Unmanned aerial vehicle autonomous path planning and high-precision surveying and mapping integrated system

Abstract

The invention discloses an unmanned aerial vehicle autonomous path planning and high-precision surveying and mapping integrated system, and relates to the technical field of unmanned aerial vehicle surveying and mapping. The system comprises a terrain modeling module, a task configuration and scheduling module, a path planning module, an unmanned aerial vehicle flight control module, a surveying and mapping sensing module, a data caching and transmission module and a ground control and result processing module. The terrain modeling module constructs three-dimensional terrain modeling through remote sensing image interpretation and a digital elevation model; the path planning module fuses simulated annealing, an adaptive genetic algorithm and a large neighborhood search algorithm to realize route static optimization and multi-aircraft cooperative path generation; the flight control module autonomously completes flight according to the path instruction; the data caching module provides breakpoint resume and edge relay support; and the ground platform completes image processing, splicing modeling and result exporting. According to the invention, intelligent route planning and high-precision and multi-source surveying and mapping data acquisition under complex terrains are realized, and the surveying and mapping automation level and the data accuracy are remarkably improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of unmanned aerial vehicle surveying and mapping technology, and particularly relates to an unmanned aerial vehicle autonomous path planning and high-precision surveying and mapping integrated system. BACKGROUND

[0002] In the current surveying and mapping operation practice, unmanned aerial vehicles have been widely used in geographic information collection, terrain modeling, disaster monitoring and other fields due to their strong maneuverability, low cost and high resolution. A typical unmanned aerial vehicle surveying and mapping system usually relies on a preset route to perform a task, and surveying and mapping data is mainly collected through images, laser radars or GNSS positioning devices, and image stitching and result output are completed with ground software, which basically meets the operation needs of small range and flat terrain.

[0003] However, in the prior art, most systems lack intelligent route planning function based on real terrain data, resulting in repeated routes, high occlusion rate or unreasonable overlapping of flight strips in complex terrain; surveying and mapping operations often rely on manual configuration of routes, lack of automatic task division and multi-unmanned aerial vehicle coordination mechanism, and seriously affect the surveying and mapping efficiency; the perception module of the unmanned aerial vehicle is scattered during flight, data synchronization is difficult, and position information drift or surveying and mapping image mismatch easily occurs, thereby affecting the result accuracy. In addition, if the link is unstable during data transmission, data loss and result interruption are also easily caused, affecting the continuity and reliability of the system.

[0004] In view of the above problems, the present application provides an unmanned aerial vehicle autonomous path planning and high-precision surveying and mapping integrated system, which automatically plans a route under the constraint of real three-dimensional terrain, realizes multi-machine cooperation and path reconstruction by fusing multiple optimization algorithms, and realizes high-precision, high-efficiency and high-stability closed-loop control of the whole surveying and mapping process by combining multi-source sensor synchronous perception and breakpoint transmission mechanism. SUMMARY

[0005] The present application provides an unmanned aerial vehicle autonomous path planning and high-precision surveying and mapping integrated system to solve the problems of the prior art that the unmanned aerial vehicle route cannot be autonomously optimized, multi-machine tasks are difficult to be cooperatively allocated, and the surveying and mapping data collection accuracy and stability are insufficient.

[0006] To solve the above technical problems, the present application provides the following technical scheme: an unmanned aerial vehicle autonomous path planning and high-precision surveying and mapping integrated system, comprising: a terrain modeling module for acquiring remote sensing images and generating a digital elevation model and a three-dimensional terrain model of a target area, the terrain modeling module comprising a remote sensing image access unit, an image interpretation unit, a DEM generation unit and a three-dimensional modeling unit; The task configuration and scheduling module is configured to receive surveying and mapping task requirements, divide surveying areas, and perform unmanned aerial vehicle scheduling and allocation. The path planning module is electrically connected to the terrain modeling module and the task configuration and scheduling module, and is configured to generate an optimized flight path according to three-dimensional terrain data and task allocation information. The unmanned aerial vehicle flight control module is electrically connected to the path planning module, and is configured to control the unmanned aerial vehicle to automatically perform a flight task according to the flight path instruction. The surveying and mapping sensing module is electrically connected to the flight control module, and is configured to perform image acquisition, position recording, and elevation measurement during flight. The data buffering and transmission module is electrically connected to the surveying and mapping sensing module, and is configured to buffer, encode, and wirelessly transmit collected data. The ground control and result processing module is connected to the wireless communication interface unit, and is configured to receive surveying and mapping data and complete image processing, result modeling, and output.

[0007] The image interpretation unit is configured to extract terrain and landform features, object boundaries, and shadow areas in the image, and input the extracted structured interpretation data to the DEM generation unit through a data channel for subsequent elevation modeling and three-dimensional reconstruction.

