Unmanned aerial vehicle cluster autonomous operation method based on UWB technology in GNSS denial environment

Abstract

The invention discloses an unmanned aerial vehicle cluster autonomous operation method based on a UWB technology in a GNSS denial environment, and the method comprises the steps: carrying out the time synchronization through employing the UWB technology, and building a high-precision time reference; uWB technology is used to carry out mutual distance measurement of the unmanned aerial vehicle, and an observation equation is established; establishing a high-precision space reference by using a free network adjustment method; unmanned aerial vehicle cooperative positioning is carried out by using a UWB ranging function and extended Kalman filtering; an NVIDIA development board and flight control software are used to carry out autonomous flight control on the unmanned aerial vehicle; and the real-time monitoring of the video stream of the unmanned aerial vehicle is realized by using the load equipment and UWB communication function. According to the invention, by combining the UWB technology with the unmanned aerial vehicle, the establishment of the high-precision space-time reference of the unmanned aerial vehicle can be effectively realized, the autonomous operation capability of the unmanned aerial vehicle cluster is improved, the dependence of the unmanned aerial vehicle on GNSS observation is eliminated, the application scene of the unmanned aerial vehicle in a GNSS rejection environment is expanded, the operation cost of the unmanned aerial vehicle is reduced, and the unmanned aerial vehicle cluster operation efficiency is improved. And the autonomous operation flexibility of the unmanned aerial vehicle is improved.

Description

Technical Field

[0001] The present invention relates to the field of unmanned aerial vehicle positioning and control, and in particular to a method for autonomous operation of a cluster of unmanned aerial vehicles based on improved UWB technology. Background Art

[0002] With the rapid development of drone technology in recent years, drones are being widely used in various fields. Drones often use GNSS+ inertial navigation for positioning and navigation. In this way, drones work in the GNSS time and space reference and rely on the receiver's continuous observation of GNSS satellites. However, with the expansion of drone application areas, especially in GNSS-denied operating environments, this positioning method has great limitations. Therefore, it is necessary to conduct research on the problem of drone collaborative operations in GNSS-denied environments, so that multi-drone systems can perform more complex multi-drone collaborative tasks such as reconnaissance, search and rescue, and mapping in a wider range of scenarios.

[0003] At present, in GNSS-denied environments, such as tunnels, dense forests, and battlefields with GNSS signal interference, drones mostly use visual positioning and navigation. Although this method gets rid of the dependence on GNSS signals, it cannot independently establish and maintain the space-time reference. The establishment of its space-time reference requires the establishment of additional base stations on the ground, which obviously increases the cost of drone operations. At the same time, in some areas that are difficult for manpower to reach, such as more dangerous battlefields and more cramped underground spaces, ground base stations cannot be established, which also makes it impossible for drones to operate in this way. There are certain limitations in usage costs and usage scenarios. Summary of the invention

[0004] The purpose of the present invention is to address the deficiencies of the above-mentioned prior art and propose an autonomous operation method for a UAV cluster based on UWB technology in a GNSS-denied environment. By using UWB technology to establish a high-precision space-time reference, and under this space-time reference, using UWB modules and development boards to develop corresponding control software, the collaborative positioning, flight control and real-time monitoring of video streams of UAV clusters in a GNSS-denied environment can be realized, so as to expand the application scenarios of UAVs.

[0005] In order to achieve the purpose of the present invention, the present invention provides a UAV cluster autonomous operation method based on UWB technology in a GNSS denial environment, which is divided into a high-precision space-time benchmark establishment stage and an autonomous operation stage.

[0006] High-precision spatiotemporal benchmark establishment phase:

[0007] (1) Establishment of a high-precision time reference: Using the time synchronization function of the UWB hardware device, the UAV slave node time reference is aligned with the master node time reference to achieve the establishment of a high-precision time reference;

[0008] (2) Establishment of high-precision spatial reference: The master node combines the ranging signals into observation equations, constrains the coordinate origin, orientation and scale, and uses the least squares method to perform adjustment and solve to obtain a high-precision spatial reference.

