Return flight control method and device, chip and unmanned aerial vehicle

Abstract

The invention relates to the technical field of aircrafts and navigation, in particular to a return flight control method and device, a chip and an unmanned aerial vehicle. The method comprises the following steps: after the autonomous aircraft takes off, acquiring motion state information of the autonomous aircraft through satellite positioning equipment and / or one or more sensors; when the autonomous aircraft flies until the satellite positioning equipment can provide a positioning signal with specified precision, the current position information of the autonomous aircraft at the current moment is obtained through the satellite positioning equipment, and the initial position information of the autonomous aircraft is reversely deduced through the current position information and the historical motion state information; the initial position information is used for performing return flight control on the autonomous aircraft during return flight of the autonomous aircraft. Therefore, based on the multi-source navigation data, the positioning blind area in the take-off stage is avoided through the mode of calculating the initial position in a delayed mode, the problem that the homeward voyage precision of the lightweight aircraft is insufficient in the environment that GPS signals are interfered is solved, and therefore the homeward voyage precision is improved, and the safety and reliability of the system are improved.

Description

Technical Field

[0001] The present disclosure relates to the field of aircraft and navigation technology, and in particular to a return control method, device, chip and unmanned aerial vehicle. Background Art

[0002] With the rapid development of drone technology, drones have been widely used in many fields, such as aerial photography, topographic mapping, agricultural plant protection, search and rescue, etc. When drones are performing tasks, an accurate and reliable navigation and positioning system is the key to ensure that they fly according to the predetermined route and achieve precise position control and attitude adjustment. At present, the navigation and positioning system of drones mainly relies on two technologies: inertial navigation system and global positioning system (GPS).

[0003] Among them, the inertial navigation system plays an important role in UAV navigation because of its characteristics of not relying on external information, strong autonomy, and not susceptible to electromagnetic interference. However, the inertial navigation system obtains navigation information through integral calculation. This mechanism causes the positioning error to accumulate over time. After a long flight, the error gradually increases, seriously affecting the navigation accuracy. Especially in UAV applications that require high-precision positioning and long-term operation, the limitations of the inertial navigation system are particularly obvious.

[0004] The Global Positioning System (GPS) has become another important means of navigation and positioning for drones due to its advantages such as high precision, all-weather and global coverage. However, the accuracy, availability, continuity and integrity of GPS signals will be seriously affected in certain environments, such as urban canyons, dense forests and other areas with severe obstruction, or in environments with strong electromagnetic interference. In these environments, drones may not be able to receive enough GPS satellite signals or the signal quality is poor, resulting in the inability to autonomously navigate and position. In serious cases, the drone may be forced to land or crash, which seriously threatens flight safety.

[0005] For lightweight aircraft, such as small multi-rotor aircraft, due to their small size and light weight, they usually do not have high-precision positioning sensors such as real-time kinematic (RTK), but are only equipped with relatively basic positioning equipment such as GPS and optical flow sensors. In an environment where GPS signals are easily interfered with, the positioning accuracy of the aircraft may not converge to a high-precision state. If the aircraft takes off in this state, the return position of the aircraft is often based on the latitude and longitude information at the time of takeoff, which may cause a large deviation in the return position in actual operation, affecting the execution effect of the flight mission and flight safety.

[0006] How to improve the return accuracy of lightweight aircraft in a GPS interference environment, and thereby improve the safety and reliability of the system, is an urgent problem to be solved. Summary of the invention

[0007] In order to solve the problems in the related art, the embodiments of the present disclosure provide a return control method, device, chip and drone.

[0008] In a first aspect, an embodiment of the present disclosure provides a return control method, which is applied to an autonomous aircraft, wherein the autonomous aircraft includes a satellite positioning device and one or more sensors, wherein the sensors are used to measure motion state information of the autonomous aircraft, and the method includes: After the autonomous aircraft takes off, obtaining the motion state information of the autonomous aircraft through the satellite positioning device and / or the one or more sensors; When the autonomous aircraft flies to a point where the satellite positioning device can provide a positioning signal with a specified accuracy, obtaining the current position information of the autonomous aircraft at the current moment through the satellite positioning device; Based on the motion state information obtained from the autonomous aircraft from the take-off time to the current time, and the current position information, the initial position information of the autonomous aircraft at the take-off time is calculated; wherein the initial position information is used to control the return of the autonomous aircraft during the return of the autonomous aircraft.

[0009] According to an embodiment of the present disclosure, the method further includes: Acquire the initial relative position information of the autonomous aircraft at the take-off time, wherein the initial relative position information is used to describe the position information of the take-off point in the relative coordinate system; The step of calculating the initial position information of the autonomous aircraft at the take-off time according to the motion state information of the autonomous aircraft acquired from the take-off time to the current time and the current position information comprises: According to the motion state information of the autonomous aircraft acquired from the take-off time to the current time, estimating the current relative position information of the autonomous aircraft at the current time, wherein the current relative position information is used to describe the information of the current position in the relative coordinate system; Calculate the initial position information of the autonomous aircraft at the take-off time according to the initial relative position information, the current relative position information and the current position information; wherein the initial position information and the current position information are both position information in a geographic coordinate system; The performing return control on the autonomous aircraft includes: controlling the autonomous aircraft to return to the take-off point.

[0010] According to an embodiment of the present disclosure, when the autonomous aircraft includes a plurality of sensors, the plurality of sensors include: an inertial measurement unit IMU and an optical flow sensor, the satellite positioning device includes: a global positioning system GPS receiver; the motion state information includes: acceleration information, angular velocity information and attitude information obtained based on the inertial measurement unit IMU, optical flow velocity information obtained based on the optical flow sensor, and GPS velocity information obtained based on the GPS receiver; The estimating the current relative position information of the autonomous aircraft at the current moment according to the motion state information of the autonomous aircraft acquired from the take-off moment to the current moment includes: The current relative position information is obtained using a Kalman filter algorithm based on the acceleration information, angular velocity information, speed information and attitude information obtained by the autonomous aircraft from the take-off moment to the current moment; wherein the speed information includes: optical flow speed information or GPS speed information or fused speed information obtained based on the optical flow speed information and the GPS speed information.

[0011] According to an embodiment of the present disclosure, the Kalman filter algorithm is an extended Kalman filter algorithm, and the current relative position information is obtained by using the Kalman filter algorithm according to the acceleration information, angular velocity information, speed information and attitude information acquired by the autonomous aircraft from the take-off time to the current time, including: Based on the relative position information, acceleration information, angular velocity information and attitude information of the autonomous aircraft at the historical moment, predicting the estimated velocity and estimated position of the autonomous aircraft at the next moment of the historical moment; updating the speed estimate and position estimate of the autonomous aircraft at the next moment in the historical moment based on the speed information of the autonomous aircraft at the next moment in the historical moment; wherein the historical moment is a moment before the current moment; By iterative prediction and updating process, an updated position estimate of the autonomous aircraft at the current moment is obtained; The updated estimated position of the autonomous aerial vehicle at the current moment is used as the current relative position information.

[0012] According to an embodiment of the present disclosure, the Kalman filter algorithm is an error state Kalman filter algorithm, and the current relative position information is obtained by using the Kalman filter algorithm according to the acceleration information, angular velocity information, speed information and attitude information acquired by the autonomous aircraft from the take-off time to the current time, including: Based on the relative position information, acceleration information, angular velocity information and attitude information of the autonomous aircraft at the historical moment, predicting the estimated velocity and estimated position of the autonomous aircraft at the next moment of the historical moment; Predicting a velocity error estimate and a position error estimate of the autonomous aircraft at the next moment in the historical moment based on the position error and the velocity error of the autonomous aircraft at the historical moment; updating the speed error estimate and the position error estimate based on the speed information of the autonomous aircraft at the next moment after the historical moment, to obtain an updated speed error estimate and an updated position error estimate; updating a velocity estimate of the autonomous aircraft at a next moment in the historical moment based on the updated velocity error estimate; updating the position estimate of the autonomous aircraft at a moment after the historical moment based on the updated position error estimate; wherein the historical moment is a moment before the current moment; By iterative prediction and updating process, an updated position estimate of the autonomous aircraft at the current moment is obtained; The updated estimated position of the autonomous aerial vehicle at the current moment is used as the current relative position information.

[0013] According to an embodiment of the present disclosure, the calculating the initial position information of the autonomous aircraft at the take-off time according to the initial relative position information, the current relative position information and the current position information includes: Based on the difference between the current relative position information and the initial relative position information and the current position information, the initial position information of the autonomous aircraft is obtained through reverse calculation; wherein the difference is used to describe the horizontal displacement information of the autonomous aircraft at the current moment relative to the take-off moment.

