UWB-based heterogeneous unmanned aerial vehicle cluster outdoor cooperative positioning method and multi-unmanned aerial vehicle system

Abstract

A UWB-based heterogeneous unmanned aerial vehicle cluster outdoor cooperative positioning method comprises the following steps: grouping an unmanned aerial vehicle cluster, taking a part of unmanned aerial vehicles as master unmanned aerial vehicles, and taking the other unmanned aerial vehicles as slave unmanned aerial vehicles; establishing a flight inertial coordinate system by using one of the main unmanned aerial vehicles, and determining the main positions of the other main unmanned aerial vehicles in the flight inertial coordinate system by using a first positioning method; determining the relative position of the slave unmanned aerial vehicle relative to the master unmanned aerial vehicle in the flight inertial coordinate system by using a second positioning method; inertial motion data of the slave unmanned aerial vehicle is obtained, and a slave position of the slave unmanned aerial vehicle is generated through an improved extended Kalman filtering model based on the relative position and the inertial motion data. According to the UWB-based heterogeneous unmanned aerial vehicle cluster outdoor cooperative positioning method and the multi-unmanned aerial vehicle system provided by the invention, the positioning precision is higher, and the cost is lower.

Description

Technical Field

[0001] This invention relates to the field of multi-UAV collaboration technology, specifically a UWB-based heterogeneous UAV swarm outdoor collaborative positioning method and multi-UAV system. Background Technology

[0002] With the development of drone technology, multi-drone collaborative operations are increasingly being used in fields such as agricultural plant protection, logistics transportation, search and rescue, and aerial performances. The key to achieving efficient collaboration lies in each drone obtaining its own precise position information in the global coordinate system.

[0003] Currently, most drones operating collaboratively use civilian satellites for positioning. However, the positioning accuracy of civilian satellites is insufficient. To accurately locate each drone, ground base stations are necessary, but these are expensive and costly, and even in medium to large-scale drone swarms, positioning accuracy is difficult to guarantee. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a UWB-based outdoor cooperative positioning method for heterogeneous UAV swarms and a multi-UAV system, which offers higher positioning accuracy and lower cost.

[0005] To achieve the above objectives, the specific solution adopted by this invention is as follows: an outdoor cooperative positioning method for heterogeneous UAV swarms based on UWB, comprising: The drone swarm is grouped into master drones and slave drones. Establish a flight inertial coordinate system using one of the master UAVs, and use the first positioning method to determine the principal positions of the other master UAVs in the flight inertial coordinate system; The second positioning method is used to determine the relative position of the UAV with respect to the master UAV in the flight inertial coordinate system; Acquire inertial motion data from the UAV, and generate the UAV's slave position based on the relative position and inertial motion data using an improved extended Kalman filter model.

[0006] As a further optimization of the above-mentioned UWB-based heterogeneous UAV swarm outdoor cooperative positioning method: the method of establishing a flight inertial coordinate system using one of the master UAVs and determining the main positions of the other master UAVs in the flight inertial coordinate system using the first positioning method includes: Based on a preset optimization objective function, the position of the main UAV is initialized using a genetic algorithm; Select one of the main UAVs to establish a flight inertial coordinate system; The first positioning method is used to determine the primary position of other master UAVs in the flight inertial coordinate system, wherein the first positioning method is the RTK-GPS positioning method.

[0007] As a further optimization of the above-mentioned UWB-based heterogeneous UAV swarm outdoor cooperative positioning method, the optimization objective function is: ; in, The location set of the main drone. The number of sampling points in the unmanned swarm operation task area. The geometrical precision factor is GDOP. For the first The geometric precision factor of each sampling point, and , Let be the observation matrix, and have ; in, This is the position of the i-th master drone. It is the geometric distance between the i-th master drone and the point to be located.