[0009] Autonomous operation stage:

[0010] (3) Using UWB modules to realize the collaborative positioning function of drones;

[0011] (4) Use the NVIDIA Jetson Nano development board to realize the autonomous flight control function of the drone;

[0012] (5) Use the payload camera to realize the real-time monitoring function of the drone video stream.

[0013] Compared with the prior art, the present invention has the following significant improvements:

[0014] (1) Use UWB technology to establish high-precision spatiotemporal references, expanding the application scenarios of drones in GNSS-denied environments;

[0015] (2) It realizes autonomous operation of UAVs, gets rid of the dependence of UWB technology on ground base stations, and improves the flexibility of UWB technology;

[0016] (3) In collaborative positioning, the relative motion equations of the master and slave machines are used to constrain the Kalman filter to improve the accuracy and reliability of the solution results. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0018] Figure 1 This is a connection diagram of the drone of the present invention.

[0019] Figure 2 A flow chart is provided for establishing the high-precision time reference proposed in the present invention.

[0020] Figure 3 A flow chart is provided for establishing the high-precision spatial reference proposed in the present invention.

[0021] Figure 4 This is a flow chart of the collaborative positioning proposed by the present invention.

[0022] Figure 5 This is a flowchart of autonomous operation of UAV swarm based on UWB technology in GNSS-denied environment. DETAILED DESCRIPTION

[0023] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments; based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0024] like Figure 2 As shown in the figure, a method for autonomous operation of UAV cluster based on UWB technology in a GNSS-denied environment is shown in the figure. The specific steps are as follows:

[0025] High-precision space-time benchmark establishment stage: With the help of UWB's high-precision ranging and high-precision timing functions, the mutual ranging and time synchronization technology between drone nodes can achieve the establishment of centimeter-level space benchmarks and picosecond-level time benchmarks (i.e. high-precision space-time benchmarks). Its accuracy is comparable to that of GNSS space-time benchmarks, and can meet the operational needs of drone clusters. The specific steps for establishing space-time benchmarks are as follows:

[0026] Step 1: Establishment of high-precision time base:

[0027] By utilizing the hardware time synchronization function of the UWB module, the time base of all nodes of the drone is aligned with the A0 node, thereby establishing a high-precision time base for the drone.

[0028] Step 2: Establishment of high-precision spatial reference;

[0029] The UWB observations and the least squares free network adjustment method are used to establish a high-precision spatial reference for UAVs. The specific steps are as follows:

[0030] Step 2.1: Mark the drone reference base station as A0, and the slave stations as A1, A2, ..., A i , ..., A j , ..., A n ; Where n represents a total of n drone nodes, the baseline A0-A1 is defined as the X-axis direction of the coordinate system, and the coordinate of drone A0 is (0, 0, 0);

[0031] Step 2.2: All drone nodes broadcast ranging signals with timestamps via UWB;

[0032] Step 2.3: When the drone node receives the ranging signal, the distance between the drone nodes is calculated by the timestamp. The specific calculation method is as follows:

[0033] Set UAV A i The node receives the data from drone A. j The ranging signal of the node, where the timestamp in the ranging signal is tj , the time when the ranging signal is received is t i , then A i To A j The distance is:

[0034] d ij =c×(t i -t j )

[0035] Where, d ij is the distance from UAV Ai to Aj, in meters; c is the speed of light;

[0036] Step 2.4: The drone node sends the calculated distance, time, node and other information to the A0 node;

[0037] Step 2.5: In order to eliminate the rank deficiency of the free network observation coefficient matrix, it is necessary to introduce the reference constraints of the coordinate system. Usually, a fixed point, a fixed astronomical azimuth, two fixed zenith distances and a fixed scale ratio are taken. Here, the coordinates of UAV A0 are specified as (0, 0, 0); the azimuths of vectors A0-A1 and A0-A2 are true values; the zenith distances of vectors A0-A3 and A0-A4 are true values, and the zenith distances of vectors A0-A5 and A0-A6 are true values; the scale ratio is fixed to 1.

[0038] Step 2.6: After receiving the UAV ranging information, the A0 node obtains the coordinates of each UAV node through free network adjustment calculation and establishes a high-precision spatial reference. The specific calculation method is as follows:

[0039] The UWB observation equation can be written as:

[0040]

[0041] Where, L ij represents the distance observation from UAV Ai to Aj, x i ,y i 、z i Represent the three-dimensional coordinates of the drone Ai, x j ,y j 、z j They represent the three-dimensional coordinates of the UAV Aj, and ε represents the observation error.

