising a processor and a memory, wherein the memory stores at least one program code, and the at least one program code is loaded and executed by the processor so that the electronic device implements any of the above-mentioned drone control methods.

[0020] On the other hand, a computer-readable storage medium is provided, in which at least one program code is stored. The at least one program code is loaded and executed by a processor so that a computer implements any of the above-mentioned drone control methods.

[0021] On the other hand, a computer program or computer program product is also provided, wherein the computer program or computer program product stores at least one computer instruction, and the at least one computer instruction is loaded and executed by a processor so that the computer implements any of the above-mentioned drone control methods.

[0022] In the technical solution provided by the embodiments of the present disclosure, the space along a drone's flight path is detected to obtain information about objects within the space. The drone is capable of detecting objects within the space beyond a first distance, which is determined based on at least one of the obstacle's shape, size, and relative speed to the drone. Objects within the space are identified to obtain attribute information, including at least one of the object's shape, size, relative speed to the drone, and relative position to the drone. Based on the attribute information, the drone determines whether obstacle avoidance is necessary. If obstacle avoidance is determined to be necessary, the drone is controlled to avoid the obstacle. If obstacle avoidance is not determined to be necessary, the drone is controlled to maintain its flight path. Compared to the traditional safety distance setting, the establishment of the first distance takes into account the characteristics of different obstacles and scenarios, ensuring safe flight while enhancing the accuracy of obstacle avoidance decisions, reducing unnecessary obstacle avoidance maneuvers, and improving the drone's flight efficiency. Designing avoidance maneuvers based on the drone's type or size can improve obstacle avoidance efficiency and avoid secondary risks caused by the drone's inherent inability to complete the avoidance maneuver. When returning to the original flight path after the obstacle avoidance operation is completed, the planned path must still ensure that the distance between the drone and the object in the space is not less than the second distance. This can give the drone sufficient response time when an accident occurs, thereby ensuring the drone's flight safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0024] FIG1 is a schematic diagram of the overall architecture of an exemplary detection and obstacle avoidance system provided by an embodiment of the present disclosure;

[0025] FIG2 is an exemplary operation flow chart of a DAA system provided by an embodiment of the present disclosure;

[0026] FIG3 is a schematic diagram of the overall architecture of an exemplary short-range DAA system provided by an embodiment of the present disclosure;

[0027] FIG4 is a schematic diagram of the overall architecture of an exemplary mid- and long-range DAA system provided by an embodiment of the present disclosure;

[0028] FIG5 is a schematic diagram of a system configuration of an exemplary radar system provided by an embodiment of the present disclosure;

[0029] FIG6 is a schematic diagram of a system configuration of an exemplary optical sensor provided by an embodiment of the present disclosure;

[0030] FIG7 is a schematic diagram of a system configuration of an exemplary processing unit provided in an embodiment of the present disclosure;

[0031] FIG8 is a schematic diagram of the overall architecture of an exemplary short-, medium-, and long-range hybrid DAA system provided by an embodiment of the present disclosure;

[0032] FIG9 is a flow chart of an exemplary drone control method provided by an embodiment of the present disclosure;

[0033] FIG10 is a structural block diagram of an exemplary drone control device provided by an embodiment of the present disclosure;

[0034] FIG11 is a structural block diagram of an exemplary electronic device provided by an embodiment of the present disclosure. DETAILED DESCRIPTION

[0035] In order to make the objectives, technical solutions and advantages of the present disclosure more clear, the embodiments of the present disclosure will be further described in detail below with reference to the accompanying drawings.

[0036] As drone applications increase in complex, low-altitude airspace, particularly in urban areas, safety challenges intensify. Drones operating in these areas must not only avoid ground objects such as tall buildings, cranes, power grids, and mobile network antennas, but also be mindful of other users sharing the airspace, such as low-flying helicopters, other drones, and kites. Effective drone control methods are needed to enable drones to autonomously detect and avoid obstacles.

