A combined offline and online method for UAV terrain-simulating flight path planning

Abstract

This invention discloses a combined offline and online method for planning a UAV's terrestrial flight path. This method belongs to the field of UAV flight path planning. Specifically, in the offline mode, before executing a flight mission, a series of interpolated waypoint latitudes and longitudes are obtained based on the selected route using a waypoint interpolation algorithm. An elevation query method is then used to obtain altitude information stored in an elevation database, and the target altitude of each UAV waypoint is calculated, thereby completing the route planning for the entire mission process. In the online mode, during the flight mission, a real-time ground laser ranging system is used to obtain the UAV's real-time ground altitude information. After calibrating the measurement error with the UAV's flight attitude information, the measurement is compared with the UAV's actual flight altitude. The UAV's ground altitude is calibrated based on the pre-planned route, thereby improving the UAV's terrestrial flight accuracy. This invention achieves high-precision terrestrial contour flight route planning for long-endurance UAVs in complex environments.

Description

Technical Field

[0001] The present invention belongs to the technical field of unmanned aerial vehicle (UAV) flight path planning, and in particular relates to an offline and online combined unmanned aerial vehicle (UAV) terrain-simulating flight path planning method. Background Art

[0002] UAV terrain-simulating flight at a constant altitude above the ground means that the drone maintains a constant altitude above the ground during flight. This mode is a common requirement for drones to perform missions. High-precision terrain-simulating flight at a constant altitude has significant advantages in many mission scenarios. For example, it can effectively improve the utilization efficiency of pesticides and fertilizers during farmland plant protection operations; it can obtain more accurate topographic and geological information during land surveying, geological exploration and other tasks; and it can effectively improve the accuracy and stability of animal tracking and identification during ecological monitoring. Based on the above wide range of application scenarios, the development of route planning technology for UAV terrain-simulating flight at a constant altitude can effectively enhance the versatility and efficiency of UAVs, which is of great significance to the development of UAVs.

[0003] A key challenge in developing drone flight path planning technology for terrain-following contour flight is obtaining accurate and effective ground altitude data. Numerous methods for acquiring altitude data have been developed, employing various distance sensors, including lidar, millimeter-wave radar, ultrasonic sensors, and binocular ranging sensors, to improve data accuracy. However, existing ranging technologies often come with limitations. For example, ultrasonic sensors are significantly affected by the propagation medium, millimeter-wave radar is easily obstructed by obstacles, and binocular vision sensors are significantly limited by the computing power of the onboard hardware. Therefore, when designing drone flight algorithms for terrain-following contour flight, it is necessary to address these issues and propose solutions based on practical conditions to ensure high-precision terrain-following contour flight. Summary of the Invention

[0004] To solve the above problems, the present invention proposes a combined offline and online method for UAV terrain-simulating flight path planning, which realizes high-precision contour-simulating flight of UAV.

[0005] The present invention provides a method for planning a UAV terrain-simulating flight path by combining offline and online methods, including an offline mode and an online mode, specifically:

[0006] Offline mode steps:

[0007] 101: Obtain the straight-line distance Dist between two adjacent waypoints based on the latitude and longitude information of each waypoint on the initial route.

[0008] 102: Determine the number n of waypoints to be inserted by specifying the step length.

[0009] 103: According to the number of inserted waypoints n, the latitude and longitude of each waypoint are calculated in sequence according to the specified step length Steplen. The calculation method adopts geometric spherical linear interpolation to achieve equal interval interpolation.

[0010] 104: Perform elevation query based on the latitude and longitude of each waypoint in turn. The elevation data is obtained by online query based on the open-elevation open source database. The method is:

[0011] First, a query URL is generated according to the specified format based on the latitude and longitude of the target waypoint to be queried, and an elevation query request is sent to the cloud, waiting for the return result. If the return is successful, the file is read, and if the return fails, the query is repeated.

[0012] After the query is successful, the information stored in the JSON format file will be read, and the elevation information will be read and saved to a list according to the information format.

[0013] After the query is completed, the read buffer is cleared, and the waypoint corresponding to the currently saved elevation information is detected based on the number of queried waypoints and the total number of waypoints to see if it is the last waypoint. If so, the query step is completed, otherwise return to the first step to generate the elevation query URL of the next target waypoint.

[0014] 105: After obtaining the elevation values ​​of the original route waypoints and the inserted waypoints in step 104, the target altitude of each waypoint is obtained according to the ground altitude data and the altitude of the simulated contour flight target.

