Unmanned aerial vehicle channel inspection path optimization method based on dynamic environment perception

Abstract

The invention discloses an unmanned aerial vehicle channel inspection path optimization method based on dynamic environment perception, and relates to the technical field of unmanned aerial vehicle intelligent path planning. Comprising the following steps: establishing a three-dimensional environment model based on channel geographic information data and surveying and mapping point cloud data, and generating an initial routing inspection path set with a coverage range meeting requirements; predicting and generating an obstacle envelope sequence changing along with time by using a dynamic obstacle trajectory collected by an airborne radar and a camera, performing space-time superposition calculation on the obstacle envelope sequence and the initial inspection path, and removing path fragments conflicting with a future obstacle movement range to obtain an inspection path set after obstacle avoidance correction; and according to elevation information in the three-dimensional environment model, predicting a local area and a spatial range in which communication shielding may occur in the inspection path of the unmanned aerial vehicle, carrying out secondary correction on a path segment which is about to enter a communication shielding area, and outputting a corresponding flight height and speed sequence.

Description

Technical Field

[0001] This invention relates to the field of intelligent path planning technology for unmanned aerial vehicles (UAVs), and in particular to a method for optimizing UAV airway inspection paths based on dynamic environmental perception. Background Technology

[0002] With the rapid development of drone technology and intelligent sensing technology, drones are increasingly being widely used in fields such as power line inspection, traffic monitoring, disaster assessment, and logistics delivery. Particularly in waterway inspection scenarios, drones offer advantages such as rapid response, low cost, and high maneuverability, enabling multi-dimensional inspections in complex geographical environments. However, traditional waterway inspection path planning methods largely rely on static geographic information, such as two-dimensional maps or simplified geographic models, often failing to fully utilize real-time sensing data to model dynamic environments. In recent years, the combination of 3D mapping point cloud technology, LiDAR, and airborne camera systems has enabled drones to acquire more detailed spatial structure information during flight and possess the ability to dynamically identify obstacles. Furthermore, research integrating environmental modeling and path planning is constantly emerging, attempting to use 3D environmental models to assist drones in selecting safer and more efficient routes during inspection tasks.

[0003] However, existing UAV route planning for airway inspection has the following shortcomings: On the one hand, although existing technologies can combine geographic information systems and point cloud data to generate 3D environment models, most route planning is still based on static scene assumptions, which makes it difficult to cope with dynamic obstacles in the airway that change over time, such as construction machinery or temporary floating objects, which may cause path conflicts and flight risks. On the other hand, existing research often adopts local correction strategies in obstacle avoidance, only detouring around the obstacle at the current moment, without predicting and modeling the future trajectory of the obstacle, resulting in insufficient global robustness of the planned path. In addition, communication obstruction is also a weak link in existing technologies, especially in areas with high bridges, mountain valleys, or dense large buildings, where signal loss or interruption often occurs in the UAV path. Existing methods usually fail to take communication obstruction areas into account during the path generation stage, which can easily lead to interruption of inspection tasks or delay in information transmission. Summary of the Invention

[0004] In view of the problems existing in the current UAV airway inspection path optimization technology, this invention is proposed.

[0005] Therefore, the problem to be solved by this invention is how to model and correct dynamic obstacle trajectories and future communication obstruction areas in order to improve the safety and continuity of inspection paths.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: In a first aspect, the present invention provides a method for optimizing UAV airway inspection paths based on dynamic environmental perception. The method includes: establishing a three-dimensional environment model based on airway geographic information data and mapping point cloud data, and generating an initial inspection path set with sufficient coverage; using dynamic obstacle trajectories collected by airborne radar and cameras to predict and generate a time-varying obstacle envelope sequence, and performing spatiotemporal overlay calculations with the initial inspection paths to eliminate path segments that conflict with the future movement range of obstacles, thus obtaining an obstacle avoidance-corrected inspection path set; based on elevation information in the three-dimensional environment model, predicting local areas and spatial ranges where communication obstruction may occur in the UAV inspection path, and performing secondary corrections on inspection path segments that are about to enter the communication obstruction area, and outputting the corresponding flight altitude and speed sequences.

[0007] As a preferred embodiment of the UAV airway inspection path optimization method based on dynamic environment perception described in this invention, the three-dimensional environment model includes airway length, airway width, water depth, terrain undulation, and obstacle location.

