CN111830547B - Bridge unmanned aerial vehicle detection method and system based on multi-source sensor fusion - Google Patents
Bridge unmanned aerial vehicle detection method and system based on multi-source sensor fusion Download PDFInfo
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- 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
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
- G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/42—Determining position
- G01S19/45—Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement
- G01S19/46—Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement the supplementary measurement being of a radio-wave signal type
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
- G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
- G01C21/16—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
- G01C21/165—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation combined with non-inertial navigation instruments
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- 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
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
- G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/42—Determining position
- G01S19/45—Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement
- G01S19/47—Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement the supplementary measurement being an inertial measurement, e.g. tightly coupled inertial
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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
- G05D1/104—Simultaneous control of position or course in three dimensions specially adapted for aircraft involving a plurality of aircrafts, e.g. formation flying
Abstract
The invention discloses a bridge unmanned aerial vehicle detection method and system based on multi-source sensor fusion, wherein the method comprises the following steps: the method comprises the following steps that a plurality of reference unmanned aerial vehicles form a reference formation, flight channels of all reference unmanned aerial vehicles in the reference formation are set, all reference unmanned aerial vehicles are started and respectively hover at specified positions, and the reference formation calculates aerial position information to provide reference coordinates for operating unmanned aerial vehicles; many operation unmanned aerial vehicles constitute the operation formation, set for the flight route of all operation unmanned aerial vehicles difference and cover whole settlement area in order to guarantee all operation unmanned aerial vehicles 'detection range, all operation unmanned aerial vehicles begin to carry out flight detection according to the benchmark coordinate for be responsible for pressing close to the flight of bridge bottom in order to detect the bridge crack, simultaneously through self carry on ultra wide band sensor and inertial sensor calculate relative benchmark unmanned aerial vehicle's positioning result. According to the invention, the high-precision positioning of the multiple unmanned aerial vehicles during the detection of the bottom of the bridge is realized through the mutual matching of the reference unmanned aerial vehicle and the operation unmanned aerial vehicle.
Description
Technical Field
The invention relates to the technical field of unmanned aerial vehicle application, in particular to a bridge unmanned aerial vehicle detection method and system based on multi-source sensor fusion.
Background
In recent years, the flight performance of Unmanned Aerial Vehicles (UAVs) has been significantly improved, and the Unmanned Aerial vehicles are applied to various industrial fields such as agriculture, measurement, inspection, logistics and the like. Especially, the unmanned aerial vehicle is used for carrying out efficient detection on the infrastructure, people pay attention to the unmanned aerial vehicle, and efficient detection operation is expected to be achieved through the cooperative unmanned aerial vehicle system. However, research and application of cooperative Unmanned Aerial Vehicle (UAV) systems is generally limited to outdoor experiments using conventional Global Navigation Satellite System (GNSS) receivers for Navigation. As is well known, when an unmanned aerial vehicle is under a bridge, due to the fact that satellite signals cannot be normally received, GNSS signals are weak and problems such as multipath effect are accompanied, high-precision positioning of the unmanned aerial vehicle under the bridge faces challenges.
The development of Ultra Wide Band (UWB) communications provides high precision positioning. Base stations with known positions are deployed in space and exchange Radio Frequency (RF) messages with mobile nodes that need to be located. The signal characteristics include: received Signal Strength Indicator (RSSI), Time Of Flight (ToF), Direction Of Arrival (DoA), and the like. In a wireless positioning system, UWB has the unique advantages of high Time resolution, low power consumption, high penetration, high transmission data rate, etc., and provides accurate positioning using a Time Difference of Arrival (TDOA) and Angle of Arrival (AOA) positioning method for UWB signals.
In addition, an Inertial Navigation System (INS) of a popular intelligent device integrated by an Inertial Measurement Unit (IMU) includes a gyroscope, an accelerometer, a magnetometer, a barometer, and the like, and can provide information such as a position, a speed, and an attitude of a moving target. The INS is relatively independent, does not depend on external equipment, and can be used for pedestrian tracking, vehicle-mounted inertial positioning, robot positioning and the like.
The problem that prior art exists is that unmanned aerial vehicle is weak at the underbridge GNSS signal, and unmanned aerial vehicle detects the unable problem of fixing a position of operation position.
Accordingly, the prior art is yet to be improved and developed.
Disclosure of Invention
The invention mainly aims to provide a bridge unmanned aerial vehicle detection method and system based on multi-source sensor fusion, and aims to solve the problems that in the prior art, GNSS signals of an unmanned aerial vehicle under a bridge are weak, and the unmanned aerial vehicle detection operation position cannot be positioned.