[0015] 106: Based on the target altitude of each waypoint obtained in step 105, an altitude check is performed to ensure the safety and effectiveness of the route.

[0016] Online mode steps:

[0017] 201: Install a lidar distance sensor on the drone and establish a data link between the drone flight control system and the laser ranging system, and transmit the drone's altitude above the ground measured by the sensor to the drone flight control system in real time.

[0018] 202: Calculate the actual altitude of the drone based on the aircraft attitude angle and the measured altitude of the drone to eliminate the deviation.

[0019] 203: Setting a calibration threshold for the real-time ground height Hei of the UAV obtained in step 202 and calibrating the UAV flight altitude.

[0020] The advantages of the present invention are:

[0021] 1. The present invention combines offline and online UAV terrain-simulating flight path planning methods, providing a complete set of UAV terrain-simulating flight path planning methods, and realizing high-precision altitude-simulating flight of UAVs;

[0022] 2. This integrated offline and online UAV terrain-simulating flight path planning method incorporates an elevation interpolation query algorithm and a waypoint switching algorithm to ensure the accuracy and safety of terrain-simulating flight. The elevation interpolation query improves the fit of the terrain-simulating route by adding transition waypoints to the route at a specified step size, thereby achieving precision control of the terrain-simulating flight. The waypoint switching algorithm, taking into account the performance limitations of the UAV, verifies the elevation differences between waypoints and provides targeted waypoint switching for flight segments that exceed the climb or descent capability limits, ensuring flight safety.

[0023] 3. It has obvious advantages in meeting the needs of terrain-simulating flight in complex mountain and forest environments. The offline pre-planning of high-precision terrain-simulating flight routes for drones and other aircraft achieved through satellite elevation data query makes the drones independent of real-time distance sensors to a certain extent, avoiding the impact on flight safety and accuracy when the distance sensors are affected by complex environments. At the same time, the use of lidar as a real-time distance sensor can effectively avoid the impact of the forest environment, further improve the accuracy of terrain-simulating flight, and make up for the insufficient accuracy of offline planning methods.

[0024] 4. The offline and online combined UAV terrain-simulating flight path planning method of the present invention has lower structural weight load and airborne equipment requirements, which can greatly reduce the structural weight and UAV cost, especially in missions with low cost and long flight time. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 This is the offline planning process of the terrain-simulating flight route of the present invention;

[0026] Figure 2 This is the implementation process of the elevation online query of the present invention;

[0027] Figure 3 This is the height switching control method of the present invention;

[0028] Figure 4 This is the implementation process of the real-time laser ranging ground height calibration of the present invention.

[0029] Figure 5 Schematic diagram of the tilt angle error calibration method of the UAV of the present invention. DETAILED DESCRIPTION

[0030] The present invention will be further described in detail below with reference to the accompanying drawings.

[0031] The present invention provides a combined offline and online method for planning a UAV's terrain-simulating flight path, including an offline mode and an online mode. The offline mode uses an elevation query method to obtain altitude information from an elevation database, pre-planning the entire mission process before the UAV mission begins. The target altitude of each UAV waypoint is calculated based on the altitude, thereby achieving offline pre-planning of the UAV's terrain-simulating flight route. The online mode uses a real-time ground laser ranging system to obtain the UAV's real-time ground altitude information. Combined with the UAV's current flight altitude information, the UAV's ground altitude is calibrated based on the pre-planned route to improve the UAV's terrain-simulating flight accuracy.

[0032] The specific methods of the above two modes are as follows:

[0033] The offline planning mode of the UAV terrain-simulating flight route first selects waypoints. According to the target mission requirements, a series of waypoints are selected as the initial route, and the waypoints are connected by straight lines. Then, an elevation interpolation query is performed based on the selected waypoints. The purpose of this step is to generate a route that fits the ground altitude fluctuations by performing waypoint elevation queries according to the specified step size to achieve terrain-simulating flight. Figure 1 The specific steps are as follows:

[0034] 101: Based on the latitude and longitude information of each waypoint on the initial route, obtain the straight-line distance Dist between two adjacent waypoints in sequence. This distance calculation can be performed using the Haversine formula. When the earth is approximated as a sphere, this formula can calculate the shortest distance between two waypoints on the sphere using the longitude and latitude of the two waypoints. The specific calculation method is as follows:

[0035]

[0036] Among them, α is the angle formed by two adjacent waypoints and the center of the sphere; Δφ=φ2-φ1 is the latitude difference between the two adjacent waypoints (unit: radians); Δλ=λ2-λ1 is the longitude difference between the two adjacent waypoints (unit: radians), and the coefficient R is the radius of the earth, usually 6371km.