[0008] As a preferred embodiment of the UAV airway inspection path optimization method based on dynamic environment perception described in this invention, the generation of the three-dimensional environment model includes: dividing the airway mapping point cloud data into a two-dimensional grid according to a fixed spatial resolution Δx and Δy, with each grid point recording the average water depth value; generating a continuous three-dimensional terrain surface model based on the water depth data of the two-dimensional grid using triangulation interpolation, wherein the spatial range of the three-dimensional terrain surface model corresponds to the airway length and width, and reflects water depth undulations in the vertical direction; mapping obstacle detection data into three-dimensional voxel units in the airway coordinate system, and aligning the boundaries of the three-dimensional voxel units with the airway coordinate system; embedding the three-dimensional voxel units into the three-dimensional terrain surface model to obtain a three-dimensional environment model containing obstacle distribution; assigning obstacle weights to each three-dimensional voxel unit containing or adjacent to an obstacle; the obstacle weights are calculated by multiplying the inverse of the distance between the obstacle and the center point of the three-dimensional voxel unit by the obstacle scale factor.

[0009] As a preferred embodiment of the UAV route inspection path optimization method based on dynamic environment perception described in this invention, the generation of the initial inspection path set includes: projecting a three-dimensional environment model onto a two-dimensional plane, dividing the route into inspection strips along the route width direction, the width of which is the effective scanning bandwidth of the UAV; the two-dimensional plane is the route length × route width; generating a longitudinal main path along the route length direction with the route starting point as the starting point; the longitudinal main path is formed by connecting the left and right boundary points segment by segment and accumulating the three-dimensional voxel units with obstacle weight values ​​higher than a preset weight threshold using a Dijkstra search method between each section; using the longitudinal main path as the skeleton, generating on the left and right sides of each longitudinal path segment sequentially... A lateral scanning path is generated. If a voxel cell with an obstacle weight higher than a preset weight threshold is encountered during the scanning process, the path is locally bypassed and then continues to connect in a serpentine manner. The longitudinal main path and the lateral serpentine scanning path are spliced ​​together to obtain a hybrid path set. For the generated hybrid path set, the coverage and theoretical energy consumption are calculated: the coverage is obtained by statistically analyzing the ratio of the number of three-dimensional voxel cells traversed by the hybrid path to the total number of three-dimensional voxel cells in the channel; the theoretical energy consumption is calculated by weighting the hybrid path length with the obstacle weights of the three-dimensional voxel cells traversed. Hybrid paths with a coverage lower than the coverage threshold or a theoretical energy consumption higher than the energy consumption threshold are removed, and an initial inspection path set is output, while retaining the coverage index and theoretical energy consumption information of each initial inspection path.

[0010] As a preferred embodiment of the UAV airway inspection path optimization method based on dynamic environment perception described in this invention, the method for predicting and generating time-varying obstacle envelope sequences includes: using obstacle trajectory data collected by airborne radar and cameras, including position and velocity vectors, and performing time synchronization and coordinate mapping on the collected obstacle trajectory data to make it consistent with the coordinate system of the three-dimensional environment model; for each dynamic obstacle, using a standard Kalman filter to generate a future position prediction sequence, and generating a three-dimensional envelope of spatial range for the predicted position, and adding a valid time interval to each three-dimensional envelope to form a set of obstacle envelope sequences.

[0011] As a preferred embodiment of the UAV flight path optimization method based on dynamic environment perception described in this invention, the step of eliminating path segments that conflict with the future movement range of obstacles includes: for each inspection path in the initial inspection path set, taking a set of inspection path points, each inspection path point including three-dimensional coordinates; determining the expected arrival time of the inspection path points based on the UAV flight speed and the distance between the inspection path points; checking whether each inspection path point falls within the spatial range of any obstacle envelope in the obstacle envelope sequence, and whether the expected arrival time is within the corresponding time window; if there is a case of falling into the obstacle envelope, then the inspection path point is marked as a conflict point; if there are consecutive conflict points, then the corresponding inspection path segment is removed from the original inspection path, and the inspection path set after obstacle avoidance correction is obtained, and the start and end points of the removed inspection path segments are marked.

[0012] As a preferred embodiment of the UAV airway inspection path optimization method based on dynamic environment perception described in this invention, the method for determining the estimated arrival time of inspection path points includes: assigning an initial takeoff time to the starting point of the inspection path; for any point on the inspection path, the estimated arrival time is obtained by adding the ratio of the spatial distance between the two points to the UAV flight speed based on the estimated arrival time of the previous point; the spatial distance between the two points is calculated by the three-dimensional coordinate difference; the UAV flight speed is a preset constant inspection speed, set according to the UAV model parameters and inspection task requirements.

[0013] As a preferred embodiment of the UAV airway inspection path optimization method based on dynamic environment perception described in this invention, the method for predicting the local area and spatial range where communication obstruction may occur in the UAV inspection path includes: spatially mapping each inspection path in the obstacle avoidance correction set to a three-dimensional environment model to obtain the corresponding terrain height of the inspection path point; for each inspection path point, calculating the elevation difference with the terrain, and if it is less than the set minimum height threshold for UAV communication safety, then marking the inspection path point as potentially having a communication obstruction risk; merging inspection path points with consecutive communication obstruction risks, outputting the inspection path segment with communication obstruction risk, and recording the spatial range and the expected time window of occurrence.