In order to achieve the purpose, the invention provides a bridge unmanned aerial vehicle detection method based on multi-source sensor fusion, which comprises the following steps:
the method comprises the following steps that a plurality of reference unmanned aerial vehicles form a reference formation, and each reference unmanned aerial vehicle is provided with an ultra wide band signal receiver, a global navigation satellite system signal receiver and a lightweight panoramic camera;
setting flight channels of all reference unmanned aerial vehicles in the reference formation, starting all reference unmanned aerial vehicles and hovering the reference unmanned aerial vehicles at specified positions respectively, receiving GNSS signals by the reference formation, and calculating to obtain aerial position information to provide reference coordinates for the operation unmanned aerial vehicles;
a plurality of operation unmanned aerial vehicles form an operation formation, and each operation unmanned aerial vehicle is provided with an ultra wide band sensor and an inertia measurement unit;
setting different flight routes of all the operation unmanned aerial vehicles in the operation formation to ensure that the detection ranges of all the operation unmanned aerial vehicles cover the whole set area, and starting all the operation unmanned aerial vehicles to execute flight detection according to the reference coordinates provided by the reference formation;
all operation unmanned aerial vehicles in the operation formation are used for being responsible for being close to bridge bottom and fly according to the flight route in order to detect the bridge crack, simultaneously through self carrying on ultra wide band sensor calculates relative benchmark unmanned aerial vehicle's positioning result.
Optionally, the method for detecting a bridge unmanned aerial vehicle based on multi-source sensor fusion, wherein all the unmanned aerial vehicles in the operation formation are used for flying according to a flight path to detect a bridge crack close to the bottom of the bridge, and meanwhile, the relative positioning result is calculated through the ultra-wideband sensor carried by the unmanned aerial vehicles, and then the method further comprises:
and after the operation formation finishes the detection of the current area, the reference formation flies to the next designated area, and the operation formation flies to the next designated area for operation according to the reference formation and the planned path.
Optionally, the method for detecting the bridge unmanned aerial vehicle based on the multi-source sensor fusion further includes:
in the process that the operation unmanned aerial vehicle is close to the reference unmanned aerial vehicle, the reference unmanned aerial vehicle detects that the operation unmanned aerial vehicle is close, when the operation unmanned aerial vehicle surpasses a safe area and is close to the reference unmanned aerial vehicle, the reference unmanned aerial vehicle flies along the avoiding distance and the direction set by the flight channel so as to avoid the operation unmanned aerial vehicle, and simultaneously marks the positioning time of the reference unmanned aerial vehicle before the position is moved and after the movement is completed, and the reference positioning point is resolved again.
Optionally, the bridge unmanned aerial vehicle detection method based on multi-source sensor fusion, wherein the reference unmanned aerial vehicle senses the approaching operation unmanned aerial vehicle through the lightweight panoramic camera, and detects a distance between the reference unmanned aerial vehicle and the flight of the operation unmanned aerial vehicle.
Optionally, the method for detecting the bridge unmanned aerial vehicle based on the multi-source sensor fusion further includes:
the safe distance between the benchmark unmanned aerial vehicle and the flight of operation unmanned aerial vehicle is preset, and when the benchmark unmanned aerial vehicle is detected, the distance between the flight of the benchmark unmanned aerial vehicle and the flight of the operation unmanned aerial vehicle is greater than when the safe distance is short, the risk of flight collision between the benchmark unmanned aerial vehicle and the operation unmanned aerial vehicle is represented.
Optionally, the method for detecting the bridge unmanned aerial vehicle based on the multi-source sensor fusion further includes:
the reference formation is used for flying on a flight channel outside a delay bridge set by an unmanned aerial vehicle flight control system, staying at a specified positioning point, providing a high-precision satellite positioning result through the global navigation satellite system signal receiver, and taking the positioning result as an initial coordinate value of a positioning base station.
Optionally, the method for detecting the bridge unmanned aerial vehicle based on the fusion of the multi-source sensors includes calculating by using a fixed interval smoothing filter algorithm when calculating the positioning result of the operation unmanned aerial vehicle relative to the reference unmanned aerial vehicle.
Optionally, the bridge unmanned aerial vehicle detection method based on multi-source sensor fusion, wherein the reference formation is composed of at least four reference unmanned aerial vehicles.
Optionally, in the method for detecting a bridge unmanned aerial vehicle based on multi-source sensor fusion, at least four reference unmanned aerial vehicles fly along a designated air route outside the bridge as reference stations.
In addition, in order to achieve the above object, the present invention further provides a bridge unmanned aerial vehicle detection system based on multi-source sensor fusion, wherein the bridge unmanned aerial vehicle detection system based on multi-source sensor fusion comprises: reference formation and job formation;
the reference formation comprises a plurality of reference unmanned aerial vehicles, and each reference unmanned aerial vehicle is provided with an ultra-wideband signal receiver, a global navigation satellite system signal receiver and a lightweight panoramic camera;
the operation formation is composed of a plurality of operation unmanned aerial vehicles, and each operation unmanned aerial vehicle is provided with an ultra wide band sensor and an inertia measurement unit;
all the reference unmanned aerial vehicles in the reference formation hover at the designated positions according to the flight channel, the reference formation receives GNSS signals, and the aerial position information obtained through calculation provides reference coordinates for the operation unmanned aerial vehicles;
all the unmanned aerial vehicles in the operation formation fly according to different flight routes to ensure that the detection ranges of all the unmanned aerial vehicles cover the whole set area, and all the unmanned aerial vehicles execute flight detection according to the reference coordinates provided by the reference formation;
all operation unmanned aerial vehicles in the operation formation are used for being responsible for being close to bridge bottom and fly according to the flight route in order to detect the bridge crack, simultaneously through self carrying on ultra wide band sensor calculates relative benchmark unmanned aerial vehicle's positioning result.
