WO2019119243A1 - 无人机避障方法及无人机 - Google Patents

无人机避障方法及无人机 Download PDF

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Publication number
WO2019119243A1
WO2019119243A1 PCT/CN2017/117043 CN2017117043W WO2019119243A1 WO 2019119243 A1 WO2019119243 A1 WO 2019119243A1 CN 2017117043 W CN2017117043 W CN 2017117043W WO 2019119243 A1 WO2019119243 A1 WO 2019119243A1
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WIPO (PCT)
Prior art keywords
track
obstacle
waypoint
radar
echo
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PCT/CN2017/117043
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English (en)
French (fr)
Inventor
王俊喜
王春明
吴旭民
石仁利
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深圳市大疆创新科技有限公司
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Application filed by 深圳市大疆创新科技有限公司 filed Critical 深圳市大疆创新科技有限公司
Priority to PCT/CN2017/117043 priority Critical patent/WO2019119243A1/zh
Priority to CN201780005013.XA priority patent/CN108513644A/zh
Publication of WO2019119243A1 publication Critical patent/WO2019119243A1/zh
Priority to US16/879,482 priority patent/US20200285254A1/en

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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
    • G05D1/10—Simultaneous control of position or course in three dimensions
    • G05D1/101—Simultaneous control of position or course in three dimensions specially adapted for aircraft
    • G05D1/106—Change initiated in response to external conditions, e.g. avoidance of elevated terrain or of no-fly zones
    • G05D1/1064—Change initiated in response to external conditions, e.g. avoidance of elevated terrain or of no-fly zones specially adapted for avoiding collisions with other aircraft
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
    • G05D1/10—Simultaneous control of position or course in three dimensions
    • G05D1/101—Simultaneous control of position or course in three dimensions specially adapted for aircraft
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/88—Radar or analogous systems specially adapted for specific applications
    • G01S13/93—Radar or analogous systems specially adapted for specific applications for anti-collision purposes
    • G01S13/933—Radar or analogous systems specially adapted for specific applications for anti-collision purposes of aircraft or spacecraft
    • G01S13/935—Radar or analogous systems specially adapted for specific applications for anti-collision purposes of aircraft or spacecraft for terrain-avoidance
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B64—AIRCRAFT; AVIATION; COSMONAUTICS
    • B64C—AEROPLANES; HELICOPTERS
    • B64C1/00—Fuselages; Constructional features common to fuselages, wings, stabilising surfaces or the like
    • B64C1/36—Fuselages; Constructional features common to fuselages, wings, stabilising surfaces or the like adapted to receive antennas or radomes
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B64—AIRCRAFT; AVIATION; COSMONAUTICS
    • B64C—AEROPLANES; HELICOPTERS
    • B64C39/00—Aircraft not otherwise provided for
    • B64C39/02—Aircraft not otherwise provided for characterised by special use
    • B64C39/024—Aircraft not otherwise provided for characterised by special use of the remote controlled vehicle type, i.e. RPV
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/35—Details of non-pulse systems
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
    • G05D1/0055—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot with safety arrangements
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
    • G05D1/08—Control of attitude, i.e. control of roll, pitch, or yaw
    • G05D1/0808—Control of attitude, i.e. control of roll, pitch, or yaw specially adapted for aircraft
    • G05D1/0816—Control of attitude, i.e. control of roll, pitch, or yaw specially adapted for aircraft to ensure stability
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
    • G05D1/10—Simultaneous control of position or course in three dimensions
    • G05D1/101—Simultaneous control of position or course in three dimensions specially adapted for aircraft
    • G05D1/102—Simultaneous control of position or course in three dimensions specially adapted for aircraft specially adapted for vertical take-off of aircraft
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B64—AIRCRAFT; AVIATION; COSMONAUTICS
    • B64U—UNMANNED AERIAL VEHICLES [UAV]; EQUIPMENT THEREFOR
    • B64U10/00—Type of UAV
    • B64U10/10—Rotorcrafts
    • B64U10/13—Flying platforms
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B64—AIRCRAFT; AVIATION; COSMONAUTICS
    • B64U—UNMANNED AERIAL VEHICLES [UAV]; EQUIPMENT THEREFOR
    • B64U10/00—Type of UAV
    • B64U10/10—Rotorcrafts
    • B64U10/13—Flying platforms
    • B64U10/16—Flying platforms with five or more distinct rotor axes, e.g. octocopters
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B64—AIRCRAFT; AVIATION; COSMONAUTICS
    • B64U—UNMANNED AERIAL VEHICLES [UAV]; EQUIPMENT THEREFOR
    • B64U2201/00—UAVs characterised by their flight controls
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B64—AIRCRAFT; AVIATION; COSMONAUTICS
    • B64U—UNMANNED AERIAL VEHICLES [UAV]; EQUIPMENT THEREFOR
    • B64U2201/00—UAVs characterised by their flight controls
    • B64U2201/10—UAVs characterised by their flight controls autonomous, i.e. by navigating independently from ground or air stations, e.g. by using inertial navigation systems [INS]
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/02—Systems using reflection of radio waves, e.g. primary radar systems; Analogous systems
    • G01S13/06—Systems determining position data of a target
    • G01S13/08—Systems for measuring distance only
    • G01S13/32—Systems for measuring distance only using transmission of continuous waves, whether amplitude-, frequency-, or phase-modulated, or unmodulated
    • G01S13/34—Systems for measuring distance only using transmission of continuous waves, whether amplitude-, frequency-, or phase-modulated, or unmodulated using transmission of continuous, frequency-modulated waves while heterodyning the received signal, or a signal derived therefrom, with a locally-generated signal related to the contemporaneously transmitted signal
    • G01S13/343—Systems for measuring distance only using transmission of continuous waves, whether amplitude-, frequency-, or phase-modulated, or unmodulated using transmission of continuous, frequency-modulated waves while heterodyning the received signal, or a signal derived therefrom, with a locally-generated signal related to the contemporaneously transmitted signal using sawtooth modulation
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/88—Radar or analogous systems specially adapted for specific applications
    • G01S13/93—Radar or analogous systems specially adapted for specific applications for anti-collision purposes
    • G01S13/933—Radar or analogous systems specially adapted for specific applications for anti-collision purposes of aircraft or spacecraft
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/03—Details of HF subsystems specially adapted therefor, e.g. common to transmitter and receiver

Definitions

  • the present invention relates to the field of flight technology, and in particular, to a drone obstacle avoidance method and a drone.
  • the external environment is mainly perceived by an optical lens such as an ultrasonic wave, a binocular vision, or a laser to realize obstacle avoidance of the drone.
  • optical lenses are sensitive to external conditions such as illumination and climatic conditions, and radars are less sensitive to external conditions, and can be used even in radars such as rain, fog, and dust, which are still effective and weatherproof.
  • the radar performs obstacle sensing and realizes obstacle avoidance of the drone according to the obstacles sensed by the radar.
  • the invention provides a UAV obstacle avoidance method and a UAV, which are used for solving the problem of misjudgment of obstacles in the prior art in the process of using a radar to perform obstacle-aware UAV operations.
  • the present invention provides a method for obstacle avoidance of a drone, comprising:
  • the obstacle is subjected to obstacle avoidance processing.
  • the present invention provides a drone that includes a rack, a controller disposed on the rack, and the agricultural drone further includes: a rack mounted on the rack or the rack Radar on the load;
  • the radar is used to acquire measurement data
  • the controller is connected to the radar for determining, according to the measured data output by the radar, an obstacle track relative to a flight path of the drone, and performing the obstacle according to the flight track Obstacle avoidance treatment.
  • the unmanned aerial vehicle obstacle avoidance method and the drone provided by the present invention determine the flight path of the obstacle relative to the drone according to the measured data output by the radar, and refrain the obstacle according to the flight track
  • the obstacle processing makes the radar output the measurement data based on the clutter, but since the clutter does not have the corresponding obstacle, according to the measurement data of the radar based on the clutter output, it is not possible to determine the obstacle relative to the drone.
  • the flight path so as to avoid obstacles according to the radar based on the clutter output measurement data, and solve the problem of obstacle misjudgment when the obstacle is processed according to the flight path. .
  • FIG. 1 is a flow chart of a first embodiment of an obstacle avoidance method for a drone according to the present invention
  • FIG. 2 is a schematic structural view of a radar of the present invention
  • FIG. 3 is a flow chart of a second embodiment of a method for obstacle avoidance of a drone according to the present invention.
  • FIG. 4 is a schematic diagram showing the relationship between an echo of the present invention and a first associated wave gate
  • Figure 5 is a schematic diagram showing the relationship between the radar, the obstacle and the Cartesian coordinate system of the present invention.
  • Embodiment 6 is a flowchart of Embodiment 3 of a method for obstacle avoidance of a UAV according to the present invention.
  • FIG. 7 is a schematic diagram of generating a candidate track according to the present invention.
  • Embodiment 8 is a flowchart of Embodiment 4 of a method for avoiding obstacles of a drone according to the present invention.
  • FIG. 9 is a schematic diagram of determining measurement data satisfying a preset condition according to the present invention.
  • Figure 10 is a schematic structural view of the drone of the present invention.
  • Figure 11 is a diagram showing the physical structure of the drone of the present invention.
  • the invention is applied to a drone, and the drone is equipped with a radar, which can detect an obstacle and output measurement data obtained based on the detection of the obstacle.