[0014] According to an embodiment of the present disclosure, the initial position information of the autonomous aircraft is obtained by reverse calculation based on the difference between the current relative position information and the initial relative position information and the current position information, including: The initial position information of the autonomous aircraft is obtained by the following formula : ; ; in, represents the latitude coordinate in the initial position information, represents the longitude coordinate in the initial position information, represents the altitude in the initial position information, where the altitude in the initial position information is the altitude of the autonomous aircraft at the take-off time, represents the latitude coordinate in the current location information, represents the longitude coordinate in the current location information, , , represents the eastward displacement of the autonomous aircraft at the current time relative to the take-off time, represents the northward displacement of the autonomous aircraft at the current time relative to the take-off time, represents the meridian curvature radius, , a and They are the semi-major axis of the earth and the square of the first eccentricity in the WGS-84 ellipsoid model parameters, a=6,378,137m, .

[0015] According to an embodiment of the present disclosure, the method further includes: Based on the current position information and the motion state information of the autonomous aircraft obtained from the current moment to the specified moment, the specified position information of the autonomous aircraft at the specified moment is calculated; wherein the specified moment is the moment after the current moment when the satellite positioning device cannot provide a positioning signal of a specified accuracy.

[0016] In a second aspect, an embodiment of the present disclosure provides a return control device, which is provided in an autonomous aircraft, wherein the autonomous aircraft includes a satellite positioning device and one or more sensors, wherein the sensors are used to measure motion state information of the autonomous aircraft, and the device includes: a motion state information acquisition module, configured to acquire the motion state information of the autonomous aircraft through the satellite positioning device and / or the one or more sensors after the autonomous aircraft takes off; a current position information acquisition module, configured to acquire the current position information of the autonomous aircraft at the current moment through the satellite positioning device when the autonomous aircraft flies to a position where the satellite positioning device can provide a positioning signal with a specified accuracy; The initial position information acquisition module is configured to calculate the initial position information of the autonomous aircraft at the take-off time based on the motion state information obtained from the take-off time to the current time, and the current position information of the autonomous aircraft; wherein the initial position information is used to control the return of the autonomous aircraft during the return of the autonomous aircraft.

[0017] In a third aspect, a chip is provided in an embodiment of the present disclosure, comprising the device described in the second aspect; or, comprising a memory and a processor; wherein the memory is used to store one or more computer instructions, wherein the one or more computer instructions are executed by the processor to implement any method described in the first aspect.

[0018] In a fourth aspect, an embodiment of the present disclosure provides a drone, comprising the device described in the second aspect; or, comprising the chip described in the third aspect; or, comprising a memory and a processor; wherein the memory is used to store one or more computer instructions, wherein the one or more computer instructions are executed by the processor to implement any one of the methods described in the first aspect.

[0019] According to the technical solution provided by the embodiments of the present disclosure, when there is no or insufficient satellite signal during the take-off phase, the motion state information (such as acceleration, angular velocity, speed) of the autonomous aircraft (such as a drone) is obtained through a satellite positioning device and / or one or more sensors. During the satellite signal recovery phase, when the autonomous aircraft flies to an area with good satellite signals, the current position information of the autonomous aircraft at the current moment is obtained through the satellite positioning device, and the initial position information is dynamically inferred through the current position information and historical motion state information, and the autonomous aircraft is controlled to return based on the initial position, thereby avoiding the limitation of the traditional method that requires the take-off point to be accurately recorded in advance. This dynamic calibration method can adapt to situations where the take-off point position is unknown or inaccurately recorded, thereby improving the flexibility and practicality of the system.

[0020] In addition, the present disclosure realizes the backward correction of the initial positioning error through the innovative method of "using the current position to reverse the initial position", and combines the multi-sensor fusion algorithm to significantly improve the navigation accuracy and return reliability of the autonomous aircraft in complex environments. The initial position accuracy reversed is significantly higher than that of pure inertial navigation or a single sensor solution, and is particularly suitable for long-duration flight missions. Thus, by delaying the calculation of the initial position, the positioning blind spot and GPS dependence problems in the take-off phase are avoided, so that the aircraft can still establish an accurate return reference point in a complex environment (such as indoors, urban canyons), significantly improving the return reliability, and solving the problem of insufficient return accuracy of lightweight aircraft in an environment where GPS signals are interfered, thereby significantly improving the return accuracy and improving the safety and reliability of the system. In addition, there is no need to rely on high-precision RTK-GPS or laser radar, and high-precision positioning is achieved only through low-cost conventional satellite positioning equipment and sensors and other basic equipment, thereby further reducing the complexity and cost of the system. In addition, this technical solution not only reduces the dependence on a single sensor or a single positioning device, but also provides a universal solution for low-cost and high-precision navigation, which has significant engineering practical value.

[0021] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Other features, objectives and advantages of the present disclosure will become more apparent through the following detailed description of non-limiting embodiments in conjunction with the accompanying drawings. In the accompanying drawings: Figure 1 A flow chart showing a return control method according to an embodiment of the present disclosure; Figure 2 A flow chart of a method for calculating initial position information according to an embodiment of the present disclosure is shown; Figure 3 A schematic diagram of a return trajectory of an autonomous aircraft in a relative coordinate system according to an embodiment of the present disclosure is shown; Figure 4 A flow chart of a method for estimating current relative position information using an EKF algorithm according to an embodiment of the present disclosure is shown; Figure 5 A flow chart of a method for estimating current relative position information using an ESKF algorithm according to an embodiment of the present disclosure is shown; Figure 6 A flowchart showing another return control method according to an embodiment of the present disclosure; Figure 7 A schematic structural diagram of a return control device according to an embodiment of the present disclosure is shown; Figure 8 A schematic structural diagram of another return control device according to an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0023] Hereinafter, exemplary embodiments of the present disclosure will be described in detail with reference to the accompanying drawings so that those skilled in the art can easily implement them. In addition, for the sake of clarity, parts not related to the description of the exemplary embodiments are omitted in the accompanying drawings.

[0024] In the present disclosure, it should be understood that terms such as "include" or "have" are intended to indicate the presence of features, numbers, steps, behaviors, components, parts, or a combination thereof disclosed in the present specification, and are not intended to exclude the possibility that one or more other features, numbers, steps, behaviors, components, parts, or a combination thereof exist or are added.

[0025] It should also be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present disclosure may be combined with each other. The present disclosure will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0026] As mentioned above, small multi-rotor aircraft usually do not have high-precision positioning sensors such as real-time kinematic (RTK) due to their small size and light weight, but are only equipped with relatively basic positioning equipment such as GPS and optical flow sensors. In an environment where GPS signals are susceptible to interference, the positioning accuracy of the aircraft may not converge to a high-precision state. If the aircraft takes off in this state, the return position of the aircraft is often based on the longitude and latitude information at the time of takeoff, which may cause a large deviation in the return position in actual operation, affecting the execution effect of the flight mission and flight safety.

[0027] In an environment where GPS signals are lost or interfered with (such as indoors, in urban canyons), existing solutions use sensor data to record flight trajectories, and call trajectory points in reverse order to generate control instructions when returning home. The PID (Proportional-Integral-Derivative) algorithm is used to adjust the flight state. To correct the accumulated error, visual feature matching, optical flow closed loop or lidar scanning are used to optimize the path. Although this method does not rely on GPS, long-distance return requires frequent corrections, and the accuracy is affected by sensor drift.

[0028] In order to solve the problem in the prior art that small multi-rotor aircraft cannot obtain high-precision position information when the GPS signal is interfered, resulting in large errors in the return flight, the inventors of the present invention have optimized and improved the existing return flight position acquisition scheme after careful research and consideration, and provided an efficient, reliable and low-cost return flight control solution.

[0029] Figure 1 A flow chart of a return control method according to an embodiment of the present disclosure is shown, which is applied to an autonomous aircraft, wherein the autonomous aircraft includes a satellite positioning device and one or more sensors, and the sensors are used to measure the motion state information of the autonomous aircraft.

[0030] The autonomous aircraft in the present disclosure refers to an aircraft that can achieve autonomous flight through onboard sensors, navigation systems and control algorithms without relying on real-time manual control. In addition to existing types of drones (such as small multi-rotor aircraft and fixed-wing drones), it can also include other types of aircraft derived from the development of science and technology in the future, such as unmanned airships, unmanned rockets, unmanned flying cars, etc.