The invention forms a reference formation by a plurality of reference unmanned aerial vehicles, and each reference unmanned aerial vehicle is provided with an ultra-wideband signal receiver, a global navigation satellite system signal receiver and a lightweight panoramic camera; setting flight channels of all reference unmanned aerial vehicles in the reference formation, starting all reference unmanned aerial vehicles and hovering the reference unmanned aerial vehicles at specified positions respectively, receiving GNSS signals by the reference formation, and calculating to obtain aerial position information to provide reference coordinates for the operation unmanned aerial vehicles; a plurality of operation unmanned aerial vehicles form an operation formation, and each operation unmanned aerial vehicle is provided with an ultra wide band sensor and an inertia measurement unit; setting different flight routes of all the operation unmanned aerial vehicles in the operation formation to ensure that the detection ranges of all the operation unmanned aerial vehicles cover the whole set area, and starting all the operation unmanned aerial vehicles to execute flight detection according to the reference coordinates provided by the reference formation; all operation unmanned aerial vehicles in the operation formation are used for being responsible for being close to bridge bottom and fly according to the flight route in order to detect the bridge crack, simultaneously through self carrying on ultra wide band sensor calculates relative benchmark unmanned aerial vehicle's positioning result. According to the invention, the high-precision positioning of the multiple unmanned aerial vehicles during the detection of the bottom of the bridge is realized through the mutual matching of the reference unmanned aerial vehicle and the operation unmanned aerial vehicle.
Drawings
FIG. 1 is a flow chart of a preferred embodiment of the bridge unmanned aerial vehicle detection method based on multi-source sensor fusion according to the present invention;
FIG. 2 is a schematic distribution diagram of a reference unmanned aerial vehicle and a working unmanned aerial vehicle working cooperatively in the bridge unmanned aerial vehicle detection method based on multi-source sensor fusion according to the invention;
FIG. 3 is a schematic block diagram of an integrated navigation system in a preferred embodiment of the bridge unmanned aerial vehicle detection method based on multi-source sensor fusion of the present invention;
FIG. 4 is a schematic diagram illustrating a schematic diagram of an interval smoothing method in an embodiment of the bridge unmanned aerial vehicle detection method based on multi-source sensor fusion;
FIG. 5 is a schematic diagram of a bridge unmanned aerial vehicle detection system based on multi-source sensor fusion according to a preferred embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer and clearer, the present invention is further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
In a preferred embodiment of the present invention, as shown in fig. 1, the method for detecting a bridge unmanned aerial vehicle based on multi-source sensor fusion includes the following steps:
step S10, forming a reference formation by a plurality of reference unmanned aerial vehicles, wherein each reference unmanned aerial vehicle is provided with an ultra-wideband signal receiver, a global navigation satellite system signal receiver and a lightweight panoramic camera;
step S20, setting flight channels of all the reference unmanned aerial vehicles in the reference formation, starting all the reference unmanned aerial vehicles and hovering the reference unmanned aerial vehicles at specified positions respectively, receiving GNSS signals by the reference formation, and calculating to obtain aerial position information to provide reference coordinates for the operation unmanned aerial vehicle;
step S30, forming a job formation by a plurality of operation unmanned aerial vehicles, wherein each operation unmanned aerial vehicle is provided with an ultra-wideband sensor and an inertia measurement unit;
step S40, setting different flight routes of all the unmanned aerial vehicles in the operation formation to ensure that the detection ranges of all the unmanned aerial vehicles cover the whole set area, and starting all the unmanned aerial vehicles to execute flight detection according to the reference coordinates provided by the reference formation;
and S50, enabling all the operation unmanned aerial vehicles in the operation formation to fly close to the bottom of the bridge according to the flight route so as to detect the cracks of the bridge, and meanwhile, calculating the positioning result of the unmanned aerial vehicles relative to the reference through the ultra-wideband sensors carried by the unmanned aerial vehicles.
Specifically, as shown in fig. 2, a plurality of reference drones form a reference formation, in the present invention, the reference formation is composed of at least four reference drones, and fig. 2 shows four reference drones, namely, at least four reference drones form a reference formation as a positioning reference station, wherein each reference drone in the reference formation is provided with an ultra wide band signal receiver (UWB receiver), a global navigation satellite system signal receiver (GNSS signal receiver) and a light-weight panoramic camera (which is convenient for the reference drone to be arranged above or below), the benchmark formation sets a channel flying along the outer side of the bridge through an unmanned aerial vehicle flight control system and stays at a designated positioning point, high-precision satellite positioning results are provided through a GNSS signal receiver (global navigation satellite system signal receiver), and the positioning results are used as initial coordinate values of a UWB base station (positioning base station).
