Unmanned aerial vehicle automatic path-finding method based on satellite-based enhancement

Abstract

The invention relates to the technical field of satellite positioning, in particular to an unmanned aerial vehicle automatic path-finding method based on satellite-based enhancement, and the method comprises the steps: S1, setting a flight target of an unmanned aerial vehicle; s2, acquiring a real-time position of a satellite; s3, judging whether the unmanned aerial vehicle arrives at the flight target based on the real-time position, and if so, ending; if the unmanned aerial vehicle does not arrive, front obstacle information of the unmanned aerial vehicle is extracted; s4, on the basis of the front obstacle information and the flying target, an A * algorithm is adopted to search a movable direction with the minimum current cost, and the unmanned aerial vehicle is controlled to move towards the movable direction with the minimum current cost; and S5, circulating the steps S2-S4 until the unmanned aerial vehicle reaches the flight target. According to the invention, the environment perception, path planning and obstacle avoidance capabilities of the unmanned aerial vehicle can be improved, and the method is suitable for efficient and safe autonomous flight in a complex scene.

Description

Technical Field

[0001] The present invention relates to the technical field of satellite positioning, and particularly to an automatic path finding method for an unmanned aerial vehicle (UAV) based on satellite-based augmentation. Background Art

[0002] Existing path finding technologies rely on networks. For example, networks are required to obtain route data and perform path planning. Therefore, in a network-free environment, the positioning of UAVs faces the problem of decreased satellite positioning accuracy. Since real-time orbit correction data cannot be obtained, the positioning error may reach the meter level. In addition, the lack of network support causes UAVs to be unable to update route data in real time, seriously affecting their autonomous path finding and navigation capabilities. Moreover, many existing satellite-based augmentation technologies only use a single HAS or a single PPP-B2b, and do not adopt combined technologies, which have certain limitations. Therefore, the present invention proposes an efficient UAV positioning and autonomous path finding method. Summary of the Invention

[0003] The purpose of the present invention is to provide an automatic path finding method for an unmanned aerial vehicle based on satellite-based augmentation, which combines YOLOv, depth cameras, the A* algorithm, and satellite-based augmentation positioning to perform automatic path finding for UAVs, improve the environmental perception, path planning, and obstacle avoidance capabilities of UAVs, and is suitable for efficient and safe autonomous flight in complex scenarios.

[0004] To achieve the above purpose, the present invention provides the following solution:

[0005] An automatic path finding method for an unmanned aerial vehicle based on satellite-based augmentation, comprising:

[0006] S1. Set the flight target of the UAV;

[0007] S2. Obtain the real-time position of the satellite;

[0008] S3. Based on the real-time position, determine whether the UAV has reached the flight target. If it has reached, end; if not, extract the information of the obstacles in front of the UAV;

[0009] S4. Based on the information of the obstacles in front and the flight target, use the A* algorithm to find the movable direction with the minimum current cost, and control the UAV to move in the movable direction with the minimum current cost;

[0010] S5. Loop S2 - S4 until the UAV reaches the flight target.

[0011] Optionally, in S1, the flight target includes: the end position, speed limit, and altitude limit of the UAV.

[0012] Optionally, in S2, obtaining the real-time position of the satellite includes:

[0013] Receive the broadcast ephemeris, observed value data and real-time position correction issued by the satellite;

[0014] Merge the real-time position correction with the broadcast ephemeris to generate a real-time precise ephemeris and orbit;

[0015] Obtain the real-time position of the satellite through the real-time precise ephemeris and orbit.

[0016] Optionally, merging the real-time position correction with the broadcast ephemeris to generate a real-time precise ephemeris and orbit includes:

[0017] Calculate the correction of the satellite orbit in the body-fixed coordinate system at the current moment, and transfer the correction of the satellite orbit to the Earth-centered Earth-fixed coordinate system;

[0018] Correct the correction of the satellite orbit in the Earth-centered Earth-fixed coordinate system to the satellite orbit calculated by the broadcast ephemeris to obtain a real-time precise orbit;

[0019] Calculate the correction of the satellite clock error at the current moment, and correct the correction of the satellite clock error to the satellite clock calculated by the broadcast ephemeris to obtain a real-time precise clock error.