[0031] The satellite positioning device in the present disclosure is used to receive satellite signals from a global navigation satellite system (such as GPS, BeiDou, etc.) to obtain three-dimensional position coordinates (longitude, latitude, altitude), speed and time information, and can be a GPS receiver, BeiDou receiver, GLONASS receiver, etc.

[0032] The sensors in the present disclosure include but are not limited to: inertial measurement unit IMU and optical flow sensor. Among them, IMU usually contains a three-axis accelerometer, a three-axis gyroscope and a three-axis magnetometer. These sensors can provide a variety of information about the drone. The acceleration, angular velocity and attitude of the autonomous aircraft can be measured through IMU; the pixel changes between consecutive image frames can be analyzed through the optical flow sensor to calculate the relative speed of the aircraft relative to the ground (optical flow speed).

[0033] The motion state information in the present disclosure is data describing the motion characteristics of the autonomous aircraft, including but not limited to speed, acceleration, angular velocity, attitude information, etc. Among them, the motion state information is obtained by sensors and / or satellite positioning devices set on the autonomous aircraft. For example: the GPS speed of the autonomous aircraft can be obtained through a GPS receiver, the optical flow velocity of the autonomous aircraft can be obtained through an optical flow sensor, and the acceleration, angular velocity and attitude information of the autonomous aircraft can be obtained through an IMU. Among them, since the optical flow sensor measures the motion of the drone based on visual information, and calculates the horizontal motion speed of the drone relative to the ground by analyzing the motion of pixels in continuous image frames, the optical flow speed is a two-dimensional horizontal speed relative to the ground; the GPS speed is the speed of the drone relative to the earth's surface calculated based on the satellite positioning system. It calculates the speed by measuring the rate of change of the position of the drone in three-dimensional space, so it is a three-dimensional speed, including horizontal speed (eastward speed and northward speed) and vertical speed (ascending or descending speed). The horizontal speed in the optical flow speed and GPS speed both represent the horizontal motion speed of the drone relative to the ground.

[0034] like Figure 1 As shown, the method includes the following steps S110~S130: In step S110, after the autonomous aircraft takes off, the motion state information of the autonomous aircraft is acquired through the satellite positioning device and / or the one or more sensors.

[0035] Before the autonomous aircraft takes off, the sensors and satellite positioning equipment are started. After that, the sensor data is initialized, the sensor errors (such as the zero bias of the IMU, etc.) are calibrated, and the satellite positioning equipment (such as the GPS receiver) is initialized. The GPS receiver will automatically search for satellite signals. During a cold start, it takes 30 seconds to several minutes to search for satellite signals. During this period, the positioning accuracy is low (the error may be hundreds of meters), and it cannot meet the high-precision initial position information required for return. During this period, if the aircraft takes off before the positioning accuracy converges to a high accuracy, and obtains the current take-off longitude and latitude through the GPS receiver and uses it as the return position, it is easy to produce large errors when returning.

[0036] In the early stage of autonomous aircraft takeoff, although the GPS position accuracy is in the unconverged stage, since GPS velocity calculation is based on Doppler frequency shift, the measurement of Doppler frequency shift is relatively independent of position calculation and has low sensitivity to errors, even when the position accuracy has not converged, the velocity accuracy may reach a high level quickly. Therefore, under normal circumstances, the velocity accuracy given by GPS during this period will be better than the position accuracy. In addition, since the working principles of various sensors (such as IMU and optical flow sensors) do not rely on satellite signals, they can generally work normally when the aircraft takes off.

[0037] Based on the above considerations, in order to avoid the positioning blind spot during the take-off phase, the present invention does not directly obtain the take-off position information through the satellite positioning device after the autonomous aircraft takes off, but first obtains the motion state information of the autonomous aircraft through the satellite positioning device and / or the one or more sensors, and then reversely infers the take-off position information based on the obtained motion state information by combining subsequent steps S120 and S130.

[0038] According to an embodiment of the present disclosure, when acquiring the motion state information of the autonomous aircraft through the satellite positioning device and / or the one or more sensors, the satellite positioning device and the sensor may be implemented in the following four combinations: Method 1: Obtain the motion state information of the autonomous aircraft through a satellite positioning device and a sensor. For example, obtain the GPS speed of the autonomous aircraft through a GPS receiver, and obtain the acceleration, angular velocity and attitude of the autonomous aircraft through an IMU, or obtain the GPS speed and optical flow speed of the autonomous aircraft through a GPS receiver and an optical flow sensor respectively.

[0039] Method 2: Obtain the motion state information of the autonomous aircraft through satellite positioning equipment and multiple sensors. For example, obtain the GPS speed of the autonomous aircraft through a GPS receiver, obtain the acceleration, angular velocity and attitude of the autonomous aircraft through an IMU, and obtain the optical flow speed of the autonomous aircraft through an optical flow sensor.

[0040] Method 3: Obtain the motion state information of the autonomous aircraft through a sensor or satellite positioning device. For example, obtain the GPS speed of the autonomous aircraft through a GPS receiver, or obtain the acceleration, angular velocity and attitude of the autonomous aircraft through an IMU, or obtain the optical flow speed of the autonomous aircraft through an optical flow sensor.

[0041] Method 4: obtaining the motion state information of the autonomous aircraft through multiple sensors, for example, obtaining the acceleration and angular velocity of the autonomous aircraft through an IMU, and obtaining the optical flow velocity of the autonomous aircraft through an optical flow sensor.

[0042] Methods 1 and 2 above are suitable for scenarios where the GPS positioning signal is weak but the speed accuracy is still acceptable. Combining IMU and optical flow sensor can improve reliability. Method 3 is suitable for scenarios where a single sensor can meet the mission requirements, such as indoor hovering (using optical flow sensor) or flying in an open environment (using GPS). The most suitable sensor can be selected according to the flight environment and mission requirements. For example, optical flow sensor is preferred when flying at low altitude; GPS is preferred in an open environment. Method 4 is suitable for scenarios where GPS signal is completely unavailable. The combination of optical flow sensor and IMU can provide reliable motion status information.

[0043] In the specific implementation, the various modes can be dynamically switched based on the current accuracy parameters of the satellite positioning equipment and sensors configured on the autonomous aircraft, and combined with the current flight environment conditions and mission requirements. By reasonably designing the execution conditions of each mode, it can be ensured that the autonomous aircraft can obtain high-precision motion state information in different environments, thereby supporting reliable return control.

[0044] In addition, the acquisition frequency of motion state information is related to the data update frequency of each sensor and satellite positioning device, and can be reasonably set according to sensor performance, control requirements and computing resources. In some scenarios, the acquisition frequency can be dynamically adjusted to optimize performance and resource utilization. For example: during high-dynamic flight, increase the acquisition frequency of IMU and optical flow sensors (such as IMU 500Hz, optical flow 50Hz); during low-dynamic flight or hovering, reduce the acquisition frequency of IMU and optical flow sensors (such as IMU 100Hz, optical flow 10Hz); when the GPS signal is good, only rely on GPS data; when the GPS signal is unavailable, increase the acquisition frequency of IMU and optical flow sensors to make up for the lack of GPS data. By reasonably setting the acquisition frequency, while ensuring the acquisition of motion state information, the utilization of computing resources can be optimized to ensure the efficient operation of autonomous aircraft.

[0045] It should be noted that: before executing this step S110, that is, when the autonomous aircraft takes off (that is, at the take-off time), it can be determined whether the satellite positioning device can provide a positioning signal of a specified accuracy. If not, it means that the autonomous aircraft took off when the satellite positioning device did not converge to high accuracy. Then, this step S110 and subsequent steps S120 and S130 are executed again. If the positioning signal of the specified accuracy can be provided, it means that the autonomous aircraft took off when the satellite positioning device converged to high accuracy. At this time, the take-off position information can be directly obtained through the satellite positioning device and used as the return position.

[0046] In step S120, when the autonomous aircraft flies to a position where the satellite positioning device can provide a positioning signal with a specified accuracy, the current position information of the autonomous aircraft at the current moment is acquired through the satellite positioning device.

[0047] After the autonomous aircraft takes off, it will continuously monitor (in real time or at preset time intervals) the positioning accuracy of the satellite positioning device. Once it is detected that the autonomous aircraft has flown to a point where the satellite positioning device can provide a positioning signal of a specified accuracy, it means that it has converged to a high-precision state. In this case, the current position information obtained by the satellite positioning device is highly accurate.