  • the measurement data may be measurement data output by the radar after detecting an obstacle, or it may be that the radar detects clutter, such as ground clutter, etc., instead of detecting the measurement data output by the obstacle.
  • the present invention is for solving the problem of misjudgment of obstacles in the process of the UAV operation using the radar for obstacle sensing.
  • FIG. 1 is a flowchart of Embodiment 1 of a method for avoiding obstacles of a UAV according to the present invention.
  • the execution body of the embodiment may be a controller of the UAV.
  • the method in this embodiment may include:
  • Step 101 Determine a flight path of the obstacle relative to the drone according to the measured data output by the radar.
  • the measurement data may specifically include one or more of an obstacle's speed, distance, and azimuth.
  • the radar is divided according to the antenna of the radar, and the radar may specifically refer to a radar whose antenna is a directional antenna, or may also be a radar whose antenna is a rotating antenna. If the radar specifically refers to a radar whose antenna is a directional antenna, the number of the radars may be multiple, and the multiple radars are respectively used to detect obstacles of different directions of the drone. For example, six radars respectively emit radar waves toward the front, the front lower, the lower, the rear, the lower rear, and the upper side of the drone. If the radar specifically refers to a radar whose antenna is a rotating antenna, the radar can be continuously rotated.
  • the method of this embodiment may further include: controlling the continuous rotation of the radar to obtain measurement data of the radar during continuous rotation.
  • the radar when the radar continuously rotates, the radar emits radar waves at least directly toward the front, the front lower, the lower side, the front side, the lower side, and the right side of the drone.
  • the direction of the rotation axis of the radar may be parallel to the pitch axis of the drone.
  • the radar for the position where the radar is installed on the drone, it can be flexibly designed according to requirements, which is not limited by the present invention.
  • the direction of the radar wave can be flexibly designed according to requirements, which is not limited by the present invention.
  • the radar may be a continuous wave radar or a pulse radar.
  • the radar is a Frequency Modulated Continuous Wave (FMCW) radar, which mainly includes a signal processing module and a radio frequency front end.
  • the signal processing module includes a controller (for example, a digital signal processing (DSP) chip, etc.), and the signal processing module is mainly used to generate a modulated signal, and is collected according to A/D.
  • the difference frequency signal determines the distance.
  • the signal processing module may further include a memory for storing data such as a flash memory (FLASH), a random access memory (RAM), a read-only memory (ROM), and the like.
  • the RF front-end uses one-shot dual-receiver, which includes one transmit path and two receive paths.
  • the modulation waveform generated by the signal processing module is voltage-regulated by a Voltage Controlled Oscillator (VCO) to generate a chirp signal (the transmission frequency of the chirp signal can be at 24 GHz), and the chirp signal is in the pair.
  • VCO Voltage Controlled Oscillator
  • PA Power Amplifier
  • it is transmitted through the transmitting antenna TX (here, the transmitting antenna TX emits a radar wave).
  • the echoes of the radar wave emitted by the transmitting antenna after being reflected by the target are received by the receiving path through the receiving antennas RX1 and RX2, and the received signal is low-noise amplified by a low noise amplifier (LNA, Low Noise Amplifier), and low noise is performed.
  • the amplified signal is mixed (wherein the mixing is specifically mixing a signal corresponding to the radar wave and a signal corresponding to the echo) to obtain a difference frequency signal. Further, after the difference frequency signal passes through the A/D acquisition and enters the signal processing module, the signal processing module determines the measurement data according to the difference frequency signal.
  • the receiving path and the transmitting path may further include a power splitter (referred to as a power split).
  • the above receiving antenna and transmitting antenna may specifically adopt a microstrip antenna.
  • the flight path is referenced by the flying drone, and the flight path exists with respect to the drone. Therefore, even if the radar outputs measurement data based on the clutter, since the clutter does not have a corresponding obstacle, it is impossible to determine the flight of the obstacle relative to the drone based on the measurement data of the radar based on the ground clutter output. track.
  • the present invention is not limited to the specific implementation manner for determining the flight path of the obstacle relative to the drone.
  • the two measurement data can be used as the waypoints respectively, and the routes formed by the two waypoints are determined as obstacles relative to each other.
  • the flight track may include at least two waypoints, and the information of each waypoint may include one or more of position, speed, angle, and the like.
  • Step 102 Perform obstacle avoidance processing on the obstacle according to the flight path.
  • the flight path or the flying height of the unmanned aerial vehicle may be adjusted according to the obstacle track relative to the flight path of the drone to perform obstacle avoidance processing on the obstacle.
  • the flight attitude of the drone may be controlled according to the flight path of the obstacle relative to the drone, The obstacle is treated to avoid obstacles.
  • the flight attitude may include dive, climb, acceleration, deceleration, roll, and the like. It should be noted that the present invention is not limited to a specific implementation manner for performing obstacle avoidance processing on the obstacle according to the flight path. Those skilled in the art may design a corresponding obstacle avoidance strategy to avoid obstacles according to actual needs.
  • the obstacle is subjected to obstacle avoidance processing according to the flight track, so that even the radar is based on the clutter output.
  • the measurement data but because the clutter does not have a corresponding obstacle, according to the measurement data of the radar based on the clutter output, it is not possible to determine the flight path of the obstacle relative to the drone, and thus according to the flight
  • the trajectory avoids the obstacle avoidance based on the measurement data of the radar based on the clutter output, and solves the problem of the obstacle misjudgment.
  • FIG. 3 is a flow chart of a second embodiment of a method for avoiding obstacles of a drone according to the present invention.
  • This embodiment mainly describes a specific implementation method for determining the flight path of an obstacle relative to a drone based on the measured data of the radar output on the basis of the embodiment shown in FIG.
  • the method in this embodiment may include:
  • Step 301 Determine a first predicted waypoint of the current moment of the obstacle according to a flight path of the obstacle relative to the drone according to the previous time.
  • the predicted waypoint of the current moment of the obstacle is determined based on the flight path of the obstacle relative to the drone at the previous time. Due to the flight path of the obstacle relative to the drone at the previous moment, the flight law of the obstacle relative to the drone may be reflected, so the obstacle is relative to the drone based on the previous moment The flight path can determine the first predicted waypoint. It should be noted that, the specific implementation manner of determining the first predicted waypoint of the current moment of the obstacle according to the flight path of the obstacle according to the previous time is not limited by the present invention.
  • the movement law of the obstacle may be determined according to the flight path of the obstacle relative to the drone at the previous time, and according to the obstacle The motion law determines the first predicted waypoint.
  • the step 301 may include: determining a motion model of the obstacle according to a flight path of the obstacle relative to the drone according to a previous time; and determining, according to the motion model, the obstacle at the current time The first predicted waypoint of the object.
  • the motion model may represent the first predicted waypoint of the obstacle at the current time as a function of the waypoint of the previous time (eg, the previous time).
  • the motion model may specifically be a normal speed model, and the normal speed model may acquire flight speed information of the drone in real time.
  • the motion model of the obstacle may be determined by using one or more of the obstacle, the position, the speed, the angle, and the like of the waypoint in the flight path of the drone as the state variable.
  • the state variable When the state variable is selected in the position, velocity, angle, etc. of the waypoint, the calculation amount can be prevented from increasing with the increase of the number of state variables according to the principle of a set of variables with the smallest number of dimensions and comprehensively reflecting the dynamic characteristics.
  • the state variable may include speed.
  • the determining, according to the motion model, the first predicted waypoint of the obstacle at the current moment specifically: determining, according to the motion model, an estimated waypoint of the obstacle at the current moment; according to the previous one The waypoint of the moment and the estimated waypoint, based on the Kalman algorithm, determine the first predicted waypoint of the obstacle at the current moment.
  • the waypoint of the previous moment can be used as the measured value in the Kalman filter algorithm
  • the estimated waypoint is used as the predicted value in the Kalman filter algorithm
  • the estimated value calculated by the Kalman filter algorithm is For the first predicted waypoint.
  • the specific implementation manner of determining the first predicted waypoint of the obstacle at the current time according to the motion model is not limited in the present invention.
  • the estimated waypoint of the obstacle at the current time determined according to the motion model may be used as the first predicted waypoint, or may be obtained by using the first estimated waypoint and the flight path The waypoints of the previous moment are weighted to determine the first predicted waypoint.
  • Step 302 Determine a first associated wave gate according to the first predicted waypoint.
  • the first associated wave gate may be a spatial region centered on the first predicted waypoint, and the first associated wave gate may specifically be a rectangular wave gate, a circular wave gate, or a circular wave. Doors, spherical gates or sector gates.
  • the relevant echo falling within the first associated gate has a high probability; on the other hand, It is not allowed to have too many irrelevant echoes in the first associated gate.
  • the relevant echo can be understood as the measurement data of the corresponding measurement data and the above-mentioned flight track, and the irrelevant echo can be understood as the measurement data of the corresponding measurement data and the flight track.
  • Step 303 If the echo of the radar falls within the first associated wave gate at the current time, determine a current waypoint of the flight track according to the measured data corresponding to the echo.
  • the first associated gate is a spherical wave gate, and the coordinate system is a Cartesian coordinate system.
  • the range of the first associated wave gate may be the following formula (1).
  • (x 0 , y 0 ) can represent the coordinates corresponding to the first predicted waypoint in the Cartesian coordinate system
  • (x k , y k ) can represent the coordinates of the measured data corresponding to the echo in the Cartesian coordinate system
  • K It can represent the radius of the spherical gate.