[0037] The Obstacle Detection and Avoidance (ODA) system is one of the key onboard systems enabling autonomous drone flight. By sensing the surrounding environment, the ODA system can determine the size and distance of obstacles, estimate the flight trajectory, and calculate the probability of collision with detected obstacles. Based on this, the drone's flight control system can autonomously decelerate, hover, or circumvent obstacles to ensure flight safety. The functionality and performance of the ODA system are closely related to the drone's flight speed, which directly affects its range. Fault detection also significantly impacts obstacle avoidance performance, preventing instability and unintended braking.

[0038] First, let’s explain the vocabulary that may be used to describe the detection and obstacle avoidance system:

[0039] Field of view: The angular range of the target observed by the sensor in one dimension;

[0040] Failure protection: The system can still operate safely when any single component fails;

[0041] Fault diagnosis and isolation: When any fault occurs, the process of checking and isolating the fault point so that the fault does not affect the safe operation of the system;

[0042] Processing unit: a device that transmits and processes information from interface systems (such as sensors and drone control systems);

[0043] UAV control system: a system that transmits and processes UAV control signals and UAV actuators;

[0044] Detection and Obstacle Avoidance System: A system consisting of sensors and a processing unit, including software, equipped with an interface to send information to the UAV traffic management system or the UAV operator;

[0045] Detection: Determining the presence or absence of a target can trigger subsequent operations of the detection and obstacle avoidance system;

[0046] Identification: Determine the type of target object, such as responding drones, non-responding drones, and other hazards, as well as supporting information such as distance to the target object, estimated speed, and complete trajectory, providing accurate predictions of the target object's future location;

[0047] Maneuvering: The control of a drone to change its trajectory or speed to avoid collision, such as horizontal turns, hovering, deceleration, acceleration, climbing and descending.

[0048] Figure 1 is a schematic diagram of the overall architecture of an exemplary detection and avoidance (DAA) system provided by an embodiment of the present disclosure. The DAA system performs non-responsive detection and avoidance of target objects, regardless of whether a UAV Traffic Management (UTM) system exists. In an operating scenario where the DAA and UTM systems work together, the DAA system has an interface with the UTM or the drone operator / drone control terminal for sending information about detection and avoidance. The DAA system uses sensors for environmental perception. The sensors used include but are not limited to microwave radars, lidars, cameras, infrared sensors, ultrasonic sensors, volume sensors, or a combination of multiple sensors thereof.

[0049] More specifically, the DAA system should have environmental perception capabilities, which enable it to perceive the surrounding environment and detect objects during flight; situational awareness capabilities, which determine whether there are potential risks that affect flight safety based on detected obstacles and related data analysis (such as flight speed, heading, altitude and absolute position, high-precision maps); target avoidance capabilities, which conduct path avoidance decision-making processes and execute avoidance actions based on information obtained through situational awareness; fault detection and isolation capabilities, which monitor the operation of the DAA system and identify faults that may cause system abnormalities; fault indication capabilities, which provide timely feedback of fault information when a fault occurs; and fault correction capabilities, which correct the fault without affecting the operation of the DAA system and eliminate negative impacts when a fault occurs.

[0050] The DAA system remains active throughout the flight, detecting and avoiding both static and dynamic objects. Static objects generally refer to those fixed to the ground, such as buildings, trees, towers, poles, and power lines. Dynamic objects typically include birds and aircraft.

[0051] Figure 2 is a flowchart of an exemplary DAA system operation according to an embodiment of the present disclosure. In the embodiment shown in Figure 2, the DAA system operation process includes: object detection; target identification based on the detection results; determining whether obstacle avoidance is necessary based on the identification results; if obstacle avoidance is not necessary, maintaining the original flight path; if obstacle avoidance is necessary, performing obstacle avoidance and checking the results; if the risk has been eliminated, returning to the original flight path; if the risk has not been eliminated, re-determining whether obstacle avoidance is necessary.

[0052] Based on the range of the sensor, DAA systems can be divided into short-range DAA systems and medium- and long-range DAA systems. It should be understood that the distinction between short-range DAA systems and medium- and long-range DAA systems is not strict.

