A multi-rotor unmanned aerial vehicle terrain mapping adaptive route planning method

Abstract

The application belongs to the technical field of unmanned aerial vehicle surveying and mapping, and particularly relates to a multi-rotor unmanned aerial vehicle terrain surveying and mapping self-adaptive flight route planning method. The method collects terrain three-dimensional data through a laser radar, a high-definition camera and an elevation sensor, extracts slope, relief and roughness core terrain features through noise reduction processing, dynamically calculates a flight route adjustment step, a flight height and a speed in combination with unmanned aerial vehicle performance parameters and surveying and mapping accuracy requirements, generates a flight route path adapted to the terrain, cooperatively controls an unmanned aerial vehicle flight attitude, real-time check comprehensive surveying and mapping errors, triggers flight route optimization when the errors exceed a standard, improves multi-rotor unmanned aerial vehicle flight stability and surveying and mapping definition under complex terrain, guarantees surveying and mapping accuracy, adapts to multi-scene terrain surveying and mapping, and improves operation efficiency and reliability.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) surveying technology, specifically to an adaptive flight path planning method for terrain surveying using a multi-rotor UAV. Background Technology

[0002] Multi-rotor UAV topographic mapping is a core operational method in fields such as land surveying, engineering surveying, and 3D terrain modeling. The terrain adaptability and dynamic control capability of flight path planning directly determine the accuracy, completeness, and overall operational efficiency of the mapping results. In scenarios such as complex undulating terrain and irregular survey areas, higher requirements are placed on the intelligence and adaptability of flight path planning.

[0003] In existing multi-rotor UAV topographic mapping route planning, the common approach is to use preset fixed routes or adjust routes based on simple shape parameters. This has a core flaw: it cannot achieve dynamic route planning based on real-time terrain features. The planning logic is seriously out of touch with the actual terrain features. It cannot accurately adapt to complex undulating terrain and irregular survey areas, nor has it established a precise correlation model between terrain features and route parameters. Furthermore, it lacks a real-time accuracy verification and dynamic route correction mechanism during the surveying process. This results in frequent omissions, duplicate surveys, or insufficient surveying accuracy in surveying operations, making it difficult to balance surveying efficiency and accuracy in complex terrain scenarios.

[0004] Therefore, developing an adaptive flight path planning method for multi-rotor UAV terrain mapping that can adapt to complex terrain and has dynamic optimization capabilities has become an urgent technical problem to be solved in the field of UAV intelligent mapping. Summary of the Invention

[0005] The purpose of this invention is to provide an adaptive flight path planning method for terrain mapping using a multi-rotor UAV, which solves the problems of existing technologies being unable to achieve dynamic flight path planning based on real-time terrain features, having poor adaptability to complex terrain, and being unable to balance mapping efficiency and accuracy.

[0006] To achieve the above objectives, the present invention provides the following technical solution: An adaptive flight path planning method for terrain mapping using a multi-rotor UAV includes the following steps: Step 1: 3D Terrain Feature Perception Core terrain feature parameters are obtained through three-dimensional terrain feature perception. Step 2: Adaptive route dynamic generation: Dynamically generate adapted flight routes based on terrain features; Step 3: Coordinated Attitude Control of the UAV: Coordinated control of drone flight attitude; Step 4: Surveying accuracy verification: Real-time verification of surveying accuracy and optimization of flight routes based on error results.

[0007] Furthermore, in step one, the onboard lidar, high-definition visible light camera and elevation sensor collect raw three-dimensional terrain data. After noise reduction processing by Gaussian filtering algorithm, three core terrain feature parameters, namely terrain slope, undulation and surface roughness, are extracted, and the undulation and surface roughness are normalized.

[0008] Furthermore, in step two, by combining core terrain feature parameters, UAV preset performance parameters and mapping accuracy requirements, the flight path adjustment step size, planned flight altitude, planned flight speed and planned heading angle are calculated to complete the generation and optimization of adaptive flight path.

[0009] Furthermore, in step two, when it is determined that the terrain is undulating, the planned heading angle is automatically adjusted to keep the flight path direction parallel to the terrain slope, thereby reducing the adjustment range of the UAV's flight attitude.

[0010] Furthermore, in step two, when the surface texture is determined to be complex, the planned flight speed is automatically adjusted to ensure the clarity of the survey image acquisition.

[0011] Furthermore, in step three, the planned flight path parameters are compared with the actual flight parameters of the UAV, and the flight altitude and heading angle that exceed the deviation range are corrected in real time. The turning radius of the flight path is controlled to meet the performance requirements of the UAV, and the actual flight and attitude data are fed back in real time.