  • the echo corresponding to (x i , y i ) in FIG. 4 falls within the first associated wave gate, and the echo corresponding to (x n , y n ) does not fall within the first associated wave gate, that is, falls into the first One is associated with the wave gate.
  • the measurement data of the radar output is usually the data in the polar coordinate system, and the controller processes the data in the Cartesian coordinate system, so the measurement data in the polar coordinate system of the radar output can be coordinated.
  • the conversion is converted to measurement data in a Cartesian coordinate system.
  • the distance R, azimuth of the obstacle The relationship between the coordinate x and the coordinate x in the Cartesian coordinate system can be as shown in equation (2), R, The relationship between the coordinates y in the Cartesian coordinate system can be as shown in equation (3).
  • X and Y in FIG. 5 are two coordinate axes of the Cartesian coordinate system.
  • determining the current waypoint of the obstacle according to the measurement data corresponding to the echo including: The measurement data corresponding to the echo is used as the current waypoint of the flight track.
  • determining the current waypoint of the obstacle according to the measurement data corresponding to the echo including:
  • One echo is selected among the plurality of echoes, and the measured data corresponding to the selected echo is used as the current waypoint of the flight track.
  • the selecting one echo among the plurality of echoes includes: selecting one echo among the plurality of echoes based on a nearest neighbor method.
  • the update vector v i (k) of the ith echo at the k+1th time is determined by the following formula (4). +1).
  • z i (k) represents the measurement data corresponding to the echo at the kth time.
  • the distance g i (k+1) is determined by the following formula (5).
  • the specific implementation manner of selecting one echo in multiple echoes based on the nearest neighbor method is not limited in the present invention.
  • a plurality of echoes may be selected corresponding to the first predicted waypoint. The echo is the closest echo.
  • the obstacle is not fixed, so in addition to determining the flight path, a new flight path different from the flight track may be determined. Therefore, when the echo of the radar does not fall into the first associated wave gate at the current time, further, a new flight path can be determined based on the measured data.
  • the processing manner of determining the new flight track according to the measurement data may be similar to the processing method for generating the candidate track in the embodiment shown in FIG. 6, and details are not described herein again.
  • the first predicted waypoint of the current moment of the obstacle is determined according to the flight path of the obstacle relative to the drone according to the previous time, and the first predicted waypoint is determined according to the first predicted waypoint.
  • An associated wave gate if the echo of the radar falls within the first associated wave gate at the current moment, determining a current waypoint of the flight track according to the measured data corresponding to the echo, and realizing the basis
  • the measured data of the radar output determines the flight path of the obstacle relative to the drone.
  • FIG. 6 is a flowchart of Embodiment 3 of a method for avoiding obstacles of a UAV according to the present invention.
  • This embodiment mainly describes an optional implementation manner when the echo of the radar does not fall into the first associated wave gate at the current moment on the basis of the embodiment shown in FIG. 3.
  • the method in this embodiment may include:
  • Step 601 If the echo of the radar does not fall into the first associated wave gate at the current moment, determine whether the echo falls within the second associated portal; the second associated portal is according to the Second, the associated wave gate determined by the waypoint is predicted, and the second predicted waypoint is a predicted waypoint determined according to the candidate track.
  • the obstacle is not fixed during the flight of the drone.
  • the first associated wave gate When the first associated wave gate is inside, it may be further determined whether it falls within the second associated wave gate determined based on the candidate track.
  • the number of the candidate trackes may be one or more, which is not limited in the present invention.
  • the first correlation wave is The door is similar and will not be described here.
  • Step 602 Determine, if the echo falls within the second associated wave gate, a current waypoint of the candidate track according to the measured data corresponding to the echo.
  • step 602 is similar to step 303, and details are not described herein again.
  • Step 603 If the echo does not fall within the second associated wave gate, generate a candidate track according to the measured data corresponding to the echo.
  • the obstacle is not fixed, so in addition to the above-mentioned flight track and candidate track, it is possible to determine a new one that is different from the above-mentioned flight track and candidate track.
  • Candidate track the generation of the track needs to be considered based on these two aspects.
  • the candidate track can be generated in the following manner.
  • the second measurement data includes a first measurement data whose degree of difference between the first measurement data and the measurement data at a previous time of the first measurement data is less than or equal to a preset difference degree.
  • the candidate track includes the waypoint information determined according to each first measurement data, M is a positive integer greater than or equal to 2, and K is a positive integer less than or equal to M.
  • the tracks can be The quality is managed. The higher the quality of the track, the higher the accuracy of the track. The lower the quality of the track, the lower the accuracy of the track.
  • the quality of the track can be managed as follows:
  • the current waypoint may be the candidate track or the current waypoint of the flight track.
  • the candidate track may be a flight path of the obstacle or may not be a flight path of the obstacle, and the flight track may become a certain time after a certain period of time.
  • the track can be managed according to the quality of the track. Specifically, candidate track and flight track are managed according to the track quality.
  • the managing the candidate track and the flight track according to the track quality including: when the track quality of the flight track is less than or equal to a first preset track quality, The flight aircraft is used as a candidate track; when the track quality of the candidate track is greater than or equal to the second predetermined track quality, the candidate track is used as a flight track.
  • first preset track quality and the second preset track quality may be flexibly designed according to requirements, which is not limited by the present invention.
  • the track can also be deleted.
  • “deletion” can be understood as an operation opposite to the above “generating”.
  • the managing the candidate track and the flight track according to the track quality may further include: when the track quality of the candidate track is less than or equal to the third preset track quality, The candidate track is deleted, and the third preset track quality is smaller than the first preset track quality.
  • the echo of the radar does not fall into the first associated wave gate at the current time, it is determined whether the echo falls within the second associated wave gate, if the echo falls into the Determining, in the second associated wave gate, a current waypoint of the candidate track according to the measured data corresponding to the echo, if the echo does not fall within the second associated wave gate, according to The measurement data corresponding to the echo generates a candidate track, and on the basis of the flight path of the obstacle, the candidate track generation and update are realized, and the accuracy of the flight path of the obstacle is improved.
  • FIG. 8 is a flowchart of Embodiment 4 of a method for avoiding obstacles of a drone according to the present invention. This embodiment is implemented in the above Based on the example, an alternative implementation between the measurement data using the radar output is described. As shown in FIG. 8, the method in this embodiment may include:
  • Step 801 Determine measurement data that meets a preset condition in the measurement data output by the radar.
  • the radar usually has a large detection range, and the range of obstacles that the drone needs to consider may be only a part of the detection range, so it is possible to determine and avoid the measurement data from the radar output according to preset conditions.
  • Barrier-related measurement data the measurement data satisfying the preset condition in the measurement data of the radar output can be regarded as reliable data that can be used, and the measurement data that does not satisfy the preset condition in the measurement data of the radar output can be regarded as useless data that cannot be used.
  • the preset condition includes: a distance threshold condition and/or an angle threshold condition.
  • the distance threshold condition may be defined by one or more preset distances.
  • the distance threshold condition when defined by a preset distance, may be greater than or equal to the preset distance, or less than or equal to the pre-predetermined distance. Setting a distance; when defined by two preset distances, a preset distance 1 and a preset distance 2, the distance threshold condition may be greater than or equal to the preset distance 1 and less than or equal to the preset distance 2.
  • the angle threshold condition may be defined by one or more preset angles. For example, when defined by a preset angle, the angle threshold condition may be greater than or equal to the preset angle, or less than or equal to the preset.
  • the angle threshold condition may be greater than or equal to the preset angle 1 and less than or equal to the preset angle 2 when defined by two preset angles, the preset angle 1 and the preset angle 2.
  • step 801 may be specifically shown in FIG.
  • Step 802 Determine, according to the measured output data of the radar that meets the preset condition, an obstacle track relative to a flight path of the drone.
  • Step 803 Perform obstacle avoidance processing on the obstacle according to the flight path.
  • step 803 is similar to step 102, and details are not described herein again.
  • determining the measurement data that meets the preset condition in the measurement data output by the radar, and determining the flight of the obstacle relative to the drone according to the measurement data of the radar output that meets the preset condition The track can reduce the amount of calculation of the data, thereby reducing the burden on the controller, increasing the processing speed, and at the same time reducing the possibility of false track formation.
  • the drone 1000 of the present embodiment may include a rack 1001, a controller 1002 disposed on the rack 1001, and a radar 1004 mounted on the rack 1001 or on the load 1003 of the rack 1001. .
  • the radar 1004 is configured to acquire measurement data.
  • the controller 1002 is communicably connected to the radar 1004, and is configured to determine, according to the measured data output by the radar, an obstacle track relative to the flight path of the drone, and according to the The flight path is used to perform obstacle avoidance processing on the obstacle.
  • the controller 1002 determines the flight path of the obstacle relative to the drone according to the measured data output by the radar, and specifically includes:
  • the current waypoint of the flight track is determined according to the measured data corresponding to the echo.
  • the controller 1002 determines the current waypoint of the obstacle according to the measurement data corresponding to the echo, specifically including :
  • the measurement data corresponding to the echo is used as the current waypoint of the flight path.
  • the controller 1002 determines the current waypoint of the obstacle according to the measurement data corresponding to the echo, which specifically includes:
  • One echo is selected among the plurality of echoes, and the measured data corresponding to the selected echo is used as the current waypoint of the flight track.