[0020] Optionally, calculating the correction of the satellite orbit in the body-fixed coordinate system at the current moment and transferring the correction of the satellite orbit to the Earth-centered Earth-fixed coordinate system includes:

[0021]

[0022] δX = [e along e cross e radial δO;

[0023]

[0024] e radial = e along × e cross ;

[0025] Among them, δO represents the correction of the satellite orbit in the body-fixed coordinate system at the current moment, t represents the current moment, t0 represents the reference moment, δO radial 、δO along 、δO cross respectively represent the radial, normal, and tangential corrections of the satellite orbit in the body-fixed coordinate system at the reference moment, respectively represent the radial, normal, and tangential velocity corrections of the satellite orbit in the body-fixed coordinate system at the reference moment, δX represents the correction of the satellite orbit in the Earth-centered Earth-fixed coordinate system, r represents the satellite position calculated by the broadcast ephemeris, represents the satellite velocity calculated by the broadcast ephemeris, e along 、ecross , e radial respectively represent the radial, normal, and tangential unit vectors of the satellite.

[0026] Optionally, correcting the corrections of the satellite orbit in the Earth-Centered Earth-Fixed (ECEF) coordinate system to the satellite orbit calculated from the broadcast ephemeris to obtain the real-time precise orbit includes:

[0027] X orbit = X broadcast - δX;

[0028] where X orbit represents the precise orbit of the satellite, X broadcast represents the satellite orbit calculated from the broadcast ephemeris, and δX represents the correction of the satellite orbit in the ECEF coordinate system.

[0029] Optionally, calculating the satellite clock correction at the current moment, and correcting the satellite clock calculated from the broadcast ephemeris with the satellite clock correction to obtain the real-time precise clock includes:

[0030] δC = C0 + C1(t - t0) + C2(t - t0) 2 ;

[0031]

[0032] where δC represents the satellite clock correction at the current moment, t represents the current moment, t0 represents the reference moment, C0, C1, and C2 represent the polynomial coefficients of the clock correction, t satellite represents the precise clock of the satellite, t broadcast represents the satellite clock calculated from the broadcast ephemeris, and c represents the speed of light in vacuum.

[0033] Optionally, obtaining the real-time position of the satellite through the real-time precise ephemeris and orbit includes:

[0034]

[0035] where P represents the pseudorange observation value, L represents the carrier phase observation value, p represents the geometric distance between the satellite and the receiver, c represents the speed of light in vacuum, dt r represents the receiver clock error, dt s represents the satellite clock error, T represents the tropospheric delay error, b r represents the code pseudorange hardware delay between the receiver antenna and the signal, b s represents the code pseudorange hardware delay between the signal transmitter at the satellite end and the satellite antenna, e represents the pseudorange measurement error, λ represents the carrier wavelength, N represents the integer ambiguity, B r represents the phase hardware delay of the receiver, B srepresents the phase hardware delay at the satellite end, and ε represents the carrier phase measurement error.

[0036] Optionally, in step S3, extracting the front obstacle information of the UAV includes:

[0037] Mount a depth camera on the UAV to obtain RGB images and depth images;

[0038] Process the RGB images and depth images based on the pre-trained YOLOv framework to obtain the category, confidence, and bounding box of the obstacles;

[0039] Based on the real-time position of the satellite, extract the depth value within each obstacle bounding box, obtain the obstacle coordinates, and encode and save them using an octree.

[0040] Optionally, in step S4, based on the front obstacle information and the flight target, using the A* algorithm to find the current minimum-cost movable direction includes:

[0041] Traverse the saved octree containing obstacle coordinates and mark the free nodes;

[0042] Use the free nodes as the passable area of the A* algorithm, calculate the cost from all movable nodes in the passable area to the end point using the Euclidean distance, and select the passable path with the minimum Euclidean distance as the current minimum-cost movable direction.