[0048] According to the embodiments of the present disclosure, when judging whether the satellite positioning device can provide a positioning signal of a specified accuracy, the judgment can be made by one or more parameters among the positioning accuracy, number of satellites and signal strength obtained from the GPS signal. For example: judging whether the number of received satellites is greater than a threshold value (such as more than 4), whether the geometric precision factor (GDOP) or the horizontal precision factor (HDOP) is less than a threshold value (such as HDOP<2), and whether the received satellite signal strength is sufficient (such as signal-to-noise ratio SNR>30 dB). In specific implementation, a comprehensive judgment can be made based on the number of satellites, positioning accuracy and signal strength, thereby effectively avoiding positioning errors caused by insufficient single conditions. For example: when the number of satellites>4, HDOP<2, SNR>30dB, it is judged as high-precision positioning.

[0049] The current location information in this disclosure refers to the location information corresponding to the "current moment", which is the moment when the autonomous aircraft is monitored to fly to a position where the satellite positioning device can provide a positioning signal with a specified accuracy. The current location information includes but is not limited to the horizontal position in the geographic coordinate system, that is, the longitude and latitude information.

[0050] In step S130, the initial position information of the autonomous aircraft at the take-off time is calculated based on the motion state information of the autonomous aircraft from the take-off time to the current time, and the current position information; wherein the initial position information is used to control the return of the autonomous aircraft during the return of the autonomous aircraft.

[0051] In the present disclosure, the autonomous aircraft relies on the initial position information corresponding to the take-off time during the return flight, and does not limit whether it returns in a straight line or along a flight trajectory that has been flown. Among them, when calculating the initial position information, the motion state information obtained by the autonomous aircraft from the take-off time to the current time is used. Since the current time in the present disclosure is the initial time when the satellite positioning device can provide a positioning signal with a specified accuracy, it is generally not too long away from the take-off time. At this time, the auxiliary information (such as relative displacement information) calculated based on the motion state information obtained during this period will not accumulate too many errors due to time. Therefore, the initial position calculated in combination with the current position information obtained at the current time will be relatively accurate.

[0052] When calculating the initial position information, there will be different calculation methods and processes based on different motion state information and combined with the current position information. The present disclosure provides the following specific implementation methods for calculating the initial position information. Those skilled in the art should understand that the following method for calculating the initial position information is only a specific example and is not a technical means to limit the protection scope of the present disclosure. In specific implementation, different calculation methods can be used according to the different motion state information obtained in the specific application scenario. As long as the current position information is combined in the calculation, it is within the protection scope defined by the present disclosure.

[0053] Figure 2 FIG. 1 is a flow chart showing a method for calculating initial position information according to an embodiment of the present disclosure. Figure 2 As shown, the following steps S210-230 are included: In step S210, initial relative position information of the autonomous aircraft at the take-off time is acquired, where the initial relative position information is used to describe the position information of the take-off point in a relative coordinate system.

[0054] According to an embodiment of the present disclosure, the performing return control on the autonomous aircraft includes: controlling the autonomous aircraft to return to the take-off point.

[0055] In the present disclosure, the take-off time generally refers to the time when the aircraft changes from a stationary state on the ground to a flying state in the air. Common definitions include those based on sensor data (such as accelerometers, barometers, IMUs), control instructions (such as throttle instructions, mode switching), and external signals (such as GPS, visual sensors). For example: when the flight control system issues a take-off command (such as the throttle value exceeds a certain threshold), it can be considered that the drone starts to take off; for another example: when the drone switches from a ground mode (such as standby mode) to a flight mode (such as a fixed altitude mode or a hovering mode), it can be considered that the drone starts to take off. The setting method of the take-off time can be defined according to the specific implementation.

[0056] In addition, the take-off point refers to the position of the autonomous aircraft at the time of take-off, and the initial relative position information is used to describe the relative position information of the take-off point, and the initial relative position information includes: the horizontal position and the height of the autonomous aircraft at the time of take-off. Among them, the horizontal position in the initial relative position information is usually expressed by relative coordinates. The relative coordinate system in the present disclosure refers to the coordinate system relative to the take-off point, which can be expressed by the local coordinate system of the geographic coordinate system (such as the northeast sky coordinate system), that is, the east position and the north position relative to the take-off point are used to express it respectively. The origin and direction of the relative coordinate system are fixed and do not change with the attitude of the aircraft. It is used to describe the position of the aircraft relative to the take-off point.

[0057] Figure 3FIG. 2 shows a schematic diagram of the return trajectory of an autonomous aircraft in a relative coordinate system according to an embodiment of the present disclosure. Figure 3 As shown, in general, the origin is the take-off point, expressed as (0, 0), the first value refers to the east position, and the second value refers to the north position, both in meters; the altitude of the autonomous aircraft at the moment of take-off can be obtained by a barometer or an ultrasonic sensor, wherein the altitude of the autonomous aircraft at the moment of take-off can be the altitude or the relative altitude, and the relative altitude refers to the vertical distance of the aircraft relative to another reference point (such as the ground, a building or a plane).

[0058] In step S220, the current relative position information of the autonomous aircraft at the current moment is estimated based on the motion state information of the autonomous aircraft from the take-off moment to the current moment, and the current relative position information is used to describe the current position information in the relative coordinate system.

[0059] In the present disclosure, the position of the autonomous aircraft at the current moment is the current position, such as Figure 3 For example, assuming the take-off point is (0, 0) and the current position of the autonomous aircraft is (120, 100), it means that the current position relative to the take-off point is 120 meters eastward and 100 meters northward. From the take-off time to the current time, that is: Figure 3 During the period shown by the middle blue track, the autonomous aircraft is in a stage where the satellite navigation signal is weak. The current time may be the start time when the satellite positioning device can provide a positioning signal with a specified accuracy.

[0060] The following describes the process of estimating the current relative position information in the present disclosure through a specific embodiment.

[0061] In this specific embodiment, the autonomous aerial vehicle includes multiple sensors, and the multiple sensors include: an inertial measurement unit IMU and an optical flow sensor. The satellite positioning device includes: a global positioning system GPS receiver; the motion state information includes: acceleration information, angular velocity information and attitude information obtained based on the inertial measurement unit IMU, optical flow velocity information obtained based on the optical flow sensor, and GPS velocity information obtained based on the GPS receiver.

[0062] According to an embodiment of the present disclosure, estimating the current relative position information of the autonomous aircraft at the current moment according to the motion state information of the autonomous aircraft acquired from the take-off moment to the current moment includes: The current relative position information is obtained using a Kalman filter algorithm based on the acceleration information, angular velocity information, speed information and attitude information obtained by the autonomous aircraft from the take-off moment to the current moment; wherein the speed information includes: optical flow speed information or GPS speed information or fused speed information obtained based on the optical flow speed information and the GPS speed information.

[0063] In the present disclosure, after the autonomous aircraft takes off, starting from the take-off moment, the Kalman filter algorithm is used to iteratively estimate the relative position information corresponding to each moment from the take-off moment to the current moment in real time. For example, the relative position information at the take-off moment (i.e., the initial relative position information) is used to estimate the relative position information at the next moment after the take-off moment, and the relative position information at the next moment after the take-off moment is used to estimate the relative position information at the next moment after the take-off moment, until the current moment, the current relative position information can be directly obtained. That is to say, at each moment from the take-off moment to the current moment, the motion state information of the autonomous aircraft is not simply obtained, but after the acquisition, the relative position information at the moment is calculated using the Kalman filter algorithm, so that after real-time iterative estimation, the current relative position information of the autonomous aircraft at the current moment can be directly obtained at the current moment.

[0064] When estimating the relative position information, firstly, the velocity estimate is predicted based on the acceleration information measured by the IMU. Since the acceleration information measured by the IMU is in the body coordinate system, it is necessary to convert the attitude information measured by the IMU (including the three attitude angles of roll angle, pitch angle and heading angle) into the acceleration in the relative coordinate system, and then obtain the velocity estimate based on the acceleration in the relative coordinate system. Among them, in order to make the attitude information more accurate, the change of each attitude angle is obtained based on the angular velocity measured by the IMU, and then each attitude angle is updated based on the change. After obtaining the velocity estimate, the position estimate is predicted based on the velocity estimate. Finally, the velocity estimate and position estimate are updated using the velocity information measured by the optical flow sensor and / or GPS receiver. After the iterative prediction and update process, the current relative position information is finally obtained.

[0065] When determining the speed information, it can be based on the following method: According to the preset time period (one or more time periods), the current optical flow velocity information and the current GPS velocity information obtained are judged to see whether they meet the accuracy conditions. If the optical flow velocity meets the accuracy conditions and the GPS velocity does not meet the accuracy conditions, the optical flow velocity is used in the current time period; if the GPS velocity meets the accuracy conditions and the optical flow velocity does not meet the accuracy conditions, the GPS velocity is used in the current time period; if both meet the accuracy conditions, or in scenarios that require higher velocity estimation accuracy (such as precise hovering or low-speed flight), the fused velocity is used in the current time period. When calculating the fused velocity based on the optical flow velocity information and the GPS velocity information, the weighted average method can be used for calculation.