  • the controller 1002 selects one echo among the multiple echoes, and specifically includes:
  • one echo is selected among multiple echoes.
  • controller 1002 is further configured to:
  • the second associated portal is based on the second predicted navigation Point determining the associated wave gate, the second predicted waypoint being a predicted waypoint determined according to the candidate track;
  • the candidate track is generated according to the measured data corresponding to the echo.
  • the controller 1002 generates a candidate track according to the measurement data corresponding to the echo, which specifically includes:
  • the second measurement data includes the first
  • the first measurement data of the difference between the measurement data and the measurement data at the previous moment of the first measurement data is less than or equal to a preset difference degree
  • the candidate track includes the first measurement data according to each
  • M is a positive integer greater than or equal to 2
  • K is a positive integer less than or equal to M.
  • controller 1002 is further configured to:
  • the smaller the degree of difference the better the track quality; the greater the degree of difference, the worse the track quality.
  • controller 1002 is further configured to manage the candidate track and the flight track according to the track quality.
  • the controller 1002 manages the candidate track and the flight track according to the track quality, and specifically includes:
  • the flight aircraft is used as a candidate track
  • the candidate track is used as a flight track.
  • the controller 1002 manages the candidate track and the flight track according to the track quality, and further includes:
  • the candidate track is deleted, and the third preset track quality is less than the first preset track quality.
  • the controller 1002 determines, according to the flight path of the obstacle in the previous moment, the first predicted waypoint of the current moment of the obstacle, which includes:
  • the controller 1002 determines, according to the motion model, a first predicted waypoint of the obstacle at the current moment, specifically:
  • the first predicted waypoint of the obstacle at the current moment is determined.
  • controller 1002 is further configured to: before using the measurement data output by the radar, determine the measurement data that meets the preset condition in the measurement data output by the radar;
  • the controller 1002 determines, according to the measured data of the radar output, the flight path of the obstacle relative to the unmanned aerial vehicle, and specifically includes: determining, according to the measured output data of the radar that meets the preset condition, that the obstacle is relatively free Flight path of man and machine.
  • the preset condition includes: a distance threshold condition, and/or an angle threshold condition.
  • the controller 1002 performs obstacle avoidance processing on the obstacle according to the flight path, and specifically includes:
  • the flight attitude of the drone is controlled to perform obstacle avoidance processing on the obstacle.
  • controller 1002 is further configured to:
  • the radar when the radar continuously rotates, the radar emits radar waves at least directly toward the front, the front lower, the lower side, the front side, the lower side, and the right side of the drone.
  • the direction of the axis of the radar is parallel to the pitch axis of the drone.
  • the UAV in the present invention may specifically be a multi-rotor UAV, such as a quadrotor UAV.
  • the radar 1004 is a radar whose antenna is a rotating antenna, and the installation position of the radar 1004 on the drone 1000 is only an example.
  • FIG. 11 is a schematic diagram showing a physical structure diagram of a drone, and is not a limitation on the structure of the drone. The present invention does not specifically limit the structure of the drone.
  • the controller in the UAV of the present embodiment can be used to implement the technical solution of the method embodiment shown in FIG. 1 , FIG. 3 , FIG. 6 or FIG. 8 , and the implementation principle and technical effects are similar, and details are not described herein again.
  • the steps can be completed by the relevant hardware of the program instructions.
