WO2022109846A1 - 可移动平台的状态检测方法、装置和系统、可移动平台 - Google Patents
可移动平台的状态检测方法、装置和系统、可移动平台 Download PDFInfo
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- G—PHYSICS
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- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M13/00—Testing of machine parts
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- B—PERFORMING OPERATIONS; TRANSPORTING
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- B64F5/60—Testing or inspecting aircraft components or systems
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- G01H—MEASUREMENT OF MECHANICAL VIBRATIONS OR ULTRASONIC, SONIC OR INFRASONIC WAVES
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- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M7/00—Vibration-testing of structures; Shock-testing of structures
- G01M7/02—Vibration-testing by means of a shake table
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Definitions
- the present disclosure relates to the technical field of mobile platforms, and in particular, to a state detection method, device and system for a mobile platform, and a mobile platform.
- the mobile platform can carry various loads to perform different tasks, the mobile platform has obtained relatively rapid development and wide application in many fields.
- abnormal conditions such as aging and damage of structural parts will inevitably occur.
- One detection method is to use flaw detection equipment for abnormality detection.
- the flaw detection equipment is expensive, resulting in a high cost for the status detection of the movable platform.
- the present disclosure provides a state detection method, device and system for a movable platform, and a movable platform, which can reduce the cost of performing state detection on the movable platform.
- an embodiment of the present disclosure provides a method for detecting a state of a movable platform, the method comprising: acquiring a response signal generated by a key point on the movable platform to an excitation output by an excitation source; acquiring the key The characteristic information of the response signal of the point; based on the characteristic information of the response signal of the key point, the state detection is performed on the movable platform.
- an embodiment of the present disclosure provides an apparatus for detecting a state of a movable platform, including a processor, where the processor is configured to execute the following method: acquiring key points on the movable platform generated by an excitation output from an excitation source The response signal of the key point is obtained; the characteristic information of the response signal of the key point is obtained; based on the characteristic information of the response signal of the key point, the state detection is performed on the movable platform.
- an embodiment of the present disclosure provides a state detection system for a movable platform, the system includes: a motor for outputting excitation; an inertial measurement unit, located near key points of the movable platform, for collecting the The key point is a response signal generated by the excitation output by the excitation source; and a processor, connected in communication with the inertial measurement unit, for executing the method described in any embodiment of the present disclosure.
- an embodiment of the present disclosure provides a movable platform, where the movable platform includes: the state detection apparatus described in any embodiment of the present disclosure, or the state detection system described in any embodiment of the present disclosure.
- an embodiment of the present disclosure provides a computer-readable storage medium, including instructions, which, when executed on a computer, cause the computer to execute the method described in any embodiment of the present disclosure.
- the response signal generated by the key point for the excitation is obtained, and only the characteristic information of the response signal can be obtained, and the available information can be determined based on the characteristic information of the response signal.
- the state detection of the mobile platform does not require the use of special flaw detection equipment for the state detection, which reduces the detection cost of the state detection of the movable platform.
- FIG. 1 is a flowchart of a state detection method for a movable platform according to an embodiment of the present disclosure.
- FIG. 2 is a schematic diagram of a response signal according to an embodiment of the present disclosure.
- FIG. 3 is a schematic diagram of a frequency spectrum of a response signal according to an embodiment of the present disclosure.
- FIG. 4 is a schematic diagram of a resonance state detection process according to an embodiment of the present disclosure.
- FIG. 5 is a schematic diagram of a fatigue state detection process according to an embodiment of the present disclosure.
- FIG. 6 is a schematic diagram of a mechanical state detection process according to an embodiment of the present disclosure.
- FIG. 7 is a schematic diagram of a state detection apparatus according to an embodiment of the present disclosure.
- FIG. 8 is a schematic diagram of a state detection system according to an embodiment of the present disclosure.
- first, second, third, etc. may be used in this disclosure to describe various pieces of information, such information should not be limited by these terms. These terms are only used to distinguish the same type of information from each other.
- first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information, without departing from the scope of the present disclosure.
- word "if” as used herein can be interpreted as "at the time of” or "when” or "in response to determining.”
- Movable platforms can carry various loads to perform different tasks, so they have been rapidly developed and widely used in many fields.
- the UAV has good stability, strong anti-interference ability, can actively hover, and has relatively low requirements for take-off and landing conditions. It can be executed by the UAV carrying the imaging load. image capture tasks.
- the present disclosure provides a state detection method for a movable platform, so as to solve the problem that it is difficult to accurately detect the state of a movable platform and a load that exists for a long time in actual use, thereby affecting the working effect of the load and the driving of the movable platform. security issues.
- the method includes:
- Step 101 Acquire the response signal generated by the key point on the movable platform for the excitation output by the excitation source
- Step 102 Obtain characteristic information of the response signal of the key point
- Step 103 Perform state detection on the movable platform based on the characteristic information of the response signal of the key point.
- the movable platform in the embodiments of the present disclosure includes but is not limited to unmanned aerial vehicles, unmanned vehicles, unmanned ships, movable robots and other equipment, and the key points on the movable platform include but are not limited to arms, blades, and connections between arms At least one of the parts, the middle frame of the fuselage, the shock-absorbing ball, the arm rotating shaft, the motor shaft, the motor base and the load.
- the state detection of the movable platform in the embodiment of the present disclosure includes the state detection of the movable platform itself, and the state detection of the load (eg, camera) on the movable platform.
- the state of the load and the state of the movable platform itself can be detected synchronously, asynchronously, or separately.
- the method of the embodiment of the present disclosure may be executed by a processor built in the movable platform, for example, may be executed by a flight control unit on an unmanned aerial vehicle, or by an electronic control unit (Electronic Control Unit, ECU) on an unmanned vehicle. Alternatively, it can also be executed through a cloud server or a terminal device (for example, a dedicated remote controller or a smart terminal such as a mobile phone) that communicates with the mobile platform.
- a processor built in the movable platform for example, may be executed by a flight control unit on an unmanned aerial vehicle, or by an electronic control unit (Electronic Control Unit, ECU) on an unmanned vehicle.
- ECU Electronic Control Unit
- it can also be executed through a cloud server or a terminal device (for example, a dedicated remote controller or a smart terminal such as a mobile phone) that communicates with the mobile platform.
- an excitation signal (abbreviated as excitation) may be output through an excitation source, and a response signal of one or more key points to the excitation may be acquired.
- the excitation may be a specific form of signal, eg, a frequency sweep signal, a pulsed signal, or the like.
- the response signal may include, but is not limited to, at least one of an acceleration response signal, an angular velocity response signal, a displacement response signal, a stress response signal, and a strain response signal.
- the response signals in the present disclosure all refer to the response signals in the time domain, and the frequency domain response signals refer to the response signals in the frequency domain.
- the acceleration response signal and the angular velocity response signal can be measured by the inertial measurement unit (Inertial Measurement Unit, IMU) on the movable platform, or measured by the acceleration sensor and the angular velocity sensor respectively, the displacement response signal, the stress response signal and the strain response signal can be They are acquired by displacement sensor, stress sensor and strain sensor respectively.
- IMU Inertial Measurement Unit
- Any of the above-mentioned collection devices for collecting response signals may be a device that is built into the movable platform, or may be an additional device installed on the movable platform.
- the collecting device may further perform at least one of filtering, amplifying, adjusting, analog differential processing and analog-digital conversion processing on the response signal.
- the response signals of multiple key points can be collected by a collection device on the movable platform, or a multi-point collection system can be formed by multiple collection devices installed on the movable platform. Installed near a key point, the response signal of each key point can be collected by one or more collection devices in the multi-point collection system.
- the location and number of the acquisition devices in the multi-point acquisition system can be determined based on the accuracy requirements of the state detection, the size and weight constraints of the movable platform and other reasons.
- the reliability of the collection device for collecting the response signal of the key point is higher.
- the confidence level of the collection device closer to the key point can be set to be higher, and the confidence level of the collection device farther away from the key point can be set to be lower, and each collection device is based on the confidence level.
- the collected response signals are weighted and averaged to obtain the final response signal, thereby improving the reliability of the collected response signal.
- step 102 and step 103 characteristic information of the response signal of the key point can be acquired, and the movable platform can be detected based on the characteristic information of the response signal of the key point.
- the state detection can be performed by the processor on the movable platform, or the feature information can also be sent to the control terminal, so that the control terminal based on the feature information of the response signal of the key point, The platform performs state detection.
- the characteristic information of a response signal may include, but is not limited to, at least one of amplitude, frequency, phase, frequency spectrum, and the like of the response signal.
- the characteristic information of the response signal of the key point on the movable platform is related to the state of the movable platform, and when the state of the movable platform changes, the characteristic information of the response signal will also change accordingly. For example, when a structural member on the movable platform has a crack, the response signal of the structural member has different characteristic information from the response signal of the structural member when the structural member does not have a crack. Therefore, the state of the movable platform can be determined based on the characteristic information of the response signals of the key points.
- the feature information of the response signal of the key point may be compared with the feature information of the eigenmodel of the movable platform to obtain a comparison result, and based on the comparison result, the movable platform Check status.
- the intrinsic model is obtained by modeling by means of computer aided engineering (Computer Aided Engineering, CAE), or obtained by measurement. This method is called model identification.
- CAE Computer Aided Engineering
- model identification the state of the movable platform can be determined only by comparing the characteristic information of the response signal of the key point with the characteristic information of the intrinsic model.
- the implementation method is simple and the detection efficiency is high. And no special testing equipment is needed, which reduces the testing cost.
- the above detection method can detect various structural parts inside the movable platform, which solves the problem that the internal damage of the movable platform is difficult to observe, or the state of the movable platform with a larger model is inconvenient to detect. It is a movable platform of various structural forms and sizes, and has a wide range of applications.
- the excitation in the embodiments of the present disclosure may be output by a power system (such as a motor and/or a propeller) on the movable platform, or may be output by an external device of the movable platform, and the manner of output excitation may be determined based on the state to be detected .
- the state to be detected may include, but is not limited to, at least one of a resonance state, a fatigue state and a mechanical state of the movable platform.
- Resonance state detection is used to determine whether the frequency of the power system that provides the driving force for the movable platform and the natural mode frequency of the movable platform resonate during the driving process of the movable platform, for example, to determine whether the blade of the UAV is in resonance.
- the fatigue state detection is used to determine the fatigue damage degree of the structural parts on the movable platform, so as to control the fatigue life of the movable platform.
- Mechanical condition detection is used to determine the mechanical condition of the structural parts on the movable platform, for example, the degree of wear, the mechanical bite force, the presence of cracks, etc.
- the power generated for driving the movable platform to travel may be used as the excitation, and the excitation may be generated by a motor on the movable platform.
- each motor can be individually controlled by issuing control commands through the control program.
- the motors of the three axes of roll, yaw and pitch can be controlled by control commands, thereby controlling the rotation speed of the corresponding blades , and then effectively realize the control of UAV motion and obtain higher UAV stability.
- the detection of the resonance state or the fatigue state can be carried out while the movable platform is traveling.