[0066] Among them, when judging whether the current optical flow velocity information meets the accuracy conditions, it can be judged according to the ground texture, lighting conditions, flight altitude and other conditions. If the ground texture is rich, the lighting conditions are moderate, and the flight altitude is low (usually less than 10 meters), the current optical flow velocity information meets the accuracy conditions; when judging whether the current GPS speed information meets the accuracy conditions, it can be judged according to the speed error and speed precision factor. The smaller the speed error and the speed precision factor, the higher the GPS speed accuracy.

[0067] The present invention continuously monitors the speed accuracy of GPS and optical flow sensors, and dynamically selects a higher-precision speed according to the monitoring results, thereby utilizing the respective advantages of optical flow speed and GPS speed in different environments and flight states (for example, when flying at low altitudes, the optical flow speed has a higher accuracy; in an open environment, the GPS speed has a higher accuracy), which can significantly improve the accuracy of speed measurement and thus improve the accuracy of position estimation, while better adapting to complex environments and optimizing resource utilization.

[0068] In addition, the Kalman filter algorithm may be an extended Kalman filter (EKF) algorithm or an error state Kalman filter (ESKF) algorithm.

[0069] The specific implementation process of estimating the current relative position information based on the EKF algorithm and the ESKF algorithm is described below.

[0070] Figure 4 FIG. 2 is a flow chart showing a method for estimating current relative position information using an EKF algorithm according to an embodiment of the present disclosure. Figure 4 As shown, the following steps S410-440 are included: In step S410, based on the relative position information, acceleration information, angular velocity information and attitude information of the autonomous aircraft at the historical moment, the speed estimate and position estimate of the autonomous aircraft at the next moment of the historical moment are predicted.

[0071] The relative position information in the present disclosure refers to the position information of the autonomous aircraft in the relative coordinate system corresponding to the historical moment. For example, if the historical moment is the flight moment, the relative position information of the autonomous aircraft at the historical moment is the initial relative position information.

[0072] This step S410 is a prediction process for estimating the current relative position information based on the EKF algorithm.

[0073] When executing this step, first define the state vector and observation vector.

[0074] The state vector X is expressed as: ; The observation vector is expressed as: ; in: ; ; ; ; in:

[0075] ; ; ; in,( , ) represents the horizontal position of the autonomous aircraft in the relative coordinate system, represents the east position of the autonomous aircraft in the relative coordinate system, represents the north position of the autonomous aircraft in the relative coordinate system, and represent the eastward position of the autonomous aircraft in the relative coordinate system at time k and time k-1 respectively, and They represent the north position of the autonomous aircraft in the relative coordinate system at time k and time k-1 respectively, ( , ) represents the horizontal speed of the autonomous aircraft in the relative coordinate system, represents the eastward speed of the autonomous aircraft in the relative coordinate system, represents the northward speed of the autonomous aircraft in the relative coordinate system, and represent the eastward speed of the autonomous aircraft in the relative coordinate system at time k and time k-1 respectively, and represent the northward speed of the autonomous aircraft in the relative coordinate system at time k and time k-1 respectively, is the eastward acceleration of the autonomous aircraft in the relative coordinate system, is the northward acceleration of the autonomous aircraft in the relative coordinate system, is the eastward acceleration of the autonomous aircraft in the relative coordinate system at time k-1, is the northward acceleration of the autonomous aircraft in the relative coordinate system at time k-1, is the acceleration component of the autonomous aircraft along the x-axis of the body in the acceleration, is the acceleration component of the autonomous aircraft along the y-axis of the body in the acceleration, is the acceleration component of the autonomous aircraft along the z-axis of the body in the acceleration, is the roll angle in the attitude information, is the pitch angle in the attitude information, is the heading angle in the attitude information, and are the roll angles in the attitude information at time k and time k-1 respectively, and The pitch angles in the attitude information are k and k-1 respectively. and are the heading angles in the attitude information at time k and time k-1 respectively, is the angular velocity component along the x-axis of the body in the angular velocity at time k-1, is the angular velocity component along the y-axis of the body in the angular velocity at time k-1, is the angular velocity component along the z-axis of the body in the angular velocity at time k-1, represents the time step, represents the eastward speed in the speed information, Indicates the north speed in the speed information.

[0076] Specifically, the state equation corresponds to: ; Among them, k-1 moment is the historical moment, k moment is the moment after the historical moment, is the state estimate (velocity estimate and position estimate) at time k-1, is the state estimate (velocity estimate and position estimate) at time k, is the control input vector at time k (including acceleration and angular velocity), is the state noise at time k; Specifically, the observation equation corresponds to: ; in, represents the eastward speed in the speed information corresponding to time k, represents the northward speed in the speed information corresponding to time k, is the observation noise at time k.

[0077] Then, the state prediction and covariance prediction are performed based on the above state equation: Among them, the state prediction is carried out according to the state equation: ; The state covariance matrix is ​​predicted based on the state equation: ; in, is the state transfer matrix, is the state control matrix, and Determined according to the state equation and observation equation, is the process noise covariance matrix, which is set according to the IMU acceleration noise, angular velocity noise and model uncertainty. is the velocity estimate and position estimate predicted at time k, yes The corresponding state covariance, yes The corresponding state covariance, is the transpose operation.

[0078] In step S420, the speed estimate and position estimate of the autonomous aircraft at the next moment in the historical moment are updated based on the speed information of the autonomous aircraft at the next moment in the historical moment; wherein the historical moment is a moment before the current moment.

[0079] This step S420 is an update process of estimating the current relative position information based on the EKF algorithm.

[0080] First, the speed information of the autonomous aircraft at the next moment of the historical moment is obtained, and the speed information includes: optical flow speed information or GPS speed information or fused speed information obtained based on the optical flow speed information and the GPS speed information.

[0081] Then, calculate the Kalman gain : ; in, is the observation matrix, is the observation noise covariance matrix, which is set according to the accuracy of the optical flow sensor or GPS velocity measurement.

[0082] After that, update the state estimate (velocity estimate and position estimate): ; Finally, update the state covariance matrix: ; In step S430, an iterative prediction and updating process is performed until an updated position estimate of the autonomous aircraft at the current moment is obtained.

[0083] Starting from the take-off moment, steps S410 and S420 are repeatedly executed, and the extended Kalman filter algorithm is used to iteratively estimate the relative position information of each moment from the take-off moment to the current moment, until an updated position estimate of the autonomous aircraft at the current moment is obtained.

[0084] In step S440, the updated estimated position of the autonomous aerial vehicle at the current moment is used as the current relative position information.

[0085] Specifically, the current relative position information is extracted from the state vector ( , ).

[0086] According to an embodiment of the present disclosure, the current relative position information includes the horizontal position of the autonomous aircraft at the current moment in a relative coordinate system.

[0087] Figure 5 A flow chart of a method for estimating current relative position information using an ESKF algorithm according to an embodiment of the present disclosure is shown.

[0088] like Figure 5 As shown, the following steps S510-570 are included: In step S510, based on the relative position information, acceleration information, angular velocity information and attitude information of the autonomous aircraft at the historical moment, the speed estimate and position estimate of the autonomous aircraft at the next moment of the historical moment are predicted.

[0089] In step S520, a velocity error estimate and a position error estimate of the autonomous aircraft at the next moment in the historical moment are predicted based on the position error and the velocity error of the autonomous aircraft at the historical moment.

[0090] Steps S510 and S520 are the prediction process of estimating the current relative position information based on the ESKF algorithm. Different from the EKF algorithm, the prediction process in the ESKF algorithm includes nominal state prediction and error state prediction.

[0091] When executing this step, first define the nominal state vector, error state vector and observation vector.

[0092] The nominal state vector X is expressed as: ; Error state vector It is expressed as: ; in, represents the eastward position error of the autonomous aircraft in the relative coordinate system, represents the north position error of the autonomous aircraft in the relative coordinate system, represents the eastward velocity error of the autonomous aircraft in the relative coordinate system, It represents the north velocity error of the autonomous aircraft in the relative coordinate system.