As shown in fig. 2, the operation formation is composed of multiple operation unmanned aerial vehicles (i.e., the detection unmanned aerial vehicle in fig. 2) (three operation unmanned aerial vehicles are shown in fig. 2), and the multiple operation unmanned aerial vehicles form the operation formation and simultaneously perform bridge crack detection; every operation unmanned aerial vehicle in the operation formation carries ultra wide band sensor (UWB sensor) and inertial measurement unit (MEMS, Microelectromechanical Systems, micro electro mechanical system, inertial measurement unit, HG1120CA 50), operation unmanned aerial vehicle in the operation formation is mainly responsible for pressing close to the bridge bottom, detects the bridge crack, solves the positioning result for benchmark unmanned aerial vehicle through the UWB sensor of self carrying simultaneously.
According to the bridge crack detection method, at least four reference unmanned aerial vehicles form a reference formation to serve as a positioning reference station, a plurality of operation unmanned aerial vehicles form an operation formation to simultaneously perform bridge crack detection, the reference unmanned aerial vehicles in the reference formation carry UWB/GNSS/panoramic cameras to fly along the set track on the outer side of a bridge, the operation unmanned aerial vehicles under the bridge are positioned through GNSS signals, the operation unmanned aerial vehicles on two sides carry UWB receivers and an INS system to assist in positioning, and operation is detected simultaneously.
Further, as shown in fig. 2, in the process that the operating unmanned aerial vehicle approaches the reference unmanned aerial vehicle, the reference unmanned aerial vehicle detects the approaching operating unmanned aerial vehicle, when the operating unmanned aerial vehicle approaches the reference unmanned aerial vehicle beyond the safe area, the reference unmanned aerial vehicle flies along the set avoidance distance (i.e. the set moving distance in fig. 2) and direction of the flight channel to avoid the operating unmanned aerial vehicle, and meanwhile, the positioning time of the reference unmanned aerial vehicle before and after the position movement is marked, and the reference positioning point is resolved again; the reference unmanned aerial vehicle senses the approaching operation unmanned aerial vehicle through the lightweight panoramic camera and detects the distance between the flight of the reference unmanned aerial vehicle and the flight of the operation unmanned aerial vehicle; the safe distance between the reference unmanned aerial vehicle and the operation unmanned aerial vehicle is preset (namely the safe distance set in figure 2), and when the distance between the reference unmanned aerial vehicle and the operation unmanned aerial vehicle is detected to be greater than the safe distance, the risk of flight collision between the reference unmanned aerial vehicle and the operation unmanned aerial vehicle is indicated.
That is, in the invention, the unmanned aerial vehicle is detected under the bridge and provided with a protection mechanism to prevent collision of the unmanned aerial vehicle in the operation process, as shown in fig. 2, the reference unmanned aerial vehicle in the reference formation carries a small panoramic camera (i.e. a lightweight panoramic camera), so as to timely detect a target in the process that the operation unmanned aerial vehicle approaches the reference unmanned aerial vehicle, when the operation unmanned aerial vehicle approaches the reference unmanned aerial vehicle beyond a safe area, the protection mechanism makes a response, the reference unmanned aerial vehicle flies along the avoidance distance and direction set by a navigation channel to avoid the operation unmanned aerial vehicle, and simultaneously marks the positioning time (used for re-resolving the position of the operation unmanned aerial vehicle under the bridge) of the reference unmanned aerial vehicle before and after the movement is completed.
Further, after the operation formation completes the detection of the current area, the reference formation flies to the next designated area, and the operation formation flies to the next designated area for operation according to the reference formation and the planned path. After the operation unmanned aerial vehicle performs the current area detection operation, the reference unmanned aerial vehicle starts to move to the station, which is similar to the control network operation method, for example, the two foremost unmanned aerial vehicles start to fly to the next designated location point first, the following unmanned aerial vehicles sequentially fly to the previous unmanned aerial vehicle location point, and then the operation formation of the operation unmanned aerial vehicles sequentially fly to the designated area along the planned path to perform the operation.
According to the method, a reference formation channel and a stop point position are set, a detection route of the unmanned aerial vehicle is planned, after the reference formation flies to a specified position, firstly, a GNSS signal receiver is subjected to smooth calculation, a GNSS positioning calculation result is set as an initial coordinate during UWB data analysis, then, the initial position of the unmanned aerial vehicle subjected to UWB calculation is set as an initial value of an IMU sensor, and then, the attitude data of the unmanned aerial vehicle subjected to IMU calculation is combined, so that high-precision positioning under a bridge is achieved. During operation, the unmanned aerial vehicle operates to resolve the attitude through the IMU, and the UWB is assisted to perform accurate positioning. Operation unmanned aerial vehicle is being close arbitrary benchmark unmanned aerial vehicle's in-process, and the back is sensed through panoramic camera by the benchmark unmanned aerial vehicle that is close, can be used for fusing the location with panoramic picture information, also can be used to the range finding, judges whether two unmanned aerial vehicles are in danger, avoids benchmark unmanned aerial vehicle and operation unmanned aerial vehicle's collision.