- the movable platform Since the power generated by driving the movable platform to travel is directly used as the excitation, there is no need to input additional excitation.
- the movable platform is an unmanned aerial vehicle
- the detection of the resonance state and the fatigue state during the flight of the unmanned aerial vehicle can also reduce the ground restraint zone. error, and improve the detection accuracy.
- the amplitude information of the response signal may be determined as characteristic information of the response signal. For example, if the amplitude of the response signal is greater than the first preset value, it is determined that the resonance state of the movable platform is an abnormal state, that is, resonance occurs. For another example, if the amplitude of the response signal is less than or equal to the first preset value, it is determined that the resonance state of the movable platform is a normal state. By detecting the resonance state, it can be determined whether the movable platform is in a dangerous state.
- the first preset value corresponding to the response signal of a key point may be determined based on the amplitude of the same key point in the eigenmodel, for example, it may be determined to be K times the amplitude of the same key point in the eigenmodel, where K is greater than 1 number of.
- the frequency spectrum of the response signal can also be determined as characteristic information of the response signal.
- the frequency spectrum can be obtained by performing a Fast Fourier Transform (Fast Fourier Transform, FFT) on the response signal, and a specific form of the frequency spectrum is shown in FIG. 3 . For example, if the frequency spectrum includes a point whose frequency is within the natural frequency range of the movable platform and whose amplitude is greater than the second preset value, it is determined that the resonance state of the movable platform is an abnormal state.
- FFT Fast Fourier Transform
- the frequency spectrum does not include a point whose frequency is within the natural frequency band of the movable platform and whose amplitude is greater than the second preset value, it is determined that the resonance state of the movable platform is a normal state. If the frequency spectrum of the response signal includes the natural frequency range of the movable platform (that is, the natural modal frequency point), it indicates that there is a danger of resonance. At this time, by further judging the amplitude of the frequency spectrum, it can be determined whether resonance occurs. If the amplitude exceeds the second preset value, it means that resonance occurs, otherwise, it means that resonance does not occur.
- the second preset value corresponding to the response signal of a key point may be determined based on the amplitude of the spectrum of the same key point in the eigenmodel, for example, may be determined to be P times the amplitude of the spectrum of the same key point in the eigenmodel, P is a number greater than 1.
- the movable platform is an unmanned aerial vehicle as an example for description.
- the abnormal protection mode of the drone can be set through a server communicating with the drone or an application (Application, APP) on the control terminal, for example, when a slight abnormal situation is detected, the drone is controlled Continue the flight; control the drone to return when a more serious abnormal situation is detected. You can also trigger the resonance state detection process through the APP.
- a response signal eg, acceleration response 403 generated by a key point on the UAV with respect to the excitation signal may be collected by a data collection device such as an IMU.
- step 404 if the amplitude A of the acceleration response is greater than the maximum allowable amplitude A max , then step 410 is performed to record data information, including the amplitude A of the acceleration response, the ratio of the amplitude A exceeding the maximum amplitude A max , One or more of the status detection results (for example, whether the status is abnormal, the degree of abnormality, etc.), the current time, and other information.
- step 402 may also be returned to continue state detection.
- step 405 is executed to perform FFT transformation on the acceleration response to obtain the frequency spectrum of the acceleration response.
- step 406 it is determined whether the frequency of the frequency spectrum of the acceleration response matches the natural mode frequency of the drone (ie, whether the two overlap). If they match, it means that the two overlap, and in step 407, it is further judged whether the amplitude of the frequency spectrum of the acceleration response matches the amplitude of the eigenmodel of the UAV. If it matches, that is, the amplitude of the frequency spectrum of the acceleration response is much larger than the amplitude of the eigenmodel, it is considered that resonance occurs, and the UAV is judged to be in an abnormal state.
- step 410 may be performed to record data information, including the magnitude of the spectrum, the magnitude and frequency of the spectrum, and the magnitude of the eigenmodel and the eigenmode One or more kinds of information such as state frequency points.
- step 408 may be performed to issue an alarm through the APP, and step 409 may be performed to protect the drone from abnormality based on the abnormality protection mode.
- the amplitude information and/or phase information of the response signal may be used as characteristic information of the response signal.
- the fatigue damage of the key point can be determined, and the state of the movable platform can be detected based on the fatigue damage of the key point.
- the fatigue damage of a key point in a certain period of time can be counted by the rainflow counting method.
- the fatigue damage of multiple key points on the movable platform can be obtained separately, the total fatigue damage of the movable platform can be determined based on the fatigue damage of the multiple key points, and the fatigue state of the movable platform can be determined based on the total fatigue damage of the movable platform. test.
- the fatigue state of the movable platform is an abnormal state. If the total fatigue damage is less than or equal to the third preset value, it is determined that the fatigue state of the movable platform is a normal state.
- the fatigue state of the movable platform can also be determined based on the fatigue damage of each key point. For example, when there is a key point where the fatigue damage exceeds the preset value, it is determined that the fatigue state of the movable platform is an abnormal state, and when there is no key point where the fatigue damage exceeds the preset value, it is determined that the fatigue state of the movable platform is a normal state . Assuming that the fatigue damage of the i-th key point in the j-th time period is d ij , the preset value may be 1/s, where s is the safety margin, that is, when the fatigue damage of the i-th key point is When the damage meets the following conditions, the fatigue state of the movable platform is determined to be normal:
- the abnormal protection mode of the drone can be set through the server communicating with the drone or the APP on the control terminal.
- data collection can be performed through devices such as IMU to obtain acceleration response 503, and in step 505, data statistics are performed by combining the acceleration response 503 and the eigenmodel 504 of the movable platform using the rainflow counting method, that is, statistical Fatigue damage at one or more critical points on a movable platform.
- the total fatigue damage D of the movable platform is calculated based on the statistical fatigue damage of each key point, and in step 507, the fatigue state of the movable platform is determined based on the total fatigue damage D.
- step 508 is performed to record data information, including fatigue damage, total fatigue damage, and current time at each key point. If the fatigue state is abnormal, that is, the total fatigue damage D exceeds the fatigue damage threshold, step 509 and step 510 are performed, alarming through the APP, and protection of the drone based on a preset abnormal protection mode.
- the above embodiment collects the response signals at the designated key points, according to the intrinsic model of the movable platform, uses the rainflow counting method combined with the time domain data information to count the fatigue damage at the key points, and uses the time domain information and fast Fourier.
- the leaf transform determines the frequency and amplitude of characteristic points (eg, extreme points) in the frequency domain response signal to determine whether the movable platform is in a dangerous state.
- the obtained fatigue damage information and dangerous state information can be provided to the user through the interactive module.
- users can also assist in judging whether it is necessary to repair or replace structural components according to the status of key points. After-sales can use the results recorded by this method to more accurately locate the problem and help determine responsibility.
- the UAV needs to meet various flight conditions, resulting in a wide range of blade rotation speed distribution, and the frequency range of vibration excitation caused by the blade is also wide, which is easy to overlap with some natural frequencies of the UAV, thus Causes resonance leading to flight accidents.
- the time-domain data information collected by the IMU the amplitude of the instant-domain response signal
- the use of the fast Fourier transform (FFT) to determine the frequency and amplitude of the frequency-domain response signal combined with the natural frequency of the UAV's own eigenmodel, it can be It is judged whether the drone is in a dangerous state, and if a dangerous state occurs, corresponding operations are performed according to the dangerous state protection strategy, which improves the reliability of the drone.
- FFT fast Fourier transform
- the motor and/or the blade or the external excitation source on the movable platform can be used as the excitation source to output excitation, wherein the motor and/or the blade or the external excitation source can be used in the movable platform.
- the excitation is output in response to the received input signal.
- the sweep frequency signal can be injected into the motor control signal of the drone and the load, and the imbalance between the blades and each shaft arm can be used without borrowing other additional equipment. Based on the implementation of effective stimulus input.
- the input signal may be generated under the condition that a preset condition is satisfied.
- the preset condition may be that the time interval between the current time and the last time the state detection of the movable platform reaches a preset duration, or it may be that a detection trigger for the state detection of the movable platform is received.
- Instructions, or the movable platform performs a specified task (for example, a shooting task), or other conditions, which are not limited in the present disclosure.
- At least one of amplitude information and phase information of the response signal may be used as characteristic information of the response signal.
- the characteristic information of the response signal of the key point can be compared with the characteristic information of the eigenmodel of the movable platform, based on the characteristic information of the response signal of the key point and the characteristic information of the same key point in the eigenmodel.
- the difference between the feature information is used to detect the state of the movable platform. For example, for a certain key point, the amplitude spectrum curve of the response signal of the key point can be obtained, the extreme value point in the amplitude spectrum curve can be determined, and then the extreme value point and the amplitude spectrum curve of the key point in the eigenmodel can be determined.
- the difference between the positions of the extreme points and the difference between the values of the extreme points in the two curves is used to detect the state of the movable platform according to at least one of the two differences.
- the phase spectrum curve of the response signal of the key point can be obtained, the extreme value point in the phase spectrum curve can be determined, and then the position of the extreme value point in the phase spectrum curve of the key point and the key point in the eigenmodel can be determined.
- the difference between the two curves, and the difference between the values of the extreme points in the two curves, state detection is performed on the movable platform according to at least one of the two differences.
- the difference is less than a fourth preset value, it is determined that the mechanical state of the movable platform is a normal state. In some embodiments, if the difference is greater than the fourth preset value, it is determined that the mechanical state of the movable platform is an abnormal state.
- step 601 the mechanical state detection process can be triggered through the APP, and in step 602, the decision control module on the UAV outputs decision control instructions for controlling the motion execution module to output excitation in step 603.
- step 604 the drone or the load on the drone may generate a response signal to the excitation.
- step 605 and step 606 the response signal may be collected by means such as an IMU, and data processing such as filtering and amplification may be performed on the response signal.
- step 607 based on the processed response signal and the eigenmodel of the movable platform, it can be determined whether each key point has abnormal feature points, for example, abnormal amplitude or abnormal phase of extreme value points.
- step 609 If there is an abnormality in the feature point, an alarm prompt is output in step 609, and if there is no abnormality in the feature point, in step 608, the state detection result is fed back to the drone and the user through the APP.
- the drone can also be protected against anomalies based on the state detection results.
- the response signal processed in step 606 may also be fed back to step 603, so as to adjust information such as the amplitude or change rate of the excitation signal according to the response signal.
- the eigenmodels describing the mechanical state of the movable platform itself and the load on the movable platform can be obtained respectively.
- the difference between the characteristic information of the eigenmodel before and after is used to judge the mechanical state of the movable platform and the load, so as to evaluate whether there is damage to the mechanical structure and whether the degree of wear affects safe use. If the difference exceeds the allowable range, a replacement, repair or hazard alert is triggered.
- users can also judge whether they need to be repaired or replaced by themselves based on the test results of the drone and the load. After-sales can use the test results to more accurately locate the problem and provide help in determining responsibility.
- the first preset value, the second preset value, the third preset value, the fourth preset value and the response At least one of the frequencies of the feature points in the signal is corrected.