[0037] 102: After obtaining the straight-line distance between two adjacent waypoints, the number of waypoints to be inserted, n, can be determined by specifying the step length:

[0038]

[0039] Where n is the number of inserted waypoints, rounded down; Steplen is the specified step size. The calculation of the number of inserted waypoints is limited by the Steplen value. This parameter effectively adjusts the accuracy of terrain-simulating flight. As the step size decreases, the number of inserted waypoints increases, and the alignment between the route and the ground elevation increases. However, this also increases the complexity of offline route pre-planning calculations and the time required to generate the route. To avoid excessive planning time while maintaining a certain level of accuracy, the specified step size for each segment should be adjusted based on the actual terrain conditions. For segments with gently undulating terrain, a larger step size is used to save planning time. For segments with more undulating terrain, a smaller step size is used to ensure terrain-simulating flight accuracy.

[0040] 103: Based on the number of pre-inserted waypoints, the longitude and latitude of each waypoint are calculated in sequence according to the specified step length Steplen. To achieve evenly spaced interpolation, geometric spherical linear interpolation (Geometric Slerp) is required. First, the longitude and latitude are converted to a Cartesian coordinate system. Based on the above assumption that the earth is a perfect sphere, the conversion formula can be obtained as follows:

[0041] x=cosφcosλ

[0042] y=sinφsinλ

[0043] z=sinφ

[0044] At this time, the coordinates of the two adjacent waypoints are:

[0045] P1=(cosφ1cosλ1,sinφ1sinλ1,sinφ1)

[0046] P2=(cosφ2cosλ2,sinφ2sinλ2,sinφ2)

[0047] Calculated using spherical linear interpolation method:

[0048] cosθ=P1·P2θ=cos -1 (cosθ)

[0049] Where θ is the angle between two adjacent waypoints. If we want to interpolate n points between the two points, the ratio of each interpolation point is:

[0050]

[0051] Then the Cartesian coordinates of each interpolation point are:

[0052]

[0053] Then, the Cartesian coordinates are converted into longitude and latitude coordinates according to the above conversion formula to obtain the longitude and latitude coordinates of each inserted waypoint.

[0054] 104: Perform elevation query based on the longitude and latitude of each waypoint in turn. Elevation data query depends on the digital elevation database. Usually, this type of map data is stored and published in the form of a digital elevation model. The model is a physical ground model that represents the ground elevation in the form of a set of ordered numerical arrays. The ground elevation information is stored in grid units, and the terrain features are stored in the format of a discretized rectangular grid array by cutting the map. The direct elevation query and call of the model requires offline storage of huge map information. The online elevation query method based on the open source geographic information database can effectively solve this problem. By combining the longitude and latitude information in the prescribed URL format, the cloud database can be accessed through the network to obtain the elevation information of the corresponding point. Based on the above method, the present invention proposes an online elevation query process based on the open-elevation open source database:

[0055] like Figure 2 As shown, first generate a query URL according to the specified format based on the latitude and longitude of the target waypoint to be queried, and send an elevation query request to the cloud, waiting for the return result. If the return is successful, read the file, and if the return fails, repeat the query;

[0056] After the query is successful, the information stored in the JSON format file will be read and the elevation information will be read and saved to a list according to the information format;

[0057] After the query is completed, the read buffer is cleared, and the waypoint corresponding to the currently saved elevation information is detected based on the number of queried waypoints and the total number of waypoints to see if it is the last waypoint. If so, the query step is completed, otherwise return to the first step to generate the elevation query URL of the next target waypoint.

[0058] 105: After obtaining the elevation values ​​of the original route waypoints and the inserted waypoints in step 104, the target altitude of each waypoint can be obtained based on the ground altitude data and the altitude of the simulated contour flight target above the ground:

[0059] THei=GHei+Error

[0060] Among them, THei is the target altitude, GHei is the ground altitude, and Error is the deviation between the target altitude and the ground altitude, that is, the height of the target relative to the ground during the ground simulation flight, which is manually set according to the aircraft's flight altitude requirements.