  • the aforementioned program can be stored in a computer readable storage medium.
  • the program when executed, performs the steps including the foregoing method embodiments; and the foregoing storage medium includes various media that can store program codes, such as a ROM, a RAM, a magnetic disk, or an optical disk.

Abstract

一种无人机避障方法及无人机。该方法包括：根据雷达输出的量测数据，确定障碍物相对无人机的飞行航迹；根据所述飞行航迹，对所述障碍物进行避障处理。本发明解决了障碍物误判的问题。

Description

无人机避障方法及无人机 技术领域
本发明涉及飞行技术领域，尤其涉及一种无人机避障方法及无人机。
背景技术
通常，无人机在作业的过程中，飞行走廊上的山丘、树木等自然物体及电力线、电线杆、建筑物等都会对无人机的安全飞行带来极大隐患。
现有技术中，主要采用超声波、双目视觉、激光等光学镜头对外部环境进行感知，来实现无人机的避障。但是，光学镜头对光照、气候条件等外部条件比较敏感，而雷达具有对外部条件不太明感，即使在雨、雾、尘等恶劣气候下雷达仍然有效以及全天候性等特点，因此也可以使用雷达进行障碍物感知，根据雷达感知到的障碍物，实现无人机的避障。
但是，现有技术中，在使用雷达进行障碍物感知的无人机作业的过程中，存在障碍物误判的问题。
发明内容
本发明提供一种无人机避障方法及无人机，用于解决现有技术中在使用雷达进行障碍物感知的无人机作业的过程中，存在的障碍物误判的问题。
第一方面，本发明提供一种无人机避障方法，包括：
根据雷达输出的量测数据，确定障碍物相对无人机的飞行航迹；
根据所述飞行航迹，对所述障碍物进行避障处理。
第二方面，本发明提供一种无人机，其包括机架，设置于所述机架的控制器，所述农业无人机还包括：安装于所述机架上或所述机架的负载上的雷达；
所述雷达，用于获取量测数据；
所述控制器，与所述雷达通信连接，用于根据所述雷达输出的量测数据，确定障碍物相对无人机的飞行航迹，并根据所述飞行航迹，对所述障碍物进行避障处理。
本发明提供的无人机避障方法及无人机，通过根据雷达输出的量测数据，确定障碍物相对无人机的飞行航迹，根据所述飞行航迹，对所述障碍物进行避障处理，使得即使雷达基于杂波输出了量测数据，但是由于杂波并不存在对应的障碍物，因此根据雷达基于杂波输出的量测数据，并不能确定出障碍物相对无人机的飞行航迹，从而在根据所述飞行航迹，对所述障碍物进行避障处理时，避免了根据雷达基于杂波输出了量测数据进行障碍物避障，解决了障碍物误判的问题。
附图说明
为了更清楚地说明本发明实施例或现有技术中的技术方案，下面将对实施例或现有技术描述中所需要使用的附图作一简单地介绍，显而易见地，下面描述中的附图是本发明的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。
图1为本发明无人机避障方法实施例一的流程图
图2为本发明雷达的结构示意图；
图3为本发明无人机避障方法实施例二的流程图；
图4为本发明回波与第一关联波门的关系示意图；
图5为本发明雷达、障碍物以及笛卡尔坐标系的关系示意图；
图6为本发明无人机避障方法实施例三的流程图；
图7为本发明生成候选航迹的示意图；
图8为本发明无人机避障方法实施例四的流程图；
图9为本发明确定满足预设条件的量测数据的示意图；
图10为本发明无人机的结构示意图；
图11为本发明无人机的实体结构图。
具体实施方式
为使本发明实施例的目的、技术方案和优点更加清楚，下面将结合本发明实施例中的附图，对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例是本发明一部分实施例，而不是全部的实施例。基于本发明中的实施例，本领域普通技术人员在没有作出创造性劳动前提下所获 得的所有其他实施例，都属于本发明保护的范围。
本发明应用于无人机，所述无人机上安装有雷达，所述雷达可以对障碍物进行探测，输出基于对障碍物的探测所获得的量测数据。其中，所述量测数据可能是雷达在探测到障碍物后输出的量测数据，或者，也可能雷达检测到杂波，例如地杂波等，而并非探测到障碍物所输出的量测数据。本发明，用于解决现有技术中，在使用雷达进行障碍物感知的无人机作业的过程中，存在障碍物误判的问题。
图1为本发明无人机避障方法实施例一的流程图，本实施例的执行主体可以为所述无人机的控制器。如图1所示，本实施例的方法可以包括：
步骤101、根据雷达输出的量测数据，确定障碍物相对无人机的飞行航迹。
本步骤中，所述量测数据具体可以包括障碍物的速度、距离、方位角中的一个或多个。可选的，根据雷达的天线进行划分，所述雷达具体可以指其天线为定向天线的雷达，或者也可以是指其天线为旋转天线的雷达。若所述雷达具体指其天线为定向天线的雷达，则所述雷达的个数可以为多个，该多个雷达分别用于探测所述无人机不同方位的障碍物。例如6个雷达，分别朝向所述无人机的正前方、前下方、正下方、正后方、后下方、正上方发射雷达波。若所述雷达具体指其天线为旋转天线的雷达，则所述雷达可以连续转动，本实施例的方法还可以包括：控制所述雷达连续转动，获取所述雷达在连续转动时的量测数据。可选的，在所述雷达连续转动时，所述雷达至少朝向所述无人机的正前方、前下方、正下方、正后方、后下方、正上方发射雷达波。进一步可选的，所述雷达的转轴方向可以平行于所述无人机的俯仰轴。
需要说明的是，对于所述雷达在所述无人机上安装的位置，可以根据需求灵活设计，本发明对此并不作限定。所述雷达波的发射方向，可以根据需求灵活设计，本发明对此并不作限定。
可选的，按照所述雷达的探测原理进行划分，所述雷达可以为连续波雷达或者脉冲雷达。如图2所示，以所述雷达为调频连续波(FMCW，Frequency Modulated Continuous Wave)雷达为例，其主要包括信号处理模块和射频前端。其中，信号处理模块包括控制器(例如，数字信号处理(DSP，Digital Signal Process)芯片等)，信号处理模块主要用于产生调制信号，并根据A/D采集 的差频信号确定距离。信号处理模块还可以包括例如闪存(FLASH)、随机存取存储器(RAM，Random-Access Memory)、只读存储器(ROM，read-only memory)等用于存储数据的存储器。射频前端采用一发双收，即包括一个发射通路和两个接收通路。对于发射通路，通过压控振荡器(VCO，Voltage Controlled Oscillator)对信号处理模块产生的调制波形进行调压以产生线性调频信号(线性调频信号的发射频率可以在24GHz)，并在对线性调频信号通过功率放大器(PA，Power Amplifier)进行放大后，通过发射天线TX发出(这里，发射天线TX发出的即为雷达波)。发射天线发出的雷达波经目标反射后的回波被接收通路通过接收天线RX1和RX2接收，对接收到的信号通过低噪声放大器(LNA，Low Noise Amplifier)进行低噪声放大，并对进行低噪声放大后的信号进行混频(其中，混频具体是将雷达波对应的信号与回波对应的信号进行混频)来得到差频信号。进一步的，该差频信号经过A/D采集进入信号处理模块后，由信号处理模块根据该差频信号确定上述量测数据。其中，上述接收通路和发射通路还分别可以包括功率分配器(简称功分)。上述接收天线和发射天线具体可以采用微带天线。
需要说明是，对于运动状态为运动的障碍物或者运动状态为静止的障碍物，以飞行的无人机为参考，相对于无人机均存在飞行航迹。因此，即使雷达基于杂波输出了量测数据，但是由于杂波并不存在对应的障碍物，因此根据雷达基于地杂波输出的量测数据，并不能确定出障碍物相对无人机的飞行航迹。
需要说明的是，对于确定障碍物相对无人机的飞行航迹的具体实现方式，本发明并不作限定。例如，当雷达两次探测输出的两个测量数据之间的关系满足预设关系时，可以这两个测量数据分别作为航点，并将这两个航点组成的航线，确定为障碍物相对于无人机的飞行航线。其中，所述飞行航迹可以包括至少两个航点，每个航点的信息可以包括：位置、速度、角度等中的一个或多个。
步骤102、根据所述飞行航迹，对所述障碍物进行避障处理。
本步骤中，具体可以根据所述障碍物相对无人机的飞行航迹，调整所述无人机的飞行航线或者飞行高度，以对所述障碍物进行避障处理。可选的，可以根据所述障碍物相对于无人机的飞行航迹，控制所述无人机的飞行姿态， 以对所述障碍物进行避障处理。所述飞行姿态可以包括俯冲、爬升、加速、减速、横滚等。需要说明的是，对于根据所述飞行航迹对所述障碍物进行避障处理的具体实现方式，本发明并不作限定，本领域技术人员可以根据实际需要设计相应的避障策略进行避障。
本实施例中，通过根据雷达输出的量测数据，确定障碍物相对无人机的飞行航迹，根据所述飞行航迹，对所述障碍物进行避障处理，使得即使雷达基于杂波输出了量测数据，但是由于杂波并不存在对应的障碍物，因此根据雷达基于杂波输出的量测数据，并不能确定出障碍物相对无人机的飞行航迹，从而在根据所述飞行航迹，对所述障碍物进行避障处理时，避免了根据雷达基于杂波输出的量测数据进行障碍物避障，解决了障碍物误判的问题。
图3为本发明无人机避障方法实施例二的流程图。本实施例在图1所示实施例的基础上主要描述了根据雷达输出的量测数据，确定障碍物相对无人机的飞行航迹一种具体的实现方式。如图3所示，本实施例的方法可以包括：
步骤301、根据之前时刻所述障碍物相对于所述无人机的飞行航迹，确定所述障碍物当前时刻的第一预测航点。
本步骤中，所述障碍物当前时刻的预测航点，即第一预测航点，是基于之前时刻所述障碍物相对于无人机的飞行航迹确定的。由于之前时刻所述障碍物相对于所述无人机的飞行航迹，可以体现出所述障碍物相对于所述无人机的飞行规律，因此基于之前时刻所述障碍物相对于无人机的飞行航迹可以确定出所述第一预测航点。需要说明的是，对于根据之前时刻所述障碍物相对于所述无人机的飞行航迹，确定所述障碍物当前时刻的第一预测航点的具体实现方式，本发明并不作限定。例如，可以根据之前时刻所述障碍物相对于无人机的飞行航迹，确定所述障碍物的运动规律(例如，匀速直线运动规律，匀加速直线运动规律等)，并根据所述障碍物的运动规律确定所述第一预测航点。