[0053] FIG3 is a schematic diagram of the overall architecture of an exemplary short-range DAA system provided by an embodiment of the present disclosure. In the short-range DAA system, sensors obtain data on drones and obstacles, such as detection data, identification data, and self-test data generated by sensors when monitoring their own operation, and transmit the data to a processing unit. The processing unit processes this data and obtains the drone position information and control information fed back by the drone control unit at the same time, thereby obtaining status information corresponding to the above various data, such as detection status, identification status, obstacle avoidance status, and system self-test status. The processing unit sends this status information to the drone control unit. The drone control unit can use this data to interact with the UTM or drone control terminal.

[0054] Depending on the type and size of the drone, evasive maneuvers may vary. For example, a lightweight and agile drone may choose to maneuver around and / or return to its original flight path to avoid an obstacle. A heavy-duty drone, on the other hand, may choose to slow down, hover, or issue an alarm to avoid a collision.

[0055] A drone must be able to detect obstacles beyond the minimum detection distance to allow sufficient time to evade them. This minimum detection distance is dependent on factors such as the shape and size of the obstacle, and the relative speed between the obstacle and the drone. The table below lists the minimum detection distances under different conditions.

[0056] At the same time, after performing an evasive maneuver, the drone must still maintain a certain distance from the obstacle, known as a safe distance. This safe distance can be a pre-selected estimated distance, related to the time required for sensing, avoiding obstacles, and responding to emergencies. In other words, the safe distance is sufficient for the drone to complete these operations. The safe distance is also related to the drone's speed and / or the relative speed between the drone and the obstacle. In one possible implementation, the safe distance = relative speed between the drone and the obstacle × 0.5.

[0057] Short-range DAA systems often come into play in landing scenarios. During landing, the system detects objects on the landing surface. If any obstacles hinder landing, the drone replans its path to land in a safe landing zone. Obstacles that affect landing include objects on the ground, such as people, animals, and anything else that could damage the drone. They also include terrain that poses a threat to landing, including slopes that affect the drone's stable landing or slopes that interfere with the propellers during landing or may cause damage to the aircraft. Surface types that are unsuitable for drone landing, such as uneven surfaces, water surfaces, potholes, etc.

[0058] The failure protection function enables the DAA system to promptly detect possible failures in sensing components through self-diagnostic checks, isolate the fault point and send relevant information to the UTM or drone operator or the drone's control system, so that the drone can activate a backup obstacle avoidance protection plan, or control the drone to stop flying, make an emergency landing, etc., to avoid causing greater losses.

[0059] To minimize the impact of faults on drone flight, the DAA system includes fault diagnosis and management capabilities. The DAA system is capable of real-time detection, diagnosis, and management of the entire drone system's operating status, system faults, and anomalies. Working in conjunction with the flight control system, the DAA system can correct system faults, anomalies, or conditions that could endanger flight safety, and take appropriate safety measures as needed. During aircraft power-up, system initialization, operation, takeoff, flight, descent, and landing, if any anomaly is detected that could affect flight safety or reduce safety margins, the system provides immediate indications and warnings to the UTM or drone operator.

[0060] Fault detection includes power-on self-test and emergency management. After the system is powered on, it automatically completes the basic status check of all equipment in the drone system. When any fault occurs that affects flight safety or normal system operation, an alarm message is provided to the relevant alarm device; system initialization monitoring and management. During the system initialization process, all factors affecting the initial alignment are monitored to ensure that the system remains in the required initial alignment state. If the initial alignment is abnormal, appropriate safety measures are taken; pre-flight status diagnosis and management. After receiving the take-off command, a system check of the system function and status and the drone operator's operating instructions are immediately completed to determine whether the drone meets the take-off conditions. Otherwise, the drone is restricted from taking off; in-flight monitoring and management. During the flight of the aircraft, the DAA system should monitor the external environment and other factors, and take measures to put the aircraft into a relatively safe state when danger occurs.