[0042] The above formula is nonlinear and difficult to solve using the least squares method. Therefore, it is necessary to use Taylor expansion to linearize all observations and write them in the following matrix form:

[0043] L=BX+Δ

[0044] Among them, L is the observation value vector, B is the observation equation coefficient matrix, X is the parameter vector to be estimated, and Δ is the random error vector. The corresponding error equation can be written as:

[0045]

[0046] Where V is the correction vector, is the estimated vector of the parameters, and the initial value vector of the parameters is X 0 , and record the observed value l = L-BX 0 , then the error equation can be written as follows:

[0047]

[0048] In the formula, Represents the vector of parameter corrections to be estimated.

[0049] Add the constraints in step 2.5 to the error equation and eliminate the rank deficiency of the B matrix before solving the problem. Let the weight matrix of the estimated parameters be P. Under the least squares principle V T When PV = min, we can get the normal equation of the error equation, and solve it to get

[0050]

[0051] The coordinate correction number of the drone position can be obtained;

[0052] Add the correction value of the drone position coordinates to the initial coordinate value, and the updated drone coordinates are:

[0053]

[0054] In the formula, X is the parameter vector to be estimated, X 0 is the initial value vector of the parameters, Represents the vector of parameter corrections to be estimated.

[0055] Next, the corrected coordinates are substituted into the error equation and the calculation results are iterated until the coordinate correction number is less than the set threshold. The drone position coordinate solution is completed, thereby establishing a high-precision spatial reference.

[0056] Autonomous operation stage:

[0057] S3: Use the observation values ​​in the UWB module to perform collaborative positioning of drone clusters. The steps are as follows:

[0058] Step 3.1: Let the i-th drone node be the node to be located, and its number is A i , A i The node receives the ranging information from other nodes;

[0059] Step 3.2: Ai The node sends the ranging information to the A0 master node, which processes the ranging information;

[0060] Step 3.3: Node A0 linearizes the distance information and the coordinates of the drone node and establishes the observation equation:

[0061] L k =B k X k +ω m

[0062] Where, L k is the observation value vector at the kth moment, B k is the observation equation coefficient matrix at the kth moment, X k is the parameter vector to be estimated at the kth moment, ω m is the measurement error vector;

[0063] Step 3.4: Establish the state equation of the node to be located

[0064] X k =Φ k,k-1 X k-1 +Γ k W k

[0065] In the formula, Φ k,k-1 is the state transfer matrix from the k-1th moment to the kth moment, X k-1 is the parameter vector to be estimated at the k-1th moment, Γ k is the dynamic noise matrix, W k is dynamic noise;

[0066] Step 3.5: According to the observation equations and observation equations in steps 3.3 and 3.4, use the extended Kalman filter method to estimate the state X k The specific estimation method is as follows:

[0067] Assume the state of the drone at the kth moment is X k , and its filtering value is According to the filter value of the previous moment Calculate predicted values for:

[0068]

[0069] According to the filter variance matrix P at the k-1th moment k-1 and is the variance matrix Q of the system noise at the kth moment k , calculate the prediction error variance matrix as

[0070]

[0071] Step 3.6: Calculate the gain matrix K k

[0072]

[0073] In the formula, B k Represents the coefficient matrix of the observation equation at time k, R k represents the measurement noise at time k.

[0074] Step 3.7: Calculate the filter estimate and variance matrix

[0075]

[0076] P k / k =[IK k B k ]P k,k-1

[0077] Where, L k represents the observed value at time k, P k represents the filter variance matrix at time k, I is the unit matrix, and finally the result of the estimated coordinates is obtained.

[0078] S4: Use the NVIDIA development board to control the autonomous flight of the drone. The steps are as follows:

[0079] Step 4.1: Connect the drone to the NVIDIA development board with flight control software and UWB module;

[0080] Step 4.2: According to the flight mission, enter the flight control script in the development board, which includes the flight angular acceleration, flight speed, flight time, etc.

[0081] Step 4.3: The flight control software reads the flight control commands line by line and controls the drone to execute the flight instructions.