可选的，步骤301具体可以包括：根据之前时刻所述障碍物相对于所述无人机的飞行航迹，确定所述障碍物的运动模型；根据所述运动模型，确定当前时刻所述障碍物的第一预测航点。其中，所述运动模型可以将当前时刻所述障碍物的第一预测航点表示为之前时刻(例如，前一时刻)航点的函数。 在选择运动模型时，可以根据所述障碍物的运动状态以及所述雷达的实时性来综合考虑。例如，当所述障碍物的运动状态为静止时，运动模型具体可以为常速模型，该常速模型可以实时获取无人机的飞行速度信息。具体的，可以将之前时刻所述障碍物相对于所述无人机的飞行航迹中航点的位置、速度、角度等中的一个或多个作为状态变量，确定所述障碍物的运动模型。在航点的位置、速度、角度等中选择状态变量时，可以依据维数最少且能全面反映动态特性的一组变量的原则，防止计算量随状态变量数目的增加而增加。可选的，所述状态变量可以包括速度。
可选的，所述根据所述运动模型，确定当前时刻所述障碍物的第一预测航点，具体可以包括：根据所述运动模型确定当前时刻所述障碍物的估计航点；根据前一时刻的航点以及所述估计航点，基于卡尔曼算法，确定当前时刻所述障碍物的第一预测航点。具体的，可以将所述前一时刻的航点作为卡尔曼滤波算法中的测量值，将所述估计航点作为卡尔曼滤波算法中的预测值，采用卡尔曼滤波算法计算出来的估计值即为所述第一预测航点。
需要说明的是，对于根据所述运动模型，确定当前时刻所述障碍物的第一预测航点的具体实现方式，本发明并不作限定。例如，可以将根据所述运动模型确定的当前时刻所述障碍物的所述估计航点作为所述第一预测航点，或者，可以通过对所述第一估计航点和所述飞行航迹前一时刻的航点进行加权，确定所述第一预测航点。
步骤302、根据所述第一预测航点，确定第一关联波门。
本步骤中，所述第一关联波门可以是指以所述第一预测航点为中心的一个空间区域，所述第一关联波门具体可以为矩形波门、环形波门、圆形波门、球形波门或扇形波门等。在确定所述第一关联波门的形状和尺寸时，可以基于如下两方面进行考虑：一方面，要使落入第一关联波门内的有关回波有很高的概率；另一方面，不允许第一关联波门内有过多无关回波。这里，有关回波可以理解为对应的量测数据与上述飞行航迹有关的量测数据，无关回波可以理解为对应的量测数据与上述飞行航迹无关的量测数据。
步骤303、若当前时刻所述雷达的回波落入所述第一关联波门内，则根据所述回波对应的量测数据确定所述飞行航迹的当前航点。
本步骤中，以第一关联波门为球形波门，坐标系为笛卡尔坐标系为例， 第一关联波门的范围可以为如下公式(1)。
Figure PCTCN2017117043-appb-000001
其中，(x0，y0)可以表示笛卡尔坐标系下第一预测航点对应的坐标，(xk，yk)可以表示笛卡尔坐标系下回波对应的量测数据的坐标，K可以表示球形波门的半径。
例如，图4中(xi,yi)对应的回波落入第一关联波门内，(xn,yn)对应的回波未落入第一关联波门内，即落入第一关联波门之外。
需要说明的是，通常雷达输出的量测数据为极坐标系下的数据，而控制器处理的是笛卡尔坐标系下的数据，因此对于雷达输出的极坐标系下的量测数据可以进行坐标系转换，转换为笛卡尔坐标系下的量测数据。例如，假设雷达、障碍物以及笛卡尔坐标系之间的关系如图5所示，则障碍物的距离R、方位角
Figure PCTCN2017117043-appb-000002
与笛卡尔坐标系下的坐标x之间的关系可以如公式(2)所示，R、
Figure PCTCN2017117043-appb-000003
与笛卡尔坐标系下的坐标y之间的关系可以如公式(3)所示。
Figure PCTCN2017117043-appb-000004
Figure PCTCN2017117043-appb-000005
需要说明的是，图5中X和Y为笛卡尔坐标系的两条坐标轴。
可选的，当落入所述第一关联波门内的回波的个数为一个时，所述根据所述回波对应的量测数据确定所述障碍物的当前航点，包括：将所述回波对应的量测数据作为所述飞行航迹的当前航点。
可选的，当落入所述关联波门内的回波的个数为多个时，所述根据所述回波对应的量测数据确定所述障碍物的当前航点，包括：
在多个回波中选择一个回波，并将所选择的回波对应的量测数据作为所述飞行航迹的当前航点。可选的，所述在多个回波中选择一个回波，包括：基于最近邻法，在多个回波中选择一个回波。
具体的，首先可以根据第k+1时刻的第i个回波zi(k+1)，采用如下公式(4)确定第k+1时刻的第i个回波的更新向量vi(k+1)。
vi(k+1)＝zi(k+1)-zi(k)      公式(4)
其中，zi(k)表示第k时刻的回波对应的量测数据。
其次，根据vi(k+1)，采用如下公式(5)确定距离gi(k+1)。
Figure PCTCN2017117043-appb-000006
其中，
Figure PCTCN2017117043-appb-000007
表示vi(k+1)的转置，S-1(k+1)表示新息协方差矩阵。
最后，选择多个回波中使得gi(k+1)最小的回波。
需要说明的是，对于基于最近邻法，在多个回波中选择一个回波的具体实现方式，本发明并不作限定，例如，也可以选择多个回波中与第一预测航点对应的回波距离最近的回波。
可选的，在无人机飞行的过程中，障碍物并不是固定不变的，因此除了确定上述飞行航迹之外，还可以确定区别于上述飞行航迹的新的飞行航迹。因此，当当前时刻所述雷达的回波未落入所述第一关联波门内时，进一步的，可以根据量测数据确定新的飞行航迹。这里，关于根据量测数据确定新的飞行航迹的处理方式与图6所示实施例中生成候选航迹的处理方式可以类似，在此不再赘述。
本实施例中，通过根据之前时刻所述障碍物相对于所述无人机的飞行航迹，确定所述障碍物当前时刻的第一预测航点，根据所述第一预测航点，确定第一关联波门，若当前时刻所述雷达的回波落入所述第一关联波门内，则根据所述回波对应的量测数据确定所述飞行航迹的当前航点，实现了根据雷达输出的量测数据，确定障碍物相对无人机的飞行航迹。
图6为本发明无人机避障方法实施例三的流程图。本实施例在图3所示实施例的基础上主要描述了若当前时刻所述雷达的回波未落入所述第一关联波门内时的一种可选的实现方式。如图6所示，本实施例的方法可以包括：
步骤601、若当前时刻所述雷达的回波未落入所述第一关联波门内，则判断所述回波是否落入第二关联波门内；所述第二关联波门为根据第二预测航点确定的关联波门，所述第二预测航点为根据候选航迹确定的预测航点。
本步骤中，在无人机飞行的过程中，障碍物并不是固定不变的。为了提高障碍物的飞行航迹的准确率，除了上述飞行航迹之外，还可以确定出一些可能成为障碍物的飞行航迹的候选航迹，当当前时刻所述雷达的回波未落入所述第一关联波门内时，可以进一步的判断是否落入基于候选航迹确定的第二关联波门内。其中，所述候选航迹的个数可以为一个或多个，本发明对此并不作限定。
需要说明的是，关于所述第二关联波门的相关内容，与上述第一相关波 门类似，在此不再赘述。
步骤602、若所述回波落入所述第二关联波门内，则根据所述回波对应的量测数据确定所述候选航迹的当前航点。
需要说明的是，步骤602与步骤303类似，在此不再赘述。
步骤603、若所述回波未落入所述第二关联波门内，则根据所述回波对应的量测数据生成候选航迹。
本步骤中，在无人机飞行的过程中，障碍物并不是固定不变的，因此除了上述飞行航迹和候选航迹之外，还可以确定区别于上述飞行航迹和候选航迹的新的候选航迹。可选的，在尽快针对障碍物建立航迹以及尽量避免虚假航迹两方面的基础上，生成航迹时需要基于这两方面进行考虑。
可选的，可以采用如下方式生成候选航迹。
当雷达连续M个时刻分别输出的各第一量测数据中，第二量测数据的个数大于或等于K个时，确定生成所述候选航迹。
其中，所述第二量测数据包括第一量测数据与所述第一量测数据前一时刻的量测数据之间的差异程度小于或等于预设差异程度的第一量测数据，所述候选航迹包括根据各第一量测数据确定的航点信息，M为大于或等于2的正整数，K为小于或等于M的正整数。
具体的，假设当第i个时刻的第一量测数据与其前一时刻的量测数据之间的差异程度小于或等于预设差异程度时，Zi＝1，当第i个时刻的第一量测数据与其前一时刻的量测数据之间的差异程度大于预设差异程度时Zi＝0，则如图7所示，可以先判断Z0至ZM-1连续的M个Zi之和K是否大于或等于M，此时Z0至ZM-1可以认为是位于滑动窗口内，当K大于或等于M时，生成候选航迹，当K小于M时，进一步判断Z1至ZM连续的M个Zi之和K是否大于或等于M，即将滑动窗口向右移一次，当K大于或等于M时，生成候选航迹，当K小于M时，进一步判断Z2至ZM+1连续的M个Zi之和K是否大于或等于M，……。需要说明的是，Z0可以默认等于1或者0。
需要说明的是，在无人机飞行的过程中，障碍物并不是固定不变的，为了能够确定航迹(例如，飞行航迹以及候选航迹)的准确性，进一步的，可以对航迹的质量进行管理，航迹的质量越高可以表示航迹的准确性越高，航迹的质量越低可以表示航迹的准确性越低。
可选的，可以采用如下方式对航迹的质量进行管理：
根据所述当前航点与所述第一预测航点之间的差异程度，对所述飞行航迹的航迹质量进行更新；根据所述当前航点与所述第二预测航点之间的差异程度，对所述候选航迹的航迹质量进行更新；其中，差异程度越小，航迹质量越好；差异程度越大，航迹质量越差。需要说明的是，所述当前航点可以为上述候选航迹或上述飞行航迹的当前航点。
需要说明的是，在无人机飞行的过程中，上述候选航迹可能能够成为障碍物的飞行航迹，也可能不能成为障碍物的飞行轨迹，所述飞行航迹在一段时间之后也可能成为候选航迹。因此，可选的，在对航迹的质量进行管理的基础上，进一步的，还可以根据航迹的质量对航迹进行管理。具体的，根据所述航迹质量对候选航迹和飞行航迹进行管理。
可选的，所述根据所述航迹质量对候选航迹和飞行航迹进行管理，包括：当所述飞行航迹的航迹质量小于或等于第一预设航迹质量时，将所述飞行航机作为候选航迹；当所述候选航迹的航迹质量大于或等于第二预设航迹质量时，将所述候选航迹作为飞行航迹。