[0061] Figure 4 is a schematic diagram of the overall architecture of an exemplary medium- and long-range DAA system provided by an embodiment of the present disclosure. The sensor and the processing unit are integrated into a detection and identification unit. The detection status and detection data of the sensor are sent to the UTM or drone operator through the processing unit. The sensors in the detection and identification unit include radar and optical sensors, which send detection data and identification data to the processing unit respectively. The navigation sensor is independent of the detection and identification unit. In some feasible embodiments, the detection objects of the medium- and long-range DAA system are mainly manned aircraft with a relative speed of 200 kilometers per hour.

[0062] Figure 5 is a schematic diagram of the system configuration of an exemplary radar system provided by an embodiment of the present disclosure. The radar system consists of an antenna, a transmitter / receiver, a signal processor, and an interface. The radar input data includes the current drone status (power status, data transmission status, etc.) and current drone data (speed, roll angle, pitch angle, true angle, etc.). The radar output data includes the track ID assigned by the radar to the target object, the target object's tilt range relative to the current drone, azimuth angle, elevation angle, etc.

[0063] Figure 6 is a schematic diagram of an exemplary optical sensor system configuration provided by an embodiment of the present disclosure. The optical sensor can identify target objects within a range of 500 meters in 360-degree azimuth. The optical sensor can also utilize machine learning to achieve archiving and analysis functions. The input data of the optical sensor includes signals from a camera. The output data of the optical sensor includes information about the target object, including location information and identification information. The identification information can be a number assigned to the target object by the optical sensor.

[0064] Figure 7 is a schematic diagram of the system configuration of an exemplary processing unit provided by an embodiment of the present disclosure. The processing unit can decide whether it is necessary to avoid the target object based on the information obtained from the optical sensor and the radar. If the decision requires avoidance, the processing unit sends an avoidance request to the control system of the drone. The input data of the processing unit includes the distance between the target object and the drone, the orientation information of the target object, etc. from the radar, and the orientation information of the target object from the optical sensor. Optionally, some optical sensors that can obtain depth information, such as binocular vision sensors, can also obtain the distance between the target object and the drone. The output data of the processing unit includes specific avoidance action instructions (such as left or right, etc.).

[0065] Taking the DAA system's operational flow shown in Figure 2 as an example, each step corresponds to the required sensors. For example, radar is used for object detection, optical sensors are used for target identification, obstacle avoidance verification, and returning to the original flight path, and the processing unit is responsible for executing obstacle avoidance and resuming flight. At each step, the DAA system notifies the UTM or drone operator of relevant information, including drone status and execution results, through the processing unit. Furthermore, in addition to autonomous drone control, each step can also be switched to UTM or drone operator control.

[0066] Preferably, for object detection, 2 kilometers is the radar detection range required to avoid a collision with an aircraft traveling at a relative speed of 200 km / h. The DAA system detects dynamic objects and monitors the distance to them. For target identification, 750 meters is the 360-degree optical sensor detection range required to avoid a collision with an aircraft traveling at a relative speed of 200 km / h. For determining whether obstacle avoidance is necessary, 1.0 second is the typical time required for a computer program to process data, allowing for sufficient sampling to eliminate noise. A detection time of 1.0 second or less is sufficient to avoid a collision with an aircraft traveling at a relative speed of 200 km / h.

[0067] In some feasible implementations, after successfully avoiding an obstacle, the drone does not return to the original flight path, but instead replans the flight path or executes a designated flight path based on instructions from the UTM or the drone operator.

[0068] FIG8 is a schematic diagram of the overall architecture of an exemplary short, medium and long-range hybrid DAA system provided by an embodiment of the present disclosure. Compared with the DAA system shown in FIG4 , the sensor only includes an optical sensor unit. In a feasible embodiment, the detection target of the hybrid DAA system is mainly a drone with a relative speed of 100 kilometers per hour. Radar equipment can also be used when it is necessary to reduce the weight of the aircraft. Applicable scenarios may include pesticide spraying, maritime inspection, and cargo delivery.