Further, as shown in fig. 3, the positioning method of UWB and INS is integrated in the present invention, a strapdown inertial navigation principle (an inertial measurement device is directly and fixedly connected to a carrier, and then the output of the inertial measurement device is converted to a parameter of a navigation coordinate system through a mathematical platform, also called a strapdown matrix, to perform navigation calculation) is adopted to perform tightly integrated positioning on the UWB and IMU inertial units, the integrated navigation positioning principle is shown in fig. 3, and a measurement equation and a state equation established by integrated navigation are as follows:
the measurement equation is as follows:
is provided with
To detect (work) the position of the drone to be positioned,
for the known position of the base station drone i
Representing the difference in distance of the detecting drone from base station drone i and base station l (representing the letter l), the measured value TDOA obtained by the UWB is:
wherein the content of the first and second substances,
representing the UWB measurement, c representing the wave propagation speed,
in order to be a measure of the TDOA,
in order to measure the true value of the value,
to measure the error. In the absence of measurement errors:
wherein
、
Respectively, of the UWB signal emitted by the working (detecting) drone arriving at base station i and base station l. By doppler measurement of the base station, the change in the distance from the working drone to the reference drone, i.e., the time difference of arrival rate (TDOA rate), can be obtained. Let the true velocity vector of the carrier be
Then, the TDOA rate to the ith base station measured by the drone is detected as:
in the formula:
wherein the content of the first and second substances,
indicating the distance change rate from the real position of the working unmanned aerial vehicle to the ith base station,
are respectively as
,
,
The partial derivative in the direction of the light,
the error is measured for the TDOA rate.
The TDOA of the INS-laden working drone to the jth base station may be expressed by:
wherein the content of the first and second substances,
the distance difference between the working unmanned plane I and the reference unmanned planes I and I is shown,
and the distance between the I-th working unmanned aerial vehicle and the reference unmanned aerial vehicles I and l is shown. INS true position
Taking a first term from Taylor expansion:
wherein:
The TDOA expression from the position of the working unmanned aerial vehicle to the ith base station calculated by the INS is as follows:
wherein
、
Is composed of
Error in the estimated value.
To sum up, the TDOA and TDOA rate measurement errors are:
wherein the content of the first and second substances,
for operation unmanned aerial vehicle S to benchmark unmanned aerial vehicle
And
the distance difference of (a).
The state equation is as follows:
the system state variables are:
wherein the content of the first and second substances,
is a three-axis attitude error angle,
in order to be able to determine the speed error,
in order to be a position error,
in order for the accelerometer to measure the error,
for gyroscope measurement errors, a 13-dimensional state equation can be established according to the relationship of the combined model as
Wherein C is a specific force coefficient, and f is an acceleration. The above equation can be simplified as:
wherein A is a state matrix, and W is a control quantity; and:
further, in the detection of abnormal values in an extended kalman filter model (EKF), the extended kalman filter can achieve the effect of real-time positioning by constructing a state model and a measurement model for measurement data from a moving point to a base station. Classical extended kalman filtering does not involve outlier detection, but the quality of outlier detection has a large impact on the accuracy of the results. And (4) carrying out abnormal value detection by using the innovation and the residual error obtained in the EKF process to form a double-layer abnormal value detection mechanism.
The first layer is optimization of the extended kalman filter to measure the noise covariance to correct the state estimate using innovation correction methods. First, the innovation of EKF is calculated by the following formula (1):
wherein the content of the first and second substances,
representing an innovation sequence;
is a zero mean white gaussian noise sequence of the filter;
error as innovation;
is a positioning estimated value; h is a parameter of the measuring system;
is a measured value. However, for a general sub-optimal kalman filter, the innovation sequence thereof exhibits the following relationship:
suppose that:
；（2）
by derivation:
wherein the content of the first and second substances,
the flux is shown to be,
it is shown that it is desirable to,
is the Kalman gain;
is a state transition matrix;
is a matrix of the units,
representing the transpose of the measurement matrix. For the filter:
where R represents the measurement error matrix.
Are not used effectively. As the measured values increase, the state noise Q and the measurement noise R are accurately estimatedBoth the matrix P and the gain K will be suboptimal. Optimal Q, R, K can be obtained as follows: by transforming equation (3), one can obtain:
the optimized K can be written as:
the derived Q is:
wherein the content of the first and second substances,
denotes the autocorrelation coefficient of the measurement value, and M denotes the covariance. In actual practice, if a batch of observations can be stored, these calculations can be repeated to improve the estimation.
The second layer is outlier detection, which can be detected by comparing the residual size to an empirical threshold, since the residual can reflect the measurement error. The remaining amount is calculated as follows:
wherein the content of the first and second substances,
representing the result of the extended Kalman filter, the residual error of each estimated value
And a set threshold value
A comparison is made. If it is not
The measurement is considered to be an outlier; otherwise, the measurement is not an abnormal value, and after the abnormal value is eliminated, all the remaining measurement values are reused for measurement updating in the Kalman filter; then, the estimation state is updated again.