[0093] The observation vector is expressed as: ; in: ; ; ; ; in:

[0094] ; ; ; in,( , ) represents the horizontal position of the autonomous aircraft in the relative coordinate system, represents the east position of the autonomous aircraft in the relative coordinate system, represents the north position of the autonomous aircraft in the relative coordinate system, and represent the eastward position of the autonomous aircraft in the relative coordinate system at time k and time k-1 respectively, and They represent the north position of the autonomous aircraft in the relative coordinate system at time k and time k-1 respectively, ( , ) represents the horizontal speed of the autonomous aircraft in the relative coordinate system, represents the eastward speed of the autonomous aircraft in the relative coordinate system, represents the northward speed of the autonomous aircraft in the relative coordinate system, and represent the eastward speed of the autonomous aircraft in the relative coordinate system at time k and time k-1 respectively, and represent the northward speed of the autonomous aircraft in the relative coordinate system at time k and time k-1 respectively, is the eastward acceleration of the autonomous aircraft in the relative coordinate system, is the northward acceleration of the autonomous aircraft in the relative coordinate system, is the eastward acceleration of the autonomous aircraft in the relative coordinate system at time k-1, is the northward acceleration of the autonomous aircraft in the relative coordinate system at time k-1, is the acceleration component of the autonomous aircraft along the x-axis of the body in the acceleration, is the acceleration component of the autonomous aircraft along the y-axis of the body in the acceleration, is the acceleration component of the autonomous aircraft along the z-axis of the body in the acceleration, is the roll angle in the attitude information, is the pitch angle in the attitude information, is the heading angle in the attitude information, and are the roll angles in the attitude information at time k and time k-1 respectively, and The pitch angles in the attitude information are k and k-1 respectively. and are the heading angles in the attitude information at time k and time k-1 respectively, is the angular velocity component along the x-axis of the body in the angular velocity at time k-1, is the angular velocity component along the y-axis of the body in the angular velocity at time k-1, is the angular velocity component along the z-axis of the body in the angular velocity at time k-1, represents the time step, represents the eastward speed in the speed information, Indicates the north speed in the speed information.

[0095] Specifically, the state equation includes a nominal state equation and an error state equation, wherein the nominal state equation corresponds to: ; Among them, k-1 moment is the historical moment, k moment is the moment after the historical moment, is the nominal state estimate (velocity estimate and position estimate) at time k-1, is the nominal state estimate (velocity estimate and position estimate) at time k, is the control input vector at time k (including acceleration and angular velocity), is the state noise at time k.

[0096] The error state equation corresponds to: ; is the error state at time k-1, is the error state at time k, which represents the deviation between the real state and the nominal state, wherein the error state includes velocity error and position error, which are related to acceleration deviation (caused by accelerometer bias error, projection error and sensor noise) and angular velocity deviation (error caused by gyroscope bias and noise accumulation). The error state equation is obtained by linearizing the nominal state after clarifying the coupling relationship between position / velocity error and acceleration deviation, angular velocity deviation, sensor bias and noise.

[0097] Specifically, the observation equation describes the relationship between the observed quantity and the error state vector, which corresponds to: ; in, is the observation matrix, is the observation noise at time k.

[0098] Then, the nominal state prediction is performed according to the nominal state equation: ; The error state prediction is performed according to the error state equation: ; in, is the state transfer matrix, is the state control matrix, and Determined according to the nominal state equation and observation equation, is the velocity estimate and position estimate predicted at time k, are the velocity error estimate and position error estimate predicted at time k.

[0099] In step S530, the speed error estimate and the position error estimate are updated based on the speed information of the autonomous aircraft at the next moment of the historical moment to obtain an updated speed error estimate and an updated position error estimate.

[0100] In step S540, the velocity estimate of the autonomous aircraft at the next moment in the historical moment is updated based on the updated velocity error estimate.

[0101] In step S550, the position estimate of the autonomous aircraft at a moment next to the historical moment is updated based on the updated position error estimate; wherein the historical moment is a moment before the current moment.

[0102] Steps S530, S540, and S550 are an update process for estimating the current relative position information based on the ESKF algorithm.

[0103] First, update the error state covariance matrix: ; in, is the process noise covariance matrix, which is set according to the IMU acceleration noise, angular velocity noise and model uncertainty. yes The corresponding error state covariance, yes The corresponding error state covariance.

[0104] The speed information of the autonomous aircraft at a moment next to the historical moment is obtained, where the speed information includes: optical flow speed information or GPS speed information or fused speed information obtained based on the optical flow speed information and the GPS speed information.

[0105] Then, calculate the Kalman gain : ; in, is the observation matrix, is the observation noise covariance matrix, which is set according to the accuracy of the optical flow sensor or GPS velocity measurement.

[0106] Afterwards, update the error state estimate (including velocity error estimate and position error estimate): ; Update the nominal state estimate based on the error state estimate: ; Finally, update the error state covariance matrix: ; yes The corresponding error state covariance, is the identity matrix.

[0107] In step S560, an iterative prediction and updating process is performed until an updated position estimate of the autonomous aircraft at the current moment is obtained.

[0108] Starting from the take-off moment, steps S510 to S550 are repeatedly executed, and the error state Kalman filter algorithm is used to iteratively estimate the relative position information of each moment from the take-off moment to the current moment, until an updated position estimate of the autonomous aircraft at the current moment is obtained.

[0109] In step S570, the updated estimated position of the autonomous aerial vehicle at the current moment is used as the current relative position information.

[0110] Specifically, the current relative position information is extracted from the nominal state vector ( , ).

[0111] The present disclosure estimates the current relative position information by fusing multi-sensor navigation data and using the Kalman filter algorithm. Compared with the acceleration integration method based on IMU and the speed integration method based on optical flow sensor, when IMU calculates speed and position through acceleration integration, the accelerometer has zero bias and white noise, and the error will accumulate over time, causing the speed estimation to drift (especially during long-term integration), resulting in inaccurate estimation results, and the influence of sensor noise cannot be effectively reduced. The speed integration method based on optical flow sensor also has such problems. In the present disclosure, the speed estimation of IMU is updated by the speed information of optical flow sensor and / or GPS receiver, which can effectively reduce the error accumulation, and through real-time correction, the IMU error can be corrected in time, the accuracy of speed estimation can be improved, and the position change of the drone can be predicted more accurately, thereby improving the accuracy of position estimation. Therefore, the scheme using the Kalman filter algorithm provided by the embodiment of the present disclosure has beneficial technical effects such as high precision, real-time and robustness. Through multi-sensor data fusion and recursive update, the Kalman filter algorithm can effectively reduce the influence of sensor noise and improve the accuracy and reliability of result estimation.

[0112] In addition, the solution based on the ESKF algorithm reduces the linearization error by separating the nominal state and the error state, has better numerical stability, and is suitable for high-precision estimation and dynamic environments; the solution based on the EKF algorithm is simple to implement and has low computational complexity, which is suitable for general nonlinear systems and scenarios with limited computing resources. In specific implementation, the appropriate algorithm can be selected according to specific needs and project conditions.

[0113] In step S230, the initial position information of the autonomous aircraft at the take-off time is calculated based on the initial relative position information, the current relative position information and the current position information; wherein the initial position information and the current position information are both position information in a geographic coordinate system.

[0114] According to an embodiment of the present disclosure, the calculating the initial position information of the autonomous aircraft at the take-off time according to the initial relative position information, the current relative position information and the current position information includes: Based on the difference between the current relative position information and the initial relative position information and the current position information, the initial position information of the autonomous aircraft is obtained through reverse calculation; wherein the difference is used to describe the horizontal displacement information of the autonomous aircraft at the current moment relative to the take-off moment.

[0115] According to an embodiment of the present disclosure, the initial position information of the autonomous aircraft is obtained by reverse calculation based on the difference between the current relative position information and the initial relative position information and the current position information, including: The initial position information of the autonomous aircraft is obtained by the following formula : ; ; in, represents the latitude coordinate in the initial position information, represents the longitude coordinate in the initial position information, represents the altitude in the initial position information, where the altitude in the initial position information is the altitude of the autonomous aircraft at the take-off time, represents the latitude coordinate in the current location information, represents the longitude coordinate in the current location information, , , represents the difference between the current relative position information and the initial relative position information, represents the eastward displacement of the autonomous aircraft at the current time relative to the take-off time, represents the northward displacement of the autonomous aircraft at the current time relative to the take-off time, represents the meridian curvature radius, , a and They are the semi-major axis (equatorial radius) of the Earth and the square of the first eccentricity in the WGS-84 ellipsoid model parameters (WorldGeodetic System 1984), a=6,378,137m, Among them, the WGS-84 ellipsoid model parameters are the global geographic coordinate system standard used by the GPS system.