- the characteristic information of the response signals of the key points obtained multiple times may be averaged, and any one or more of the above-mentioned preset values may be modified according to the average value.
- the driving state of the movable platform may be controlled based on the state detection result, so as to improve the safety of the movable platform during driving.
- the driving state may include at least one of speed, position, and attitude.
- the safety margin of the movable platform may be acquired; in the case that the state detection result indicates that the movable platform is in an abnormal state, the driving state of the movable platform is controlled based on the safety margin.
- the safety margin is used to characterize the safety requirements of the movable platform during driving, and the larger the safety margin, the higher the safety requirements.
- the safety margin can be represented by numerical values, for example, a value of "1" indicates a relatively loose safety margin, a value of "2" indicates a standard safety margin, and a value of "3" indicates a strict safety margin, etc.
- the movable platform When the safety margin is the first margin, the movable platform can be controlled to continue driving until a driving state control command is received; when the safety margin is the second margin, the movable platform can be controlled Stop driving; when the safety margin is a third margin, control the movable platform to return to a designated location; wherein the first margin is less than the second margin, and the second margin is less than the the third margin.
- the first margin, the second margin and the third margin may be 1, 2 and 3, respectively.
- the above is only an exemplary illustration, and the representation, quantity and execution logic of safety margins are not limited to this. Execute logic.
- the movable platform is a UAV
- the UAV when the safety margin is 1, the UAV can be controlled to continue flying until a new specific instruction given by the interaction module is received; when the safety margin is 2 , control the drone to immediately switch to hover mode and wait for the interactive module to give new specific instructions; when the safety margin is 3, control the drone to automatically fine-tune the flight speed according to the current flight status and wait for the interactive module to give a new command Specific instructions; when the safety margin is 4, control the drone to return home immediately.
- the state detection result may be sent to the control terminal, and the control terminal may display the detection result, or output prompt information based on the detection result.
- the state detection result may also be stored, for example, directly stored in a storage unit on the movable platform, or stored in a cloud server or control terminal that communicates with the movable platform.
- the control unit on the movable platform can also output prompt information directly based on the state detection result.
- the prompt information may include, but is not limited to, at least any of the following: information used to characterize whether the current state of the movable platform is a normal state or an abnormal state, information used to characterize the degree of abnormality (for example, attention, replacement, danger, etc.) , the identification information of the abnormal key point, the current time, the time of the last state detection, etc.
- An embodiment of the present disclosure further provides a state detection apparatus for a movable platform, including a processor, where the processor is configured to execute the following methods:
- state detection is performed on the movable platform.
- the state detection includes resonance state detection.
- the characteristic information of the response signal includes an amplitude value of the response signal; the processor is configured to: if the amplitude value of the response signal is greater than a first preset value, determine whether the movable platform has an amplitude value.
- the resonance state is an abnormal state; and/or if the amplitude of the response signal is less than or equal to the first preset value, it is determined that the resonance state of the movable platform is a normal state.
- the characteristic information of the response signal includes a frequency spectrum of the response signal; the processor is configured to: if the frequency included in the frequency spectrum is within the natural frequency band range of the movable platform, and the amplitude is greater than At the point of the second preset value, it is determined that the resonance state of the movable platform is an abnormal state; if the frequency not included in the frequency spectrum is within the natural frequency range of the movable platform, and the amplitude is greater than the second preset value point, it is determined that the resonance state of the movable platform is a normal state.
- the state detection includes fatigue state detection.
- the processor is configured to: determine the fatigue damage of the key point based on the characteristic information of the response signal of the key point; and perform a state on the movable platform based on the fatigue damage of the key point detection.
- the number of the key points is greater than 1; the processor is configured to: determine the total fatigue damage of the movable platform based on the fatigue damage of the plurality of key points; based on the total fatigue damage of the movable platform damage, and perform state detection on the movable platform.
- the processor is configured to: if the total fatigue damage is greater than a third preset value, determine that the fatigue state of the movable platform is an abnormal state; and/or if the total fatigue damage is less than or equal to For the third preset value, it is determined that the fatigue state of the movable platform is a normal state.
- the state detection includes at least one of resonance state detection and fatigue detection; the excitation source is a motor on the movable platform; the excitation output by the excitation source is for driving the The power generated by the travel of the movable platform.
- condition detection includes mechanical condition detection.
- the processor is configured to: compare the characteristic information of the response signal of the key point with the characteristic information of the key point in the eigenmodel of the movable platform; based on the characteristic information of the key point In response to the difference between the feature information of the signal and the feature information of the key points in the intrinsic model of the movable platform, the state detection is performed on the movable platform.
- the processor is configured to: if the difference is less than a fourth preset value, determine that the mechanical state of the movable platform is a normal state; and/or if the difference is greater than the fourth preset value value, it is determined that the mechanical state of the movable platform is an abnormal state.
- the characteristic information includes amplitude information and/or phase information of the response signal.
- the processor is further configured to: based on the characteristic information of the response signal of the key point, determine the first preset value, the second preset value, the third preset value and the fourth preset value. At least one of the set value and the frequency of the feature point in the response signal is corrected.
- the excitation source is a motor and/or a paddle on the movable platform or an external excitation source; the excitation source responds to the received input when the movable platform is in a hovering state signal to output excitation.
- the input signal is generated when a preset condition is met.
- the preset condition includes at least any one of the following: the time interval between the current time and the last time the state detection of the movable platform is performed reaches a preset time length, and the reception of a response to the movable platform The platform performs a detection trigger instruction for state detection, and the movable platform performs a specified task.
- the processor is configured to: compare the feature information of the response signal of the key point with the feature information of the eigenmodel of the movable platform to obtain a comparison result; based on the comparison result, to The movable platform performs state detection.
- the eigenmodel is obtained by modeling, or by measurement.
- the excitation is a frequency sweep signal or a pulse signal.
- the apparatus further includes: controlling the driving state of the movable platform based on the state detection result.
- the processor is configured to: obtain a safety margin of the movable platform; and in a case that the state detection result indicates that the movable platform is in an abnormal state, control, based on the safety margin, The driving state of the movable platform.
- the processor is configured to: when the safety margin is the first margin, control the movable platform to continue driving until a driving state control command is received; when the safety margin is the first margin When the safety margin is the second margin, the movable platform is controlled to stop driving; when the safety margin is the third margin, the movable platform is controlled to return to the designated location; wherein the first margin is smaller than the second margin margin, the second margin is smaller than the third margin.
- the response signal includes at least one of an acceleration response signal, an angular velocity response signal, a displacement response signal, a stress response signal, and a strain response signal.
- the response signal is acquired by at least one of an inertial measurement unit, an acceleration sensor, an angular velocity sensor, a displacement sensor, a stress sensor, and a strain sensor on the movable platform.
- the processor is further configured to: send the status detection result to the control terminal; and/or store the status detection result; and/or output prompt information based on the status detection result.
- the processor is configured to: send the information of the feature point to the control terminal, so that the control terminal performs the status on the movable platform based on the feature information of the response signal of the key point detection.
- the key points include at least any one of the following: an arm, a blade, an arm connecting piece, a midframe of the fuselage, a shock absorbing ball, an arm rotating shaft, a motor shaft, a motor base, and a load.
- FIG. 7 shows a schematic diagram of the hardware structure of a more specific state detection apparatus provided by an embodiment of this specification.
- the device may include: a processor 701 , a memory 702 , an input/output interface 703 , a communication interface 704 and a bus 705 .
- the processor 701 , the memory 702 , the input/output interface 703 and the communication interface 704 realize the communication connection among each other within the device through the bus 705 .
- the clock synchronization apparatus is used to execute the above method applied to the first subsystem
- the processor 701 is the first processor
- the communication interface 704 is the first communication interface.
- the processor 701 is a second processor
- the communication interface 704 is a second communication interface.
- the processor 701 can be implemented by a general-purpose CPU (Central Processing Unit, central processing unit), a microprocessor, an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), or one or more integrated circuits, etc. program to implement the technical solutions provided by the embodiments of this specification.
- a general-purpose CPU Central Processing Unit, central processing unit
- a microprocessor central processing unit
- an application specific integrated circuit Application Specific Integrated Circuit, ASIC
- ASIC Application Specific Integrated Circuit
- the memory 702 can be implemented in the form of a ROM (Read Only Memory, read-only memory), a RAM (Random Access Memory, random access memory), a static storage device, a dynamic storage device, and the like.
- the memory 702 may store the operating system and other application programs. When implementing the technical solutions provided by the embodiments of this specification through software or firmware, the relevant program codes are stored in the memory 702 and invoked by the processor 701 for execution.
- the input/output interface 703 is used to connect the input/output module to realize the input and output of information.
- the input/output/module can be configured in the device as a component (not shown in the figure), or can be externally connected to the device to provide corresponding functions.
- the input device may include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc.
- the output device may include a display, a speaker, a vibrator, an indicator light, and the like.
- the communication interface 704 is used to connect a communication module (not shown in the figure), so as to realize the communication interaction between the device and other devices.
- the communication module may implement communication through wired means (eg, USB, network cable, etc.), or may implement communication through wireless means (eg, mobile network, WIFI, Bluetooth, etc.).
- Bus 705 includes a path to transfer information between the various components of the device (eg, processor 701, memory 702, input/output interface 703, and communication interface 704).
- the above-mentioned device only shows the processor 701, the memory 702, the input/output interface 703, the communication interface 704 and the bus 705, in the specific implementation process, the device may also include necessary components for normal operation. other components.
- the above-mentioned device may only include components necessary to implement the solutions of the embodiments of the present specification, rather than all the components shown in the figures.
- an embodiment of the present disclosure further provides a state detection system, where the system includes:
- the motor 801 is used for output excitation
- an inertial measurement unit 802 located near a key point of the movable platform, for collecting the response signal generated by the key point to the excitation output by the excitation source;
- a processor 803, connected in communication with the inertial measurement unit, is configured to execute the method described in any embodiment of the present disclosure.
- the system further includes: a control terminal 804, configured to perform at least any one of the following operations: trigger the motor output excitation; receive the detection result output by the processor 803; output to the processor 803 Display and/or store the detection result of the mobile platform; control the driving state of the movable platform based on the detection result; display prompt information based on the detection result output by the processor 803 .
- a control terminal 804 configured to perform at least any one of the following operations: trigger the motor output excitation; receive the detection result output by the processor 803; output to the processor 803 Display and/or store the detection result of the mobile platform; control the driving state of the movable platform based on the detection result; display prompt information based on the detection result output by the processor 803 .
- the control terminal 804 may communicate with the processor 803, and the control terminal 804 may include an interaction module (eg, a touch screen, a button), and the user may send a test result download instruction to the processor 803 through the touch screen, and receive the sent by the processor 803. test results.
- the processor 803 may also actively push the detection result to the control terminal 804 after acquiring the detection result.
- the control terminal 804 may further include a display module for displaying the detection result.
- the control terminal 804 may further include a storage module for storing the detection result.
