An unmanned aerial vehicle navigation positioning system and method for oil and gas inspection

Abstract

This invention discloses a drone navigation and positioning system and method for oil and gas inspection: Using an extended Kalman filter algorithm, the prior state estimate and prior error covariance are calculated using data from the drone's IMU module; the optimal Kalman gain, posterior state estimate, and posterior error covariance are calculated using data from the drone's GPS module, the prior state estimate, and the posterior error covariance; the estimated position of the drone is obtained based on the optimal Kalman gain, posterior state estimate, and posterior error covariance; the location of the inspected pipeline point is determined using the estimated position; the drone's onboard flight control module controls the drone to fly according to the inspected pipeline point location, and the posterior error covariance is updated using the estimated position and the inspected pipeline point location; the above process is repeated until the inspection mission ends. This invention can provide accurate positioning data for drones and ground stations, avoiding problems caused by inaccurate drone positioning to a certain extent.

Description

Technical Field

[0001] This invention belongs to the field of unmanned aerial vehicles (UAVs), and specifically relates to a navigation and positioning system and method for oil and gas inspection. Background Technology

[0002] Unmanned aerial vehicles (UAVs), also known as drones, are unmanned aircraft controlled by radio remote control equipment and ground station program control devices. They are high-tech products integrating aerodynamics, materials mechanics, automatic control technology, and software technology. With the continuous development of society, natural gas, a green and environmentally friendly clean energy source, is becoming increasingly important in human life. However, frequent catastrophic accidents caused by sudden natural gas leaks result in significant casualties and property losses, severely damaging the ecological environment. As a result, UAVs are gradually emerging in the field of oil and gas inspection, and their safety, efficiency, and diverse data capabilities are becoming a widely chosen method.

[0003] During routine inspections, the remote and harsh environment means that relying solely on GPS positioning cannot guarantee that the inspection route will not deviate, resulting in cameras failing to capture complete pipelines and causing omissions in the inspection results. Furthermore, in mountainous areas with complex terrain, signal loss can easily lead to drone crashes. Summary of the Invention

[0004] To address the aforementioned issues, this invention provides a drone navigation and positioning system and method for oil and gas inspection. This invention can provide accurate positioning data for drones and ground stations, thereby mitigating problems such as reduced inspection effectiveness and collisions with mountains caused by inaccurate drone positioning.

[0005] To solve the above-mentioned technical problems, the present invention adopts the following technical solution:

[0006] A navigation and positioning method for unmanned aerial vehicles (UAVs) used for oil and gas inspection involves flight positioning during UAV patrol through the following steps:

[0007] S1, using the extended Kalman filter algorithm, calculates the prior state estimate and prior error covariance using the drone acceleration and incremental angle in the drone's IMU module;

[0008] S2, by using the extended Kalman filter algorithm, the optimal Kalman gain, posterior state estimate and posterior error covariance are calculated using the drone speed and drone incremental angle from the drone's GPS module, as well as the prior state estimate and prior error covariance.

[0009] S3. The estimated position of the UAV is obtained based on the optimal Kalman gain, posterior state estimate, and posterior error covariance.

[0010] S4, use the estimated location to determine the location of the inspection pipeline point of the UAV;

[0011] S5, the onboard flight control module of the UAV controls the UAV to fly according to the location of the inspected pipeline point, and updates the posterior error covariance using the estimated location and the location of the inspected pipeline point;

[0012] S6, repeat S1-S5 until the inspection task is completed.

[0013] Preferably, the prior state estimation as follows:

[0014]

[0015] The prior error covariance P k|k-1 as follows:

[0016]

[0017] Where k is the loop number. This is the posterior estimate for the (k-1)th iteration. and u k-1 Let ω be the control matrix for the k-th cycle. k-1 Let ι be an 1×1 dimensional system noise vector, where ι is the dimension of the state parameters. F is a nonlinear function estimated a priori. k-1 Let P be the state transition matrix for the (k-1)th cycle. k-1|k-1 G is the posterior estimate of the covariance for the (k-1)th iteration. k-1 Let Q be the control input matrix for the (k-1)th cycle. k-1 Let be the covariance matrix of the (k-1)th cycle, assuming it follows a multivariate zero-mean normal distribution. It is the transpose of the control input matrix for the (k-1)th cycle.