[0116] According to the technical solution provided by the embodiments of the present disclosure, when there is no or insufficient satellite signal during the take-off phase, the motion state information (such as acceleration, angular velocity, speed) of the autonomous aircraft (such as a drone) is obtained through a satellite positioning device and / or one or more sensors. During the satellite signal recovery phase, when the autonomous aircraft flies to an area with good satellite signals, the current position information of the autonomous aircraft at the current moment is obtained through the satellite positioning device, and the initial position information is dynamically inferred through the current position information and historical motion state information, and the autonomous aircraft is controlled to return based on the initial position, thereby avoiding the limitation of the traditional method that requires the take-off point to be accurately recorded in advance. This dynamic calibration method can adapt to situations where the take-off point position is unknown or inaccurately recorded, thereby improving the flexibility and practicality of the system.

[0117] In addition, the present disclosure realizes the backward correction of the initial positioning error through the innovative method of "using the current position to reverse the initial position", and combines the multi-sensor fusion algorithm to significantly improve the navigation accuracy and return reliability of the autonomous aircraft in complex environments. The initial position accuracy reversed is significantly higher than that of pure inertial navigation or a single sensor solution, and is particularly suitable for long-duration flight missions. Thus, by delaying the calculation of the initial position, the positioning blind spot and GPS dependence problems in the take-off phase are avoided, so that the aircraft can still establish an accurate return reference point in a complex environment (such as indoors, urban canyons), significantly improving the return reliability, and solving the problem of insufficient return accuracy of lightweight aircraft in an environment where GPS signals are interfered, thereby significantly improving the return accuracy and improving the safety and reliability of the system. In addition, there is no need to rely on high-precision RTK or laser radar, and high-precision positioning is achieved only through low-cost conventional satellite positioning equipment and sensors and other basic equipment, thereby further reducing the complexity and cost of the system. In addition, this technical solution not only reduces the dependence on a single sensor, but also provides a universal solution for low-cost and high-precision navigation, which has significant engineering practical value.

[0118] Figure 6 FIG. 1 is a flow chart showing another return control method according to an embodiment of the present disclosure. Figure 6 As shown, the method further includes step S140 in addition to steps S110, S120 and S130: In step S140, the designated position information of the autonomous aircraft at the designated time is calculated based on the current position information and the motion state information of the autonomous aircraft acquired from the current time to the designated time; wherein the designated time is the time after the current time when the satellite positioning device cannot provide a positioning signal of a specified accuracy.

[0119] According to an embodiment of the present disclosure, calculating the designated position information of the autonomous aircraft at the designated time according to the current position information and the motion state information of the autonomous aircraft acquired from the current time to the designated time includes: estimating the designated relative position information of the autonomous aircraft at the designated time according to the motion state information of the autonomous aircraft acquired from the current time to the designated time, the designated relative position information being used to describe information of the designated position in a relative coordinate system; The designated position information of the autonomous aircraft at the take-off time is calculated according to the designated relative position information, the current relative position information and the current position information; wherein the designated position information is position information in a geographic coordinate system.

[0120] Specifically, estimating the specified relative position information of the autonomous aircraft at the specified time according to the motion state information of the autonomous aircraft acquired from the current time to the specified time includes: The specified relative position information is obtained using a Kalman filter algorithm based on the acceleration information, angular velocity information, speed information and attitude information obtained by the autonomous aircraft from the current moment to the specified moment; wherein the speed information includes: optical flow speed information or GPS speed information or fused speed information obtained based on the optical flow speed information and the GPS speed information.

[0121] When using the Kalman filter algorithm to obtain the specified relative position information, the extended Kalman filter algorithm or the error state Kalman filter algorithm can also be used. The specific process can refer to steps S410, S420, S430 and S440, or steps S510, S520, S530, S540, S550, S560 and S570 respectively, except that the historical moments involved in the process are the moments between the specified moment and the current moment (excluding the specified moment), and through the iterative prediction and update process, what is finally obtained is the updated position estimate of the autonomous aircraft at the specified moment, and the updated position estimate of the autonomous aircraft at the specified moment is the specified relative position information.

[0122] According to an embodiment of the present disclosure, the calculating the designated position information of the autonomous aerial vehicle at the designated time according to the designated relative position information, the current relative position information and the current position information includes: According to the difference between the designated relative position information and the current relative position information and the current position information, the designated position information of the autonomous aircraft is obtained through forward calculation; wherein the difference is used to describe the horizontal displacement information of the autonomous aircraft at the designated moment relative to the current moment.

[0123] In the present disclosure, the position of the autonomous aircraft at the specified time is the specified position, such as Figure 3 As shown, assuming that the position information corresponding to the specified position (ie, the specified relative position information) is (240, 60), it means that the autonomous aircraft is displaced 140 meters eastward and 20 meters northward relative to the current moment at the specified moment.

[0124] According to an embodiment of the present disclosure, the step of obtaining the designated position information of the autonomous aircraft by forward calculation based on the difference between the designated relative position information and the current relative position information and the current position information includes: The specified position information of the autonomous aircraft is obtained by the following formula : ; ; in, represents the latitude coordinate in the specified location information, represents the longitude coordinate in the specified location information, represents the latitude coordinate in the current location information, represents the longitude coordinate in the current location information, , , represents the difference between the specified relative position information and the current relative position information, represents the eastward displacement of the autonomous aircraft at the specified time relative to the current time, represents the northward displacement of the autonomous aircraft at the specified time relative to the current time, represents the meridian curvature radius, , a and They are the semi-major axis (equatorial radius) of the Earth and the square of the first eccentricity in the WGS-84 ellipsoid model parameters, a=6,378,137m, .

[0125] In the present disclosure, the displacement (eastward displacement and westward displacement) of the autonomous aircraft at the specified moment relative to the current moment, and the displacement (eastward displacement and westward displacement) of the autonomous aircraft at the current moment relative to the take-off moment, can be a positive value or a negative value. When it is a positive value, that is, when the eastward displacement or the northward displacement is a positive value, it means that the current position is located in the east or north direction relative to the take-off point, or the specified position is located in the current position; when it is a negative value, that is, when the eastward displacement or the northward displacement is a negative value, it means that the current position is located in the west or south direction relative to the take-off point, or the specified position is located in the current position.

[0126] The present invention forward infers the designated position information at a designated time in the future based on the current position information. When the GPS signal is blocked or interfered with during the subsequent flight, resulting in the inability to provide high-precision positioning information, by forward inferring the designated position information, even when the satellite signal is unreliable, the accurate position of the autonomous aircraft can continue to be estimated after the satellite signal is lost, thereby reducing navigation errors caused by signal interruption and improving the emergency handling capability and flight safety of the aircraft in complex environments.

[0127] Figure 7 FIG. 2 shows a schematic diagram of a structure of a return control device according to an embodiment of the present disclosure. Figure 7 As shown, the device is arranged on an autonomous aircraft, and the autonomous aircraft includes a satellite positioning device and one or more sensors, and the sensors are used to measure the motion state information of the autonomous aircraft. The device 700 includes: a motion state information acquisition module, which is configured to acquire the motion state information of the autonomous aircraft through the satellite positioning device and / or the one or more sensors after the autonomous aircraft takes off; a current position information acquisition module, which is configured to acquire the current position information of the autonomous aircraft at the current moment through the satellite positioning device when the autonomous aircraft flies to a position where the satellite positioning device can provide a positioning signal with a specified accuracy; an initial position information acquisition module, which is configured to calculate the initial position information of the autonomous aircraft at the take-off moment based on the motion state information acquired from the autonomous aircraft from the take-off moment to the current moment, and the current position information; wherein the initial position information is used to perform return control on the autonomous aircraft during the return of the autonomous aircraft.

[0128] Figure 8 FIG. 2 is a schematic diagram showing the structure of another return control device according to an embodiment of the present disclosure. Figure 8As shown, in addition to the motion state information acquisition module, the current position information acquisition module and the initial position information acquisition module, the device 800 also includes: a designated position information acquisition module, which is configured to calculate the designated position information of the autonomous aircraft at the designated time based on the current position information and the motion state information of the autonomous aircraft acquired from the current time to the designated time; wherein the designated time is the time after the current time when the satellite positioning device cannot provide a positioning signal of a specified accuracy.

[0129] The present disclosure also provides a chip, comprising the device as described in the above device embodiment; or, comprising a memory and a processor; wherein the memory is used to store one or more computer instructions, wherein the one or more computer instructions are executed by the processor to implement the method described in any one of the above method embodiments.