[0082] Step 5: Use the payload camera to monitor the drone video stream in real time. The steps are as follows:

[0083] Step 5.1: The drone reads the real-time video stream from the camera through the payload interface and transmits it to the NVIDIA development board;

[0084] Step 5.2: Use the ffmpeg library to read the camera's real-time video stream in image format through the video stream processing program in the development board;

[0085] Step 5.3: The processing program preprocesses the video stream image and encodes the image in H264 format;

[0086] The key parameters for the preprocessing of video stream images are: image resolution is 1920×1080, image frame number is 30fps, image bit rate is 4000kb / s, and image time base is 0.033s.

[0087] Step 5.4: The processing program uses the UWB device communication function to transmit the encoded video stream to the RTSP server in real time using the RTSP protocol;

[0088] Step 5.5: The host computer uses the RTSP protocol to pull the video stream from the RTSP server in real time, decodes it in H264, and plays it in real time.

[0089] In this embodiment, the autonomous operation method of drone cluster based on UWB technology in GNSS-denied environment designed by the present invention applies UWB technology to autonomous establishment of high-precision time and space reference and autonomous operation of drones, realizing the operation capability of drones in GNSS-denied environment. At the same time, UWB technology also integrates communication capability, greatly reducing the cost of drone deployment. Compared with the traditional operation mode of drones with GNSS+inertial navigation, the cost of this embodiment is relatively low, the deployment method is relatively simple, and the drones are free from dependence on GNSS observation during operation, which greatly expands the application scenarios of drones.

[0090] The advantages of the UWB technology used in the present invention are: ① the deployment cost is low and the layout is relatively simple; ② there is no need to set up a ground base station, and the space-time benchmark can be constructed and maintained by the UWB equipment carried by the drone; ③ the application of UWB technology is not limited to benchmark construction and collaborative positioning. Its characteristics that can be used for data transmission enable drone clusters to transmit monitoring images, perform flight control and other functions, and its application is more extensive.

[0091] The above is a detailed description of the specific implementation examples of the present invention. It should be noted that the present invention is not limited to the above specific implementation examples, and those skilled in the art can make various modifications or variations within the scope of the claims, which do not affect the essential content of the present invention.

Claims

1. A method for autonomous operation of a UAV swarm based on UWB technology in a GNSS-denied environment, comprising: High-precision space-time benchmark establishment stage: including the establishment of high-precision time benchmark and high-precision space benchmark, that is, to achieve the establishment of centimeter-level space benchmark and picosecond-level time benchmark; Autonomous operation stage: Under the high-precision space-time reference, the coordinated positioning, flight control, communication and video stream monitoring of drone clusters are realized.

2. The method for autonomous operation of UAV swarm based on UWB technology in a GNSS-denied environment according to claim 1, characterized in that: The steps to establish a high-precision time reference are to use the time synchronization function of the UWB hardware device to align the drone slave node time reference with the master node reference to achieve the establishment of a high-precision time reference.

3. The method for autonomous operation of UAV cluster based on UWB technology in GNSS-denied environment according to claim 1, characterized in that: The steps to establish a high-precision spatial reference are: Step 2.1: Mark the reference base station drone as A0, and the slave drones as A1, A2, ...A n , where n represents a total of n drone nodes; the baseline A0-A1 is defined as the X-axis direction of the coordinate system, and the coordinate of drone A0 is the origin; Step 2.2: All drone nodes broadcast ranging signals with timestamps via UWB; Step 2.3: When the drone node receives the ranging signal, the distance between the drone nodes is calculated through the timestamp; Step 2.4: The drone node sends the calculated distance, time, and node information to the A0 node; Step 2.5: In order to eliminate the rank deficiency of the coefficient matrix of the free network observation equation, it is necessary to introduce the reference constraint of the coordinate system. Usually, a fixed point, a fixed astronomical azimuth, two fixed zenith distances and a fixed scale ratio are taken. Here, the coordinates of the reference base station A0 are selected as the true value of the origin coordinates, the azimuths of vectors A0-A1 and A0-A2 are true values; the zenith distances of vectors A0-A3 and A0-A4 are true values, and the zenith distances of vectors A0-A5 and A0-A6 are true values; the scale ratio is fixed to 1; Step 2.6: After receiving the UAV ranging information, the A0 node obtains the coordinates of each UAV node through free network adjustment calculation and establishes a high-precision spatial reference.