[0043] The beneficial effects of the present invention are:

[0044] The present invention combines YOLOv, depth camera, A* algorithm, and satellite-based augmentation positioning for automatic pathfinding of UAVs, and can give full play to the advantages of each technology. YOLOv provides real-time target detection and accurately identifies obstacles; the depth camera supplements depth information to achieve three-dimensional environmental perception; the A* algorithm plans an efficient path through heuristic search, taking into account both real-time performance and optimality; satellite-based augmentation positioning provides global position information to ensure precise positioning and navigation of the UAV in the absence of a network. This multi-technology fusion solution significantly improves the environmental perception, path planning, and obstacle avoidance capabilities of the UAV, and is suitable for efficient and safe autonomous flight in complex scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0046] Figure 1Flow chart of real-time positioning of the unmanned aerial vehicle according to an embodiment of the present invention;

[0047] Figure 2 Flow chart of a method for automatic path finding of an unmanned aerial vehicle based on satellite-based augmentation according to an embodiment of the present invention. Detailed implementation manners

[0048] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0049] To make the above objects, features, and advantages of the present invention more obvious and understandable, the present invention will be further described in detail below in conjunction with the accompanying drawings and specific implementation manners.

[0050] The satellite-based augmentation system (SBAS) is a technology that enhances the performance of the Global Navigation Satellite System (GNSS) through satellite signals, aiming to improve positioning accuracy, integrity, availability, and continuity. SBAS uses ground base stations to monitor GNSS signals, calculate errors, and generate correction information, which is then broadcast to users through geostationary satellites. The BeiDou Navigation Satellite System (BDS) and the Galileo satellite navigation system (GALILEO) have respectively achieved satellite-based augmentation functions through PPP-B2b services and HAS services, providing high-precision and highly reliable positioning services for users.

[0051] Using YOLOv and a depth camera for obstacle extraction is an efficient technology that combines object detection and depth perception. By synchronously acquiring RGB images and depth information through the depth camera, YOLOv can detect obstacles in the image in real time and output the category and bounding box. Combining the depth values to calculate the three-dimensional coordinates of the obstacles can accurately extract the position and size information of the obstacles. This technology features strong real-time performance and high accuracy and is widely used in fields such as robot navigation, autonomous driving, and intelligent monitoring. Although it faces challenges such as depth noise, occlusion, and computing resources, its robustness and practicality can be effectively improved through methods such as depth filtering, multi-sensor fusion, and model optimization.

[0052] The A* algorithm is an efficient heuristic search algorithm widely used in real-time automatic pathfinding, such as in game development, robot navigation, and autonomous driving. It dynamically selects the optimal path during the search process by combining the actual cost (the cost of the path from the starting point to the current node) and the heuristic cost (the estimated cost from the current node to the end point). The core advantage of the A* algorithm lies in its ability to balance search efficiency and path quality, using heuristic functions (such as Manhattan distance or Euclidean distance) to quickly narrow the search scope, thereby finding an optimal or near-optimal path in real-time in complex environments. In real-time applications, the A* algorithm can adapt to dynamic environmental changes through incremental search, dynamic map updates, and optimized heuristic functions to ensure the real-time and accuracy of path planning, making it an important tool in the field of automatic pathfinding.

[0053] Based on the above content, this embodiment provides an automatic pathfinding method for an unmanned aerial vehicle (UAV) based on satellite-based augmentation, including:

[0054] S1. Set the flight target of the UAV;

[0055] S2. Obtain the real-time position of the satellite;

[0056] S3. Based on the real-time position, determine whether the UAV has reached the flight target. If it has reached, end; if not, extract the information of the obstacles in front of the UAV;

[0057] S4. Based on the information of the obstacles in front and the flight target, use the A* algorithm to find the movable direction with the minimum current cost, and control the UAV to move in the movable direction with the minimum current cost;

[0058] S5. Loop S2 - S4 until the UAV reaches the flight target.

[0059] Specifically, this embodiment combines YOLOv, depth camera, A* algorithm, and satellite-based augmentation positioning for automatic pathfinding of UAVs, which can give full play to the advantages of each technology. YOLOv provides real-time target detection and accurately identifies obstacles; the depth camera supplements depth information to achieve three-dimensional environmental perception; the A* algorithm plans an efficient path through heuristic search, taking into account both real-time and optimality; satellite-based augmentation positioning provides global position information to ensure precise positioning and navigation of the UAV in the absence of a network. This multi-technology fusion scheme significantly improves the environmental perception, path planning, and obstacle avoidance capabilities of the UAV, and is suitable for efficient and safe autonomous flight in complex scenarios.