- the control terminal 804 may also communicate with the movable platform to control the driving state (eg, speed, pose, etc.) of the movable platform based on the detection result.
- the control terminal 804 may also output prompt information based on the detection result, for example, output voice prompt information through a voice playback module, or output text prompt information through a display screen.
- the prompt information may be used to prompt the user of the current state of the movable platform.
- the embodiments of the present disclosure further provide a movable platform, including the state detection apparatus of the movable platform according to any embodiment of the present disclosure, or including the status detection system of the movable platform according to any embodiment of the present disclosure.
- Embodiments of the present disclosure also provide a computer-readable storage medium, including instructions, which, when executed on a computer, cause the computer to perform the method described in any of the foregoing embodiments.
- Computer-readable media includes both persistent and non-permanent, removable and non-removable media, and storage of information may be implemented by any method or technology.
- Information may be computer readable instructions, data structures, modules of programs, or other data.
- Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read only memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), Flash Memory or other memory technology, Compact Disc Read Only Memory (CD-ROM), Digital Versatile Disc (DVD) or other optical storage, Magnetic tape cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission medium that can be used to store information that can be accessed by a computing device.
- computer-readable media does not include transitory computer-readable media, such as modulated data signals and carrier waves.
- a typical implementation device is a computer, which may be in the form of a personal computer, laptop computer, cellular phone, camera phone, smart phone, personal digital assistant, media player, navigation device, email sending and receiving device, game control desktop, tablet, wearable device, or a combination of any of these devices.
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Abstract
一种可移动平台的状态检测方法、装置和系统、可移动平台，获取可移动平台上的关键点针对激励源输出的激励所产生的响应信号（101）；获取关键点的响应信号的特征信息（102）；基于关键点的响应信号的特征信息，对可移动平台进行状态检测（103）。
Description
本公开涉及可移动平台技术领域，尤其涉及可移动平台的状态检测方法、装置和系统、可移动平台。
由于可移动平台能够搭载各类负载来执行不同的任务，因此，可移动平台在许多领域获得了较为迅速的发展和广泛的应用。在可移动平台使用过程中，不可避免地会出现结构件老化和损伤等异常情况，为了保障可移动平台的安全性，需要对可移动平台的状态进行检测，以确定是否存在异常情况。一种检测方式是采用探伤设备进行异常检测，然而，探伤设备价格昂贵，导致对可移动平台的状态检测成本较高。
发明内容
本公开提供了一种可移动平台的状态检测方法、装置和系统、可移动平台，可以降低对可移动平台进行状态检测的成本。
第一方面，本公开实施例提供一种可移动平台的状态检测方法，所述方法包括：获取所述可移动平台上的关键点针对激励源输出的激励所产生的响应信号；获取所述关键点的响应信号的特征信息；基于所述关键点的响应信号的特征信息，对所述可移动平台进行状态检测。
第二方面，本公开实施例提供一种可移动平台的状态检测装置，包括处理器，所述处理器用于执行以下方法：获取所述可移动平台上的关键点针对激励源输出的激励所产生的响应信号；获取所述关键点的响应信号的特征信息；基于所述关键点的响应信号的特征信息，对所述可移动平台进行状态检测。
第三方面，本公开实施例提供一种可移动平台的状态检测系统，所述系统包括：电机，用于输出激励；惯性测量单元，设于可移动平台的关键点附近，用于采集所述关键点针对所述激励源输出的激励所产生的响应信号；以及处理器，与所述惯性测量单元通信连接，用于执行本公开任一实施例所述的方法。
第四方面，本公开实施例提供一种可移动平台，所述可移动平台包括：本公开任一实施例所述的状态检测装置，或者本公开任一实施例所述的状态检测系统。
第五方面，本公开实施例提供一种计算机可读存储介质，包括指令，当其在计算机上运行时，使得所述计算机执行本公开任一实施例所述的方法。
本公开的实施例提供的技术方案可以包括以下有益效果：
本公开实施例中，通过向可移动平台上的关键点输出激励，获取该关键点针对激励所产生的响应信号，只需获取该响应信号的特征信息，即可基于响应信号的特征信息对可移动平台进行状态检测，无需采用专用的探伤设备进行状态检测，降低了对可移动平台进行状态检测的检测成本。
图1是本公开实施例的可移动平台的状态检测方法的流程图。
图2是本公开实施例的响应信号的示意图。
图3是本公开实施例的响应信号的频谱的示意图。
图4是本公开实施例的共振状态检测过程的示意图。
图5是本公开实施例的疲劳状态检测过程的示意图。
图6是本公开实施例的机械状态检测过程的示意图。
图7是本公开实施例的状态检测装置的示意图。
图8是本公开实施例的状态检测系统的示意图。
这里将详细地对示例性实施例进行说明，其示例表示在附图中。下面的描述涉及附图时，除非另有表示，不同附图中的相同数字表示相同或相似的要素。以下示例性实施例中所描述的实施方式并不代表与本公开相一致的所有实施方式。相反，它们仅是与如所附权利要求书中所详述的、本公开的一些方面相一致的装置和方法的例子。
在本公开使用的术语是仅仅出于描述特定实施例的目的，而非旨在限制本公开。在本公开说明书和所附权利要求书中所使用的单数形式的“一种”、“所述”和“该”也旨在包括多数形式，除非上下文清楚地表示其他含义。还应当理解，本文中使用的 术语“和/或”是指并包含一个或多个相关联的列出项目的任何或所有可能组合。
应当理解，尽管在本公开可能采用术语第一、第二、第三等来描述各种信息，但这些信息不应限于这些术语。这些术语仅用来将同一类型的信息彼此区分开。例如，在不脱离本公开范围的情况下，第一信息也可以被称为第二信息，类似地，第二信息也可以被称为第一信息。取决于语境，如在此所使用的词语“如果”可以被解释成为“在……时”或“当……时”或“响应于确定”。
可移动平台能够搭载各类负载来执行不同的任务，因此在许多领域获得了较为迅速的发展和广泛的应用。以可移动平台为无人机为例，无人机的稳定性好、抗干扰能力强、能够主动悬停并且对于起飞和降落的条件要求相对较低，可以通过无人机搭载成像负载来执行图像拍摄任务。
然而，一方面，无人机上的桨叶在转动时，由于桨叶的生产及安装的偏差会产生同桨叶转速频率相关的振动激励，由于桨叶与空气存在相对运动，会产生同桨叶转速及桨叶的叶数相关的振动激励。上述激励属于交变载荷，无人机上的结构件在交变载荷的作用下容易产生裂纹，进而发生疲劳损坏。另一方面，桨叶的转动频率与无人机的固有模态频点之间存在重合。一种典型的无人机的桨叶转速在每分钟3000转到5000转之间，如果要避免无人机的固有模态频点与无人机桨叶的转动频率出现重合，一种方式是提高固有模态频点，使固有模态频点高于桨叶的最高转动频率。但是，这种方式会显著提高无人机的重量和成本。另一种方式是降低固有模态频点，使固有模态频点低于桨叶的最低转动频率。但是，这种方式会导致固有模态频点与无人机的飞控的频带相耦合，导致飞控异常。因此，桨叶的转动频率与无人机的固有模态频点之间的重合难以避免，从而使得桨叶转动频率与固有模态频点可能会出现共振，导致结构件失效。除此之外，无人机在飞行过程中，还可能出现各种飞行工况，导致无人机上的结构件可能出现不同程度的磨损或裂纹。