[0069] Taking the operating process of the DAA system provided in Figure 2 as an example, preferably, in object detection, through image processing, 250 meters is the detection range required to avoid a collision with a drone with a relative speed of 100 kilometers per hour. The DAA system detects dynamic objects and monitors the distance from itself to the object. In target recognition, 250 meters is the detection range of the 360-degree optical sensor required to avoid a collision with an aircraft with a relative speed of 100 kilometers per hour. In determining whether obstacle avoidance is necessary, 1.0 second is the general time required for a computer program to process data, which allows for sufficient sampling to eliminate noise. A detection time of 1.0 second or less is sufficient to avoid a collision with an aircraft flying at a relative speed of 100 kilometers per hour.

[0070] FIG9 is a flow chart of an exemplary drone control method provided by an embodiment of the present disclosure.

[0071] S901: Detect the space along the flight path of the UAV to obtain information about objects in the space.

[0072] The drone has the ability to detect objects in space beyond a first distance, and the first distance is set according to at least one of the shape, size, and relative speed of the obstacle to the drone.

[0073] The sensors used for detection include one or more of microwave radar, lidar, camera, infrared sensor, ultrasonic sensor, and volume sensor.

[0074] The first distance refers to the minimum detection distance at which a drone can avoid an obstacle threatening its flight after detecting it. The determination of this first distance is related to one or more factors, including the shape and size of the obstacle, and the relative speed between the obstacle and the drone. While preferred values ​​for the minimum detection distance under different conditions have been provided above, it should be understood that the minimum detection distance setting is not limited to these values. It should also be understood that the minimum detection distance is different from the safety distance required to maintain a safe driving interval; rather, it is the minimum distance at which a drone can detect an obstacle and perform necessary evasive maneuvers. A drone does not calculate in real time whether its distance to an obstacle meets the minimum detection distance. In fact, since the minimum detection distance is determined a posteriori after an obstacle is detected, it is not possible for a drone to obtain this value in real time. In one feasible embodiment, the drone is pre-tested to ensure that its detection capabilities meet the minimum detection distance requirement. Compared to traditional safety distance settings, the establishment of a minimum detection distance takes into account the characteristics of different obstacles and scenarios. This not only ensures safe flight for the drone, but also enhances the accuracy of obstacle avoidance decisions, reduces unnecessary obstacle avoidance maneuvers, and improves the drone's flight efficiency.

[0075] Objects detected by a drone in space may or may not interfere with the drone's normal flight. It should be understood that objects in space include not only objects within the drone's flight path, but also objects that at least partially occupy a predetermined range around the flight path.

[0076] S902: Identify objects in the space to obtain attribute information of the objects in the space.

[0077] Attribute information can include the object's shape, size, relative speed, relative heading, relative position relative to the drone, and whether the object can interact with the drone. For example, the object could be another drone or a manned aircraft with communication capabilities. This step is typically performed by a visual sensor, but can also be performed by other sensors, as well as sensors and analyzers with machine learning capabilities. This disclosure is not limited to this.

[0078] In some feasible implementations, the drone's onboard system is communicatively connected to a remote server or drone control terminal used to control the drone. The drone can also transmit information acquired through sensors to a remote server, such as a cloud server, UTM system, or to a remote or near-field drone operator (responsible for operating the drone control terminal), thereby handing the task of analyzing the attribute information of objects in the space to the remote server or drone operator / drone control terminal. The remote server or drone operating terminal also receives identification information about objects in the space to obtain the attribute information of the objects in the space. This can reduce the computing pressure of the drone and reduce the drone's energy consumption. In addition, generally, the remote server has more powerful processing capabilities than the onboard computing unit and can obtain more accurate calculation results.

[0079] S903: Determine whether the drone needs to avoid obstacles based on the attribute information of the objects in the space.

[0080] The drone's flight trajectory is estimated, and based on the attributes of objects within the space, the system determines the likelihood that these objects will affect the drone's normal flight. If an object is judged to be likely to affect the drone's normal flight, such as causing a collision or adversely affecting the flight environment, the system initiates obstacle avoidance. If an object is judged to be non-impactful, obstacle avoidance is not necessary.