[0008] As a further optimization of the above-mentioned UWB-based heterogeneous UAV swarm outdoor cooperative positioning method: the method for determining the master position of other master UAVs in the flight inertial coordinate system using the first positioning method includes: The latitude, longitude, and altitude coordinates of all main UAVs were determined using the RTX-GPS method; The latitude, longitude, and altitude coordinates of the main UAV are converted to geographic coordinates in the geocentric coordinate system. The conversion method is as follows: ; in, These represent latitude, longitude, and altitude in latitude, longitude, and altitude coordinates, respectively. For the first eccentricity, Let be the radius of curvature of the circle, and , This is the length of the semi-major axis of the Earth's ellipsoid; The main position of the UAV is obtained by mapping the geographic coordinates to the flight inertial coordinate system. The mapping method is as follows: ; in, The geographic coordinates of the main UAV used to establish the flight inertial coordinate system. For the geographic coordinates of other main drones, Let be a rotation matrix, and we have: ; in, The latitude, longitude, and altitude coordinates are the origin of the flight inertial coordinate system.

[0009] As a further optimization of the above-mentioned UWB-based heterogeneous UAV swarm outdoor cooperative positioning method: four master UAVs are set up, the second positioning method is the UWB positioning method, and the method for determining the relative position of the slave UAVs with respect to the master UAVs in the flight inertial coordinate system using the second positioning method includes: ; in, The main position of the main drone. This represents the relative distance between the drone and the host location.

[0010] As a further optimization of the above-mentioned UWB-based heterogeneous UAV swarm outdoor cooperative positioning method: four master UAVs are set up, and the state space of the Kalman filter model is: ; ; ; in, For state variables, Here is the state transition matrix. For the measurement equation, and ,in This represents the real-time distance between the slave drone and the i-th master drone, generated based on inertial motion data. The relative distance between the drone and the i-th master drone.

[0011] As a further optimization of the above-mentioned UWB-based heterogeneous UAV swarm outdoor cooperative localization method, the method for generating the slave position of the UAV based on relative position and inertial motion data through an improved extended Kalman filter model includes: A discrete model is constructed based on the state space: ; in, The noise at time k-1 The measurement noise at time k; An optimization model is constructed based on a discrete model. The optimization model includes state prediction equation, measurement prediction equation, first-order linearized measurement equation, one-step prediction equation of covariance matrix, filter gain equation, state update equation, and covariance update equation. The state prediction equation is: ; The measurement prediction equation is: ; The first-order linearized measurement equation is: ; The one-step prediction equation for the covariance matrix is ​​as follows: ; The filter gain equation is: ; The state update equation is: ; The covariance update equation is: ; The real-time position of the UAV in the flight inertial coordinate system is generated based on the optimization equation. The slave position of the UAV is obtained by performing an inverse coordinate transformation on the real-time position.

[0012] As a further optimization of the above-mentioned UWB-based heterogeneous UAV swarm outdoor cooperative positioning method, the method for performing coordinate inverse transformation on the real-time position is as follows: ; in, To obtain the drone's real-time location, Let be a rotation matrix, and we have: .

[0013] A multi-UAV system includes multiple master UAVs and multiple slave UAVs, wherein the master UAVs and the slave UAVs perform cooperative positioning based on the above-mentioned UWB-based heterogeneous UAV swarm outdoor cooperative positioning method.

[0014] As a further optimization of the aforementioned multi-UAV system: the master UAV includes an RTX-GPS module, a UWB base station, and a first inertial measurement unit, and the slave UAV includes a UWB tag and a second inertial measurement unit.

[0015] Beneficial effects: This invention provides a unified positioning reference for the entire UAV swarm by establishing a flight inertial coordinate system, facilitating coordinated control. It eliminates the need for ground anchor points, using the main UAV as a mobile reference, and adapts to various outdoor operating environments. Furthermore, a multi-source information fusion positioning method is designed to ensure system reliability even when some sensors fail. In addition, combining the precise ranging of UWB and the high-frequency updates of the IMU, decimeter-level positioning accuracy is achieved through tightly coupled data fusion. Finally, by using mixed formations of different UAVs, only a few main UAVs are needed to provide a positioning reference for the entire swarm, significantly reducing system costs. Attached Figure Description

[0016] Figure 1 This is a flowchart of the method of the present invention; Figure 2 This is a schematic diagram showing the results of establishing the flight inertial coordinate system; Figure 3 This is a schematic diagram of the coordinate transformation principle. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] like Figures 1 to 3 As shown, the present invention first provides an outdoor cooperative positioning method for heterogeneous UAV swarms based on UWB, including S1 to S4.