4. The method for autonomous operation of UAV cluster based on UWB technology in a GNSS-denied environment as claimed in claim 3, characterized in that: In step S2.6, the least squares principle V T When PV=min, we get the normal equation of the error equation and solve it to get: In the formula, represents the correction vector of the parameter to be estimated, B is the coefficient matrix of the observation equation, P is the weight matrix of the parameter to be estimated, l is the observation value, and V is the correction vector; Obtain the UAV position coordinate correction number, add the UAV position coordinate correction number to the initial coordinate value, obtain the updated UAV coordinate, bring the corrected coordinate into the error equation, iterate the calculation results until the coordinate correction number is less than the set threshold, complete the UAV position coordinate solution, and thus establish a high-precision spatial reference.

5. The method for autonomous operation of UAV cluster based on UWB technology in GNSS-denied environment as claimed in claim 1, characterized in that: Autonomous operation stage: The specific steps to achieve collaborative positioning, flight control, communication and video stream monitoring of drone clusters are as follows: Use UWB modules to realize the collaborative positioning function of drones; Use the development board to realize the autonomous flight control function of the drone; Use the payload camera to realize the real-time monitoring function of drone video stream.

6. The method for autonomous operation of UAV cluster based on UWB technology in GNSS-denied environment as claimed in claim 5, characterized in that: The specific steps to realize the cooperative positioning function of drones using UWB modules are as follows: Unmanned aerial vehicle A to be located i The node broadcasts its own ranging information via UWB; It is known that the drone receives the UWB ranging information and transmits it to the A0 master node; After receiving the ranging information, the A0 master node uses the Kalman filter algorithm to solve A i Node coordinates; A0 master node obtains i The relative motion equations of the nodes are added as constraints to the Kalman filter motion equations.

7. The method for autonomous operation of UAV swarm based on UWB technology in a GNSS-denied environment according to claim 5, characterized in that: Use the development board to realize the autonomous flight control function of the drone; the specific steps are: Connect the drone to the NVIDIA development board with flight control software and the UWB module; According to the flight mission, input the flight control script into the development board, which includes the flight angular acceleration, flight speed and flight time; The flight control software reads the flight control commands line by line and controls the drone to execute the flight instructions.

8. The method for autonomous operation of UAV swarm based on UWB technology in a GNSS-denied environment according to claim 5, characterized in that: The method of video stream monitoring of drone clusters is: Step 5.1: Connect the drone to the camera through the load interface, and connect the drone to the development board; Step 5.2: The drone reads the real-time video stream from the camera through the load interface and transmits it to the development board; Step 5.3: Use the video stream processing program in the development board to read the real-time video stream of the camera in image format; Step 5.4: The processing program preprocesses the video stream image and encodes the image; Step 5.5: The processing program utilizes the UWB device communication function to transmit the encoded video stream to the RTSP server in real time using the RTSP protocol; Step 5.6: The host computer uses the RTSP protocol to pull the video stream from the RTSP server in real time, decodes it in H264, and plays it in real time.

9. The method for autonomous operation of UAV cluster based on UWB technology in GNSS-denied environment as claimed in claim 6, characterized in that: Use the extended Kalman filter to estimate the state X k The specific estimation method is as follows: Assume the state of the drone at the kth moment is X k , and its filtering value is According to the filter value of the previous moment Calculate predicted values for: In the formula, Φ k,k-1 Represents the state transfer matrix from time k-1 to time k, according to the filter variance matrix P at time k-1 k-1 And the variance matrix Q of the system noise at time k k , calculate the prediction error variance matrix P k,k-1 for Calculate the gain matrix K k for In the formula, B k Represents the coefficient matrix of the observation equation at time k, R k represents the measurement noise at time k; Compute filter estimates and variance matrices P k =[I-K k B k ]P k,k-1 Where, L k represents the observed value at time k, P k represents the filter variance matrix at time k, and I is the unit matrix; finally, the result of the coordinate to be estimated is obtained.

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