[0014] As a preferred embodiment of the UAV airway inspection path optimization method based on dynamic environment perception described in this invention, the secondary correction of the inspection path segment about to enter the communication obstruction zone includes: superimposing the spatial range of the local area of ​​communication obstruction with the inspection path set after obstacle avoidance correction to determine the inspection path segment about to enter the obstruction zone; recording the start and end positions, time window, and current flight altitude for each inspection path segment; determining the unobstructed area in the three-dimensional environment model whose distance from the inspection path segment about to enter the obstruction zone does not exceed a preset distance threshold as candidate alternative inspection path segments to ensure the continuity of the inspection path and meet the constraints of flight altitude and obstacle avoidance; comprehensively scoring the candidate alternative inspection path segments, and selecting the optimal solution based on a weighted calculation of flight altitude change, obstacle weight, increase in inspection path length, and communication security priority; replacing the inspection path segment about to enter the obstruction zone with the optimized alternative inspection path segment to form a set of inspection paths after communication obstruction correction.

[0015] As a preferred embodiment of the UAV airway inspection path optimization method based on dynamic environment perception described in this invention, the obstacle scale factor is a quantitative parameter calculated based on the spatial size of the obstacle. It is obtained by normalizing the volume of the obstacle in the three-dimensional coordinate system or the projected area in the inspection direction, and is used to characterize the impact of the size of the space occupied by the obstacle on the risk of the local inspection path.

[0016] In a second aspect, the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program instructions are executed by the processor, they implement the steps of the UAV route inspection path optimization method based on dynamic environment perception as described in the first aspect of the present invention.

[0017] Thirdly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program instructions are executed by a processor, the steps of the unmanned aerial vehicle (UAV) airway inspection path optimization method based on dynamic environment perception as described in the first aspect of the present invention are implemented.

[0018] The beneficial effects of this invention are as follows: By integrating airway geographic information data and survey point cloud data to establish a three-dimensional environment model, the inspection path planning becomes comprehensive and fine-grained; by combining obstacle trajectory data collected by airborne radar and cameras, the invention predicts and generates obstacle envelope sequences that change over time, and performs spatiotemporal overlay comparison on the path to eliminate and avoid potential future conflict segments, thereby improving the safety and continuity of the path; in addition, this invention also implements a prediction and secondary correction mechanism for communication obstruction areas, which can dynamically adjust flight altitude and speed in complex terrain or highly obstructed environments to ensure the stability of data transmission.

[0019] Through the above-mentioned multi-level data processing, the present invention can provide more efficient, reliable and intelligent path planning support for UAVs in airway inspection tasks. Attached Figure Description

[0020] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a flowchart of a method for optimizing UAV airway inspection paths based on dynamic environment perception. Detailed Implementation

[0022] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0023] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0024] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0025] Figure 1 This is a flowchart of a UAV airway inspection path optimization method based on dynamic environment perception according to an embodiment of the present invention. Figure 1 As shown, the UAV route inspection path optimization method based on dynamic environment perception includes: S1: Based on waterway geographic information data and survey point cloud data, establish a three-dimensional environment model and generate an initial inspection path set with sufficient coverage.

[0026] The three-dimensional environment model includes the channel length, channel width, water depth, topographic relief, and obstacle locations.

[0027] It should be noted that directly planning flight paths on a two-dimensional map cannot accurately reflect the impact of water depth variations and obstacle distribution on UAV flight path selection, leading to potential risks in inspection paths. Therefore, this invention first generates a three-dimensional environment model that corresponds one-to-one with the actual spatial range of the waterway by fusing waterway mapping point cloud data and waterway geographic information data, and then generates a set of candidate initial inspection paths based on this model. This ensures that the inspection path generation process is based on the constraints of a real three-dimensional environment, thereby enhancing the safety and coverage of the paths.

[0028] S1.1: Generate a three-dimensional environment model.

[0029] S1.1.1: Divide the channel survey point cloud data into a two-dimensional grid with fixed spatial resolutions Δx and Δy, and record the average water depth value for each grid point.

[0030] S1.1.2: Based on the water depth data of the two-dimensional grid, a continuous three-dimensional terrain surface model is generated by triangulation interpolation. The spatial range of the three-dimensional terrain surface model corresponds to the channel length and channel width, and reflects the water depth undulation in the vertical direction.

[0031] It should be noted that directly using the grid surface would result in contour lines appearing stepped, making it difficult to accurately represent continuous changes in water depth. This invention connects grid points into an irregular triangular network through triangulation, and uses an interpolation function within the network to estimate the water depth at any point, thereby obtaining a continuous and smooth underwater topographic surface.