[0018] Preferably, the optimal Kalman gain K k as follows:

[0019]

[0020] The posterior state estimation as follows:

[0021]

[0022] The posterior error covariance P k|k as follows:

[0023] P k|k =(I n -Kk H k )P k|k-1

[0024] Where k is the loop number, P k|k-1 Let the prior error covariance be the value of the (k-1)th iteration. Let H be the measurement matrix for the k-th cycle. k Let R′ be the observation matrix for the k-th cycle. k The observation noise with zero mean Gaussian covariance in the k-th cycle. For the prior state estimation of the (k-1)th cycle, and z′ k The state measurement value for the current cycle, z′ k|k-1 I is the state measurement value for the (k-1)th cycle. n It is an n-dimensional identity matrix.

[0025] Preferred state variables q is the attitude quaternion; V NED P represents the airframe velocity of the UAV in the northeast coordinate system. NED UAV body position in the northeast coordinate system; Δθ b The incremental angular deviation of the body coordinate system; ΔV b This represents the incremental velocity deviation in the body coordinate system.

[0026] Preferably, in S4, when determining the inspection pipeline point position of the UAV using the estimated position, the estimated position is projected onto the nearest inspection pipeline point position, and this inspection pipeline point position is used as the estimated position for the next moment.

[0027] In S5, when the UAV's onboard flight control module controls the UAV to fly according to the inspection pipeline point location, the inspection pipeline point location obtained in S4 is applied to the inner and outer loop algorithms of the onboard flight control module. By combining it with the obtained map matching location, continuous system position estimation of the UAV's position is achieved.

[0028] Preferably, when projecting the estimated location to the nearest inspection pipeline point, the estimated location x1 is used as the center and R is the radius. The R value is then gradually increased within the loop. When the circle includes ≥10 inspection coordinate points, these points are x0... i Calculate the sum of the squared differences between x1 and x2, find the point x2 that is closest to x1, and calculate the distance d between x1 and x2.

[0029] The distance d is used to calculate the new posterior error covariance, resulting in the updated posterior error covariance.

[0030] x2 is used as the system position estimate for the next moment, and a new round of position calculation is performed;

[0031] Where i = 0, 1, 2, ... 9.

[0032] The present invention also provides a drone navigation and positioning system for oil and gas inspection, comprising:

[0033] IMU module: Used to acquire real-time status data of the drone, including drone acceleration and drone incremental angle;

[0034] GPS module: Used to provide real-time location data for the drone, including drone speed and incremental angle.

[0035] The airborne flight control module is used to calculate the prior state estimate and prior error covariance using the UAV's acceleration and incremental angle from the UAV's IMU module via an extended Kalman filter algorithm; to calculate the optimal Kalman gain, posterior state estimate, and posterior error covariance using the UAV's speed and incremental angle from the UAV's GPS module, along with the aforementioned prior state estimate and prior error covariance; to obtain the estimated position of the UAV based on the optimal Kalman gain, posterior state estimate, and posterior error covariance; to determine the location of the inspection pipeline point using the estimated position; to control the UAV to fly according to the inspection pipeline point location; and to update the posterior error covariance using the estimated position and the inspection pipeline point location; and to repeat the above process until the inspection mission is completed.

[0036] The present invention has the following beneficial effects:

[0037] This invention, supported by data from the UAV's IMU and GPS modules, uses an extended Kalman filter algorithm to fuse precise positioning data. This data, combined with a set of inspection data points, provides faster and more accurate positioning of the flight direction points for inspection flights, improving the effectiveness and stability of the entire UAV flight control system and providing strong support for inspection missions. Therefore, this invention can provide accurate positioning data for both the UAV and the ground station, mitigating to some extent the problems caused by inaccurate positioning, such as reduced inspection effectiveness and collisions with mountains. Attached Figure Description

[0038] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below.

[0039] Figure 1 This is a system structure diagram of the UAV navigation and positioning system for oil and gas inspection according to the present invention;

[0040] Figure 2This is a flowchart of the UAV navigation and positioning method for oil and gas inspection according to the present invention;

[0041] Figure 3 This is a schematic diagram illustrating the determination of the inspection pipeline location by the UAV using estimated location in an embodiment of the present invention. Detailed Implementation

[0042] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments.

[0043] Reference Figure 1 The UAV navigation and positioning system for oil and gas inspection of this invention is basically the same in hardware structure as a conventional UAV navigation and positioning system for oil and gas inspection. The main improvement of this invention lies in data processing. Specifically, the navigation and positioning system includes a ground station system and a UAV system. See also... Figure 1 The system on the UAV platform includes: a GPS positioning module, an IMU module, an airborne flight control module, an airborne video camera module, and a wireless communication module. The ground station system mainly includes a data processing unit and a wireless transmission module.