[0081] In some feasible implementations, the determination of whether obstacle avoidance is necessary may also be made by a remote server or a drone operator / drone operating terminal, and the decision result may be transmitted to the drone. Specifically, the remote server or drone operating terminal may have acquired or calculated identification information of objects in space during a previous operation. The drone's onboard system may receive a first instruction or a second instruction from the remote server or drone operating terminal, wherein the first instruction is used to instruct the drone to avoid obstacles, and the second instruction is used to instruct the drone to avoid obstacles according to the obstacle avoidance strategy determined by the remote server or drone operating terminal.

[0082] S904: When it is determined that the drone needs to avoid obstacles, an obstacle avoidance strategy is determined based on the type or size of the drone and attribute information of objects in the space, and the drone is controlled to avoid the obstacles.

[0083] Avoidance maneuvers vary depending on the type and size of the drone. Designing avoidance maneuvers based on the drone's specific circumstances can improve obstacle avoidance efficiency and prevent secondary risks caused by the drone's inherent inability to perform the avoidance maneuver. Furthermore, the shape, size, relative position, and speed of objects in space also influence the drone's obstacle avoidance strategy. Drones can be categorized as cargo drones and passenger drones, as well as small drones and large drones. It should be understood that small drones offer greater maneuverability, while passenger drones require higher safety requirements.

[0084] For example, a lightweight and flexible drone may choose to maneuver around and / or return to the original flight path to avoid obstacles. A drone with a large payload may choose to slow down, hover, sound an alarm, etc. to avoid collisions with obstacles. The shape of the obstacle and its positional relationship with the drone will determine the direction of the drone's obstacle avoidance (ascending, descending, circling left or right), and the relative speed between the obstacle and the drone will determine whether the drone will hover, slow down, or land to avoid the obstacle. If the obstacle is a drone or manned aircraft with communication capabilities, communication may also be used to take evasive actions against each other. It should be understood that communication functions include data communication, as well as other methods such as signal lights and alarms.

[0085] In some feasible implementations, the obstacle avoidance strategy may also be formulated by a remote server or a drone operator / drone control terminal, and specific obstacle avoidance action instructions may be sent to the drone.

[0086] The drone will perform obstacle avoidance according to the obstacle avoidance strategy or the specific obstacle avoidance action instructions received.

[0087] S905: When it is determined that the UAV does not need to avoid obstacles, control the UAV to maintain the flight path.

[0088] Obviously, when the decision does not require obstacle avoidance, the drone can maintain its original flight path and flight speed and keep detecting the space beyond the minimum detection distance.

[0089] S906: Check obstacle avoidance results.

[0090] That is, check whether the impact of objects in the space on the normal flight of the drone has been eliminated.

[0091] The process can return to steps S903-S905 to obtain the attribute information of the objects in the space after obstacle avoidance, such as the adjusted relative position relationship between the objects and the drone, the adjusted relative speed, etc. Based on the attribute information of the objects in the space after obstacle avoidance, it is determined whether the drone still needs to avoid obstacles and relevant subsequent operations are performed.

[0092] S907: When it is determined that the UAV does not need to continue to avoid obstacles, it returns to the original flight path.

[0093] A flight trajectory can be planned from the current position of the drone where the obstacle avoidance maneuver is completed to return to the original flight path and execute the flight. It should be understood that during the process of returning to the original flight path, the drone maintains detection of the space beyond the minimum detection distance to prevent the appearance of new objects that may affect the normal flight of the drone.

[0094] In one possible implementation, when planning the path back to the original flight path, the planned path must ensure that the distance between the drone and the object in space is no less than a second distance. This distance is determined by the time required for the drone to perform operations such as perception, obstacle avoidance, and emergency response. For example, the second distance = the relative speed between the drone and the object in space × 0.5. Maintaining a certain distance from obstacles gives the drone sufficient time to respond in the event of an accident, ensuring flight safety.

[0095] FIG10 is a structural block diagram of an exemplary drone control device provided in an embodiment of the present disclosure.