[0032] Specifically, firstly, all valid grid points in the two-dimensional grid are triangulated to form a set of non-overlapping triangular units; secondly, a planar interpolation function is established within each triangular unit, and the water depth at any location within the triangular unit is calculated using the water depth value at the vertex of the triangle; finally, all triangular units are spliced ​​together to obtain a continuous three-dimensional terrain surface model.

[0033] S1.1.3: In the channel coordinate system, map the obstacle detection data into three-dimensional voxel units and align the boundaries of the three-dimensional voxel units with the channel coordinate system.

[0034] Obstacle detection data typically originates from sonar scans or lidar point clouds, presenting as irregular point sets or volumetric data. Directly incorporating this data into inspection path planning makes it difficult to establish a unified computational framework. This invention proposes to transform obstacle data into regular three-dimensional voxel units through voxelization, ensuring that the obstacle distribution and the three-dimensional environment model share a consistent coordinate system.

[0035] Specifically, firstly, the voxel side length is defined, and a three-dimensional voxel grid covering the waterway area is constructed in the waterway coordinate system. Then, obstacle detection points are mapped to the corresponding voxels. If the number of obstacle points in a voxel exceeds a set threshold, the corresponding voxel is marked as an obstacle voxel. In this way, obstacle data is uniformly encoded into voxel units compatible with the terrain model. Finally, the boundaries of all voxel units are aligned with the X, Y, and Z axes of the waterway coordinate system, thus ensuring ease of indexing and calculation in the subsequent path planning process.

[0036] S1.1.4: Three-dimensional voxel units are superimposed and embedded into a three-dimensional terrain surface model to obtain a three-dimensional environment model containing obstacle distribution.

[0037] S1.1.5: Assign obstacle weights to each 3D voxel unit that contains or is adjacent to an obstacle.

[0038] Conventional methods typically employ binarization, marking obstacle voxels as "impassable" and non-obstacle voxels as "passable." This approach fails to reflect the potential risks in the obstacle's neighborhood, easily leading to planned inspection paths running too close to obstacle boundaries, creating collision hazards. To address this, this invention proposes calculating obstacle weights based on a combination of obstacle distance and scale factors, enabling inspection path planning to perceive the obstacle's influence range and size.

[0039] The specific calculation method is as follows: the obstacle weight is calculated by multiplying the inverse of the distance between the obstacle and the center point of the three-dimensional voxel unit with the obstacle scale factor, so as to reflect the comprehensive impact of the obstacle's position and volume on local spatial risk.

[0040] The distance between the obstacle and the center point of the three-dimensional voxel unit can be calculated using Euclidean distance.

[0041] Preferably, the obstacle scale factor is a quantitative parameter calculated based on the spatial dimensions of the obstacle. It is obtained by normalizing the volume of the obstacle in the three-dimensional coordinate system or the projected area in the inspection direction, and is used to characterize the impact of the size of the space occupied by the obstacle on the risk of the local inspection path.

[0042] Using the above calculation method, the closer an obstacle is to the center point of a voxel, or the larger its spatial scale, the higher its obstacle weight value, thereby effectively improving the ability to avoid high-risk areas during the inspection path planning process.

[0043] S1.2: Generate the initial inspection path set.

[0044] S1.2.1: Project the three-dimensional environment model onto a two-dimensional plane and divide it into inspection strips along the width of the channel. The width of the inspection strip is the effective scanning bandwidth of the UAV. The two-dimensional plane is the channel length × channel width.

[0045] S1.2.2: Starting from the channel starting point, a longitudinal main path is generated along the channel length direction; the longitudinal main path is formed by connecting the left and right boundary points segment by segment and accumulating them by using a Dijkstra search-based method between each section to avoid three-dimensional voxel units with obstacle weight values ​​higher than the preset weight threshold.

[0046] It should be noted that when generating the inspection path, a longitudinal master path must first be established as the skeleton for the subsequent lateral scan path generation. Conventional methods often involve generating a straight trajectory directly along the channel centerline, but this approach cannot guarantee safety in the presence of obstacles or significant water depth fluctuations. This invention proposes a longitudinal path generation method based on Dijkstra's search, which can form a more robust longitudinal master path while considering obstacle weights.

[0047] In this embodiment of the invention, the specific operations are as follows: First, the channel is divided into multiple sections along its length, each section consisting of transverse grid points. Second, the left and right boundary points on each section are selected as candidate nodes, and the connectivity cost between nodes in adjacent sections is calculated. The connectivity cost is obtained by weighting distance and obstacle weights. Third, Dijkstra's search algorithm is used to expand the channel from the starting point downstream section by section, selecting the node connection sequence with the minimum cumulative connectivity cost. Finally, the trajectories of all connected nodes are spliced ​​together to form the longitudinal main path.

[0048] In this way, the longitudinal main path can not only run through the entire length of the waterway, but also effectively avoid dangerous areas where the obstacle weight is higher than the threshold.