[0044] The GPS positioning module in the UAV system is responsible for providing the aircraft with real-time location information; the IMU module is mainly responsible for acquiring various real-time data such as the aircraft's attitude, speed, acceleration, and altitude; the airborne flight control module can realize the UAV's automatic climb and landing according to the instructions issued by the data processing unit, and perform inspection operations according to the specified route; the airborne video camera module mainly includes a high-definition camera installed on the UAV, which is responsible for capturing and collecting image information; the wireless communication module is responsible for communication between the ground and the air, transmitting or receiving various data that the aircraft needs to interact with the ground.

[0045] The wireless transmission module in the ground station system is responsible for receiving data transmitted from the UAV, primarily including real-time position data provided by the GPS module, various real-time aircraft status data obtained by the IMU module through its own sensors, and video information transmitted from the onboard video camera module. It also uploads control commands issued by the ground station to the onboard flight control module of the UAV. The data processing unit is responsible for processing the data from the IMU module to obtain the aircraft's real-time position information. Furthermore, the data processing unit determines the priority of the two types of position information: when the GPS module signal is not interfered with, the GPS module positioning information is prioritized; when the GPS signal is weak, the IMU module information is prioritized. A real-time flight path is generated and compared with the inspection route. If the error threshold is exceeded, a flight control request is issued to adjust the UAV's attitude to ensure that no inspection results are missed. The data processing unit performs image recognition on the video information obtained by the onboard video module, automatically identifying and avoiding obstacles such as mountains and trees, and projects the video information onto the screen for ground personnel to assist in control.

[0046] All data between the ground station system and the unmanned aerial vehicle (UAV) platform (i.e., the UAV system) is communicated via a wireless communication module. The ground station receives downlink data links from the UAV platform and simultaneously sends uplink data links to the UAV platform.

[0047] Before each drone inspection mission takes off, the ground station software sets up a map of the pipelines to be inspected. (See below) Figure 2 and Figure 3 Set the maximum yaw distance threshold.

[0048] Once ready, the drone takes off and flies according to the pre-set pipeline map. The ground station software receives real-time GPS location information, IMU sensor information, and video data. By processing the IMU sensor information, it calculates the drone's location in real time and displays it on the flight path map. Simultaneously, the GPS location information is also displayed on the flight path map.

[0049] The GPS signal strength is monitored in real time by ground station software. When the GPS signal strength is relatively high, the location information obtained by the GPS signal is the primary focus. When the GPS satellite acquisition strength is less than 5 satellites or the error is greater than 10m, the location information calculated by the IMU sensor is the primary focus.

[0050] When the position information deviates and exceeds the set yaw distance threshold, the flight status, including flight speed, flight altitude, and flight attitude, is adjusted through the ground station software to control the UAV.

[0051] The application background of the technical solution of this invention is pipeline inspection drones, and to provide navigation and positioning data for them.

[0052] 1. Before the aircraft takes off, the ground station system will upload the pipeline trajectory data points to be inspected to the onboard flight control system. The inspection route trajectory will form a zigzag line, which will be used for subsequent navigation data error calibration.

[0053] 2. During flight, the UAV's position and attitude data are primarily calculated and updated in real-time using the Extended Kalman Filter (EKF) algorithm. The EKF equation mainly consists of two parts: time update (prediction) and measurement update (correction). See [link to EKF algorithm]. Figure 2 The details are as follows:

[0054] (1) Prediction:

[0055] Predictive (prior) state estimation

[0056]

[0057] Prediction (prior) error covariance

[0058]

[0059] (2) Correction:

[0060] Optimal Kalman gain

[0061]

[0062] Update (posterior) state estimation

[0063]

[0064] Update (posterior) covariance

[0065] P k|k =(I n -K k H k )P k|k-1

[0066] in, and state variables q is the attitude quaternion; V NED P represents the airframe velocity of the UAV in the northeast coordinate system. NED UAV body position in the northeast coordinate system; Δθ b The incremental angular deviation of the body coordinate system; ΔV b This represents the incremental velocity deviation in the body coordinate system.

[0067] Additionally f: R n ×R m ×R n →R nand h:R n ×R m →R m It is a nonlinear function; m and n are parameters illustrating the nonlinear function, indicating the dimension of its internal terms; ω k-1 G is an ι×1 dimensional system noise vector, where ι is the dimension of the state parameters, assumed to be extracted from a zero-mean multivariate normal distribution N; k For the input control matrix; F k H is the state transition matrix; k Let Q be the observation matrix. Both Q and R follow a normal distribution, and Q... k :ω k ~N(0, Q) k ), R k :v k ~N(0, R) k ).