[0019] S1. Divide the drone swarm into groups, designating some drones as master drones and the rest as slave drones. The master drones primarily serve as positioning references, while the slave drones use the master drones for positioning. The number of master drones can be determined based on the total number of drones in the swarm, and will not be elaborated further here.

[0020] S2. Establish a flight inertial coordinate system using one of the master UAVs, and determine the principal positions of the other master UAVs in the flight inertial coordinate system using the first positioning method. More specifically, the method of establishing a flight inertial coordinate system using one of the master UAVs and determining the principal positions of the other master UAVs in the flight inertial coordinate system using the first positioning method includes S21 to S23.

[0021] S21. Based on a preset optimization objective function, the position of the main UAV is initialized using a genetic algorithm. More specifically, the optimization objective function is: ; in, The location set of the main drone. The number of sampling points in the unmanned swarm operation task area. The geometrical precision factor is GDOP. For the first The geometric precision factor of each sampling point, and , Let be the observation matrix, and have ; in, This is the position of the i-th master drone. It is the geometric distance between the i-th master drone and the point to be located.

[0022] The objective function of this optimization is to minimize the average geometric precision factor. Based on this, the population of the genetic algorithm is initialized, and multiple sets of master UAV position configuration schemes are randomly generated. Then, iterative optimization is performed through genetic operations such as selection, crossover, and mutation to finally obtain the optimal position scheme for all master UAVs. The specific process of the genetic algorithm is existing technology and will not be described in detail here.

[0023] S22. Select one of the master drones to establish a flight inertial coordinate system. This can be done by randomly selecting one master drone, or by selecting a drone closer to the center or edge based on the positions of all master drones. The distance between any two master drones can be used to determine whether a drone is closer to the center or edge, thus determining the master drone used to establish the flight inertial coordinate system. After establishing the flight inertial coordinate system, the position of the master drone used to establish it becomes the origin of the flight inertial coordinate system.

[0024] S23. Use a first positioning method to determine the principal position of other master UAVs in the flight inertial coordinate system, wherein the first positioning method is the RTK-GPS positioning method. More specifically, the method for determining the principal position of other master UAVs in the flight inertial coordinate system using the first positioning method includes S231 to S233.

[0025] S231. Use the RTX-GPS method to determine the latitude, longitude, and altitude coordinates of all main UAVs.

[0026] S232. Convert the latitude, longitude, and altitude coordinates of the main UAV to geographic coordinates in the geocentric coordinate system. The conversion method is as follows: ; in, These represent latitude, longitude, and altitude in latitude, longitude, and altitude coordinates, respectively. For the first eccentricity, Let be the radius of curvature of the circle, and , This is the length of the semi-major axis of the Earth's ellipsoid.

[0027] S233. Map the geographic coordinates to the flight inertial coordinate system to obtain the main position of the UAV. The mapping method is as follows: ; in, The geographic coordinates of the main UAV used to establish the flight inertial coordinate system. For the geographic coordinates of other main drones, Let be a rotation matrix, and we have: ; in, The latitude, longitude, and altitude coordinates are the origin of the flight inertial coordinate system.

[0028] S3. Determine the relative position of the slave drone relative to the master drone in the flight inertial coordinate system using the second positioning method. In one embodiment of the present invention, four master drones are provided, and the second positioning method is the UWB positioning method. The method for determining the relative position of the slave drone relative to the master drone in the flight inertial coordinate system using the second positioning method includes: ; in, The main position of the main drone. Let represent the relative distance between the drone and the master drone. After obtaining the master drone's position and the relative distance between the drone and the master drone, solving this system of equations will yield the drone's relative position.