[0130] The present disclosure also provides a drone, comprising the device as described in the above device embodiment; or, comprising the chip as described above, or, comprising a memory and a processor; wherein the memory is used to store one or more computer instructions, wherein the one or more computer instructions are executed by the processor to implement the method described in any one of the above method embodiments.

[0131] The present disclosure also provides a computer-readable storage medium, which may be a computer-readable storage medium included in the electronic device or computer system in the above embodiment; or a computer-readable storage medium that exists independently and is not assembled into a device. The computer-readable storage medium stores one or more programs, and the programs are used by one or more processors to execute the method described in the present disclosure.

[0132] The present disclosure also provides a computer program product, including a computer program, which implements any method described in the present disclosure when the computer program is executed by a processor.

[0133] The above description is only a preferred embodiment of the present disclosure and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the present disclosure is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the inventive concept. For example, the above features are replaced with the technical features with similar functions disclosed in the present disclosure (but not limited to) by each other.

Claims

1. A return control method, characterized in that: Applied to an autonomous aircraft, the autonomous aircraft includes a satellite positioning device and one or more sensors, the sensors are used to measure the motion state information of the autonomous aircraft, the method includes: After the autonomous aircraft takes off, obtaining the motion state information of the autonomous aircraft through the satellite positioning device and / or the one or more sensors; When the autonomous aircraft flies to a point where the satellite positioning device can provide a positioning signal with a specified accuracy, obtaining the current position information of the autonomous aircraft at the current moment through the satellite positioning device; Based on the motion state information obtained from the autonomous aircraft from the take-off time to the current time, and the current position information, the initial position information of the autonomous aircraft at the take-off time is calculated; wherein the initial position information is used to control the return of the autonomous aircraft during the return of the autonomous aircraft.

2. The method according to claim 1, characterized in that The method further comprises: Acquire the initial relative position information of the autonomous aircraft at the take-off time, wherein the initial relative position information is used to describe the position information of the take-off point in the relative coordinate system; The step of calculating the initial position information of the autonomous aircraft at the take-off time according to the motion state information of the autonomous aircraft acquired from the take-off time to the current time and the current position information comprises: According to the motion state information of the autonomous aircraft acquired from the take-off time to the current time, estimating the current relative position information of the autonomous aircraft at the current time, wherein the current relative position information is used to describe the information of the current position in the relative coordinate system; Calculate the initial position information of the autonomous aircraft at the take-off time according to the initial relative position information, the current relative position information and the current position information; wherein the initial position information and the current position information are both position information in a geographic coordinate system; The performing return control on the autonomous aircraft includes: controlling the autonomous aircraft to return to the take-off point.

3. The method according to claim 2, characterized in that in, When the autonomous aircraft includes a plurality of sensors, the plurality of sensors include: an inertial measurement unit IMU and an optical flow sensor, the satellite positioning device includes: a global positioning system GPS receiver; the motion state information includes: acceleration information, angular velocity information and attitude information obtained based on the inertial measurement unit IMU, optical flow velocity information obtained based on the optical flow sensor, and GPS velocity information obtained based on the GPS receiver; The estimating the current relative position information of the autonomous aircraft at the current moment according to the motion state information of the autonomous aircraft acquired from the take-off moment to the current moment includes: The current relative position information is obtained using a Kalman filter algorithm based on the acceleration information, angular velocity information, speed information and attitude information obtained by the autonomous aircraft from the take-off moment to the current moment; wherein the speed information includes: optical flow speed information or GPS speed information or fused speed information obtained based on the optical flow speed information and the GPS speed information.

4. The method according to claim 3, characterized in that: The Kalman filter algorithm is an extended Kalman filter algorithm, and the current relative position information is obtained by using the Kalman filter algorithm according to the acceleration information, angular velocity information, speed information and attitude information acquired by the autonomous aircraft from the take-off time to the current time, including: Based on the relative position information, acceleration information, angular velocity information and attitude information of the autonomous aircraft at the historical moment, predicting the estimated velocity and estimated position of the autonomous aircraft at the next moment of the historical moment; updating the speed estimate and position estimate of the autonomous aircraft at the next moment in the historical moment based on the speed information of the autonomous aircraft at the next moment in the historical moment; wherein the historical moment is a moment before the current moment; By iterative prediction and updating process, an updated position estimate of the autonomous aircraft at the current moment is obtained; The updated estimated position of the autonomous aerial vehicle at the current moment is used as the current relative position information.

5. The method according to claim 3, characterized in that: The Kalman filter algorithm is an error state Kalman filter algorithm, and the current relative position information is obtained by using the Kalman filter algorithm according to the acceleration information, angular velocity information, speed information and attitude information acquired by the autonomous aircraft from the take-off time to the current time, including: Based on the relative position information, acceleration information, angular velocity information and attitude information of the autonomous aircraft at the historical moment, predicting the estimated velocity and estimated position of the autonomous aircraft at the next moment of the historical moment; Predicting a velocity error estimate and a position error estimate of the autonomous aircraft at the next moment in the historical moment based on the position error and the velocity error of the autonomous aircraft at the historical moment; updating the speed error estimate and the position error estimate based on the speed information of the autonomous aircraft at the next moment after the historical moment, to obtain an updated speed error estimate and an updated position error estimate; updating a velocity estimate of the autonomous aircraft at a next moment in the historical moment based on the updated velocity error estimate; updating the position estimate of the autonomous aircraft at a moment after the historical moment based on the updated position error estimate; wherein the historical moment is a moment before the current moment; By iterative prediction and updating process, an updated position estimate of the autonomous aircraft at the current moment is obtained; The updated estimated position of the autonomous aerial vehicle at the current moment is used as the current relative position information.

6. The method according to claim 2, characterized in that The calculating the initial position information of the autonomous aircraft at the take-off time according to the initial relative position information, the current relative position information and the current position information comprises: Based on the difference between the current relative position information and the initial relative position information and the current position information, the initial position information of the autonomous aircraft is obtained through reverse calculation; wherein the difference is used to describe the horizontal displacement information of the autonomous aircraft at the current moment relative to the take-off moment.

7. The method according to claim 6, characterized in that The step of obtaining the initial position information of the autonomous aircraft by reverse calculation based on the difference between the current relative position information and the initial relative position information and the current position information comprises: The initial position information of the autonomous aircraft is obtained by the following formula : ； ； in, represents the latitude coordinate in the initial position information, represents the longitude coordinate in the initial position information, represents the altitude in the initial position information, where the altitude in the initial position information is the altitude of the autonomous aircraft at the take-off time, represents the latitude coordinate in the current location information, represents the longitude coordinate in the current location information, , , represents the eastward displacement of the autonomous aircraft at the current time relative to the take-off time, represents the northward displacement of the autonomous aircraft at the current time relative to the take-off time, represents the meridian curvature radius, , a and They are the semi-major axis of the earth and the square of the first eccentricity in the WGS-84 ellipsoid model parameters, a=6,378,137m, .

8. The method according to claim 1, characterized in that The method further comprises: Based on the current position information and the motion state information of the autonomous aircraft obtained from the current moment to the specified moment, the specified position information of the autonomous aircraft at the specified moment is calculated; wherein the specified moment is the moment after the current moment when the satellite positioning device cannot provide a positioning signal of a specified accuracy.

9. A return control device, characterized in that: The device is provided in an autonomous aircraft, wherein the autonomous aircraft includes a satellite positioning device and one or more sensors, wherein the sensors are used to measure the motion state information of the autonomous aircraft, and the device includes: a motion state information acquisition module, configured to acquire the motion state information of the autonomous aircraft through the satellite positioning device and / or the one or more sensors after the autonomous aircraft takes off; a current position information acquisition module, configured to acquire the current position information of the autonomous aircraft at the current moment through the satellite positioning device when the autonomous aircraft flies to a position where the satellite positioning device can provide a positioning signal with a specified accuracy; The initial position information acquisition module is configured to calculate the initial position information of the autonomous aircraft at the take-off time based on the motion state information obtained from the take-off time to the current time, and the current position information of the autonomous aircraft; wherein the initial position information is used to control the return of the autonomous aircraft during the return of the autonomous aircraft.

10. A chip, characterized in that: Comprising the apparatus of claim 9; or comprising a memory and a processor; wherein the memory is used to store one or more computer instructions, wherein the one or more computer instructions are executed by the processor to implement the method of any one of claims 1 to 8.

11. A drone, characterized in that: Comprising the device of claim 9; or, comprising the chip of claim 10; or, comprising a memory and a processor; wherein the memory is used to store one or more computer instructions, wherein the one or more computer instructions are executed by the processor to implement the method of any one of claims 1 to 8.

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