[0068] k is the loop number. Let u be the posterior estimate of the (k-1)th iteration. k-1 This is the control matrix for the k-th cycle. F is a nonlinear function estimated a priori. k-1 Let P be the state transition matrix for the (k-1)th cycle. k-1|k-1 G is the posterior estimate of the covariance for the (k-1)th iteration. k-1 Let Q be the control input matrix for the (k-1)th cycle. k-1 Let be the covariance matrix of the (k-1)th cycle, assuming it follows a multivariate zero-mean normal distribution. P is the transpose of the control input matrix for the (k-1)th iteration; k|k-1 For the prior error covariance, Let H be the measurement matrix for the k-th cycle. k Let R′ be the observation matrix. k The observation noise with zero mean Gaussian covariance in the k-th cycle. For prior state estimation, and z′ k The state measurement value for the current (i.e., the kth) cycle, z′ k|k-1 I is the state measurement value for the (k-1)th cycle. n It is an n-dimensional identity matrix.

[0069] 3. Obtain the UAV's inertial navigation and positioning position through the UAV's onboard inertial measurement unit (IMU sensor), and use this data as a prediction quantity for the navigation and positioning system;

[0070] state variables

[0071] Among them, attitude quaternions:

[0072]

[0073] V NED This represents the machine's velocity in the NED coordinate system.

[0074]

[0075] P NED This indicates the machine's position in the NED coordinate system:

[0076]

[0077] Δθ b This represents the incremental angular deviation (bias) in the XYZ body coordinate system:

[0078]

[0079] ΔV b This represents the incremental velocity bias in the XYZ body coordinate system.

[0080]

[0081] Posture update:

[0082] Δθ truth =Δθ meas -Δθ b

[0083] The measured value of the incremental angle is:

[0084] Δθ meas =ωΔt

[0085]

[0086] q k+1 =q k ·Δq

[0087]

[0088] Speed ​​updates:

[0089] ΔV truth =ΔV meas -ΔV b

[0090] The measured value of the incremental velocity is:

[0091] ΔV meas =aΔt

[0092]

[0093] Location update:

[0094]

[0095] Among them, the state quantities in the NED coordinate system refer to the values ​​in the north-east geodetic coordinate system; xyz is the body coordinate system, which changes with the aircraft's attitude, and some sensor measurements correspond to the body coordinate system.

[0096] 4. Use the GPS module positioning data on the UAV directly as the observation data of the navigation and positioning system.

[0097] 5. After this round of EKF calculation is completed, the calculated position will be corrected in real time according to the inspection route. This invention uses the position results of multi-sensor integrated navigation and the target data of the UAV inspection route to perform projection calculation for system position estimation at the next time step.

[0098] See Figure 2 and Figure 3 x1 is used as the EKF posterior estimate for this round. It is projected onto the nearest inspection pipeline point x2 as the system position estimate for the next time step. This position estimate is then applied to the flight control inner and outer loop algorithms. By combining it with the obtained map matching position, the accuracy of UAV position estimation is improved, and continuous system position estimation of UAV position is achieved to ensure the feasibility of the inspection effect.

[0099] The calculation process is as follows:

[0100] First, it is known that the uploaded inspection route is a dot-line diagram, which is an inspection route diagram formed by various coordinate points.

[0101] (1) With x1 as the center and R as the radius, gradually increase the R value in the loop. When the number of inspection coordinate points included in the circle is ≥10, transfer these points x0 to x1. i (i = 0, 1, 2, ...) Calculate the difference of squares between x1 and x2, and sum them to find the point x2 that is closest to x1. The distance is calculated as follows:

[0102]

[0103] Note that x is a two-dimensional coordinate at this point.

[0104] (2) Use the distance d to calculate the new measurement error matrix.

[0105] (3) Use x2 as the system position estimate for the next moment and perform a new round of position calculation.