[0029] S4. Acquire the inertial motion data of the slave drone, and generate the slave position of the slave drone based on the relative position and inertial motion data using an improved extended Kalman filter model. Taking the embodiment where the master drone has four units as an example, the state space of the Kalman filter model can be described as follows: ; ; ; in, For state variables, Here is the state transition matrix. For the measurement equation, and ,in This represents the real-time distance between the slave drone and the i-th master drone, generated based on inertial motion data. The relative distance between the drone and the i-th master drone.

[0030] Based on the aforementioned state space, methods for generating the slave position from the UAV using an improved extended Kalman filter model based on relative position and inertial motion data include S41 to S44.

[0031] S41. Construct a discrete model based on the state space: ; in, The noise at time k-1 Let be the measurement noise at time k.

[0032] S42. Construct an optimization model based on the discrete model. The optimization model includes the state prediction equation, the measurement prediction equation, the first-order linearized measurement equation, the one-step prediction equation of the covariance matrix, the filter gain equation, the state update equation, and the covariance update equation. The state prediction equation is ; The measurement prediction equation is ; The first-order linearized measurement equation is ; The one-step prediction equation for the covariance matrix is ​​as follows: ; The filter gain equation is ; The state update equation is ; The covariance update equation is .

[0033] S43. Generate the real-time position of the UAV in the flight inertial coordinate system based on the optimization equation.

[0034] S44. Perform an inverse coordinate transformation on the real-time position to obtain the slave position of the UAV. More specifically, the method for performing an inverse coordinate transformation on the real-time position is as follows: ; in, To obtain the drone's real-time location, Let be a rotation matrix, and we have: .

[0035] This invention further provides a multi-UAV system, including multiple master UAVs and multiple slave UAVs. The master UAVs and the slave UAVs perform cooperative positioning based on the aforementioned UWB-based heterogeneous UAV swarm outdoor cooperative positioning method. To ensure successful positioning based on the aforementioned positioning method, the master UAV includes an RTX-GPS module, a UWB base station, and a first inertial measurement unit, while the slave UAV includes a UWB tag and a second inertial measurement unit.

[0036] This invention provides a unified positioning reference for the entire UAV swarm by establishing a flight inertial coordinate system, facilitating coordinated control without the need for ground anchor points. Using the main UAV as a mobile reference, it can adapt to various outdoor operating environments. Furthermore, a multi-source information fusion positioning method is designed to ensure system reliability even when some sensors fail. In addition, combining the precise ranging of UWB and the high-frequency updates of the IMU, decimeter-level positioning accuracy is achieved through tightly coupled data fusion. Finally, by using mixed formations of different UAVs, only a few main UAVs are needed to provide a positioning reference for the entire swarm, significantly reducing system costs.

[0037] It should also be noted that RTK-GPS modules, UWB base stations, UWB tags, and inertial measurement units are all conventional technologies in this field and will not be described in detail here.

[0038] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A UWB-based heterogeneous unmanned aerial vehicle (UAV) swarm outdoor cooperative positioning method, characterized in that, include: The drone swarm is grouped into master drones and slave drones. Establish a flight inertial coordinate system using one of the master UAVs, and use the first positioning method to determine the principal positions of the other master UAVs in the flight inertial coordinate system; The second positioning method is used to determine the relative position of the UAV with respect to the master UAV in the flight inertial coordinate system; Acquire inertial motion data from the UAV, and generate the UAV's slave position based on the relative position and inertial motion data using an improved extended Kalman filter model.

2. The UWB-based heterogeneous UAV swarm outdoor cooperative positioning method as described in claim 1, characterized in that, The method of establishing a flight inertial coordinate system using one of the master UAVs and determining the principal positions of the other master UAVs in the flight inertial coordinate system using a first positioning method includes: Based on a preset optimization objective function, the position of the main UAV is initialized using a genetic algorithm; Select one of the main UAVs to establish a flight inertial coordinate system; The first positioning method is used to determine the primary position of other master UAVs in the flight inertial coordinate system, wherein the first positioning method is the RTK-GPS positioning method.