包括上述疲劳损伤、磨损等情况在内的各种异常情况，部分细微及内部裂纹用户一般难以通过感官直接感知到，且由于疲劳损伤和磨损等带来的变化无法定量评估，导致无法准确地暴露问题的风险。为了确定无人机是否存在上述异常情况，一种常用的检测方式是采用探伤设备进行异常检测。还有一种方式是定期将无人机返厂进行检测。然而，探伤设备价格昂贵，返厂检测的方式所带来的时间成本(包括来回物流时间、检测时间和更换时间)和维护花费较高，同时还伴随着返厂期间无人机无法使用带来的损失。综上所述，传统的无人机状态检测方式的检测成本较高。应当说明的是， 上述实施例以无人机为例，对可移动平台的状态检测过程中存在的问题进行举例说明，其他类型的可移动平台(例如，可移动机器人、无人车、无人船)也可能存在疲劳损伤、结构件磨损等异常情况，并且也存在状态检测成本高的问题。
基于此，本公开提供一种可移动平台的状态检测方法，以解决长期存在的可移动平台及负载在在实际使用中难以准确地进行状态检测，进而影响负载的工作效果及可移动平台的行驶安全的问题。如图1所示，所述方法包括：
步骤101：获取所述可移动平台上的关键点针对激励源输出的激励所产生的响应信号；
步骤102：获取所述关键点的响应信号的特征信息；
步骤103：基于所述关键点的响应信号的特征信息，对所述可移动平台进行状态检测。
本公开实施例的可移动平台包括但不限于无人机、无人车、无人船、可移动机器人等设备，可移动平台上的关键点包括但不限于机臂、桨叶、机臂连接件、机身中框、减震球、机臂转轴、电机轴、电机底座和负载中的至少一者。本公开实施例中对可移动平台的状态检测，包括对可移动平台本身的状态检测，以及对可移动平台上的负载(例如，相机)的状态检测。负载的状态与可移动平台本身的状态可以同步检测，也可以异步检测，或者是分别检测。本公开实施例的方法可由可移动平台上自带的处理器执行，例如，可以由无人机上的飞控单元执行，或者由无人车上的电子控制单元(Electronic Control Unit，ECU)执行。或者，也可以通过与可移动平台进行通信的云端服务器或者终端设备(例如，专用遥控器或者手机等智能终端)执行。
在步骤101中，可以通过激励源输出激励信号(简称激励)，并获取一个或多个关键点针对该激励的响应信号。所述激励可以是一种特定形式的信号，例如，扫频信号、脉冲信号等。所述响应信号可以包括但不限于加速度响应信号、角速度响应信号、位移响应信号、应力响应信号、应变响应信号中的至少一者。若无特别说明，本公开中的响应信号均指时域上的响应信号，频域响应信号指频域上的响应信号。其中，加速度响应信号和角速度响应信号可以由可移动平台上的惯性测量单元(Inertial Measurement Unit，IMU)，或者分别由加速度传感器和角速度传感器测量得到，位移响应信号、应力响应信号和应变响应信号可以分别由位移传感器、应力传感器和应变传感器采集得到。上述采集响应信号的任意一种采集装置可以是可移动平台上自带的 装置，也可以是额外安装到可移动平台上的装置。
采集装置在采集到响应信号之后，还可以对响应信号进行滤波、放大、调节、模拟微分处理和模数转换处理中的至少一种处理。可以通过可移动平台上的一个采集装置采集多个关键点的响应信号，也可以通过可移动平台上安装的多个采集装置构成多点采集系统，多点采集系统中的每个采集装置可以分别安装在一个关键点附近，每个关键点的响应信号可由多点采集系统中的一个或多个采集装置来采集。多点采集系统中采集装置的位置和数量，可以基于状态检测的精度要求、可移动平台的尺寸和重量限制等原因确定。在一个采集装置与关键点的位置较近时，该采集装置采集该关键点的响应信号的可靠性较高。针对一个关键点，可以将距离该关键点较近的采集装置的置信度设置为较高，将距离该关键点较远的采集装置的置信度设置为较低，并基于置信度对各个采集装置采集的响应信号进行加权平均，得到最终的响应信号，从而提高采集到的响应信号的可靠性。
在步骤102和步骤103中，可以获取关键点的响应信号的特征信息，并基于关键点的响应信号的特征信息对可移动平台进行状态检测。例如，可以由可移动平台上的处理器进行状态检测，或者，也可以将特征信息发送至控制终端，以使所述控制终端基于所述关键点的响应信号的特征信息，对所述可移动平台进行状态检测。其中，一个响应信号的特征信息可以包括但不限于该响应信号的幅值、频率、相位、频谱等中的至少一者。可移动平台上关键点的响应信号的特征信息与该可移动平台的状态相关，在可移动平台的状态改变时，响应信号的特征信息也会相应地改变。例如，可移动平台上的某个结构件出现裂纹时该结构件的响应信号与该结构件未出现裂纹时该结构件的响应信号具有不同的特征信息。因此，基于关键点的响应信号的特征信息可以确定可移动平台的状态。
在一些实施例中，可以将所述关键点的响应信号的特征信息与所述可移动平台的本征模型的特征信息进行比较，得到比较结果，基于所述比较结果，对所述可移动平台进行状态检测。其中，所述本征模型通过计算机辅助工程(Computer Aided Engineering，CAE)等方式建模获得，或者通过测量得到。这种方式称为模型辨识，通过模型辨识，只需将关键点的响应信号的特征信息与本征模型的特征信息进行比较，即可确定可移动平台的状态，实现方式简单，检测效率高，且无需专用检测设备，降低了检测成本。此外，上述检测方式可以对可移动平台内部的各种结构件进行检测，解决了可移动平台的内部损伤难以观察，或者机型较大的可移动平台的状态不便于检 测的问题，适用于各种结构形式、各种尺寸大小的可移动平台，适用范围广。
本公开实施例中的激励可以由可移动平台上的动力系统(例如电机和/或桨叶)输出，也可以由可移动平台的外接设备输出，输出激励的方式可基于待检测的状态来确定。其中，待检测的状态可包括但不限于可移动平台的共振状态、疲劳状态和机械状态中的至少一种。共振状态检测用于在可移动平台行驶过程中，确定为可移动平台的行驶提供动力的动力系统的频率与可移动平台的固有模态频点是否产生共振，例如，确定无人机的桨叶转动频率与无人机的固有模态频点是否产生共振。疲劳状态检测用于确定可移动平台上的结构件的疲劳损伤程度，从而对可移动平台进行疲劳寿命管控。机械状态检测用于确定可移动平台上的结构件的机械状态，例如，磨损程度、机械咬合力、是否存在裂纹等。
在对共振状态或者疲劳状态进行检测的情况下，可以将用于驱动可移动平台行驶所产生的动力作为所述激励，该激励可由可移动平台上的电机产生。在电机数量为多个的情况下，可以通过控制程序发出控制指令单独控制各个电机。例如，在可移动平台为无人机时，可以通过控制指令分别控制横滚轴(roll)、航向轴(yaw)和俯仰轴(pitch)这三个轴的电机，从而控制对应桨叶的转速，进而有效地实现无人机运动的控制，并且获得较高的无人机稳定性。共振状态或者疲劳状态的检测可以在可移动平台行驶过程中进行。由于直接将驱动可移动平台行驶所产生的动力作为激励，因此，无需输入额外的激励。对于可移动平台为无人机的情况，由于无人机在飞行过程中不受地面的约束，因此，在无人机飞行过程中进行共振状态和疲劳状态的检测，还能够减少由于地面约束带来的误差，提高检测精确度。
在对共振状态进行检测时，采集到的响应信号如图2所示。可以将响应信号的幅值信息确定为响应信号的特征信息。例如，若所述响应信号的幅值大于第一预设值，判定所述可移动平台的共振状态为异常状态，即发生共振。又例如，若所述响应信号的幅值小于或等于所述第一预设值，判定所述可移动平台的共振状态为正常状态。通过对共振状态进行检测，可以确定可移动平台是否处于危险状态。一个关键点的响应信号对应的第一预设值可以基于本征模型中同一关键点的幅值确定，例如，可以确定为本征模型中同一关键点的幅值的K倍，K为大于1的数。
进一步地，在对共振状态进行检测时，还可以将响应信号的频谱确定为响应信号的特征信息。所述频谱可以通过对响应信号进行快速傅里叶变换(Fast Fourier Transform，FFT)得到，频谱的一种具体形式如图3所示。例如，若所述频谱中包括 频率在所述可移动平台的固有频段范围内，且幅值大于第二预设值的点，判定所述可移动平台的共振状态为异常状态。又例如，若所述频谱中不包括频率在所述可移动平台的固有频段范围内，且幅值大于第二预设值的点，判定所述可移动平台的共振状态为正常状态。如果响应信号的频谱中包括可移动平台的固有频段范围(即固有模态频点)，则说明存在共振的危险，此时，通过进一步判断频谱的幅值，能够确定是否发生共振。如果幅值超过第二预设值，说明发生共振，反正，则说明未发生共振。一个关键点的响应信号对应的第二预设值可以基于本征模型中同一关键点的频谱的幅值确定，例如，可以确定为本征模型中同一关键点的频谱的幅值的P倍，P为大于1的数。
如图4所示，是共振状态检测过程的示意图，本实施例中以可移动平台是无人机为例进行说明。在步骤401中，可以通过与无人机通信的服务器或者控制终端上的应用程序(Application，APP)，设置无人机的异常保护模式，例如，在检测到轻微异常情况时，控制无人机继续飞行；在检测到较为严重的异常情况时，控制无人机返航。还可以通过APP触发启动共振状态检测流程。在步骤402中，可以通过IMU等数据采集装置对无人机上的关键点针对激励信号所产生的响应信号(例如，加速度响应403)进行采集。在步骤404中，如果加速度响应的幅值A大于允许的最大幅值A
max，则执行步骤410，记录数据信息，包括加速度响应的幅值A、幅值A超出最大幅值A
max的比例、状态检测结果(例如，是否异常状态、异常程度等)、当前时间等信息中的一种或多种。在执行步骤410之后，还可以返回步骤402，从而持续进行状态检测。
如果加速度响应的幅值A小于或等于允许的最大幅值A
max，则执行步骤405，对加速度响应进行FFT变换，得到加速度响应的频谱。在步骤406中，判断加速度响应的频谱的频率与无人机的固有模态频点是否匹配(即二者是否存在重叠)。如果匹配，表示二者存在重叠，在步骤407中进一步判断加速度响应的频谱的幅值与无人机的本征模型的幅值是否匹配。如果匹配，即加速度响应的频谱的幅值远大于本征模型的幅值，则认为发生共振，从而判定无人机状态异常。在加速度响应的频谱的幅值或者频率与本征模型不匹配时，可以执行步骤410，记录数据信息，包括频谱的幅值、频谱的幅值和频率，以及本征模型的幅值与固有模态频点等一种或多种信息。在无人机姿态异常的情况下，可以执行步骤408，通过APP进行报警，并执行步骤409，基于异常保护模式对无人机进行异常保护。
在对疲劳状态进行检测的情况下，可以将响应信号的幅值信息和/或相位信息作为响应信号的特征信息。可以基于某个关键点的响应信号的特征信息，确定该关键点的 疲劳损伤，并基于该关键点的疲劳损伤，对可移动平台进行状态检测。其中，一个关键点在一定时间段内的疲劳损伤可以通过雨流计数法来统计。可以分别获取可移动平台上多个关键点的疲劳损伤，基于所述多个关键点的疲劳损伤确定可移动平台的总疲劳损伤，并基于可移动平台的总疲劳损伤对可移动平台的疲劳状态进行检测。若所述总疲劳损伤大于第三预设值，判定所述可移动平台的疲劳状态为异常状态。若所述总疲劳损伤小于或等于所述第三预设值，判定所述可移动平台的疲劳状态为正常状态。
或者，也可以分别基于各个关键点的疲劳损伤，确定可移动平台的疲劳状态。例如，当存在疲劳损伤超过预设值的关键点时，判定可移动平台的疲劳状态为异常状态，当不存在疲劳损伤超过预设值的关键点时，判定可移动平台的疲劳状态为正常状态。假设第i个关键点在第j个时间段内的疲劳损伤为d