[0060] Further, in S1, the flight target includes: the end position, speed limit, and altitude limit of the UAV.

[0061] Further, in S2, obtaining the real-time position of the satellite includes:

[0062] Receive the broadcast ephemeris, observation data, and real-time position correction data broadcast by the satellite;

[0063] Merge the real-time position correction data with the broadcast ephemeris to generate a real-time precise ephemeris and orbit;

[0064] Obtain the real-time position of the satellite through the real-time precise ephemeris and orbit.

[0065] Specifically, merging the real-time position correction data with the broadcast ephemeris to generate a real-time precise ephemeris and orbit includes:

[0066] Calculate the correction of the satellite orbit in the body-fixed coordinate system at the current moment, and transfer the correction of the satellite orbit to the Earth-centered Earth-fixed coordinate system:

[0067]

[0068] δX = [e along e cross e radial δO;

[0069]

[0070] e radial = e along × e cross ;

[0071] Wherein, δO represents the correction of the satellite orbit in the body-fixed coordinate system at the current moment, t represents the current moment, t0 represents the reference moment, δO radial 、δO along 、δO cross respectively represent the radial, normal, and tangential corrections of the satellite orbit in the body-fixed coordinate system at the reference moment, respectively represent the radial, normal, and tangential velocity corrections of the satellite orbit in the body-fixed coordinate system at the reference moment, δX represents the correction of the satellite orbit in the Earth-centered Earth-fixed coordinate system, r represents the satellite position calculated by the broadcast ephemeris, represents the satellite velocity calculated by the broadcast ephemeris, e along 、e cross 、e radial respectively represent the radial, normal, and tangential unit vectors of the satellite;

[0072] Correct the correction of the satellite orbit in the Earth-centered Earth-fixed coordinate system to the satellite orbit calculated by the broadcast ephemeris to obtain a real-time precise orbit:

[0073] X orbit = X broadcast - δX;

[0074] Wherein, Xorbit Represents the precise orbit of the satellite, X broadcast Represents the satellite orbit calculated from the broadcast ephemeris, and δX represents the correction of the satellite orbit in the Earth-centered Earth-fixed coordinate system;

[0075] Calculate the satellite clock error correction at the current moment, correct the satellite clock error calculated from the broadcast ephemeris with the satellite clock error correction, and obtain the real-time precise clock error:

[0076] δC = C0 + C1(t - t0) + C2(t - t0) 2 ;

[0077]

[0078] where δC represents the satellite clock error correction at the current moment, t represents the current moment, t0 represents the reference moment, C0, C1, and C2 represent the polynomial coefficients of the clock error correction, t satellite represents the precise clock of the satellite, t broadcast represents the satellite clock calculated from the broadcast ephemeris, and c represents the speed of light in vacuum.

[0079] Specifically, obtaining the real-time position of the satellite through the real-time precise ephemeris and orbit includes:

[0080]

[0081] where P represents the pseudorange observation value, L represents the carrier phase observation value, p represents the geometric distance between the satellite and the receiver, c represents the speed of light in vacuum, dt r represents the receiver clock error, dt s represents the satellite clock error, T represents the tropospheric delay error, b r represents the code pseudorange hardware delay between the receiver antenna and the signal, b s represents the code pseudorange hardware delay between the satellite signal transmitter and the satellite antenna, e represents the pseudorange measurement error, λ represents the carrier wavelength, N represents the integer ambiguity, B r represents the phase hardware delay of the receiver, B s represents the phase hardware delay of the satellite end, and ε represents the carrier phase measurement error.

[0082] Furthermore, in step S3, extracting the information of the obstacles in front of the UAV includes:

[0083] Install a depth camera on the UAV to obtain RGB images and depth images;

[0084] Process the RGB images and depth images based on the pre-trained YOLOv framework to obtain the category, confidence, and bounding box of the obstacles;

[0085] Based on the real-time position of the satellite, extract the depth values within each obstacle bounding box, obtain the obstacle coordinates, and encode and save them using an octree.