[0096] The drone control device 1000 includes:

[0097] A sensing module 1001 is configured to detect space along the flight path of the drone to obtain information about objects within the space, wherein the drone is capable of detecting objects within the space beyond a first distance, the first distance being determined based on at least one of the shape, size, and relative speed of the obstacle to the drone;

[0098] Identification module 1002, configured to identify objects in the space to obtain attribute information of the objects in the space, the attribute information including at least one of the shape, size, relative velocity relationship with the drone, and relative position relationship of the objects in the space to the drone;

[0099] Processing module 1003, used to determine whether the drone needs to avoid obstacles based on the attribute information of the objects in the space;

[0100] The control module 1004 is used to control the drone to avoid obstacles when it is determined that the drone needs to avoid obstacles; and to control the drone to maintain the flight path when it is determined that the drone does not need to avoid obstacles.

[0101] In a feasible implementation, the processing module 1003 is also used to: obtain attribute information of objects in the space after obstacle avoidance; determine whether the drone needs to avoid obstacles based on the attribute information of objects in the space after obstacle avoidance; correspondingly, the control module 1004 is specifically used to: control the drone to avoid obstacles when it is determined that the drone needs to avoid obstacles.

[0102] In a feasible embodiment, when it is determined that the drone does not need to avoid obstacles, the processing module 1003 is further used to: plan a path to make the drone return to the flight path, wherein the planned path satisfies that the distance between the drone and the object in the space is not less than the second distance.

[0103] In a feasible implementation, the second distance is determined by the relative speed between the drone and the object in space.

[0104] In a feasible implementation, before controlling the drone to avoid obstacles, the processing module 1003 is also used to: determine an obstacle avoidance strategy based on the type or size of the drone and the attribute information of objects in the space; correspondingly, the control module 1004 is specifically used to control the drone to avoid obstacles according to the obstacle avoidance strategy.

[0105] In a feasible implementation, the drone control device 1000 is coupled to a drone onboard system, and the drone onboard system is communicatively connected to a remote server or a drone control terminal.

[0106] In a feasible embodiment, the drone control device 1000 also includes a communication module 1005, which is used to: send information about objects in the space to a remote server or a drone operation terminal; and receive identification information about the objects in the space from the remote server or the drone operation terminal to obtain attribute information of the objects in the space.

[0107] In a feasible embodiment, the identification information of objects in the space exists in a remote server or a UAV operation terminal, and the communication module 1005 is also used to: receive a first instruction or a second instruction from the remote server or the UAV operation terminal, the first instruction is used to instruct the UAV to avoid obstacles, and the second instruction is used to instruct the UAV to avoid obstacles according to the obstacle avoidance strategy determined by the remote server or the UAV operation terminal.

[0108] It should be understood that the device provided in FIG. 10 is merely an example of the division of the functional modules described above when implementing its functions. In actual applications, the functions described above can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiments are based on the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0109] 11 is a block diagram of an exemplary electronic device 1100 provided in an embodiment of the present disclosure. The electronic device 1100 may be a portable mobile terminal or an onboard computer of a drone.

[0110] Typically, the electronic device 1100 includes a processor 1101 and a memory 1102 .

[0111] The processor 1101 may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor 1101 may be implemented in at least one hardware form of DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), or PLA (Programmable Logic Array). The processor 1101 may also include a main processor and a coprocessor. The main processor is a processor for processing data in the awake state, also known as a CPU (Central Processing Unit); the coprocessor is a low-power processor for processing data in the standby state. In some embodiments, the processor 1101 may be integrated with a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content to be displayed on the display screen. In some embodiments, the processor 1101 may also include an AI (Artificial Intelligence) processor, which is used to process computing operations related to machine learning.

[0112] Memory 1102 may include one or more computer-readable storage media, which may be non-transitory. Memory 1102 may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices and flash memory storage devices. In some embodiments, the non-transitory computer-readable storage medium in memory 1102 is used to store at least one instruction, which is executed by processor 1101 to implement the drone control method provided in the method embodiment of the present disclosure.

[0113] Those skilled in the art will understand that the structure shown in FIG11 does not constitute a limitation on the electronic device 1100 , and may include more or fewer components than shown, or combine certain components, or adopt a different component arrangement.

[0114] In an exemplary embodiment, a computer-readable storage medium is further provided, in which at least one program code is stored. The at least one program code is loaded and executed by a processor to enable an electronic device to implement any of the above-mentioned drone control methods.