ij，则所述预设值可以是1/s，其中，s为安全裕度，即，当第i个关键点的疲劳损伤满足以下条件时，判定可移动平台的疲劳状态为正常状态：
D
i*s＜1。
其中，D
i＝∑
jd
ij。
如图5所示，是疲劳状态检测过程的示意图。在步骤501中，可以通过与无人机通信的服务器或者控制终端上的APP设置无人机的异常保护模式。在步骤502中，可以通过IMU等装置进行数据采集，得到加速度响应503，并在步骤505中，结合加速度响应503与可移动平台的本征模型504利用雨流计数法进行数据统计，即，统计可移动平台上一个或多个关键点的疲劳损伤。在步骤506中，基于统计的各个关键点的疲劳损伤，计算可移动平台的总疲劳损伤D，并在步骤507中基于总疲劳损伤D确定可移动平台的疲劳状态。如果疲劳状态正常，即总疲劳损伤D未超出疲劳损伤阈值(即上述第三预设值)，则执行步骤508，记录数据信息，包括各个关键点的疲劳损伤、总疲劳损伤、当前时间等。如果疲劳状态异常，即总疲劳损伤D超过疲劳损伤阈值，则执行步骤509和步骤510，通过APP报警，以及基于预先设置的异常保护模式进行无人机保护。
上述实施例通过采集指定关键点处的响应信号，根据可移动平台的本征模型，利用雨流计数法结合时域数据信息统计关键点处的疲劳损伤，以及根据时域信息及利用快速傅里叶变换确定频域响应信号中特征点(例如，极值点)的频率及幅值判断可移动平台是否处于危险状态。得到的疲劳损伤信息及危险状态信息可以通过交互模块提 供给使用者。此外，用户还可以根据关键点的状态辅助判断是否需要进行维修或者自行更换结构件，售后能够利用该方法记录的结果更加准确地定位问题并对定责提供帮助。
以无人机为例，无人机的桨叶在自身高速旋转的工况下，由于生产及安装的偏差以及气动力的耦合，会产生同桨叶转速及桨叶数相关的往复激励，无人机的各个机臂根部由于力臂原因受力情况较为恶劣，存在较大的疲劳失效风险。通过IMU采集到的振动数据(即上述响应信号)结合无人机自身的本征模型，可以反推得到各个机臂根部的具体受力情况，利用雨流计数法可以得到各个机臂根部的疲劳损伤情况，同理也可以得到无人机其他的关键点的疲劳状态的预估结果，提高了无人机的可靠性。
无人机需要满足各种飞行工况，导致桨叶转速分布范围通常较宽，进而由桨叶导致的振动激励的频带范围也较宽，容易与无人机的某些固有频率发生重叠，从而引起共振导致飞行事故。根据IMU采集的时域数据信息(即时域响应信号的幅度)及利用快速傅里叶变换FFT变换确定频域响应信号的频率及幅值，结合无人机自身的本征模型的固有频率，可以判断无人机是否处于危险状态，如果出现危险状态则根据危险状态保护策略执行相应操作，提高了无人机的可靠性。
在对机械状态进行检测的情况下，可以通过可移动平台上的电机及/或桨叶或者外接激励源作为激励源输出激励，其中，电机及/或桨叶或者外接激励源可以在所述可移动平台悬停状态下，响应于接收到的输入信号而输出激励。例如，对于无人机，若触发机械状态检测，可以将扫频信号注入到无人机和负载的电机控制信号之中，利用桨叶和各个轴臂的不平衡，在不借用其他额外设备的基础上实施有效的激励输入。
其中，所述输入信号可以在满足预设条件的情况下生成。所述预设条件可以是当前时间与上一次对所述可移动平台进行状态检测的时间之间的时间间隔达到预设时长，也可以是接收到对所述可移动平台进行状态检测的检测触发指令，或者可移动平台执行指定任务(例如，拍摄任务)，或者也可以是其他条件，本公开对此不做限制。
在对机械状态进行检测时，可以将响应信号的幅值信息和相位信息中的至少一者作为响应信号的特征信息。可以将所述关键点的响应信号的特征信息与所述可移动平台的本征模型的特征信息进行比较，基于所述关键点的响应信号的特征信息与所述本征模型中同一关键点的特征信息之间的差异，对所述可移动平台进行状态检测。例如，对于某一关键点，可以获取该关键点的响应信号的幅度谱曲线，确定幅度谱曲线中的极值点，然后确定该极值点与本征模型中该关键点的幅度谱曲线中极值点的位置之间 的差异，以及两条曲线中极值点的取值之间的差异，根据这两种差异中的至少一者对可移动平台进行状态检测。又例如，可以获取该关键点的响应信号的相位谱曲线，确定相位谱曲线中的极值点，然后确定该极值点与本征模型中该关键点的相位谱曲线中极值点的位置之间的差异，以及两条曲线中极值点的取值之间的差异，根据这两种差异中的至少一者对可移动平台进行状态检测。
在一些实施例中，若所述差异小于第四预设值，判定所述可移动平台的机械状态为正常状态。在一些实施例中，若所述差异大于所述第四预设值，判定所述可移动平台的机械状态为异常状态。
如图6所示，是机械状态检测过程的示意图。在步骤601中，可通过APP触发机械状态检测流程，在步骤602中，无人机上的决策控制模块输出决策控制指令，用于在步骤603中控制运动执行模块输出激励。在步骤604中，无人机或者无人机上的负载可针对激励产生响应信号。在步骤605和步骤606中，可以通过IMU等装置采集响应信号，以及对响应信号进行滤波、放大等数据处理。在步骤607中，可以基于处理后的响应信号与可移动平台的本征模型，判断各个关键点是否存在特征点异常，例如，极值点的幅值异常或者相位异常。如果存在特征点异常，则在步骤609中输出报警提示，如果不存在特征点异常，则在步骤608中，通过APP将状态检测结果反馈给无人机和用户。还可以基于状态检测结果对无人机进行异常保护。步骤606中处理后的响应信号还可以反馈给步骤603，以便根据响应信号调整激励信号的幅值或者变化速率等信息。
本实施例通过主动控制激励源结合采集到的响应信号，通过模型辨识，采用计算处理完成机械状态检测，可以分别得到描述可移动平台本身和可移动平台上的负载的机械状态的本征模型，利用本征模型的特征信息的前后差异来判断可移动平台和负载的机械状态，从而评估机械结构是否存在损坏及磨损程度是否影响安全使用。如果差异超过允许范围则触发更换、维修或危险提示。此外，用户还可以根据无人机及负载的检测结果辅助判断是否需要进行维修或者自行更换，售后能够利用检测结果更加准确地定位问题，并对定责提供帮助。
在一些实施例中，还可以基于所述关键点的响应信号的特征信息，对所述第一预设值、第二预设值、第三预设值、第四预设值以及所述响应信号中特征点的频率中的至少一者进行修正。例如，可以对多次获得的所述关键点的响应信号的特征信息求平均值，根据所述平均值对上述任意一种或多种预设值进行修正。
在一些实施例中，可以基于状态检测结果，控制所述可移动平台的行驶状态，以提高可移动平台行驶过程中的安全性。所述行驶状态可包括速度、位置和姿态中的至少一种。例如，可以获取所述可移动平台的安全裕度；在所述态检测结果指示所述可移动平台处于异常状态的情况下，基于所述安全裕度，控制所述可移动平台的行驶状态。所述安全裕度用于表征可移动平台行驶过程中对安全性的要求高低，安全裕度越大，表示对安全性的要求越高。安全裕度可以用数值来表示，例如，用数值“1”表示较为宽松的安全裕度，数值“2”表示标准安全裕度，数值“3”表示严格安全裕度等。
在所述安全裕度为第一裕度时，可以控制所述可移动平台继续行驶，直到接收到行驶状态控制指令；在所述安全裕度为第二裕度时，控制所述可移动平台停止行驶；在所述安全裕度为第三裕度时，控制所述可移动平台返回指定地点；其中，所述第一裕度小于所述第二裕度，所述第二裕度小于所述第三裕度。例如，所述第一裕度、第二裕度和第三裕度可以分别为1，2和3。当然，上述仅为示例性说明，安全裕度的表示方式、数量以及各个安全裕度下的执行逻辑不限于此，可以根据实际应用场景以及用户需求自行设置安全裕度以及各安全裕度下的执行逻辑。
例如，在可移动平台为无人机的情况下，可以在安全裕度为1时，控制无人机继续飞行，直到接收到交互模块给出的新的具体指令；在安全裕度为2时，控制无人机立即切换为悬停模式并等待交互模块给出新的具体指令；在安全裕度为3时，控制无人机根据当前飞行状态自动微调飞行速度并等待交互模块给出新的具体指令；在安全裕度为4时，控制无人机立即返航。
在对所述可移动平台进行状态检测之后，可以将状态检测结果发送至控制终端，控制终端可以对检测结果进行展示，或者基于检测结果输出提示信息。也可以对所述状态检测结果进行存储，例如，直接存储在可移动平台上的存储单元中，或者存储至与可移动平台进行通信的云端服务器或者控制终端上。可移动平台上的控制单元也可以直接基于所述状态检测结果输出提示信息。所述提示信息可以包括但不限于以下至少任一：用于表征可移动平台当前状态为正常状态或者异常状态的信息，用于表征异常程度的信息(例如，需注意、需更换、危险等)，异常关键点的标识信息，当前时间，上次进行状态检测的时间等。
本公开实施例还提供一种可移动平台的状态检测装置，包括处理器，所述处理器用于执行以下方法：
获取所述可移动平台上的关键点针对激励源输出的激励所产生的响应信号；
获取所述关键点的响应信号的特征信息；
基于所述关键点的响应信号的特征信息，对所述可移动平台进行状态检测。
在一些实施例中，所述状态检测包括共振状态检测。
在一些实施例中，所述响应信号的特征信息包括所述响应信号的幅值；所述处理器用于：若所述响应信号的幅值大于第一预设值，判定所述可移动平台的共振状态为异常状态；和/或若所述响应信号的幅值小于或等于所述第一预设值，判定所述可移动平台的共振状态为正常状态。
在一些实施例中，所述响应信号的特征信息包括所述响应信号的频谱；所述处理器用于：若所述频谱中包括频率在所述可移动平台的固有频段范围内，且幅值大于第二预设值的点，判定所述可移动平台的共振状态为异常状态；若所述频谱中不包括频率在所述可移动平台的固有频段范围内，且幅值大于第二预设值的点，判定所述可移动平台的共振状态为正常状态。
在一些实施例中，所述状态检测包括疲劳状态检测。
在一些实施例中，所述处理器用于：基于所述关键点的响应信号的特征信息，确定所述关键点的疲劳损伤；基于所述关键点的疲劳损伤，对所述可移动平台进行状态检测。
在一些实施例中，所述关键点的数量大于1；所述处理器用于：基于多个关键点的疲劳损伤，确定所述可移动平台的总疲劳损伤；基于所述可移动平台的总疲劳损伤，对所述可移动平台进行状态检测。
在一些实施例中，所述处理器用于：若所述总疲劳损伤大于第三预设值，判定所述可移动平台的疲劳状态为异常状态；和/或若所述总疲劳损伤小于或等于所述第三预设值，判定所述可移动平台的疲劳状态为正常状态。
在一些实施例中，所述状态检测包括共振状态检测和疲劳检测中的至少一者；所述激励源为所述可移动平台上的电机；所述激励源输出的激励为用于驱动所述可移动平台行驶所产生的动力。
在一些实施例中，所述状态检测包括机械状态检测。
在一些实施例中，所述处理器用于：将所述关键点的响应信号的特征信息与所述可移动平台的本征模型中所述关键点的特征信息进行比较；基于所述关键点的响应信 号的特征信息与所述可移动平台的本征模型中所述关键点的特征信息之间的差异，对所述可移动平台进行状态检测。
在一些实施例中，所述处理器用于：若所述差异小于第四预设值，判定所述可移动平台的机械状态为正常状态；和/或若所述差异大于所述第四预设值，判定所述可移动平台的机械状态为异常状态。