[0086] Further, in step S4, based on the information of the front obstacle and the flight target, using the A* algorithm to find the direction with the minimum current cost includes:

[0087] Traverse the saved octree containing obstacle coordinates and mark the free nodes;

[0088] Take the free nodes as the passable areas of the A* algorithm, calculate the cost from all movable nodes in the passable areas to the end point using the Euclidean distance, and select the passable path with the minimum Euclidean distance as the direction with the minimum current cost.

[0089] The following combines Figure 1 、 Figure 2 to elaborate in detail on an automatic pathfinding method for an unmanned aerial vehicle based on satellite-based augmentation proposed in this embodiment, which specifically includes the following steps:

[0090] Step 1: Set the end position, speed limit, and altitude limit of the unmanned aerial vehicle.

[0091] Step 2: Receive the broadcast ephemeris, observed value data, and real-time position correction numbers broadcast by the satellite.

[0092] Step 3: Combine the correction numbers with the broadcast ephemeris to generate real-time precise ephemeris and orbit.

[0093] Step 4: Obtain the real-time position of the satellite through the real-time precise ephemeris and orbit.

[0094] Step 5: Obtain the information of the front obstacle through the sensor.

[0095] Step 6: Identify the direction with the minimum current cost through the A* algorithm.

[0096] Step 7: Loop through steps 2 to 6 until the unmanned aerial vehicle reaches the end position.

[0097] In step 3, combining the correction numbers with the broadcast ephemeris to generate real-time precise ephemeris and orbit is as follows:

[0098] (1) Calculate the correction numbers in the satellite-fixed coordinate system at the current moment, and its formula is:

[0099]

[0100] In the formula, t0 represents the reference moment, t represents the current moment, δO represents the correction number of the satellite orbit in the satellite-fixed coordinate system at the current moment, δO radial 、δO along 、δOcross respectively represent the radial, normal, and tangential corrections of the satellite orbit in the satellite-fixed coordinate system at the reference time, respectively represent the radial, normal, and tangential velocity corrections of the satellite orbit in the satellite-fixed coordinate system at the reference time.

[0101] (2) Transform the orbit corrections of the satellite from the satellite-fixed coordinate system to the Earth-centered Earth-fixed coordinate system. The formula is as follows:

[0102] δX = [e along e cross e radial δO;

[0103]

[0104] e radial = e along ×e cross ;

[0105] In the formula, δX represents the orbit correction of the satellite in the Earth-centered Earth-fixed coordinate system, r represents the satellite position calculated from the broadcast ephemeris, represents the satellite velocity calculated from the broadcast ephemeris, e along 、e cross 、e radial respectively represent the radial, normal, and tangential unit vectors of the satellite.

[0106] (3) Correct the orbit corrections of the satellite to the satellite orbit calculated from the broadcast ephemeris to obtain the real-time precise orbit. The formula is as follows:

[0107] X orbit = X broadcast -δX;

[0108] In the formula, X orbit represents the precise orbit of the satellite, and X broadcast represents the satellite orbit calculated from the broadcast ephemeris.

[0109] (4) Calculate the satellite clock correction at the current time. The formula is as follows:

[0110] δC = C0 + C1(t - t0) + C2(t - t0) 2 ;

[0111] In the formula, δC represents the satellite clock correction at the current time, t represents the current time, t0 represents the reference time, and C0, C1, and C2 represent the polynomial coefficients of the clock correction.

[0112] (5) Correct the satellite clock correction to the satellite clock calculated from the broadcast ephemeris to obtain the real-time precise clock. The formula is as follows:

[0113]

[0114] In the formula, t satellite represents the precise clock offset of the satellite, and t broadcast represents the satellite clock offset calculated from broadcast ephemeris, and c represents the speed of light in vacuum.