[0012] Furthermore, in step four, the original terrain data, planned flight route parameters, and measured flight and attitude data are combined to calculate the flight route plane position deviation error and the flight route displacement error caused by attitude deviation, and then synthesize the comprehensive mapping error.

[0013] Furthermore, in step four, the comprehensive surveying error is compared with the preset accuracy threshold. If the comprehensive surveying error meets the standard, the current route path is maintained; if the comprehensive surveying error exceeds the standard, route optimization is triggered, and the surveying error data of each time is recorded in real time to form an accuracy log.

[0014] Compared with the prior art, the beneficial effects of the present invention are: 1. In this invention, by sensing the three-dimensional features of the terrain in the surveying area in real time, an adaptive flight path matching the slope, undulation, and roughness is dynamically generated, and the flight altitude, speed, and flight path direction are automatically adjusted, which is beneficial to improving flight stability and the clarity of surveying images under complex terrain. 2. In this invention, through attitude coordination control and real-time mapping accuracy verification mechanism, the flight path error is accurately calculated and the flight path is dynamically optimized to ensure that the accuracy meets the standards under different mapping scenarios, reduce the frequency of manual intervention, significantly improve the efficiency and reliability of topographic mapping operations, and adapt to various complex topographic mapping operations. Attached Figure Description

[0015] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings; Figure 1 This is a schematic diagram of the working method of the present invention; Figure 2 This is a schematic diagram of the system structure of the present invention. Detailed Implementation

[0016] 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.

[0017] Example 1: Traditional multi-rotor UAV topographic mapping uses a fixed flight path planning method. The flight path cannot adapt to changes in terrain slope, undulation, and roughness. It has poor flight stability in complex terrain, and the mapped images are prone to blurring. Furthermore, it does not dynamically adjust flight altitude, speed, and heading angle in combination with terrain features. The flight path design is unreasonable, and there is a lack of coordinated control between flight attitude and flight path parameters. The deviation between measured parameters and planned parameters cannot be corrected in real time, making it difficult to meet the stability and clarity requirements of complex terrain mapping.

[0018] In response to the above problems, refer to Figure 1-2 As shown in the figure, the adaptive flight path planning method for terrain mapping of multi-rotor UAVs proposed in this embodiment has the following steps: Step 1: Terrain 3D Feature Perception: The terrain 3D feature perception module collects raw 3D terrain data of the UAV-mapped area in real time, performs data denoising, feature extraction, and normalization, and outputs three core feature parameters: terrain slope (A), undulation (B), and surface roughness (C), providing accurate terrain data support for flight path generation; see the following for details: The drone is equipped with a lidar sensor, a high-definition visible light camera and an elevation sensor. After takeoff, it enters the surveying area and the terrain 3D feature perception module is activated simultaneously. The lidar sensor collects terrain elevation data at a frequency of 10Hz, the high-definition visible light camera collects surface texture data simultaneously, and the elevation sensor collects the drone's height data relative to the ground surface in real time. The collected raw data is first processed by a Gaussian filtering algorithm to remove environmental noise, which includes abnormal elevation and texture data caused by vegetation obstruction and light interference. Then, a terrain feature extraction algorithm is used to calculate the difference between the noise-reduced elevation data and extract two types of basic data: the elevation difference Dh and the horizontal distance Dx between adjacent collection points. The terrain slope A is calculated based on the geometric arctangent operation, i.e., A=arctan(Dh / Dx). Furthermore, the undulation degree B is calculated by the dispersion of elevation data, and the surface roughness C is calculated by combining the grayscale variation of surface texture. Then, the two types of parameters, undulation degree B and surface roughness C, are normalized and mapped to the numerical range of 0 to 1 to eliminate the influence of the difference in magnitude of different parameters. After processing, the three core parameters of terrain slope A, undulation B, and surface roughness C are transmitted in real time to the adaptive flight path dynamic generation module. At the same time, the original noise reduction data is backed up and transmitted to the surveying accuracy verification module for subsequent auxiliary verification of terrain features and flight path adaptability.