在一些实施例中，所述特征信息包括所述响应信号的幅值信息和/或相位信息。
在一些实施例中，所述处理器还用于：基于所述关键点的响应信号的特征信息，对所述第一预设值、第二预设值、第三预设值、第四预设值以及所述响应信号中特征点的频率中的至少一者进行修正。
在一些实施例中，所述激励源为所述可移动平台上的电机及/或桨叶或者外接激励源；所述激励源在所述可移动平台悬停状态下，响应于接收到的输入信号而输出激励。
在一些实施例中，所述输入信号在满足预设条件的情况下生成。
在一些实施例中，所述预设条件包括以下至少任一：当前时间与上一次对所述可移动平台进行状态检测的时间之间的时间间隔达到预设时长，接收到对所述可移动平台进行状态检测的检测触发指令，所述可移动平台执行指定任务。
在一些实施例中，所述处理器用于：将所述关键点的响应信号的特征信息与所述可移动平台的本征模型的特征信息进行比较，得到比较结果；基于所述比较结果，对所述可移动平台进行状态检测。
在一些实施例中，所述本征模型通过建模获得，或者通过测量得到。
在一些实施例中，所述激励为扫频信号或者脉冲信号。
在一些实施例中，所述装置还包括：基于状态检测结果，控制所述可移动平台的行驶状态。
在一些实施例中，所述处理器用于：获取所述可移动平台的安全裕度；在所述态检测结果指示所述可移动平台处于异常状态的情况下，基于所述安全裕度，控制所述可移动平台的行驶状态。
在一些实施例中，所述处理器用于：在所述安全裕度为第一裕度时，控制所述可移动平台继续行驶，直到接收到行驶状态控制指令；在所述安全裕度为第二裕度时，控制所述可移动平台停止行驶；在所述安全裕度为第三裕度时，控制所述可移动平台 返回指定地点；其中，所述第一裕度小于所述第二裕度，所述第二裕度小于所述第三裕度。
在一些实施例中，所述响应信号包括加速度响应信号、角速度响应信号、位移响应信号、应力响应信号、应变响应信号中的至少一者。
在一些实施例中，所述响应信号由可移动平台上的惯性测量单元、加速度传感器、角速度传感器、位移传感器、应力传感器、应变传感器中的至少一者采集得到。
在一些实施例中，所述处理器还用于：将状态检测结果发送至控制终端；和/或对所述状态检测结果进行存储；和/或基于所述状态检测结果输出提示信息。
在一些实施例中，所述处理器用于：将所述特征点的信息发送至控制终端，以使所述控制终端基于所述关键点的响应信号的特征信息，对所述可移动平台进行状态检测。
在一些实施例中，所述关键点包括以下至少任一：机臂、桨叶、机臂连接件、机身中框、减震球、机臂转轴、电机轴、电机底座、负载。
图7示出了本说明书实施例所提供的一种更为具体的状态检测装置的硬件结构示意图，该设备可以包括：处理器701、存储器702、输入/输出接口703、通信接口704和总线705。其中处理器701、存储器702、输入/输出接口703和通信接口704通过总线705实现彼此之间在设备内部的通信连接。在所述时钟同步装置用于执行上述应用于第一子系统的方法时，所述处理器701为第一处理器，所述通信接口704为第一通信接口。在所述时钟同步装置用于执行上述应用于第二子系统的方法时，所述处理器701为第二处理器，所述通信接口704为第二通信接口。
处理器701可以采用通用的CPU(Central Processing Unit，中央处理器)、微处理器、应用专用集成电路(Application Specific Integrated Circuit，ASIC)、或者一个或多个集成电路等方式实现，用于执行相关程序，以实现本说明书实施例所提供的技术方案。
存储器702可以采用ROM(Read Only Memory，只读存储器)、RAM(Random Access Memory，随机存取存储器)、静态存储设备，动态存储设备等形式实现。存储器702可以存储操作系统和其他应用程序，在通过软件或者固件来实现本说明书实施例所提供的技术方案时，相关的程序代码保存在存储器702中，并由处理器701来调用执行。
输入/输出接口703用于连接输入/输出模块，以实现信息输入及输出。输入输出/模块可以作为组件配置在设备中(图中未示出)，也可以外接于设备以提供相应功能。 其中输入设备可以包括键盘、鼠标、触摸屏、麦克风、各类传感器等，输出设备可以包括显示器、扬声器、振动器、指示灯等。
通信接口704用于连接通信模块(图中未示出)，以实现本设备与其他设备的通信交互。其中通信模块可以通过有线方式(例如USB、网线等)实现通信，也可以通过无线方式(例如移动网络、WIFI、蓝牙等)实现通信。
总线705包括一通路，在设备的各个组件(例如处理器701、存储器702、输入/输出接口703和通信接口704)之间传输信息。
需要说明的是，尽管上述设备仅示出了处理器701、存储器702、输入/输出接口703、通信接口704以及总线705，但是在具体实施过程中，该设备还可以包括实现正常运行所必需的其他组件。此外，本领域的技术人员可以理解的是，上述设备中也可以仅包含实现本说明书实施例方案所必需的组件，而不必包含图中所示的全部组件。
如图8所示，本公开实施例还提供一种状态检测系统，所述系统包括：
电机801，用于输出激励；
惯性测量单元802，设于可移动平台的关键点附近，用于采集所述关键点针对所述激励源输出的激励所产生的响应信号；以及
处理器803，与所述惯性测量单元通信连接，用于执行本公开任一实施例所述的方法。
在一些实施例中，所述系统还包括：控制终端804，用于执行以下至少任一操作：触发所述电机输出激励；接收所述处理器803输出的检测结果；对所述处理器803输出的检测结果进行显示和/或存储；基于所述检测结果控制所述可移动平台的行驶状态；基于所述处理器803输出的检测结果显示提示信息。
所述控制终端804可以与处理器803进行通信，控制终端804上可包括一交互模块(例如，触摸屏、按键)，用户可以通过触摸屏向处理器803发送检测结果下载指令，并接收处理器803发送的检测结果。或者，处理器803也可以在获取到检测结果之后，主动推送给控制终端804。控制终端804上还可以包括显示模块，用于对检测结果进行显示。控制终端804上还可以包括存储模块，用于对检测结果进行存储。控制终端804还可以与可移动平台进行通信，以基于检测结果控制可移动平台的行驶状态(例如，速度、位姿等)。控制终端804还可以基于检测结果输出提示信息，例如，通过语音播放模块输出语音提示信息，或者通过显示屏输出文本提示信息。所述提示信息可 用于向用户提示可移动平台的当前状态。
本公开实施例还提供一种可移动平台，包括本公开任一实施例的可移动平台的状态检测装置，或者包括本公开任一实施例的可移动平台的状态检测系统。
本公开实施例还提供一种计算机可读存储介质，包括指令，当其在计算机上运行时，使得所述计算机执行前述任一实施例所述的方法。
计算机可读介质包括永久性和非永久性、可移动和非可移动媒体可以由任何方法或技术来实现信息存储。信息可以是计算机可读指令、数据结构、程序的模块或其他数据。计算机的存储介质的例子包括，但不限于相变内存(PRAM)、静态随机存取存储器(SRAM)、动态随机存取存储器(DRAM)、其他类型的随机存取存储器(RAM)、只读存储器(ROM)、电可擦除可编程只读存储器(EEPROM)、快闪记忆体或其他内存技术、只读光盘只读存储器(CD-ROM)、数字多功能光盘(DVD)或其他光学存储、磁盒式磁带，磁带磁磁盘存储或其他磁性存储设备或任何其他非传输介质，可用于存储可以被计算设备访问的信息。按照本文中的界定，计算机可读介质不包括暂存电脑可读媒体(transitory media)，如调制的数据信号和载波。
通过以上的实施方式的描述可知，本领域的技术人员可以清楚地了解到本说明书实施例可借助软件加必需的通用硬件平台的方式来实现。基于这样的理解，本说明书实施例的技术方案本质上或者说对现有技术做出贡献的部分可以以软件产品的形式体现出来，该计算机软件产品可以存储在存储介质中，如ROM/RAM、磁碟、光盘等，包括若干指令用以使得一台计算机设备(可以是个人计算机，服务器，或者网络设备等)执行本说明书实施例各个实施例或者实施例的某些部分所述的方法。
上述实施例阐明的系统、装置、模块或单元，具体可以由计算机芯片或实体实现，或者由具有某种功能的产品来实现。一种典型的实现设备为计算机，计算机的具体形式可以是个人计算机、膝上型计算机、蜂窝电话、相机电话、智能电话、个人数字助理、媒体播放器、导航设备、电子邮件收发设备、游戏控制台、平板计算机、可穿戴设备或者这些设备中的任意几种设备的组合。
以上实施例中的各种技术特征可以任意进行组合，只要特征之间的组合不存在冲突或矛盾，但是限于篇幅，未进行一一描述，因此上述实施方式中的各种技术特征的任意进行组合也属于本公开的范围。
Claims (60)
- 一种可移动平台的状态检测方法，其特征在于，所述方法包括：获取所述可移动平台上的关键点针对激励源输出的激励所产生的响应信号；获取所述关键点的响应信号的特征信息；基于所述关键点的响应信号的特征信息，对所述可移动平台进行状态检测。
- 根据权利要求1所述的方法，其特征在于，所述状态检测包括共振状态检测。
- 根据权利要求2所述的方法，其特征在于，所述响应信号的特征信息包括所述响应信号的幅值；所述基于所述关键点的响应信号的特征信息，对所述可移动平台进行状态检测，包括：若所述响应信号的幅值大于第一预设值，判定所述可移动平台的共振状态为异常状态；和/或若所述响应信号的幅值小于或等于所述第一预设值，判定所述可移动平台的共振状态为正常状态。
- 根据权利要求2所述的方法，其特征在于，所述响应信号的特征信息包括所述响应信号的频谱；所述基于所述关键点的响应信号的特征信息，对所述可移动平台进行状态检测，包括：若所述频谱中包括频率在所述可移动平台的固有频段范围内，且幅值大于第二预设值的点，判定所述可移动平台的共振状态为异常状态；若所述频谱中不包括频率在所述可移动平台的固有频段范围内，且幅值大于第二预设值的点，判定所述可移动平台的共振状态为正常状态。
- 根据权利要求1所述的方法，其特征在于，所述状态检测包括疲劳状态检测。
- 根据权利要求5所述的方法，其特征在于，所述基于所述关键点的响应信号的特征信息，对所述可移动平台进行状态检测，包括：基于所述关键点的响应信号的特征信息，确定所述关键点的疲劳损伤；基于所述关键点的疲劳损伤，对所述可移动平台进行状态检测。
- 根据权利要求6所述的方法，其特征在于，所述关键点的数量大于1；所述基于所述关键点的疲劳损伤，对所述可移动平台进行状态检测，包括：基于多个关键点的疲劳损伤，确定所述可移动平台的总疲劳损伤；基于所述可移动平台的总疲劳损伤，对所述可移动平台进行状态检测。
- 根据权利要求7所述的方法，其特征在于，所述基于所述可移动平台的总疲劳损伤，对所述可移动平台进行状态检测，包括：若所述总疲劳损伤大于第三预设值，判定所述可移动平台的疲劳状态为异常状态；和/或若所述总疲劳损伤小于或等于所述第三预设值，判定所述可移动平台的疲劳状态为正常状态。
- 根据权利要求1所述的方法，其特征在于，所述状态检测包括共振状态检测和疲劳检测中的至少一者；所述激励源为所述可移动平台上的电机；所述激励源输出的激励为用于驱动所述可移动平台行驶所产生的动力。