[0008] The static path planning unit uses the minimum circumscribed rectangle method to geometrically fit the survey area boundary, extracts the main heading angle as the reference direction of the flight path, and uses the simulated annealing algorithm to globally optimize the flight strip spacing and flight path arrangement position under the constraints of elevation and survey area coverage rate, to ensure that the flight strip overlap rate is maintained within a predetermined range and to reduce excessive flight path overlap caused by terrain changes.

[0009] The dynamic path optimization unit reconstructs the path in real time under the sudden environmental change and realizes emergency avoidance according to the real-time collected meteorological information, the human flow and vehicle flow data in the survey area and the temporary obstacle dynamic environmental elements, by constructing a multi-constraint route adjustment model, introducing an adaptive genetic algorithm to optimize the unmanned aerial vehicle path node sequence, and combining a large neighborhood search algorithm to perform disturbance and repair operations on the generated path.

[0010] The cooperative path planning unit avoids the intersection of the flight paths of multiple unmanned aerial vehicles, the overlapping coverage of the survey area and the waste of resources by constructing a multi-unmanned aerial vehicle task allocation game model, combining the load capacity, the residual power, the distance of the voyage and the task priority parameters of the unmanned aerial vehicle, and adopting an allocation strategy based on the game equilibrium solution.

[0011] The image acquisition unit is composed of a multi-spectral imaging device with an adjustable viewing angle, has a multi-band shooting capability, is connected with a GNSS receiving unit and an image registration and time synchronization unit through a high-speed bus to form a synchronous triggering mechanism, and ensures that each frame of the collected image is paired with the corresponding position and time data.

[0012] The data buffering unit adopts a double buffering structure of a high-bandwidth input channel and a ring buffer mechanism, has a frame index table and a time stamp recording mechanism, can identify the image frames and the corresponding position data according to the acquisition order, records the frame number and the data section that have been transmitted through the breakpoint resume transmission control unit when the communication is abnormal, repositions the buffer position after the communication is restored, continues the data transmission from the interrupted frame, avoids repeated transmission and data omission.

[0013] The result evaluation unit is used for performing spatial registration comparison between the orthographic image in the surveying and mapping result and the three-dimensional terrain model generated in the modeling stage, calculating the image overlap error, the height deviation and the feature boundary offset, and feeding back the evaluation result to the path planning module through a data return link, so that the route is adjusted and the task is deployed again.

[0014] Compared with the prior art, the present application has the following beneficial effects: The present application obtains external high-resolution remote sensing image data through the remote sensing image access unit and the image interpretation unit in the terrain modeling module, combines the DEM generation unit and the three-dimensional modeling unit, extracts the ground structure features by using a ground feature recognition model based on a convolutional neural network, automatically constructs the digital elevation model and the three-dimensional terrain map of the survey area, and realizes the structured conversion from the original image to the available terrain data.

[0015] Based on the modeling result, the static path planning unit and the dynamic path optimization unit jointly perform path design in the path planning stage, the simulated annealing algorithm is used to control the overlap degree of the flight strip, and the adaptive genetic algorithm is used to optimize the flight path layout and path sequence, so that the shielding area is avoided, the turning point is optimized, and the path coverage is improved, and the traditional rule mowing type path mode is effectively replaced.

[0016] The result evaluation module is arranged in the ground processing platform, and internally includes an image error comparison unit and a surveying and mapping accuracy evaluation unit, can automatically register the unmanned aerial vehicle surveying and mapping image obtained after flight and the original remote sensing image or historical geographic database, and utilize the image error matching algorithm based on SIFT feature points and the RTK control point difference analysis method to judge the surveying and mapping error.

[0017] The high-speed data cache unit and the breakpoint resume control unit are arranged in the data cache and transmission module, the cache unit adopts a ring double-buffer structure, continuous acquisition and batch uploading can be realized in flight, the breakpoint resume control unit is based on a frame number recording mechanism and cache residual judgment logic, can automatically pause data sending when communication is interrupted, and can resume transmission from the breakpoint position after link recovery, realize stable, complete and high-frequency data return, and effectively guarantee data without loss and omission in long-distance surveying and mapping or complex environment. BRIEF DESCRIPTION OF DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments, and understand that the following drawings only show some embodiments of the present application, and should not be regarded as limiting the scope, and for those skilled in the art, other related drawings can also be obtained according to these drawings without creative labor.

[0019] Figure 1 It is a system architecture diagram of the present application. DETAILED DESCRIPTION

[0020] In order to make the purposes, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative labor fall within the protection scope of the present application. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the claimed present application, but only for selected embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative labor fall within the protection scope of the present application.