[0115] Optionally, the computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a compact disc (CD-ROM), a magnetic tape, a floppy disk, an optical data storage device, or the like.

[0116] In an exemplary embodiment, a computer program or computer program product is also provided, wherein at least one computer instruction is stored in the computer program or computer program product, and the at least one computer instruction is loaded and executed by a processor to enable the computer to implement any of the above-mentioned drone control methods.

[0117] It should be understood that the term "plurality" used herein refers to two or more. "And / or" describes a relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can mean: A exists alone, A and B exist simultaneously, or B exists alone. The character " / " generally indicates an "or" relationship between the associated objects.

[0118] The serial numbers of the embodiments of the present disclosure are for descriptive purposes only and do not represent the merits of the embodiments. The above description is merely an exemplary embodiment of the present disclosure and is not intended to limit the present disclosure. Any modifications, equivalent replacements, improvements, etc. made within the principles of the present disclosure shall be included within the scope of protection of the present disclosure.

Claims

1. A drone control method, characterized in that: include: Detecting a space along a flight path of a drone to obtain information about objects within the space, wherein the drone is capable of detecting objects within the space beyond a first distance, the first distance being determined based on at least one of a shape, a size, and a relative speed of the obstacle to the drone; Identifying objects in the space to obtain attribute information of the objects in the space, the attribute information including at least one of a shape, a size, a relative speed relationship with the drone, and a relative position relationship with the drone; Determining whether the drone needs to avoid obstacles based on attribute information of objects in the space; When it is determined that the drone needs to avoid an obstacle, controlling the drone to avoid the obstacle; When it is determined that the UAV does not need to avoid obstacles, the UAV is controlled to maintain the flight path.

2. The control method according to claim 1, characterized in that: After controlling the UAV to avoid obstacles, the method further includes: Obtaining attribute information of objects in the space after obstacle avoidance; determining whether the drone needs to avoid obstacles based on the attribute information of the objects in the space after the obstacle avoidance; When it is determined that the drone needs to avoid obstacles, the drone is controlled to avoid the obstacles.

3. The control method according to claim 2, characterized in that: If it is determined that the UAV does not need to avoid obstacles, the method further includes: A path is planned to return the drone to the flight path, wherein the planned path satisfies that a distance between the drone and the object in the space is not less than a second distance.

4. The control method according to claim 3, characterized in that: The second distance is determined by the relative speed between the drone and the object in the space.

5. The control method according to claim 1, characterized in that: Before controlling the UAV to avoid obstacles, the method further includes: Determining an obstacle avoidance strategy based on the type or size of the drone and attribute information of objects in the space; Correspondingly, controlling the drone to avoid obstacles includes: According to the obstacle avoidance strategy, the UAV is controlled to avoid obstacles.

6. The control method according to any one of claims 1 to 5, characterized in that: The control method is used for a drone onboard system, and the drone onboard system is communicatively connected to a remote server or a drone control terminal.

7. The control method according to claim 6, characterized in that: The identifying the objects in the space to obtain attribute information of the objects in the space includes: Sending information about the objects in the space to the remote server or the drone operation terminal; Receive identification information of the objects in the space from the remote server or the drone operation terminal to obtain attribute information of the objects in the space.

8. The control method according to claim 6, characterized in that: The identification information of the object in the space is stored in the remote server or the drone operation terminal, and determining whether the drone needs to avoid obstacles based on the attribute information of the object in the space includes: Receive a first instruction or a second instruction from the remote server or the drone operating terminal, where the first instruction is used to instruct the drone to avoid obstacles, and the second instruction is used to instruct the drone to avoid obstacles according to the obstacle avoidance strategy determined by the remote server or the drone operating terminal.

9. An electronic device, characterized in that: The electronic device includes a processor and a memory, wherein at least one program code is stored in the memory, and the at least one program code is loaded and executed by the processor, so that the electronic device implements the control method according to any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that At least one program code is stored in the computer-readable storage medium, and the at least one program code is loaded and executed by the processor to enable the computer to implement the control method according to any one of claims 1 to 8.

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