需要说明的是，所述第一预设航迹质量以及所述第二预设航迹质量，可以根据需求灵活设计，本发明对此并不作限定。
可选的，为了减小所维护的航迹的数目，还可以对航迹进行删除，这里“删除”可以理解为与上述“生成”相反的操作。具体的，所述根据所述航迹质量对候选航迹和飞行航迹进行管理，还可以包括：当所述候选航迹的航迹质量小于或等于第三预设航迹质量时，将所述候选航迹删除，所述第三预设航迹质量小于所述第一预设航迹质量。
本实施例中，通过若当前时刻所述雷达的回波未落入所述第一关联波门内，则判断所述回波是否落入第二关联波门内，若所述回波落入所述第二关联波门内，则根据所述回波对应的量测数据确定所述候选航迹的当前航点，若所述回波未落入所述第二关联波门内，则根据所述回波对应的量测数据生成候选航迹，在障碍物的飞行航迹的基础上，实现了候选航迹生成以及更新，提高了障碍物的飞行航迹的准确率。
图8为本发明无人机避障方法实施例四的流程图。本实施例在上述实施 例的基础上主要描述了在使用雷达输出的量测数据之间的一种可选的实现方式。如图8所示，本实施例的方法可以包括：
步骤801、确定所述雷达输出的量测数据中满足预设条件的量测数据。
本步骤中，雷达通常具有较大的探测范围，而无人机需要考虑的障碍物的范围可以仅为探测范围的部分，因此可以根据预设条件从雷达输出的量测数据中确定出与避障相关的量测数据。这里，雷达输出的量测数据中满足预设条件的量测数据可以认为是能够使用的可靠数据，雷达输出的量测数据中不满足预设条件的量测数据可以认为是不能使用的无用数据。可选的，所述预设条件包括：距离门限条件和/或角度门限条件。其中，所述距离门限条件可以由一个或者多个预设距离限定，例如，当由一个预设距离限定时，所述距离门限条件可以为大于或等于该预设距离，或者小于或等于该预设距离；当由两个预设距离，预设距离1和预设距离2限定时，所述距离门限条件可以为大于或等于预设距离1且小于或等于预设距离2。其中，所述角度门限条件可以由一个或多个预设角度限定，例如，当由一个预设角度限定时，所述角度门限条件可以为大于或等于该预设角度，或者小于或等于该预设角度；当由两个预设角度，预设角度1和预设角度2限定时，所述角度门限条件可以为大于或等于预设角度1且小于或等于预设角度2。
当所述预设条件包括距离门限条件和角度门限条件，步骤801具体的，可以例如图9所示。
步骤802、根据所述雷达输出的满足所述预设条件的量测数据，确定障碍物相对无人机的飞行航迹。
需要说明的是，关于根据量测数据确定障碍物相对于无人机的飞行航迹的具体方式可以参见上述实施例，在此不再赘述。
步骤803、根据所述飞行航迹，对所述障碍物进行避障处理。
需要说明的是，步骤803与步骤102类似，在此不再赘述。
本实施例中，确定所述雷达输出的量测数据中满足预设条件的量测数据，根据所述雷达输出的满足所述预设条件的量测数据，确定障碍物相对无人机的飞行航迹，可以减小数据的计算量，从而减轻控制器的负担，提高了处理的速度，同时，也可以降低虚假航迹形成的可能性。
图10为本发明无人机的结构示意图，图11为本发明无人机的实体结构图。如图9和图10所示，本实施例的无人机1000可以包括机架1001，设置于机架1001的控制器1002，安装于机架1001上或机架1001的负载1003上的雷达1004。其中，雷达1004，用于获取量测数据；控制器1002，与所述雷达1004通信连接，用于根据雷达输出的量测数据，确定障碍物相对无人机的飞行航迹，并根据所述飞行航迹，对所述障碍物进行避障处理。
可选的，控制器1002根据雷达输出的量测数据，确定障碍物相对于无人机的飞行航迹，具体包括：
根据之前时刻所述障碍物相对于所述无人机的飞行航迹，确定所述障碍物当前时刻的第一预测航点；
根据所述第一预测航点，确定第一关联波门；
若当前时刻所述雷达的回波落入所述第一关联波门内，则根据所述回波对应的量测数据确定所述飞行航迹的当前航点。
可选的，当落入所述第一关联波门内的回波的个数为一个时，控制器1002根据所述回波对应的量测数据确定所述障碍物的当前航点，具体包括：
将所述回波对应的量测数据作为所述飞行航迹的当前航点。
可选的，当落入所述关联波门内的回波的个数为多个时，控制器1002根据所述回波对应的量测数据确定所述障碍物的当前航点，具体包括：
在多个回波中选择一个回波，并将所选择的回波对应的量测数据作为所述飞行航迹的当前航点。
可选的，控制器1002在多个回波中选择一个回波，具体包括：
基于最近邻法，在多个回波中选择一个回波。
可选的，控制器1002还用于：
若当前时刻所述雷达的回波未落入所述第一关联波门内，则判断所述回波是否落入第二关联波门内；所述第二关联波门为根据第二预测航点确定的关联波门，所述第二预测航点为根据候选航迹确定的预测航点；
若所述回波落入所述第二关联波门内，则根据所述回波对应的量测数据确定所述候选航迹的当前航点；
若所述回波未落入所述第二关联波门内，则根据所述回波对应的量测数据生成候选航迹。
可选的，控制器1002根据所述回波对应的量测数据生成候选航迹，具体包括：
当雷达连续M个时刻分别输出的各第一量测数据中，第二量测数据的个数大于或等于K个时，确定生成所述候选航迹；所述第二量测数据包括第一量测数据与所述第一量测数据前一时刻的量测数据之间的差异程度小于或等于预设差异程度的第一量测数据，所述候选航迹包括根据各第一量测数据确定的航点信息，M为大于或等于2的正整数，K为小于或等于M的正整数。
可选的，控制器1002，还用于：
根据所述当前航点与所述第一预测航点之间的差异程度，对所述飞行航迹的航迹质量进行更新；
根据所述当前航点与所述第二预测航点之间的差异程度，对所述候选航迹的航迹质量进行更新；
其中，差异程度越小，航迹质量越好；差异程度越大，航迹质量越差。
可选的，控制器1002，还用于根据所述航迹质量对候选航迹和飞行航迹进行管理。
可选的，控制器1002根据所述航迹质量对候选航迹和飞行航迹进行管理，具体包括：
当所述飞行航迹的航迹质量小于或等于第一预设航迹质量时，将所述飞行航机作为候选航迹；
当所述候选航迹的航迹质量大于或等于第二预设航迹质量时，将所述候选航迹作为飞行航迹。
可选的，控制器1002根据所述航迹质量对候选航迹和飞行航迹进行管理，还包括：
当所述候选航迹的航迹质量小于或等于第三预设航迹质量时，将所述候选航迹删除，所述第三预设航迹质量小于所述第一预设航迹质量。
可选的，控制器1002根据之前时刻所述障碍物相对于所述无人机的飞行航迹，确定所述障碍物当前时刻的第一预测航点，具体包括：
根据之前时刻所述障碍物相对于所述无人机的飞行航迹，确定所述障碍物的运动模型；
根据所述运动模型，确定当前时刻所述障碍物的第一预测航点。
可选的，控制器1002根据所述运动模型，确定当前时刻所述障碍物的第一预测航点，具体包括：
根据所述运动模型确定当前时刻所述障碍物的估计航点；
根据前一时刻的航点以及所述估计航点，基于卡尔曼算法，确定当前时刻所述障碍物的第一预测航点。
可选的，控制器1002，还用于在使用雷达输出的量测数据之前，确定所述雷达输出的量测数据中满足预设条件的量测数据；
控制器1002根据所述雷达输出的量测数据，确定障碍物相对无人机的飞行航迹，具体包括：根据所述雷达输出的满足所述预设条件的量测数据，确定障碍物相对无人机的飞行航迹。
可选的，所述预设条件包括：距离门限条件，和/或角度门限条件。
可选的，控制器1002根据所述飞行航迹，对所述障碍物进行避障处理，具体包括：
根据所述飞行航迹，控制所述无人机的飞行姿态，以对所述障碍物进行避障处理。
可选的，控制器1002还用于：
控制所述雷达连续转动；
获取所述雷达在连续转动时的量测数据。
可选的，在所述雷达连续转动时，所述雷达至少朝向所述无人机的正前方、前下方、正下方、正后方、后下方、正上方发射雷达波。
可选的，所述雷达的转轴方向平行于所述无人机的俯仰轴。
可选的，本发明中上述无人机具体可以为多旋翼无人机，例如四旋翼无人机。
需要说明的是，图11中以雷达1004为其天线为旋转天线的雷达为例，且雷达1004在无人机1000上的安装位置仅为举例。图11只是以示例的形式示意出一种无人机的实体结构图，并不是对无人机结构的限定，本发明对无人机的结构不作具体限定。
本实施例的无人机中的控制器，可以用于执行图1、图3、图6或图8所示方法实施例的技术方案，其实现原理和技术效果类似，此处不再赘述。
本领域普通技术人员可以理解：实现上述各方法实施例的全部或部分步 骤可以通过程序指令相关的硬件来完成。前述的程序可以存储于一计算机可读取存储介质中。该程序在执行时，执行包括上述各方法实施例的步骤；而前述的存储介质包括：ROM、RAM、磁碟或者光盘等各种可以存储程序代码的介质。
最后应说明的是：以上各实施例仅用以说明本发明的技术方案，而非对其限制；尽管参照前述各实施例对本发明进行了详细的说明，本领域的普通技术人员应当理解：其依然可以对前述各实施例所记载的技术方案进行修改，或者对其中部分或者全部技术特征进行等同替换；而这些修改或者替换，并不使相应技术方案的本质脱离本发明各实施例技术方案的范围。

Claims (38)

  1. 一种无人机避障方法，其特征在于，包括：
    根据雷达输出的量测数据，确定障碍物相对无人机的飞行航迹；
    根据所述飞行航迹，对所述障碍物进行避障处理。
  2. 根据权利要求1所述的方法，其特征在于，所述根据雷达输出的量测数据，确定障碍物相对于无人机的飞行航迹，包括：
    根据之前时刻所述障碍物相对于所述无人机的飞行航迹，确定所述障碍物当前时刻的第一预测航点；
    根据所述第一预测航点，确定第一关联波门；
    若当前时刻所述雷达的回波落入所述第一关联波门内，则根据所述回波对应的量测数据确定所述飞行航迹的当前航点。
  3. 根据权利要求2所述的方法，其特征在于，当落入所述第一关联波门内的回波的个数为一个时，所述根据所述回波对应的量测数据确定所述障碍物的当前航点，包括：
    将所述回波对应的量测数据作为所述飞行航迹的当前航点。
  4. 根据权利要求2所述的方法，其特征在于，当落入所述关联波门内的回波的个数为多个时，所述根据所述回波对应的量测数据确定所述障碍物的当前航点，包括：
    在多个回波中选择一个回波，并将所选择的回波对应的量测数据作为所述飞行航迹的当前航点。