[0115] In step four, the real-time position of the satellite is obtained through real-time precise ephemeris and orbit, and the method is as follows:

[0116]

[0117] In the formula, P represents the pseudorange observation value, L represents the carrier phase observation value, p represents the geometric distance between the satellite and the receiver, c represents the speed of light in vacuum, dt r represents the receiver clock offset, dt s represents the satellite clock offset, T represents the tropospheric delay error, b r represents the code pseudorange hardware delay between the receiver antenna and the signal, b s represents the code pseudorange hardware delay between the signal transmitter at the satellite end and the satellite antenna, e represents the pseudorange measurement error, λ represents the carrier wavelength, N represents the integer ambiguity, B r represents the phase hardware delay of the receiver, B s represents the phase hardware delay at the satellite end, and ε represents the carrier phase measurement error.

[0118] In step five, a sensor is used to identify the obstacles ahead, and the identified data is saved using octree encoding. The method is as follows:

[0119] (1) Synchronously obtain the RGB image and the depth image from the depth camera.

[0120] (2) Extract the image through the YOLOv framework to obtain the category, confidence, and bounding box of the obstacle.

[0121] (3) For each detected obstacle, extract the depth value within its bounding box. The formula is as follows:

[0122]

[0123] In the formula, x and y are the real-time positions obtained in step four, u and v are the pixel coordinates in the image, and z is the depth value.

[0124] (4) Update the stored octree encoding according to the calculated obstacle coordinates.

[0125] In step six, the direction with the minimum current cost is identified through the A* algorithm. The steps are as follows:

[0126] [[ID=5

[0127] (2) Traverse the octree and mark all the idle nodes, and the idle nodes serve as the passable area for the A* algorithm.

[0128] (3) Use the Euclidean distance as the heuristic function to calculate the cost from all movable nodes to the end point. The formula is as follows:

[0129]

[0130] In the formula, h next represents the Euclidean distance of the passable path, x next , y next represents the coordinates of the passable path, x end , y end represents the end point coordinates.

[0131] (4) Select the passable path with the minimum Euclidean distance as the forward direction, and repeat (1) to (4) until the UAV reaches the end point position.

[0132] The embodiments described above are only descriptions of the preferred embodiments of the present invention, and do not limit the scope of the present invention. Without departing from the design spirit of the present invention, various deformations and improvements made by those of ordinary skill in the art to the technical solutions of the present invention shall fall within the protection scope determined by the claims of the present invention.

Claims

1. An automatic path - finding method for unmanned aerial vehicles based on satellite - based augmentation, characterized in that, Including: S1. Set the flight target of the drone; S2. Obtain the real-time position of the satellite; S3. Based on the real-time position, determine whether the drone has reached the flight target. If it has reached, end; If not, extract the information of the obstacles in front of the drone; S4. Based on the information of the obstacles in front and the flight target, use the A* algorithm to find the direction of movement with the minimum current cost, and control the drone to move in the direction of movement with the minimum current cost; S5. Loop S2 - S4 until the drone reaches the flight target.

2. The method for automatic path finding of an unmanned aerial vehicle based on satellite-based augmentation according to claim 1, wherein In S1, the flight target includes: the end position, speed limit and altitude limit of the drone.

3. The method for automatically navigating an unmanned aerial vehicle based on satellite-based augmentation according to claim 1, wherein In S2, obtaining the real-time position of the satellite includes: Receiving the broadcast ephemeris, observation data and real-time position correction number broadcast by the satellite; Combining the real-time position correction number with the broadcast ephemeris to generate real-time precise ephemeris and orbit; Obtaining the real-time position of the satellite through the real-time precise ephemeris and orbit.

4. The method for automatically navigating an unmanned aerial vehicle based on satellite-based augmentation according to claim 3, wherein Combining the real-time position correction number with the broadcast ephemeris to generate real-time precise ephemeris and orbit includes: Calculating the correction number of the satellite orbit in the satellite-fixed coordinate system at the current moment, and transferring the correction number of the satellite orbit to the Earth-centered Earth-fixed coordinate system; Correcting the correction number of the satellite orbit in the Earth-centered Earth-fixed coordinate system to the satellite orbit calculated by the broadcast ephemeris to obtain the real-time precise orbit; Calculating the satellite clock error correction number at the current moment, and correcting the satellite clock error calculated by the broadcast ephemeris to obtain the real-time precise clock error.