3. The UWB-based heterogeneous UAV swarm outdoor cooperative positioning method as described in claim 2, characterized in that, The optimization objective function is: ； in, The location set of the main drone. The number of sampling points in the unmanned swarm operation task area. The geometrical precision factor is GDOP. For the first The geometric precision factor of each sampling point, and , Let be the observation matrix, and have ； in, This is the position of the i-th master drone. It is the geometric distance between the i-th master drone and the point to be located.

4. The UWB-based heterogeneous UAV swarm outdoor cooperative positioning method as described in claim 2, characterized in that, The method for determining the principal position of other master UAVs in the flight inertial coordinate system using the first positioning method includes: The latitude, longitude, and altitude coordinates of all main UAVs were determined using the RTX-GPS method; The latitude, longitude, and altitude coordinates of the main UAV are converted to geographic coordinates in the geocentric coordinate system. The conversion method is as follows: ； in, These represent latitude, longitude, and altitude in latitude, longitude, and altitude coordinates, respectively. For the first eccentricity, Let be the radius of curvature of the circle, and , This is the length of the semi-major axis of the Earth's ellipsoid; The main position of the UAV is obtained by mapping the geographic coordinates to the flight inertial coordinate system. The mapping method is as follows: ； in, The geographic coordinates of the main UAV used to establish the flight inertial coordinate system. For the geographic coordinates of other main drones, Let be a rotation matrix, and we have: ； in, The latitude, longitude, and altitude coordinates are the origin of the flight inertial coordinate system.

5. The UWB-based heterogeneous UAV swarm outdoor cooperative positioning method as described in claim 1, characterized in that, The main UAV is configured with four units. The second positioning method is the UWB positioning method. The method for determining the relative position of the slave UAVs with respect to the main UAVs in the flight inertial coordinate system using the second positioning method includes: ； in, The main position of the main drone. This represents the relative distance between the drone and the host location.

6. The UWB-based heterogeneous UAV swarm outdoor cooperative positioning method as described in claim 1, characterized in that, The main UAV is configured with four units, and the state space of the Kalman filter model is: ； ； ； in, For state variables, Here is the state transition matrix. For the measurement equation, and ,in This represents the real-time distance between the slave drone and the i-th master drone, generated based on inertial motion data. The relative distance between the drone and the i-th master drone.

7. The UWB-based heterogeneous UAV swarm outdoor cooperative positioning method as described in claim 6, characterized in that, The method for generating the slave position from the UAV based on relative position and inertial motion data using an improved extended Kalman filter model includes: A discrete model is constructed based on the state space: ； in, The noise at time k-1 The measurement noise at time k; An optimization model is constructed based on a discrete model. The optimization model includes state prediction equation, measurement prediction equation, first-order linearized measurement equation, one-step prediction equation of covariance matrix, filter gain equation, state update equation, and covariance update equation. The state prediction equation is: ; The measurement prediction equation is: ; The first-order linearized measurement equation is: ; The one-step prediction equation for the covariance matrix is ​​as follows: ; The filter gain equation is: ; The state update equation is: ; The covariance update equation is: ; The real-time position of the UAV in the flight inertial coordinate system is generated based on the optimization equation. The slave position of the UAV is obtained by performing an inverse coordinate transformation on the real-time position.

8. The UWB-based heterogeneous UAV swarm outdoor cooperative positioning method as described in claim 7, characterized in that, The method for performing inverse coordinate transformation on the real-time position is as follows: ； in, To obtain the drone's real-time location, Let be a rotation matrix, and we have: 。 9. A multi-UAV system, characterized in that, It includes multiple master drones and multiple slave drones, wherein the master drones and the slave drones perform cooperative positioning based on a UWB-based heterogeneous drone swarm outdoor cooperative positioning method as described in any one of claims 1-8.

10. The multi-UAV system as described in claim 9, characterized in that, The master drone includes an RTX-GPS module, a UWB base station, and a first inertial measurement unit, while the slave drone includes a UWB tag and a second inertial measurement unit.

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