[0061] 106: Based on the target altitude of each waypoint obtained in step 105, an altitude check is required to ensure the safety and effectiveness of the route. The altitude check formula is:

[0062] tan -1 θ=(THei i+1 -THei i) / Dist

[0063] Among them, THEi i is the waypoint target height above the ground, THe i+1 is the target height of the next waypoint, θ is the target pitch angle of the drone. When the target pitch angle of the drone in a certain segment is greater than the maximum pitch angle of the drone, the drone cannot complete the normal straight climb. At this time, it is necessary to set the following in this segment: Figure 3 The control method of the drone circling and then flying forward / flying forward and then circling and rising ensures that the drone can safely reach the next target waypoint. For the flight segments where the drone's altitude climbing capability can meet the requirements, the normal control method of slope control is adopted to ensure the terrain-simulating flight effect.

[0064] After completing the above steps, a complete, safe and effective offline planning of the terrain-simulating flight route is achieved. By uploading the route to the drone, the drone can realize the terrain-simulating and contour flight.

[0065] The online planning mode of the UAV’s terrain-simulating flight route obtains accurate ground information in real time by installing a ground-based laser radar sensor. Based on the offline planning of the route, the corresponding height adjustment is performed according to the flight accuracy requirements to ensure the terrain-simulating flight accuracy requirements, such as Figure 4 As shown, the specific implementation steps are:

[0066] 201: Install a lidar distance sensor on the drone and establish a data link between the drone flight control system and the laser ranging system, and transmit the drone's altitude above the ground measured by the sensor to the drone flight control system in real time.

[0067] 202: Such as Figure 5 As shown in the figure, during the actual mission, the UAV's flight attitude angle will cause a deviation between the measured altitude and the actual altitude. The actual altitude must be calculated based on the aircraft attitude angle and the measured altitude to eliminate the deviation. The specific formula is as follows:

[0068] Hei=Hei0·cosθ·cosλ

[0069] Among them, Hei is the actual height of the UAV above the ground, Hei0 is the height of the UAV above the ground measured by the sensor, θ is the pitch angle of the UAV, and λ is the roll angle of the UAV.

[0070] 203: The real-time ground height Hei of the UAV obtained in step 202 is calibrated by setting a calibration threshold and calibrating the UAV flight altitude. The specific formula is as follows:

[0071] Err=THei·η

[0072] ΔH=|THei-Hei|

[0073] Where Err is the calibration threshold, ΔH is the difference between the target altitude and the actual altitude, η is the error margin (unit: %), and THei is the target altitude above the ground. According to the above formula, when ΔH is greater than the calibration threshold Err, the drone's altitude is fine-tuned by |ΔH - Err|.

[0074] According to the above steps, the terrain-simulating flight altitude calibration method using real-time lidar ranging can be implemented.

[0075] In summary, the offline and online combined UAV terrain-simulating flight path planning method of the present invention can meet the needs of UAV terrain-simulating and altitude-conforming flight in complex environments. The offline pre-planning method of elevation interpolation query combined with elevation verification can realize safe and effective path planning before the mission. The real-time ground altitude information of the ground-based laser radar can realize the accuracy calibration of terrain-simulating flight during the mission. The planning algorithm combining these two methods can make the UAV terrain-simulating flight in complex environments more accurate, reliable, and safe.

[0076] It will be understood that the above embodiments are merely exemplary embodiments for illustrating the principles of the present invention, and the present invention is not limited thereto. Those skilled in the art will appreciate that various modifications and improvements can be made without departing from the spirit and substance of the present invention, and such modifications and improvements are also considered to be within the scope of protection of the present invention.