- 根据权利要求1所述的方法，其特征在于，所述状态检测包括机械状态检测。
- 根据权利要求10所述的方法，其特征在于，所述基于所述关键点的响应信号的特征信息，对所述可移动平台进行状态检测，包括：将所述关键点的响应信号的特征信息与所述可移动平台的本征模型中所述关键点的特征信息进行比较；基于所述关键点的响应信号的特征信息与所述可移动平台的本征模型中所述关键点的特征信息之间的差异，对所述可移动平台进行状态检测。
- 根据权利要求11所述的方法，其特征在于，所述基于所述关键点的响应信号的特征信息与所述可移动平台的本征模型中所述关键点的特征信息之间的差异，对所述可移动平台进行状态检测，包括：若所述差异小于第四预设值，判定所述可移动平台的机械状态为正常状态；和/或若所述差异大于所述第四预设值，判定所述可移动平台的机械状态为异常状态。
- 根据权利要求11所述的方法，其特征在于，所述特征信息包括所述响应信号的幅值信息和/或相位信息。
- 根据权利要求11所述的方法，其特征在于，所述方法还包括：基于所述关键点的响应信号的特征信息，对所述第四预设值进行修正。
- 根据权利要求10所述的方法，其特征在于，所述激励源为所述可移动平台上的电机及/或桨叶或者外接激励源；所述激励源在所述可移动平台悬停状态下，响应于接收到的输入信号而输出激励。
- 根据权利要求15所述的方法，其特征在于，所述输入信号在满足预设条件的情况下生成。
- 根据权利要求16所述的方法，其特征在于，所述预设条件包括以下至少任一：当前时间与上一次对所述可移动平台进行状态检测的时间之间的时间间隔达到预 设时长，接收到对所述可移动平台进行状态检测的检测触发指令，所述可移动平台执行指定任务。
- 根据权利要求1所述的方法，其特征在于，所述基于所述关键点的响应信号的特征信息，对所述可移动平台进行状态检测，包括：将所述关键点的响应信号的特征信息与所述可移动平台的本征模型的特征信息进行比较，得到比较结果；基于所述比较结果，对所述可移动平台进行状态检测。
- 根据权利要求18所述的方法，其特征在于，所述本征模型通过建模获得，或者通过测量得到。
- 根据权利要求1所述的方法，其特征在于，所述激励为扫频信号或者脉冲信号。
- 根据权利要求1所述的方法，其特征在于，所述方法还包括：基于状态检测结果，控制所述可移动平台的行驶状态。
- 根据权利要求21所述的方法，其特征在于，所述基于状态检测结果，控制所述可移动平台的行驶状态，包括：获取所述可移动平台的安全裕度；在所述态检测结果指示所述可移动平台处于异常状态的情况下，基于所述安全裕度，控制所述可移动平台的行驶状态。
- 根据权利要求22所述的方法，其特征在于，所述在所述态检测结果指示所述可移动平台处于异常状态的情况下，基于所述安全裕度，控制所述可移动平台的行驶状态，包括：在所述安全裕度为第一裕度时，控制所述可移动平台继续行驶，直到接收到行驶状态控制指令；在所述安全裕度为第二裕度时，控制所述可移动平台停止行驶；在所述安全裕度为第三裕度时，控制所述可移动平台返回指定地点；其中，所述第一裕度小于所述第二裕度，所述第二裕度小于所述第三裕度。
- 根据权利要求1所述的方法，其特征在于，所述响应信号包括加速度响应信号、角速度响应信号、位移响应信号、应力响应信号、应变响应信号中的至少一者。
- 根据权利要求24所述的方法，其特征在于，所述响应信号由可移动平台上的惯性测量单元、加速度传感器、角速度传感器、位移传感器、应力传感器、应变传感 器中的至少一者采集得到。
- 根据权利要求1所述的方法，其特征在于，在基于所述关键点的响应信号的特征信息，对所述可移动平台进行状态检测之后，所述方法还包括：将状态检测结果发送至控制终端；和/或对所述状态检测结果进行存储；和/或基于所述状态检测结果输出提示信息。
- 根据权利要求1所述的方法，其特征在于，所述基于所述关键点的响应信号的特征信息，对所述可移动平台进行状态检测，包括：将所述特征点的信息发送至控制终端，以使所述控制终端基于所述关键点的响应信号的特征信息，对所述可移动平台进行状态检测。
- 根据权利要求1所述的方法，其特征在于，所述关键点包括以下至少任一：机臂、桨叶、机臂连接件、机身中框、减震球、机臂转轴、电机轴、电机底座、负载。
- 一种可移动平台的状态检测装置，包括处理器，其特征在于，所述处理器用于执行以下方法：获取所述可移动平台上的关键点针对激励源输出的激励所产生的响应信号；获取所述关键点的响应信号的特征信息；基于所述关键点的响应信号的特征信息，对所述可移动平台进行状态检测。
- 根据权利要求29所述的装置，其特征在于，所述状态检测包括共振状态检测。
- 根据权利要求30所述的装置，其特征在于，所述响应信号的特征信息包括所述响应信号的幅值；所述处理器用于：若所述响应信号的幅值大于第一预设值，判定所述可移动平台的共振状态为异常状态；和/或若所述响应信号的幅值小于或等于所述第一预设值，判定所述可移动平台的共振状态为正常状态。
- 根据权利要求30所述的装置，其特征在于，所述响应信号的特征信息包括所述响应信号的频谱；所述处理器用于：若所述频谱中包括频率在所述可移动平台的固有频段范围内，且幅值大于第二预设值的点，判定所述可移动平台的共振状态为异常状态；若所述频谱中不包括频率在所述可移动平台的固有频段范围内，且幅值大于第二预设值的点，判定所述可移动平台的共振状态为正常状态。
- 根据权利要求29所述的装置，其特征在于，所述状态检测包括疲劳状态检测。
- 根据权利要求33所述的装置，其特征在于，所述处理器用于：基于所述关键点的响应信号的特征信息，确定所述关键点的疲劳损伤；基于所述关键点的疲劳损伤，对所述可移动平台进行状态检测。
- 根据权利要求34所述的装置，其特征在于，所述关键点的数量大于1；所述处理器用于：基于多个关键点的疲劳损伤，确定所述可移动平台的总疲劳损伤；基于所述可移动平台的总疲劳损伤，对所述可移动平台进行状态检测。
- 根据权利要求35所述的装置，其特征在于，所述处理器用于：若所述总疲劳损伤大于第三预设值，判定所述可移动平台的疲劳状态为异常状态；和/或若所述总疲劳损伤小于或等于所述第三预设值，判定所述可移动平台的疲劳状态为正常状态。
- 根据权利要求29所述的装置，其特征在于，所述状态检测包括共振状态检测和疲劳检测中的至少一者；所述激励源为所述可移动平台上的电机；所述激励源输出的激励为用于驱动所述可移动平台行驶所产生的动力。
- 根据权利要求29所述的装置，其特征在于，所述状态检测包括机械状态检测。
- 根据权利要求38所述的装置，其特征在于，所述处理器用于：将所述关键点的响应信号的特征信息与所述可移动平台的本征模型中所述关键点的特征信息进行比较；基于所述关键点的响应信号的特征信息与所述可移动平台的本征模型中所述关键点的特征信息之间的差异，对所述可移动平台进行状态检测。
- 根据权利要求39所述的装置，其特征在于，所述处理器用于：若所述差异小于第四预设值，判定所述可移动平台的机械状态为正常状态；和/或若所述差异大于所述第四预设值，判定所述可移动平台的机械状态为异常状态。
- 根据权利要求39所述的装置，其特征在于，所述特征信息包括所述响应信号的幅值信息和/或相位信息。
- 根据权利要求39所述的装置，其特征在于，所述处理器还用于：基于所述关键点的响应信号的特征信息，对所述第四预设值进行修正。
- 根据权利要求38所述的装置，其特征在于，所述激励源为所述可移动平台上的电机及/或桨叶或者外接激励源；所述激励源在所述可移动平台悬停状态下，响应于 接收到的输入信号而输出激励。
- 根据权利要求43所述的装置，其特征在于，所述输入信号在满足预设条件的情况下生成。
- 根据权利要求44所述的装置，其特征在于，所述预设条件包括以下至少任一：当前时间与上一次对所述可移动平台进行状态检测的时间之间的时间间隔达到预设时长，接收到对所述可移动平台进行状态检测的检测触发指令，所述可移动平台执行指定任务。
- 根据权利要求29所述的装置，其特征在于，所述处理器用于：将所述关键点的响应信号的特征信息与所述可移动平台的本征模型的特征信息进行比较，得到比较结果；基于所述比较结果，对所述可移动平台进行状态检测。
- 根据权利要求46所述的装置，其特征在于，所述本征模型通过建模获得，或者通过测量得到。
- 根据权利要求29所述的装置，其特征在于，所述激励为扫频信号或者脉冲信号。
- 根据权利要求29所述的装置，其特征在于，所述装置还包括：基于状态检测结果，控制所述可移动平台的行驶状态。
- 根据权利要求49所述的装置，其特征在于，所述处理器用于：获取所述可移动平台的安全裕度；在所述态检测结果指示所述可移动平台处于异常状态的情况下，基于所述安全裕度，控制所述可移动平台的行驶状态。
- 根据权利要求50所述的装置，其特征在于，所述处理器用于：在所述安全裕度为第一裕度时，控制所述可移动平台继续行驶，直到接收到行驶状态控制指令；在所述安全裕度为第二裕度时，控制所述可移动平台停止行驶；在所述安全裕度为第三裕度时，控制所述可移动平台返回指定地点；其中，所述第一裕度小于所述第二裕度，所述第二裕度小于所述第三裕度。
- 根据权利要求29所述的装置，其特征在于，所述响应信号包括加速度响应信号、角速度响应信号、位移响应信号、应力响应信号、应变响应信号中的至少一者。
- 根据权利要求52所述的装置，其特征在于，所述响应信号由可移动平台上的 惯性测量单元、加速度传感器、角速度传感器、位移传感器、应力传感器、应变传感器中的至少一者采集得到。
- 根据权利要求29所述的装置，其特征在于，所述处理器还用于：将状态检测结果发送至控制终端；和/或对所述状态检测结果进行存储；和/或基于所述状态检测结果输出提示信息。
- 根据权利要求29所述的装置，其特征在于，所述处理器用于：将所述特征点的信息发送至控制终端，以使所述控制终端基于所述关键点的响应信号的特征信息，对所述可移动平台进行状态检测。
- 根据权利要求29所述的装置，其特征在于，所述关键点包括以下至少任一：机臂、桨叶、机臂连接件、机身中框、减震球、机臂转轴、电机轴、电机底座、负载。
- 一种可移动平台的状态检测系统，其特征在于，所述系统包括：电机，用于输出激励；惯性测量单元，设于可移动平台的关键点附近，用于采集所述关键点针对所述激励源输出的激励所产生的响应信号；以及处理器，与所述惯性测量单元通信连接，用于执行权利要求1至28任意一项所述的方法。
- 根据权利要57所述的系统，其特征在于，所述系统还包括：控制终端，用于执行以下至少任一操作：触发所述电机输出激励；接收所述处理器输出的检测结果；对所述处理器输出的检测结果进行显示和/或存储；基于所述检测结果控制所述可移动平台的行驶状态；基于所述处理器输出的检测结果显示提示信息。
- 一种可移动平台，其特征在于，所述可移动平台包括：权利要求29至56任意一项所述的可移动平台的状态检测装置，或者权利要求57或58所述的可移动平台的状态检测系统。
- 一种计算机可读存储介质，包括指令，其特征在于，当其在计算机上运行时，使得所述计算机执行权利要求1至28任意一项所述的方法。
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