5. The method for automatically navigating an unmanned aerial vehicle based on satellite-based augmentation according to claim 4, wherein Calculating the correction number of the satellite orbit in the satellite-fixed coordinate system at the current moment, and transferring the correction number of the satellite orbit to the Earth-centered Earth-fixed coordinate system includes: δX = [e along e cross e radial δO; e radial = e along × e cross ; Among them, δO represents the correction of the satellite orbit in the satellite-fixed coordinate system at the current moment, t represents the current moment, t0 represents the reference moment, and δO radial , δO along , δO cross represent the radial, normal, and tangential corrections of the satellite orbit in the satellite-fixed coordinate system at the reference moment, respectively. represent the radial, normal, and tangential velocity corrections of the satellite orbit in the satellite-fixed coordinate system at the reference moment, respectively. δX represents the correction of the satellite orbit in the Earth-centered Earth-fixed coordinate system, r represents the satellite position calculated from the broadcast ephemeris, represents the satellite velocity calculated from the broadcast ephemeris, and e along , e cross , e radial represent the radial, normal, and tangential unit vectors of the satellite, respectively.

6. The method for automatically navigating an unmanned aerial vehicle based on satellite-based augmentation according to claim 4, wherein Correcting the correction number of the satellite orbit in the Earth-centered Earth-fixed coordinate system to the satellite orbit calculated by the broadcast ephemeris to obtain the real-time precise orbit includes: X orbit = X broadcast - δX; Among them, X orbit represents the precise orbit of the satellite, X broadcast represents the satellite orbit calculated from the broadcast ephemeris, and δX represents the correction of the satellite orbit in the Earth-centered Earth-fixed coordinate system.

7. The method for automatically navigating an unmanned aerial vehicle based on satellite-based augmentation according to claim 4, characterized in that, Calculating the satellite clock error correction number at the current moment, and correcting the satellite clock error calculated by the broadcast ephemeris to obtain the real-time precise clock error includes: δC = C0 + C1(t - t0) + C2(t - t0) 2 ; Among them, δC represents the satellite clock error correction at the current moment, t represents the current moment, t0 represents the reference moment, C0, C1, and C2 represent the polynomial coefficients of the clock error correction, and t satellite represents the precise clock error of the satellite, and t broadcast represents the satellite clock error calculated from the broadcast ephemeris, and c represents the speed of light in vacuum.

8. The method for automatically navigating an unmanned aerial vehicle based on satellite-based augmentation according to claim 3, wherein Obtaining the real-time position of the satellite through the real-time precise ephemeris and orbit includes: Among them, P represents the pseudorange observation value, L represents the carrier phase observation value, p represents the geometric distance between the satellite and the receiver, c represents the speed of light in vacuum, dt r represents the receiver clock error, dt s represents the satellite clock error, T represents the tropospheric delay error, b r represents the code pseudorange hardware delay between the receiver antenna and the signal, b s represents the code pseudorange hardware delay between the signal transmitter at the satellite end and the satellite antenna, e represents the pseudorange measurement error, λ represents the carrier wavelength, N represents the integer ambiguity, B r represents the phase hardware delay of the receiver, B s represents the phase hardware delay at the satellite end, ε represents the carrier phase measurement error.

9. The method for automatic path finding of an unmanned aerial vehicle based on satellite-based augmentation according to claim 1, wherein In S3, extracting the information of the obstacles in front of the drone includes: Installing a depth camera on the drone to obtain RGB images and depth images; Processing the RGB images and depth images based on the pre-trained YOLOv framework to obtain the category, confidence level and bounding box of the obstacles; Based on the real-time position of the satellite, extracting the depth values within each obstacle bounding box to obtain the obstacle coordinates and encoding and storing them using an octree.

10. The method for automatically navigating an unmanned aerial vehicle based on satellite-based augmentation according to claim 1, wherein In S4, based on the information of the obstacles in front and the flight target, using the A* algorithm to find the direction of movement with the minimum current cost includes: Traversing the octree containing the obstacle coordinates and marking the free nodes; Taking the free nodes as the passable area of the A* algorithm, calculating the cost from all movable nodes in the passable area to the end using the Euclidean distance, and selecting the passable path with the minimum Euclidean distance as the direction of movement with the minimum current cost.

Images