[0019] Step 2: Adaptive Flight Route Dynamic Generation: The adaptive flight route dynamic generation module, based on the terrain slope A, undulation B, and surface roughness C output by the terrain 3D feature perception module, and combined with the UAV's own performance parameters and mapping accuracy requirements, calculates the flight route adjustment step size S, planned flight altitude Hp, and planned flight speed Vp through preset parameter association logic. This dynamically generates and optimizes the flight route, achieving real-time adaptation between the flight route and terrain features, thus solving the problems of fixed flight routes and poor adaptability in existing technologies. See below for details: After the adaptive flight path dynamic generation module is started, it first receives three core parameters transmitted by the terrain 3D feature perception module: terrain slope A, undulation B, and surface roughness C. At the same time, it calls the UAV's own preset performance parameters, including maximum flight tilt angle, maximum calibrated flight speed Vmax, minimum turning radius Rmin, etc., as well as preset mapping accuracy requirements, including resolution, overlap rate, etc. Then, the flight path is initialized. Starting from the UAV takeoff point, an initial straight flight strip is generated by combining the coordinates of the survey area boundary. Subsequently, the flight path adjustment step size S, the planned flight altitude Hp, and the planned flight speed Vp are calculated sequentially through the preset parameter association logic. Among them, the planned flight altitude Hp is the theoretical flight altitude corrected by the terrain relief B, and the initial value is the reference flight altitude Ho. The calculation logic for the correction of the planned flight altitude Hp is: the planned flight altitude Hp is equal to the reference flight altitude Ho multiplied by the sum of one and the terrain relief B, that is, Hp=Ho×(1+B). It should be noted that the terrain relief B is dimensionless, with a value range of 0 to 1, and is output after normalization processing by the terrain three-dimensional feature perception module. The larger the value, the more severe the terrain relief. The calculation logic for the flight path adjustment step size S is as follows: The flight path adjustment step size S is equal to the product of the UAV performance adjustment coefficient K (dimensionless, with a value range of 0.8 to 1.2, preferably 0.8 for small multi-rotor UAVs, 1.0 for medium-sized UAVs, and 1.2 for large UAVs, which is obtained by pre-entering the airborne controller through the UAV's factory performance calibration manual), the planned flight altitude Hp, and the tangent value of the terrain slope A, i.e., S = K × Hp × tanA; The calculation logic of the planned flight speed Vp is as follows: the planned flight speed Vp is equal to the maximum calibrated flight speed Vmax of the UAV (a fixed parameter calibrated by the UAV at the factory) multiplied by the difference between one and the surface roughness C (dimensionless, with a value range of 0 to 1, output after normalization processing by the terrain three-dimensional feature perception module), that is, Vp=Vmax×(1-C).

[0020] Based on the above calculation logic, the flight path adjustment step size S and the planned flight altitude Hp are calculated in real time. When the terrain undulation B is greater than or equal to a preset threshold of 0.6, that is, when the terrain undulation is determined to be severe, the planned heading angle Pp is automatically adjusted to make the flight path direction parallel to the terrain slope, reducing the range of UAV flight attitude adjustment and avoiding mapping blur caused by terrain undulation. At the same time, combined with the surface roughness C, when the surface roughness C is greater than or equal to a preset threshold of 0.7, that is, when the surface texture is determined to be complex, the flight speed is reduced according to the above-mentioned planned flight speed Vp calculation logic to ensure the clarity of the mapping image. After generating the initial adaptive flight path, four types of flight path parameters—planned heading angle Pp, planned flight altitude Hp, flight path adjustment step size S, and planned flight speed Vp—are transmitted in real time to the UAV attitude collaborative control module. At the same time, the flight path parameters are transmitted synchronously to the mapping accuracy verification module for accuracy verification.

[0021] Step 3: UAV Attitude Coordinated Control: The UAV attitude coordinated control module receives the planned heading angle Pp, planned flight altitude Hp, flight path adjustment step size S, and planned flight speed Vp output by the adaptive flight path dynamic generation module. Combined with real-time UAV attitude data, it coordinates and controls the UAV's flight attitude and speed to ensure the UAV flies precisely along the planned flight path. Simultaneously, it provides real-time feedback on the UAV's measured flight altitude Hs, measured flight speed Vs, and measured heading angle Ps, supporting flight path optimization and accuracy verification. This achieves coordinated matching of attitude and flight path parameters, improving flight stability. See below for details: After the UAV attitude collaborative control module is activated, it receives four types of flight path parameters in real time from the adaptive flight path dynamic generation module: planned heading angle Pp, planned flight altitude Hp, planned flight speed Vp, and flight path adjustment step size S. At the same time, it collects real-time attitude data of the UAV, including pitch angle, roll angle, and yaw angle, as well as measured flight parameters, including measured flight altitude Hs, measured flight speed Vs, and measured heading angle Ps, through the gyroscope, accelerometer, and GPS positioning module on the UAV. Furthermore, the measured flight parameters are compared with the planned flight path parameters. When the difference between the measured flight altitude Hs and the planned flight altitude Hp exceeds ±0.5 meters, the flight altitude is corrected by adjusting the UAV propeller speed to ensure that the altitude deviation is within the allowable range. When the difference between the measured heading angle Ps and the planned heading angle Pp exceeds ±3°, the heading is corrected by adjusting the UAV attitude angle to ensure that the UAV flies along the planned flight path. At the same time, the step size S is adjusted according to the flight path to control the UAV to complete the turning action when switching flight paths. The turning radius is not less than the UAV's minimum turning radius Rmin to avoid attitude loss during the turning process. Finally, the UAV attitude collaborative control module synchronously feeds back the real-time collected measured flight altitude Hs, measured flight speed Vs, measured heading angle Ps, and attitude data to the adaptive flight path dynamic generation module and the mapping accuracy verification module. The data fed back to the adaptive flight path dynamic generation module is used for iterative optimization of flight path parameters, and the data fed back to the mapping accuracy verification module is used for accuracy comparison.