[0049] S1.2.3: Using the longitudinal main path as the skeleton, transverse scanning paths are generated sequentially on the left and right sides of each longitudinal path segment. If a voxel unit with an obstacle weight higher than the preset weight threshold is encountered during the scanning process, the serpentine connection continues after local bypass.

[0050] It should be noted that after generating the longitudinal main path, to ensure that the inspection coverage meets the full width of the waterway, lateral scan paths need to be generated on both sides of the longitudinal path. The lateral scan paths are formed by sequentially arranging scan lines along the left and right sides of each segment of the longitudinal path to cover the areas not covered by the longitudinal path, thus creating a serpentine scan trajectory.

[0051] In this embodiment of the invention, the specific operations include: First, an initial set of lateral path points is generated at the start and end points of each path segment along the width of the airway. The spacing between the path points is set according to the effective scanning bandwidth of the UAV, so that the scanning path can cover the left and right sides of the longitudinal path. Then, adjacent path points are connected in sequence to form lateral scanning path segments, forming a continuous serpentine trajectory.

[0052] During the connection process, if the obstacle weight of the corresponding 3D voxel unit is found to be higher than the preset threshold for any path point, local bypass is performed at the path point. That is, under the condition of not exceeding the maximum offset distance, the position of the path point is adjusted to bypass the obstacle while maintaining the continuity of the path segment.

[0053] S1.2.4: Combine the vertical main path and the horizontal serpentine scan path to obtain a mixed path set.

[0054] First, the longitudinal main path is segmented, with the length of each segment matching the strip length of the transverse scanning path. Second, transverse paths are inserted on both sides of each segment in a serpentine pattern, ensuring that the start and end points of the transverse paths are precisely connected to the nodes of the longitudinal main path. Third, a path smoothing algorithm (such as B-spline interpolation) is used to locally adjust the splicing points to avoid abrupt path turns that could lead to flight instability. Finally, a set of interwoven longitudinal and transverse paths is obtained.

[0055] The paths generated in this way ensure both the continuity of inspections and the coverage of the waterway area, providing a variety of candidate solutions for subsequent path optimization.

[0056] It should be noted that while using only the vertical main path ensures path continuity, it results in insufficient coverage in the horizontal direction. Conversely, using only the horizontal serpentine scanning path provides high coverage but is lengthy and lacks global guidance. Therefore, this invention employs a path splicing operation, using the vertical main path as the overall framework and embedding horizontal serpentine paths into each vertical path segment to achieve a combination of vertical and horizontal coverage.

[0057] S1.2.5: Calculate coverage and theoretical energy consumption for the generated mixed path set: Coverage is obtained by statistically analyzing the ratio of the number of three-dimensional voxel units traversed by the mixed path to the total number of three-dimensional voxel units in the channel; theoretical energy consumption is calculated by weighting the mixed path length with the obstacle weights of the three-dimensional voxel units traversed.

[0058] S1.2.6: Remove mixed paths with coverage rates below the coverage threshold or theoretical energy consumption above the energy consumption threshold, output the initial inspection path set, and retain the coverage index and theoretical energy consumption information of each initial inspection path.

[0059] Through the above filtering operations, the final initial inspection path set not only has high coverage efficiency, but also ensures feasibility under energy consumption constraints, thus providing reliable inspection path candidates for actual UAV route inspection.

[0060] S2: Using the dynamic obstacle trajectories collected by airborne radar and cameras, predict and generate a time-varying obstacle envelope sequence, and perform spatiotemporal superposition calculation with the initial inspection path to remove path segments that conflict with the future movement range of obstacles, thus obtaining the inspection path set after obstacle avoidance correction.

[0061] S2.1: Predict and generate a time-varying sequence of obstacle envelopes.

[0062] Obstacle trajectory data, including position and velocity vectors, is collected using airborne radar and cameras. The collected obstacle trajectory data is time-synchronized and coordinate-mapped to be consistent with the coordinate system of the three-dimensional environment model. For each dynamic obstacle, a future position prediction sequence is generated using a standard Kalman filter. A three-dimensional envelope of the spatial range is generated for the predicted position, and a valid time interval is added to each three-dimensional envelope to form a set of obstacle envelope sequences.

[0063] S2.2: Eliminate path segments that conflict with the future movement range of obstacles.

[0064] After obtaining the obstacle envelope sequence set, it is necessary to compare it point by point with the initial inspection path set to identify and eliminate conflicting path segments, thereby obtaining the path set after obstacle avoidance correction.

[0065] The above process not only considers whether the path point coincides with the obstacle location, but also maps the expected arrival time of the path point to the time window of the obstacle envelope to ensure spatiotemporal consistency.

[0066] S2.2.1: For each inspection path in the initial inspection path set, take the inspection path point set, where each inspection path point includes three-dimensional coordinates, namely horizontal position, vertical position and height information.