Claims

1. A method for planning a UAV's terrain-simulating flight path by combining offline and online methods, characterized by: Including offline mode and online mode; The specific steps for offline mode are: 101: Obtain the straight-line distance Dist between two adjacent waypoints based on the latitude and longitude information of each waypoint on the initial route; 102: Determine the number n of waypoints to be inserted by specifying the step length; 103: According to the number of inserted waypoints n, the latitude and longitude of each waypoint is calculated in sequence according to the specified step length Steplen. The calculation method adopts geometric spherical linear interpolation to achieve equal interval interpolation; 104: Perform elevation query based on the latitude and longitude of each waypoint in turn; the elevation data is queried online based on the open-elevation open source database, using the following method: First, generate a query URL according to the specified format based on the latitude and longitude of the target waypoint to be queried, and send an elevation query request to the cloud. Wait for the return result. If the return is successful, read the file. If the return fails, repeat the query. After the query is successful, the information stored in the JSON format file will be read and the elevation information will be read and saved to a list according to the information format; After the query is completed, the read buffer is cleared, and the waypoint corresponding to the currently saved elevation information is checked based on the number of queried waypoints and the total number of waypoints to see if it is the last waypoint. If so, the query step is completed. Otherwise, return to the first step to generate the elevation query URL for the next target waypoint. 105: After obtaining the elevation values ​​of the original route waypoints and the inserted waypoints in step 104, the target altitude of each waypoint is obtained based on the ground altitude data and the altitude of the simulated contour flight target above the ground; 106: Based on the target altitude of each waypoint obtained in step 105, an altitude check is performed to ensure the safety and effectiveness of the route; The specific steps for online mode are: 201: Install a laser radar distance sensor on the UAV and establish a data link between the UAV flight control system and the laser ranging system, transmitting the UAV altitude above the ground measured by the sensor to the UAV flight control system in real time; 202: Calculate the actual altitude of the drone based on the aircraft attitude angle and the measured altitude of the drone to eliminate the deviation; 203: Setting a calibration threshold for the real-time ground height Hei of the UAV obtained in step 202 and calibrating the UAV flight altitude.

2. The offline and online combined unmanned aerial vehicle terrain simulation flight path planning method according to claim 1, characterized in that: In step 101, the straight-line distance between two adjacent waypoints is calculated using the Haversine formula. The specific calculation method is: Among them, α is the angle formed by two adjacent waypoints and the center of the sphere; Δφ=φ2-φ1 is the latitude difference between the two adjacent waypoints; Δλ=λ2-λ1 is the longitude difference between the two adjacent waypoints, and the coefficient R is the radius of the earth, usually 6371km.

3. The offline and online combined unmanned aerial vehicle terrain simulation flight path planning method according to claim 1, characterized in that: In step 102, the step length is adjusted according to the actual terrain conditions of the flight segment. For flight segments with gently undulating terrain, a large step length is used to save planning time. For flight segments with greatly undulating terrain, a small step length is used to ensure the accuracy of the terrain simulation flight.

4. The offline and online combined unmanned aerial vehicle terrain simulation flight path planning method according to claim 1, characterized in that: In step 103, the latitude and longitude of each waypoint are calculated as follows: First, convert the longitude and latitude into the Cartesian coordinate system, and the conversion formula is as follows: x=cosφcosλ y=sinφsinλ z=sinφ At this time, the coordinates of the two adjacent waypoints are: P1=(cosφ1cosλ1,sinφ1sinλ1,sinφ1) P2=(cosφ2cosλ2,sinφ2sinλ2,sinφ2) Calculated using spherical linear interpolation method: cosθ=P1·P2 θ=cos -1 (cosθ) Where θ is the angle between two adjacent waypoints. If we want to interpolate n points between the two points, the ratio of each interpolation point is: Then the Cartesian coordinates of each interpolation point are: Then, the Cartesian coordinates are converted into longitude and latitude coordinates according to the above conversion formula to obtain the longitude and latitude coordinates of each inserted waypoint.

5. The offline and online combined unmanned aerial vehicle terrain simulation flight path planning method according to claim 1, characterized in that: In step 106, the elevation verification method is: The elevation verification formula is: tan -1 θ=(THei i+1 -THei i ) / Dist Among them, Dist is the straight-line distance between two adjacent waypoints; i is the waypoint target height above the ground, THei i+1 is the target height of the next waypoint, and θ is the target climb pitch angle of the UAV.

6. The offline and online combined unmanned aerial vehicle terrain simulation flight path planning method according to claim 5, characterized in that: When the target pitch angle is greater than the maximum pitch angle of the drone, it means that the drone cannot complete normal straight climb in the current flight segment. At this time, the drone needs to be set to hover and then fly forward or fly forward and then hover and rise in the current flight segment to ensure that the drone can safely reach the next target waypoint. For segments where the drone's altitude climbing capability can meet the requirements, the normal control method of slope control is used to ensure the terrain-imitating flight effect.

7. The offline and online combined unmanned aerial vehicle terrain simulation flight path planning method according to claim 1, characterized in that: The specific method of step 203 is: Err=THei·η ΔH=|THei-Hei| Where Err is the calibration threshold, ΔH is the difference between the target altitude and the actual altitude, η is the error range, and |THei is the target altitude above the ground. According to the above formula, when ΔH is greater than the calibration threshold Err, the drone altitude is fine-tuned accordingly with an amplitude of |ΔH - Err|.

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