  5. 根据权利要求4所述的方法，其特征在于，所述在多个回波中选择一个回波，包括：
    基于最近邻法，在多个回波中选择一个回波。
  6. 根据权利要求2-5任一项所述的方法，其特征在于，所述方法还包括：
    若当前时刻所述雷达的回波未落入所述第一关联波门内，则判断所述回波是否落入第二关联波门内；所述第二关联波门为根据第二预测航点确定的关联波门，所述第二预测航点为根据候选航迹确定的预测航点；
    若所述回波落入所述第二关联波门内，则根据所述回波对应的量测数据确定所述候选航迹的当前航点；
    若所述回波未落入所述第二关联波门内，则根据所述回波对应的量测数 据生成候选航迹。
  7. 根据权利要求6所述的方法，其特征在于，所述根据所述回波对应的量测数据生成候选航迹，包括：
    当雷达连续M个时刻分别输出的各第一量测数据中，第二量测数据的个数大于或等于K个时，确定生成所述候选航迹；所述第二量测数据包括第一量测数据与所述第一量测数据前一时刻的量测数据之间的差异程度小于或等于预设差异程度的第一量测数据，所述候选航迹包括根据各第一量测数据确定的航点信息，M为大于或等于2的正整数，K为小于或等于M的正整数。
  8. 根据权利要求6或7所述的方法，其特征在于，所述方法还包括：
    根据所述当前航点与所述第一预测航点之间的差异程度，对所述飞行航迹的航迹质量进行更新；
    根据所述当前航点与所述第二预测航点之间的差异程度，对所述候选航迹的航迹质量进行更新；
    其中，差异程度越小，航迹质量越好；差异程度越大，航迹质量越差。
  9. 根据权利要求8所述的方法，其特征在于，所述方法还包括：根据所述航迹质量对候选航迹和飞行航迹进行管理。
  10. 根据权利要求9所述的方法，其特征在于，所述根据所述航迹质量对候选航迹和飞行航迹进行管理，包括：
    当所述飞行航迹的航迹质量小于或等于第一预设航迹质量时，将所述飞行航机作为候选航迹；
    当所述候选航迹的航迹质量大于或等于第二预设航迹质量时，将所述候选航迹作为飞行航迹。
  11. 根据权利要求10所述的方法，其特征在于，所述根据所述航迹质量对候选航迹和飞行航迹进行管理，还包括：
    当所述候选航迹的航迹质量小于或等于第三预设航迹质量时，将所述候选航迹删除，所述第三预设航迹质量小于所述第一预设航迹质量。
  12. 根据权利要求2-11任一项所述的方法，其特征在于，所述根据之前时刻所述障碍物相对于所述无人机的飞行航迹，确定所述障碍物当前时刻的第一预测航点，包括：
    根据之前时刻所述障碍物相对于所述无人机的飞行航迹，确定所述障碍 物的运动模型；
    根据所述运动模型，确定当前时刻所述障碍物的第一预测航点。
  13. 根据权利要求12所述的方法，其特征在于，所述根据所述运动模型，确定当前时刻所述障碍物的第一预测航点，包括：
    根据所述运动模型确定当前时刻所述障碍物的估计航点；
    根据前一时刻的航点以及所述估计航点，基于卡尔曼算法，确定当前时刻所述障碍物的第一预测航点。
  14. 根据权利要求1-13任一项所述的方法，其特征在于，在使用雷达输出的量测数据之前，还包括：
    确定所述雷达输出的量测数据中满足预设条件的量测数据；
    所述根据所述雷达输出的量测数据，确定障碍物相对无人机的飞行航迹，包括：根据所述雷达输出的满足所述预设条件的量测数据，确定障碍物相对无人机的飞行航迹。
  15. 根据权利要求14所述的方法，其特征在于，所述预设条件包括：距离门限条件，和/或角度门限条件。
  16. 根据权利要求1-15任一项所述的方法，其特征在于，所述根据所述飞行航迹，对所述障碍物进行避障处理，包括：
    根据所述飞行航迹，控制所述无人机的飞行姿态，以对所述障碍物进行避障处理。
  17. 根据权利要求1-16任一项所述的方法，其特征在于，所述方法还包括：
    控制所述雷达连续转动；
    获取所述雷达在连续转动时的量测数据。
  18. 根据权利要求17所述的方法，其特征在于，在所述雷达连续转动时，所述雷达至少朝向所述无人机的正前方、前下方、正下方、正后方、后下方、正上方发射雷达波。
  19. 根据权利要求1-18任一项所述的方法，其特征在于，所述雷达的转轴方向平行于所述无人机的俯仰轴。
  20. 一种无人机，其包括机架，设置于所述机架的控制器，其特征在于，所述农业无人机还包括：安装于所述机架上或所述机架的负载上的雷达；
    所述雷达，用于获取量测数据；
    所述控制器，与所述雷达通信连接，用于根据所述雷达输出的量测数据，确定障碍物相对无人机的飞行航迹，并根据所述飞行航迹，对所述障碍物进行避障处理。
  21. 根据权利要求20所述的无人机，其特征在于，所述控制器根据雷达输出的量测数据，确定障碍物相对于无人机的飞行航迹，具体包括：
    根据之前时刻所述障碍物相对于所述无人机的飞行航迹，确定所述障碍物当前时刻的第一预测航点；
    根据所述第一预测航点，确定第一关联波门；
    若当前时刻所述雷达的回波落入所述第一关联波门内，则根据所述回波对应的量测数据确定所述飞行航迹的当前航点。
  22. 根据权利要求21所述的无人机，其特征在于，当落入所述第一关联波门内的回波的个数为一个时，所述控制器根据所述回波对应的量测数据确定所述障碍物的当前航点，具体包括：
    将所述回波对应的量测数据作为所述飞行航迹的当前航点。
  23. 根据权利要求21所述的无人机，其特征在于，当落入所述关联波门内的回波的个数为多个时，所述控制器根据所述回波对应的量测数据确定所述障碍物的当前航点，具体包括：
    在多个回波中选择一个回波，并将所选择的回波对应的量测数据作为所述飞行航迹的当前航点。
  24. 根据权利要求23所述的无人机，其特征在于，所述控制器在多个回波中选择一个回波，具体包括：
    基于最近邻法，在多个回波中选择一个回波。
  25. 根据权利要求21-24任一项所述的无人机，其特征在于，所述控制器还用于：
    若当前时刻所述雷达的回波未落入所述第一关联波门内，则判断所述回波是否落入第二关联波门内；所述第二关联波门为根据第二预测航点确定的关联波门，所述第二预测航点为根据候选航迹确定的预测航点；
    若所述回波落入所述第二关联波门内，则根据所述回波对应的量测数据确定所述候选航迹的当前航点；
    若所述回波未落入所述第二关联波门内，则根据所述回波对应的量测数据生成候选航迹。
  26. 根据权利要求25所述的无人机，其特征在于，所述控制器根据所述回波对应的量测数据生成候选航迹，具体包括：
    当雷达连续M个时刻分别输出的各第一量测数据中，第二量测数据的个数大于或等于K个时，确定生成所述候选航迹；所述第二量测数据包括第一量测数据与所述第一量测数据前一时刻的量测数据之间的差异程度小于或等于预设差异程度的第一量测数据，所述候选航迹包括根据各第一量测数据确定的航点信息，M为大于或等于2的正整数，K为小于或等于M的正整数。
  27. 根据权利要求25或26所述的无人机，其特征在于，所述控制器，还用于：
    根据所述当前航点与所述第一预测航点之间的差异程度，对所述飞行航迹的航迹质量进行更新；
    根据所述当前航点与所述第二预测航点之间的差异程度，对所述候选航迹的航迹质量进行更新；
    其中，差异程度越小，航迹质量越好；差异程度越大，航迹质量越差。
  28. 根据权利要求27所述的无人机，其特征在于，所述控制器，还用于根据所述航迹质量对候选航迹和飞行航迹进行管理。
  29. 根据权利要求28所述的无人机，其特征在于，所述控制器根据所述航迹质量对候选航迹和飞行航迹进行管理，具体包括：
    当所述飞行航迹的航迹质量小于或等于第一预设航迹质量时，将所述飞行航机作为候选航迹；
    当所述候选航迹的航迹质量大于或等于第二预设航迹质量时，将所述候选航迹作为飞行航迹。
  30. 根据权利要求29所述的无人机，其特征在于，所述控制器根据所述航迹质量对候选航迹和飞行航迹进行管理，还包括：
    当所述候选航迹的航迹质量小于或等于第三预设航迹质量时，将所述候选航迹删除，所述第三预设航迹质量小于所述第一预设航迹质量。
  31. 根据权利要求21-30任一项所述的无人机，其特征在于，所述控制器根据之前时刻所述障碍物相对于所述无人机的飞行航迹，确定所述障碍物 当前时刻的第一预测航点，具体包括：
    根据之前时刻所述障碍物相对于所述无人机的飞行航迹，确定所述障碍物的运动模型；
    根据所述运动模型，确定当前时刻所述障碍物的第一预测航点。
  32. 根据权利要求31所述的无人机，其特征在于，所述控制器根据所述运动模型，确定当前时刻所述障碍物的第一预测航点，具体包括：
    根据所述运动模型确定当前时刻所述障碍物的估计航点；
    根据前一时刻的航点以及所述估计航点，基于卡尔曼算法，确定当前时刻所述障碍物的第一预测航点。
  33. 根据权利要求30-32任一项所述的无人机，其特征在于，所述控制器，还用于在使用雷达输出的量测数据之前，确定所述雷达输出的量测数据中满足预设条件的量测数据；
    所述控制器根据所述雷达输出的量测数据，确定障碍物相对无人机的飞行航迹，具体包括：根据所述雷达输出的满足所述预设条件的量测数据，确定障碍物相对无人机的飞行航迹。
  34. 根据权利要求33所述的无人机，其特征在于，所述预设条件包括：距离门限条件，和/或角度门限条件。
  35. 根据权利要求30-34任一项所述的无人机，其特征在于，所述控制器根据所述飞行航迹，对所述障碍物进行避障处理，具体包括：
    根据所述飞行航迹，控制所述无人机的飞行姿态，以对所述障碍物进行避障处理。
  36. 根据权利要求20-35任一项所述的无人机，其特征在于，所述控制器还用于：
    控制所述雷达连续转动；
    获取所述雷达在连续转动时的量测数据。
  37. 根据权利要求36所述的无人机，其特征在于，在所述雷达连续转动时，所述雷达至少朝向所述无人机的正前方、前下方、正下方、正后方、后下方、正上方发射雷达波。
  38. 根据权利要求20-37任一项所述的无人机，其特征在于，所述雷达的转轴方向平行于所述无人机的俯仰轴。
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