[0067] This discretization method transforms the path from a continuous curve into a finite sequence of discrete points, facilitating subsequent point-by-point checks to determine whether it overlaps with the obstacle envelope.

[0068] S2.2.2: Determine the estimated arrival time of the inspection path points based on the UAV's flight speed and the distance between inspection path points.

[0069] Determining the estimated arrival time of inspection path points includes: assigning an initial takeoff time to the starting point of the inspection path; for any point on the inspection path, the estimated arrival time is obtained by adding the ratio of the spatial distance between the two points to the UAV's flight speed to the estimated arrival time of the previous point.

[0070] The spatial distance between two points is calculated by the difference in three-dimensional coordinates.

[0071] The drone's flight speed is a preset constant inspection speed, set according to the drone model parameters and inspection task requirements. This constant speed assumption simplifies the time calculation process and avoids prediction uncertainties caused by real-time speed fluctuations.

[0072] S2.2.3: For each inspection path point, check whether it falls within the spatial range of any obstacle envelope in the obstacle envelope sequence, and whether the expected arrival time is within the time window corresponding to the obstacle envelope. If both spatial and temporal conditions are met, it indicates that the UAV will enter the future location area of ​​the obstacle at the corresponding time point, i.e., there is a situation where it falls into the obstacle envelope, thus constituting a path conflict. In this case, the corresponding inspection path point should be marked as a conflict point.

[0073] If there are consecutive conflict points (e.g., at least 3 consecutive conflict points), the corresponding inspection path segment will be removed from the original inspection path. The inspection path set after obstacle avoidance correction will be generated, and the start and end points of the removed inspection path segments will be marked.

[0074] It can be seen that the inspection path set after obstacle avoidance correction avoids conflict with the future movement range of dynamic obstacles, and improves the feasibility and safety of the inspection path.

[0075] S3: Based on the elevation information in the 3D environment model, predict the local areas and spatial ranges where communication may be blocked in the UAV inspection path, perform secondary correction on the inspection path segment that is about to enter the communication blockage area, and output the corresponding flight altitude and speed sequence.

[0076] S3.1: Predict the local areas and spatial ranges where communication may be obstructed during the UAV inspection path.

[0077] First, each inspection path in the set of inspection paths after obstacle avoidance correction is spatially mapped to the three-dimensional environment model to obtain the corresponding terrain height of the inspection path points.

[0078] The mapping operation involves spatially matching the horizontal and vertical coordinates of each inspection path point with the grid or continuous surface of the 3D environment model, and obtaining the elevation values ​​of the corresponding grid points or interpolated surfaces, which facilitates the calculation of the relative height difference between the UAV and ground obstacles.

[0079] Through the above mapping, each inspection path point not only has its own spatial coordinates, but also corresponding terrain elevation information, providing an accurate basis for communication obstruction determination.

[0080] Secondly, for each inspection path point, the elevation difference with the terrain is calculated. If it is less than the set minimum height threshold for drone communication safety, it indicates that the drone may be in an area where communication signals are blocked, and the inspection path point is marked as potentially having a communication obstruction risk.

[0081] The minimum altitude threshold for drone communication security can be set autonomously based on the characteristics of the drone communication link, antenna gain, signal attenuation parameters, and safety requirements of the inspection mission, so as to ensure that the drone can maintain basic communication quality near the marked point.

[0082] The inspection path points with continuous communication blockage risk are merged, and the inspection path segments with communication blockage risk are output, and the spatial range and the expected time window are recorded.

[0083] S3.2: Perform secondary correction on the inspection path segment that is about to enter the communication shielded area.

[0084] S3.2.1: Overlay the spatial range of the communication obstruction area with the inspection path set after obstacle avoidance correction to determine the inspection path segment that is about to enter the obstruction area, and record the start and end positions, time window and current flight altitude for each inspection path segment.

[0085] The overlay operation includes aligning the path segment with the original path in three-dimensional space and combining it with time window information to determine the specific inspection path segment that the UAV will enter during the inspection process.

[0086] During the recording process, each inspection path segment includes the start point, end point, expected time window, and current flight altitude, providing precise spatial and temporal constraints for subsequent alternative path planning.

[0087] S3.2.2: Uncovered areas in the 3D environment model whose distance from the inspection path segment about to enter the covert area does not exceed a preset distance threshold are identified as candidate alternative inspection path segments to ensure the continuity of the inspection path and meet the flight altitude and obstacle avoidance constraints.

[0088] These candidate alternative inspection route segments are spatially adjacent to the shielded area, which can ensure the continuity of the route while meeting flight altitude constraints and obstacle avoidance requirements.

[0089] S3.2.3: A comprehensive score is given to the candidate alternative inspection path segments, and the optimal solution is selected based on a weighted calculation of changes in flight altitude, obstacle weight, increase in inspection path length, and communication security priority.