[0022] Example 2: Traditional UAV topographic mapping lacks a real-time mapping accuracy verification mechanism, cannot calculate flight path plane position deviation and attitude offset error, makes it difficult to determine whether the mapping accuracy meets the standard, and cannot trigger flight path optimization in time after the error exceeds the standard, making the mapping accuracy uncontrollable and easily leading to unqualified mapping results.

[0023] In response to the above problems, refer to Figure 1-2 As shown, the difference between this embodiment and Embodiment 1 is that it also includes step four, which is a mapping accuracy verification: The mapping accuracy verification module receives the planned flight path parameters from the adaptive flight path dynamic generation module and the measured flight parameters and attitude data from the UAV attitude collaborative control module. This is combined with the raw noise reduction data from the terrain 3D feature perception module for auxiliary verification. The accuracy verification logic calculates the flight path-related errors and determines whether the errors meet the preset accuracy threshold Eo. If not, the error data is fed back to the adaptive flight path dynamic generation module, triggering flight path optimization. This achieves real-time verification and dynamic correction of flight path accuracy during the mapping process. The specific workflow is as follows: After the mapping accuracy verification module is started, it synchronously receives three types of input data: the denoised terrain raw data output by the terrain 3D feature perception module, the planned waypoint xp, planned waypoint yp, planned flight altitude Hp, flight path adjustment step size S, planned heading angle Pp and planned flight speed Vp output by the adaptive flight path dynamic generation module, and the measured waypoint xs, measured waypoint ys, measured flight altitude Hs, measured heading angle Ps, measured pitch angle θs and measured roll angle φs output by the UAV attitude collaborative control module. The two types of errors directly related to the accuracy of the flight path execution are calculated: flight path plane position deviation error E1 and flight path displacement error E2 caused by attitude deviation, and then combined into a comprehensive mapping error E. Among them, the horizontal position deviation error E1 reflects the horizontal offset of the overall flight trajectory of the UAV. Its calculation logic is as follows: the horizontal position deviation error E1 is the horizontal Euclidean distance between the UAV's measured GPS waypoint and the planned waypoint, which is equal to the square root of the sum of the squares of the differences between the measured waypoint's x-coordinate Xs and the planned waypoint's x-coordinate Xp, and the squares of the differences between the measured waypoint's y-coordinate Ys and the planned waypoint's y-coordinate Yp. ; The calculation logic for the flight path displacement error E2 caused by attitude deviation is as follows: The flight path displacement error E2 is the converted value of the deviation between the measured attitude angle of the UAV and the planned attitude requirement. It is equal to the sum of the absolute values ​​of the difference between the measured pitch angle θs and the planned pitch angle θp, and the absolute values ​​of the difference between the measured roll angle φs and the planned roll angle φp, and then multiplied by the attitude influence coefficient α (the value is 0.1 to 0.3, depending on the UAV model. Preferably, 0.1 is used for small UAVs and 0.3 is used for large UAVs), that is, E2=α×(∣θs-θp∣+∣φs-φp∣). It should be noted that the planned pitch angle θp and the planned roll angle φp are preset according to the planned flight altitude Hp and the terrain slope A. For example, the larger the terrain slope A, the greater the adaptation adjustment of the planned pitch angle θp and roll angle φp. The comprehensive surveying error E comprehensively reflects the overall accuracy of route planning and execution. Its calculation logic is as follows: the comprehensive surveying error E equals the arithmetic square root of the sum of the squares of the route plane deviation error E1 and the squares of the route attitude deviation error E2, i.e. ; The calculated comprehensive surveying error E is compared with the preset accuracy threshold Eo. Preferably, the accuracy threshold Eo is 0.1 in high-precision surveying scenarios and 0.3 in conventional surveying scenarios. If the comprehensive surveying error E is less than or equal to the accuracy threshold Eo, it is determined that the accuracy of the current route meets the requirements, and the current route path is maintained. At the same time, the accuracy qualified signal is fed back to the adaptive route dynamic generation module to maintain the current route parameters. If the comprehensive surveying error E is greater than the accuracy threshold Eo, the route accuracy is determined to be unsatisfactory. The three types of error data, E, E1 and E2, are fed back to the adaptive route dynamic generation module and the back-end terminal in real time, triggering the route optimization process. For example, the adaptive route dynamic generation module recalculates parameters such as the route adjustment step size S, the planned flight altitude Hp, and the planned heading angle Pp to optimize the route path. At the same time, error data for each step is recorded to form an accuracy log, which is used for parameter optimization in subsequent surveying and mapping tasks to ensure that the accuracy of the flight path is controllable throughout the entire surveying and mapping process.