In order to improve the robustness of the positioning system, the positioning result is smoothed by adopting inverse Kalman filtering. Fixed interval filtration process measured as shown in FIG. 4
Updated with the direction of the upper arrow, the result is represented as (A)
，
，
，...，
). When the measured value in interval T is moreWhen the new process is completed, the last positioning value is used as calibration, and the backward filtering is started, and the result is (A)
，
，
，...， 
). Since both the forward and backward filtering results are biased, both filtering results are weighted. The weight of the pre-and post-positioning results used in combination is an empirical value of 0.5. Taking the final value of the first interval smoothing result as the initial value of the second interval smoothing, and adopting the following processing method by analogy:
wherein the content of the first and second substances,
represents the weighted positioning result value and the position of the target,
the table shows the results of the inverse filtering,
which represents the result of the forward filtering,
representing the weight coefficients.
According to the invention, two groups of unmanned aerial vehicles are adopted to form a formation to carry out crack detection on the bridge, so that full-automatic bridge detection operation is realized, an unmanned aerial vehicle formation detection reference is established, at least four airplanes fly along a designated air route outside the bridge as reference stations, and operation airplanes can be increased infinitely on the premise of sufficient space and no conflict of planned paths; and set for aircraft safe interval scope, detect the distance between the aircraft through the panoramic camera, prevent that operation aircraft from bumping with the benchmark aircraft (established unmanned aerial vehicle protection mechanism promptly, protected benchmark formation unmanned aerial vehicle and operation formation unmanned aerial vehicle, prevent the accident). The invention provides an underbridge unmanned aerial vehicle positioning method fusing UWB and INS, which fuses an Ultra Wide Band (UWB) positioning method and an inertial navigation system, provides a double-layer abnormal value detection algorithm, effectively eliminates data noise and improves positioning accuracy; secondly, a fixed interval smoothing filtering algorithm is introduced in the positioning fusion process, so that the robustness of the system is ensured, and the precision of the positioning system is further improved; namely, the under-bridge detection unmanned aerial vehicle fuses UWB (ultra wide band) and INS (inertial navigation system) data through an extended Kalman filter by using a double-layer abnormal value detection method and an introduced fixed interval smoothing method, so that the cooperative high-precision positioning of multiple unmanned aerial vehicles at the bottom of a bridge is realized.
Further, the UWB and INS fusion algorithm is not limited to the method introduced by the present invention; the UWB positioning method is not limited to the method introduced in the present invention, but TOF measurement and the like may also be used; the Kalman filter used in the fusion is not limited to one, and the original Kalman filter, the unscented Kalman filter, the adaptive Kalman filter and other methods can be used for filtering fusion; outlier processing can be replaced by a variety of methods, such as replacing outlier detection by mahalanobis distance.
Further, as shown in fig. 2 and 5, based on the above-mentioned bridge unmanned aerial vehicle detection method based on multi-source sensor fusion, the present invention also provides a bridge unmanned aerial vehicle detection system based on multi-source sensor fusion, and the system includes: reference formation and job formation; the reference formation comprises a plurality of reference unmanned aerial vehicles, and each reference unmanned aerial vehicle is provided with an ultra-wideband signal receiver, a global navigation satellite system signal receiver and a lightweight panoramic camera; the operation formation comprises many operation unmanned aerial vehicles, and every operation unmanned aerial vehicle carries on ultra wide band sensor and inertia measuring unit.
All the reference unmanned aerial vehicles in the reference formation hover at the designated positions according to the flight channel, the reference formation receives GNSS signals, and the aerial position information obtained through calculation provides reference coordinates for the operation unmanned aerial vehicles; all the unmanned aerial vehicles in the operation formation fly according to different flight routes to ensure that the detection ranges of all the unmanned aerial vehicles cover the whole set area, and all the unmanned aerial vehicles execute flight detection according to the reference coordinates provided by the reference formation; all operation unmanned aerial vehicles in the operation formation are used for being responsible for being close to bridge bottom and fly according to the flight route in order to detect the bridge crack, simultaneously through self carrying on ultra wide band sensor calculates relative benchmark unmanned aerial vehicle's positioning result.
Further, in the process that the operation unmanned aerial vehicle is close to the reference unmanned aerial vehicle, the reference unmanned aerial vehicle detects that the operation unmanned aerial vehicle is close, when the operation unmanned aerial vehicle surpasses a safe area and is close to the reference unmanned aerial vehicle, the reference unmanned aerial vehicle flies along the set avoidance distance and direction of the flight channel so as to avoid the operation unmanned aerial vehicle, and simultaneously marks the positioning time of the reference unmanned aerial vehicle before the position movement and after the movement is completed, and the reference positioning point is resolved again. The benchmark unmanned aerial vehicle is close to the operation unmanned aerial vehicle through the induction of the lightweight panoramic camera, and the distance between the benchmark unmanned aerial vehicle and the flight of the operation unmanned aerial vehicle is detected. The safe distance between the benchmark unmanned aerial vehicle and the flight of operation unmanned aerial vehicle is preset, and when the benchmark unmanned aerial vehicle is detected, the distance between the flight of the benchmark unmanned aerial vehicle and the flight of the operation unmanned aerial vehicle is greater than when the safe distance is short, the risk of flight collision between the benchmark unmanned aerial vehicle and the operation unmanned aerial vehicle is represented.