[0090] The path segment about to enter the obstruction zone is replaced with an optimized alternative path segment, forming a set of inspection paths corrected for communication obstruction. This updated path set not only considers obstacle avoidance and communication safety but also outputs the flight altitude and speed sequence for each path, providing complete execution data for subsequent UAV inspection tasks. Through these operations, the UAV can dynamically adapt to communication obstruction risks in complex flight path environments, achieving safe and continuous inspections.

[0091] For each inspection path segment, the drone's flight altitude, terrain elevation, and spatial distance to surrounding obstacles are acquired along the path. By comparing the difference between the drone's flight altitude and the terrain elevation, if the difference is close to or below the minimum communication security altitude threshold, the communication security risk of the path segment increases, and the corresponding communication security priority is set to a higher value. Conversely, if the flight altitude is significantly higher than the surrounding terrain and obstacles are sparse, the communication security risk is low, and the communication security priority is set to a lower value. Specific settings can be adjusted as needed.

[0092] This embodiment also provides a computer device applicable to the UAV route inspection path optimization method based on dynamic environment perception, including a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the UAV route inspection path optimization method based on dynamic environment perception as proposed in the above embodiment.

[0093] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0094] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements the method for optimizing UAV airway inspection paths based on dynamic environment perception as proposed in the above embodiments.

[0095] In summary, this invention establishes a three-dimensional environment model by integrating airway geographic information data and survey point cloud data, enabling comprehensive and fine-grained inspection path planning. By combining obstacle trajectory data collected by airborne radar and cameras, it predicts and generates a time-varying obstacle envelope sequence and performs spatiotemporal overlay comparison of the path, enabling the elimination and avoidance of potential future conflict segments, thereby improving path safety and continuity. Furthermore, this invention also incorporates a prediction and secondary correction mechanism for communication obstruction areas, dynamically adjusting flight altitude and speed in complex terrain or highly obstructed environments to ensure data transmission stability.

[0096] Through the above-mentioned multi-level data processing, the present invention can provide more efficient, reliable and intelligent path planning support for UAVs in airway inspection tasks.

[0097] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for optimizing UAV airway inspection paths based on dynamic environment perception, characterized in that: include: Based on waterway geographic information data and survey point cloud data, a three-dimensional environment model is established, and an initial inspection path set with sufficient coverage is generated. Using the dynamic obstacle trajectories collected by airborne radar and cameras, a time-varying obstacle envelope sequence is predicted and generated. This sequence is then spatiotemporally superimposed with the initial inspection path. Path segments that conflict with the future movement range of obstacles are removed, resulting in a set of inspection paths after obstacle avoidance correction. Based on the elevation information in the 3D environment model, the local areas and spatial ranges where communication may be blocked in the UAV inspection path are predicted, and the inspection path segments that are about to enter the communication blockage area are corrected for the second time, and the corresponding flight altitude and speed sequences are output.

2. The UAV route inspection path optimization method based on dynamic environment perception as described in claim 1, characterized in that: The three-dimensional environment model includes the channel length, channel width, water depth, topographic relief, and obstacle locations.

3. The UAV route inspection path optimization method based on dynamic environment perception as described in claim 2, characterized in that: The generation of the three-dimensional environment model includes: The channel survey point cloud data is divided into a two-dimensional grid with fixed spatial resolutions Δx and Δy, and each grid point records the average water depth value. Based on the water depth data of the two-dimensional grid, a continuous three-dimensional terrain surface model is generated by triangulation interpolation. The spatial range of the three-dimensional terrain surface model corresponds to the channel length and channel width, and reflects the water depth undulation in the vertical direction. In the channel coordinate system, obstacle detection data is mapped to three-dimensional voxel units, and the boundaries of the three-dimensional voxel units are aligned with the channel coordinate system. The three-dimensional voxel units are superimposed and embedded into the three-dimensional terrain surface model to obtain a three-dimensional environment model containing the distribution of obstacles; Assign obstacle weights to each 3D voxel unit that contains or is adjacent to an obstacle; The obstacle weight is calculated by multiplying the inverse of the distance between the obstacle and the center point of the three-dimensional voxel unit by the obstacle scale factor.