[0024] The working principle of this invention is as follows: During use, three-dimensional terrain data is collected through lidar, high-definition cameras, and elevation sensors. After noise reduction and feature extraction, core parameters such as slope, undulation, and roughness are obtained. Combined with the performance of the UAV and the requirements of surveying accuracy, the flight path step length, flight altitude, and speed are dynamically calculated to generate an adaptive flight path. The flight attitude of the UAV is coordinated and controlled, and the comprehensive surveying error is checked in real time. If the error exceeds the standard, the flight path is optimized. This solves the problems of poor terrain adaptability, unstable flight attitude, and insufficient surveying accuracy of traditional fixed flight paths. It realizes real-time adaptive matching between the flight path and complex terrain, improves the flight stability of the UAV and the clarity of the surveying images, adapts to terrain surveying in multiple scenarios, and greatly improves the efficiency and reliability of operations.

[0025] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, enabling those skilled in the art to better understand and utilize it. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. An adaptive flight path planning method for terrain mapping using a multi-rotor unmanned aerial vehicle (UAV), characterized in that, Includes the following steps: Step 1: 3D Terrain Feature Perception Core terrain feature parameters are obtained through three-dimensional terrain feature perception. Step 2: Adaptive route dynamic generation: Dynamically generate adapted flight routes based on terrain features; Step 3: Coordinated Attitude Control of the UAV: Coordinated control of drone flight attitude; Step 4: Surveying accuracy verification: Real-time verification of surveying accuracy and optimization of flight routes based on error results.

2. The adaptive flight path planning method for terrain mapping using a multi-rotor UAV according to claim 1, characterized in that, In step one, raw three-dimensional terrain data is collected using the onboard lidar, high-definition visible light camera, and elevation sensor. After noise reduction using a Gaussian filtering algorithm, three core terrain feature parameters—slope, undulation, and surface roughness—are extracted.

3. The adaptive flight path planning method for terrain mapping using a multi-rotor UAV according to claim 1, characterized in that, In step two, the flight path adjustment step size, planned flight altitude, planned flight speed, and planned heading angle are calculated by combining the core terrain feature parameters, the UAV's preset performance parameters, and the mapping accuracy requirements.

4. The adaptive flight path planning method for terrain mapping using a multi-rotor UAV according to claim 2, characterized in that, In step two, when it is determined that the terrain is undulating, the planned heading angle is automatically adjusted to keep the heading parallel to the terrain slope.

5. The adaptive flight path planning method for terrain mapping using a multi-rotor UAV according to claim 4, characterized in that, When the terrain texture is determined to be complex, the planned flight speed is automatically adjusted.

6. The adaptive flight path planning method for terrain mapping using a multi-rotor UAV according to claim 1, characterized in that, In step three, the planned flight path parameters are compared with the actual flight parameters of the UAV. Flight altitude and heading angle that exceed the deviation range are corrected in real time. The turning radius of the flight path is controlled to meet the performance requirements of the UAV, and the actual flight and attitude data are fed back in real time.

7. The adaptive flight path planning method for terrain mapping using a multi-rotor UAV according to claim 1, characterized in that, In step four, the original terrain data, planned flight route parameters, and measured flight and attitude data are combined to calculate the flight route plane position deviation error and the flight route displacement error caused by attitude deviation, and then synthesize the comprehensive mapping error.

8. The adaptive flight path planning method for terrain mapping using a multi-rotor UAV according to claim 7, characterized in that, In step four, the overall mapping error is compared with the preset accuracy threshold. If the overall mapping error meets the standard, the current route path is maintained; if the overall mapping error exceeds the standard, route optimization is triggered.

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