Further, the reference formation is used for flying in a flight channel arranged outside the delay bridge and arranged by the unmanned aerial vehicle flight control system, stopping at a specified positioning point, providing a high-precision satellite positioning result through the global navigation satellite system signal receiver, and taking the positioning result as an initial coordinate value of the positioning base station.
Further, after the operation formation completes the detection of the current area, the reference formation flies to the next designated area, and the operation formation flies to the next designated area for operation according to the reference formation and the planned path.
In summary, the invention provides a bridge unmanned aerial vehicle detection method and system based on multi-source sensor fusion, and the method comprises the following steps: the method comprises the following steps that a plurality of reference unmanned aerial vehicles form a reference formation, and each reference unmanned aerial vehicle is provided with an ultra wide band signal receiver, a global navigation satellite system signal receiver and a lightweight panoramic camera; setting flight channels of all reference unmanned aerial vehicles in the reference formation, starting all reference unmanned aerial vehicles and hovering the reference unmanned aerial vehicles at specified positions respectively, receiving GNSS signals by the reference formation, and calculating to obtain aerial position information to provide reference coordinates for the operation unmanned aerial vehicles; a plurality of operation unmanned aerial vehicles form an operation formation, and each operation unmanned aerial vehicle is provided with an ultra wide band sensor and an inertia measurement unit; setting different flight routes of all the operation unmanned aerial vehicles in the operation formation to ensure that the detection ranges of all the operation unmanned aerial vehicles cover the whole set area, and starting all the operation unmanned aerial vehicles to execute flight detection according to the reference coordinates provided by the reference formation; all operation unmanned aerial vehicles in the operation formation are used for being responsible for being close to bridge bottom and fly according to the flight route in order to detect the bridge crack, simultaneously through self carrying on ultra wide band sensor calculates relative benchmark unmanned aerial vehicle's positioning result. According to the invention, the high-precision positioning of the multiple unmanned aerial vehicles during the detection of the bottom of the bridge is realized through the mutual matching of the reference unmanned aerial vehicle and the operation unmanned aerial vehicle.
It is to be understood that the invention is not limited to the examples described above, but that modifications and variations may be effected thereto by those of ordinary skill in the art in light of the foregoing description, and that all such modifications and variations are intended to be within the scope of the invention as defined by the appended claims.
Claims (7)
1. The bridge unmanned aerial vehicle detection method based on multi-source sensor fusion is characterized by comprising the following steps of:
the method comprises the following steps that a plurality of reference unmanned aerial vehicles form a reference formation, and each reference unmanned aerial vehicle is provided with an ultra wide band signal receiver, a global navigation satellite system signal receiver and a lightweight panoramic camera;
setting flight channels of all reference unmanned aerial vehicles in the reference formation, starting all reference unmanned aerial vehicles and hovering the reference unmanned aerial vehicles at specified positions respectively, receiving GNSS signals by the reference formation, and calculating to obtain aerial position information to provide reference coordinates for the operation unmanned aerial vehicles;
a plurality of operation unmanned aerial vehicles form an operation formation, and each operation unmanned aerial vehicle is provided with an ultra wide band sensor and an inertia measurement unit;
setting different flight routes of all the operation unmanned aerial vehicles in the operation formation to ensure that the detection ranges of all the operation unmanned aerial vehicles cover the whole set area, and starting all the operation unmanned aerial vehicles to execute flight detection according to the reference coordinates provided by the reference formation;
all the operation unmanned aerial vehicles in the operation formation are used for flying close to the bottom of the bridge according to the flight route so as to detect cracks of the bridge, and meanwhile, the positioning result of the relative reference unmanned aerial vehicle is calculated through the ultra-wideband sensor carried by the operation unmanned aerial vehicles;
after the operation formation finishes the detection of the current area, the reference formation flies to the next designated area, and the operation formation flies to the next designated area for operation according to the reference formation and the planned path;
when the operation unmanned aerial vehicle approaches the reference unmanned aerial vehicle, the reference unmanned aerial vehicle detects the approaching operation unmanned aerial vehicle, when the operation unmanned aerial vehicle approaches the reference unmanned aerial vehicle beyond a safe area, the reference unmanned aerial vehicle flies along an avoidance distance and a direction set by a flight channel to avoid the operation unmanned aerial vehicle, meanwhile, the positioning time of the reference unmanned aerial vehicle before position movement and after movement completion is marked, and the reference positioning point is solved again;
the reference formation is used for flying in a flight channel arranged outside a delay bridge and arranged by an unmanned aerial vehicle flight control system, staying at a specified positioning point, providing a high-precision satellite positioning result through the global navigation satellite system signal receiver, and taking the positioning result as an initial coordinate value of a positioning base station;
setting a benchmark formation channel and a stop point position, planning a detection route of the operation unmanned aerial vehicle, after the benchmark formation flies to a specified position, firstly carrying out smooth calculation on a global navigation satellite system signal receiver, setting a GNSS positioning calculation result as an initial coordinate during UWB data analysis, then setting the initial position of the operation unmanned aerial vehicle calculated by the UWB as an initial value of an IMU sensor, and combining with the aircraft attitude data calculated by the IMU to realize the high-precision positioning under a bridge.