4. The UAV route inspection path optimization method based on dynamic environment perception as described in claim 3, characterized in that: The generation of the initial inspection path set includes: The three-dimensional environment model is projected onto a two-dimensional plane, and inspection strips are divided along the width of the channel. The width of the inspection strip is the effective scanning bandwidth of the UAV. The two-dimensional plane is the channel length × channel width. Starting from the beginning of the channel, a longitudinal main path is generated along the length of the channel. The longitudinal main path is formed by connecting the left and right boundary points segment by segment and accumulating them by using a Dijkstra search-based method between each section to avoid three-dimensional voxel units with obstacle weight values ​​higher than a preset weight threshold. Using the main vertical path as the skeleton, horizontal scanning paths are generated sequentially on the left and right sides of each vertical path segment. If a voxel unit with an obstacle weight higher than the preset weight threshold is encountered during the scanning process, the serpentine connection continues after local bypass. The vertical main path and the horizontal serpentine scan path are concatenated to obtain a mixed path set; For the generated set of mixed paths, calculate coverage and theoretical energy consumption: The coverage rate is obtained by statistically analyzing the ratio of the number of three-dimensional voxel units traversed by the mixed path to the total number of three-dimensional voxel units in the channel. The theoretical energy consumption is calculated by weighting the hybrid path length with the obstacle weights passing through the three-dimensional voxel unit; Remove mixed paths with coverage rates below the coverage threshold or theoretical energy consumption above the energy consumption threshold, output the initial inspection path set, and retain the coverage index and theoretical energy consumption information of each initial inspection path.

5. The UAV route inspection path optimization method based on dynamic environment perception as described in claim 4, characterized in that: The predicted obstacle envelope sequence that varies over time includes: Obstacle trajectory data, including position and velocity vectors, is collected using airborne radar and cameras. The collected obstacle trajectory data is then synchronized in time and mapped to coordinates to ensure consistency with the coordinate system of the 3D environment model. For each dynamic obstacle, a future position prediction sequence is generated using a standard Kalman filter, and a three-dimensional envelope of the spatial range is generated for the predicted position. A valid time interval is added to each three-dimensional envelope to form a set of obstacle envelope sequences.

6. The UAV route inspection path optimization method based on dynamic environment perception as described in claim 5, characterized in that: The path segments to be eliminated that conflict with the future movement range of obstacles include: For each inspection path in the initial inspection path set, take the inspection path point set, where each inspection path point includes three-dimensional coordinates; The estimated arrival time of the inspection path points is determined based on the drone's flight speed and the distance between inspection path points. For each inspection path point, check whether it falls within the spatial range of any obstacle envelope in the obstacle envelope sequence, and whether the expected arrival time is within the corresponding time window; If a point falls within the envelope of an obstacle, then the inspection path point is marked as a conflict point. If there are consecutive conflict points, the corresponding inspection path segments will be removed from the original inspection path. The inspection path set after obstacle avoidance correction will be generated, and the start and end points of the removed inspection path segments will be marked.

7. The UAV route inspection path optimization method based on dynamic environment perception as described in claim 6, characterized in that: The estimated arrival time of the determined inspection route points includes: Assign the initial takeoff time to the starting point of the inspection route; For any point on the inspection path, the estimated arrival time is obtained by adding the ratio of the spatial distance between the two points to the drone's flight speed to the estimated arrival time of the previous point. The spatial distance between the two points is calculated by the three-dimensional coordinate difference; The drone's flight speed is a preset constant inspection speed, set according to the drone model parameters and inspection task requirements.

8. The UAV route inspection path optimization method based on dynamic environment perception as described in claim 7, characterized in that: The predicted local areas and spatial ranges where communication may be blocked in the UAV inspection path include: Each inspection path in the set of inspection paths after obstacle avoidance correction is spatially mapped to the 3D environment model to obtain the corresponding terrain height of the inspection path point; For each inspection path point, calculate the elevation difference with the terrain. If it is less than the set minimum height threshold for UAV communication safety, mark the inspection path point as potentially having a communication blockage risk. The inspection path points with continuous communication blockage risk are merged, and the inspection path segments with communication blockage risk are output, and the spatial range and the expected time window are recorded.

9. The UAV route inspection path optimization method based on dynamic environment perception as described in claim 8, characterized in that: The secondary correction of the inspection path segment that is about to enter the communication obstruction area includes: The spatial range of the communication obstruction area is superimposed with the inspection path set after obstacle avoidance correction to determine the inspection path segment that is about to enter the obstruction area. The start and end positions, time window and current flight altitude of each inspection path segment are recorded. Uncovered areas in the 3D environment model whose distance from the inspection path segment about to enter the covert area does not exceed a preset distance threshold are identified as candidate alternative inspection path segments to ensure the continuity of the inspection path and meet the flight altitude and obstacle avoidance constraints. The candidate alternative inspection path segments are comprehensively scored, and the optimal solution is selected based on a weighted calculation of changes in flight altitude, obstacle weight, increase in inspection path length, and communication security priority. The inspection path segment that is about to enter the obscured area is replaced with the optimized alternative inspection path segment, forming a set of inspection paths after communication obscuration correction.

10. The method for optimizing UAV airway inspection paths based on dynamic environment perception as described in claim 9, characterized in that: The obstacle scale factor is a quantitative parameter calculated based on the spatial dimensions of the obstacle. It is obtained by normalizing the volume of the obstacle in the three-dimensional coordinate system or the projected area in the inspection direction, and is used to characterize the impact of the size of the space occupied by the obstacle on the risk of the local inspection path.

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