Claims

1. A method for navigation and positioning of a UAV for oil and gas inspection, characterized in that, During drone patrol, flight positioning is performed using the following steps: S1, using the extended Kalman filter algorithm, calculates the prior state estimate and prior error covariance using the drone acceleration and incremental angle in the drone's IMU module; S2, by using the extended Kalman filter algorithm, the optimal Kalman gain, posterior state estimate and posterior error covariance are calculated using the drone speed and drone incremental angle from the drone's GPS module, as well as the prior state estimate and prior error covariance. S3. The estimated position of the UAV is obtained based on the optimal Kalman gain, posterior state estimate, and posterior error covariance. S4, using the estimated position, determine the location of the inspection pipeline point of the UAV. Specifically: project the estimated position onto the nearest inspection pipeline point, and use this inspection pipeline point as the estimated position for the next moment; when projecting the estimated position onto the nearest inspection pipeline point, use the estimated position... Using a circle as the center and R as the radius, the R value is gradually increased within the loop. When the circle includes ≥10 inspection points, these points are... With estimated location Perform the difference of squares and sum them to find the solution relative to the estimated location. nearest distance point and calculate and Distance between ; distance This is used to calculate the new posterior error covariance, resulting in the updated posterior error covariance; As the system's position estimate for the next moment, a new round of position calculations is performed; among them, i = 0, 1, 2, ... 9; S5, the UAV's onboard flight control module controls the UAV to fly according to the inspection pipeline point location, and updates the posterior error covariance using the estimated location and the inspection pipeline point location. Specifically, the inspection pipeline point location obtained in S4 is applied to the inner and outer loop algorithms of the onboard flight control module, and combined with the obtained map matching location to achieve continuous system position estimation of the UAV's position. S6, repeat S1-S5 until the inspection task is completed.

2. The UAV navigation and positioning method for oil and gas inspection according to claim 1, characterized in that, the prior state estimate as follows: the prior error covariance as follows: in, k This is the sequence number of the loop. This is the posterior estimate for the (k-1)th iteration. and ∈ State Quantity , This is the control matrix for the (k-1)th cycle. for A 1-dimensional system noise vector. The dimension of the state parameters. For the prior estimate of the nonlinear function, This is the state transition matrix for the (k-1)th cycle. For the posterior estimate of the covariance of the (k-1)th iteration, This is the control input matrix for the (k-1)th iteration. Let be the covariance matrix of the (k-1)th cycle, assuming it follows a multivariate zero-mean normal distribution. It is the transpose of the control input matrix for the (k-1)th cycle. 3.The method for navigation and positioning of the UAV for oil and gas inspection according to claim 1, wherein, the optimal kalman gain as follows: the posterior state estimate as follows: the posterior error covariance as follows: in, k This is the sequence number of the loop. Let the prior error covariance be the value of the (k-1)th iteration. Let be the measurement matrix for the k-th cycle. For measurement matrix The transpose of the matrix, The observation noise with zero mean Gaussian covariance in the k-th cycle. For the prior state estimation of the (k-1)th cycle, and ∈ State Quantity , This is the state measurement value for the k-th cycle. This is the state measurement value for the (k-1)th cycle. It is an n-dimensional identity matrix.

4. The navigation positioning method for the oil and gas inspection unmanned aerial vehicle according to claim 2 or 3, characterized in that, state variables q is the attitude quaternion; The velocity of the UAV in the northeast coordinate system; Position of the UAV in the northeast coordinate system; This represents the incremental angular deviation of the body coordinate system. This represents the incremental velocity deviation in the body coordinate system.

5. A UAV navigation positioning system for oil and gas inspection, characterized in that, include: IMU module: Used to acquire real-time status data of the drone, including drone acceleration and drone incremental angle; GPS module: Used to provide real-time location data for the drone, including drone speed and incremental angle. Airborne flight control module: used to calculate prior state estimate and prior error covariance using the UAV acceleration and UAV incremental angle in the UAV's IMU module through extended Kalman filter algorithm; and to calculate optimal Kalman gain, posterior state estimate and posterior error covariance using the UAV speed and UAV incremental angle in the UAV's GPS module, as well as the prior state estimate and prior error covariance. The estimated position of the UAV is obtained based on the optimal Kalman gain, posterior state estimate, and posterior error covariance. The estimated position is used to determine the location of the inspection pipeline point for the UAV. Specifically, the estimated position is projected onto the nearest inspection pipeline point, and this inspection pipeline point is used as the estimated position for the next moment. When projecting the estimated position onto the nearest inspection pipeline point, the estimated position is used as the reference point. Using a circle as the center and R as the radius, the R value is gradually increased within the loop. When the number of pipeline points included in the circle is ≥10, these points are... With estimated location Perform the difference of squares and sum them to find the solution relative to the estimated location. nearest distance point and calculate and Distance between ; distance This is used to calculate the new posterior error covariance, resulting in the updated posterior error covariance; As the system's position estimate for the next moment, a new round of position calculations is performed; among them, i = 0, 1, 2, ... 9; Control the UAV to fly according to the location of the inspection pipeline point, and update the posterior error covariance using the estimated location and the location of the inspection pipeline point. Specifically: Apply the obtained inspection pipeline point location to the inner and outer loop algorithms of the airborne flight control module, and combine it with the obtained map matching location to realize continuous system position estimation of the UAV's position; Repeat the above process until the inspection task ends.

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