2. The multi-source sensor fusion-based bridge unmanned aerial vehicle detection method of claim 1, wherein the reference unmanned aerial vehicle senses the approaching working unmanned aerial vehicle through the lightweight panoramic camera and detects a distance between the flight of the reference unmanned aerial vehicle and the flight of the working unmanned aerial vehicle.
3. The method for detecting the bridge unmanned aerial vehicle based on the multi-source sensor fusion of claim 2, wherein the method for detecting the bridge unmanned aerial vehicle based on the multi-source sensor fusion further comprises:
the safe distance between the benchmark unmanned aerial vehicle and the flight of operation unmanned aerial vehicle is preset, and when the benchmark unmanned aerial vehicle is detected, the distance between the flight of the benchmark unmanned aerial vehicle and the flight of the operation unmanned aerial vehicle is smaller than the safe distance, the risk of flight collision between the benchmark unmanned aerial vehicle and the operation unmanned aerial vehicle is represented.
4. The method for detecting the bridge unmanned aerial vehicle based on the multi-source sensor fusion of claim 1, wherein when the positioning result of the working unmanned aerial vehicle relative to the reference unmanned aerial vehicle is calculated, the calculation is performed through a fixed interval smooth filtering algorithm.
5. The multi-source sensor fusion-based bridge unmanned aerial vehicle detection method of claim 1, wherein the reference formation is composed of at least four reference unmanned aerial vehicles.
6. The method for detecting the bridge unmanned aerial vehicle based on the multi-source sensor fusion of claim 5, wherein at least four reference unmanned aerial vehicles fly along a designated air route outside the bridge to serve as reference stations.
7. The utility model provides a bridge unmanned aerial vehicle detecting system based on multisource sensor fuses which characterized in that, bridge unmanned aerial vehicle detecting system based on multisource sensor fuses includes: reference formation and job formation;
the reference formation comprises a plurality of reference unmanned aerial vehicles, and each reference unmanned aerial vehicle is provided with an ultra-wideband signal receiver, a global navigation satellite system signal receiver and a lightweight panoramic camera;
the operation formation is composed of a plurality of operation unmanned aerial vehicles, and each operation unmanned aerial vehicle is provided with an ultra wide band sensor and an inertia measurement unit;
all the reference unmanned aerial vehicles in the reference formation hover at the designated positions according to the flight channel, the reference formation receives GNSS signals, and the aerial position information obtained through calculation provides reference coordinates for the operation unmanned aerial vehicles;
all the unmanned aerial vehicles in the operation formation fly according to different flight routes to ensure that the detection ranges of all the unmanned aerial vehicles cover the whole set area, and all the unmanned aerial vehicles execute flight detection according to the reference coordinates provided by the reference formation;
all the operation unmanned aerial vehicles in the operation formation are used for flying close to the bottom of the bridge according to the flight route so as to detect cracks of the bridge, and meanwhile, the positioning result of the relative reference unmanned aerial vehicle is calculated through the ultra-wideband sensor carried by the operation unmanned aerial vehicles;
after the operation formation finishes the detection of the current area, the reference formation flies to the next designated area, and the operation formation flies to the next designated area for operation according to the reference formation and the planned path;
when the operation unmanned aerial vehicle approaches the reference unmanned aerial vehicle, the reference unmanned aerial vehicle detects the approaching operation unmanned aerial vehicle, when the operation unmanned aerial vehicle approaches the reference unmanned aerial vehicle beyond a safe area, the reference unmanned aerial vehicle flies along an avoidance distance and a direction set by a flight channel to avoid the operation unmanned aerial vehicle, meanwhile, the positioning time of the reference unmanned aerial vehicle before position movement and after movement completion is marked, and the reference positioning point is solved again;
the reference formation is used for flying in a flight channel arranged outside a delay bridge and arranged by an unmanned aerial vehicle flight control system, staying at a specified positioning point, providing a high-precision satellite positioning result through the global navigation satellite system signal receiver, and taking the positioning result as an initial coordinate value of a positioning base station;
setting a benchmark formation channel and a stop point position, planning a detection route of the operation unmanned aerial vehicle, after the benchmark formation flies to a specified position, firstly carrying out smooth calculation on a global navigation satellite system signal receiver, setting a GNSS positioning calculation result as an initial coordinate during UWB data analysis, then setting the initial position of the operation unmanned aerial vehicle calculated by the UWB as an initial value of an IMU sensor, and combining with the aircraft attitude data calculated by the IMU to realize the high-precision positioning under a bridge.
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| CN113791074A (en) * | 2021-08-12 | 2021-12-14 | 中国建筑股份有限公司 | Unmanned aerial vehicle bridge crack inspection system and method based on multi-sensor fusion |
| CN115963851A (en) * | 2021-10-13 | 2023-04-14 | 北京三快在线科技有限公司 | Unmanned aerial vehicle positioning method and device |
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