[0021] Please refer to Figure 1 It is a schematic diagram of an unmanned aerial vehicle autonomous path planning and high-precision mapping integrated system provided by an embodiment of the present application, comprising: a terrain modeling module, a task configuration and scheduling module, a path planning module, an unmanned aerial vehicle flight control module, a mapping and perception module, a data caching and transmission module, a ground control and result processing module, each functional module cooperates with each other through electrical connection, data link or wireless communication channel, and constitutes a complete mapping closed loop system, and the structures of each module are as follows: The terrain modeling module comprises a remote sensing image access unit, an image interpretation unit, a DEM generation unit and a three-dimensional modeling unit. The task configuration and scheduling module comprises a task input unit, a task decomposition unit, a collaborative scheduling unit and a soft time window control unit. The path planning module comprises a static path planning unit, a dynamic path optimization unit, a collaborative path planning unit and a path evaluation unit. The unmanned aerial vehicle flight control module comprises a flight control instruction execution unit, an attitude and height perception unit and a task execution interface unit. The mapping and perception module comprises a laser range finder unit, a GNSS receiving unit, an image acquisition unit and an image registration and time synchronization unit. The data caching and transmission module comprises a data caching unit, a wireless communication interface unit, a breakpoint resume transmission control unit and an edge relay unit. The ground control and result processing module comprises an image preprocessing unit, an image stitching and modeling unit, a result exporting unit and a result evaluation unit.

[0022] The connection relationship, electrical signal path, function and role of each module in the system are described in combination with each stage, wherein each stage includes: a terrain modeling stage, a task configuration and planning stage, a path planning stage, a flight and data acquisition stage, a data transmission and caching stage, and an image processing and result output stage. I. Terrain modeling stage In the terrain modeling stage, the image data processing unit in the ground control and result processing module first calls the remote sensing image access unit in the terrain modeling module to obtain pre-collected high-resolution remote sensing images or historical aerial images. The accessed image data is transmitted to the image interpretation unit through electrical connection for automatic identification and classification of the topographic and geomorphic features, ground object boundaries and shadow areas in the survey area.

[0023] Subsequently, the interpretation results are transmitted from the output end of the image interpretation unit to the DEM generation unit through the data bus, and the DEM generation unit generates a digital elevation model (DEM) of the target survey area based on the brightness, texture and band combination information of the remote sensing image, combined with the pixel elevation value. The DEM data is further transmitted to the three-dimensional modeling unit to build a three-dimensional terrain model containing relief, occlusion and spatial structure information, which can truly reflect the changes of ground topography, obstacle distribution and survey area boundary characteristics.

[0024] The output end of the above-mentioned three-dimensional modeling unit is connected to the data input interface of the static path planning unit in the path planning module through electrical connection, which is used to provide basic terrain environment input parameters for flight path planning. Through the integrated processing of this stage, the whole process from remote sensing image access, terrain feature extraction, DEM construction and three-dimensional terrain modeling is completed.

[0025] II. Task configuration and planning stage In the task configuration and scheduling stage, the user first inputs the survey area boundary range, task priority level, surveying and mapping accuracy requirement and time window parameters through the task configuration interface unit in the ground control and result processing module. The above parameters are transmitted to the task input unit in the task configuration and scheduling module through control instructions by the ground platform internal bus.

[0026] The task input unit analyzes the user's requirements, extracts the task target information, and outputs it to the task decomposition unit. The task decomposition unit divides the large survey area into several logical sub-tasks according to the geometric range and operation density requirements of the survey area, each sub-task containing independent spatial area, operation index and estimated time consumption data.

[0027] After the division is completed, the subtask data is transmitted to the cooperative scheduling unit, which comprehensively considers the existing number of unmanned aerial vehicles, the urgency of the task and the flight capability, intelligently allocates the task, and determines which unmanned aerial vehicle or unmanned aerial vehicles execute each subtask; if the task has a time-adjustable characteristic, the scheduling scheme will reasonably arrange the task execution period in combination with the constraint information provided by the soft time window control unit, to ensure that the overall operation achieves the optimal scheduling effect under the resource constraint condition.

[0028] Finally, the cooperative scheduling result is output in the form of a task instruction package to the cooperative path planning unit in the path planning module, which is used for the next stage of flight path generation. The transmission is completed through the electrical connection between the output interface of the task scheduling module and the input interface of the path planning module.

[0029] III. Path planning stage In the path planning stage, the system starts the path planning module to complete the flight path design and optimization of the unmanned aerial vehicle. The path planning module receives input information from two directions: On the one hand, the output end of the three-dimensional modeling unit in the terrain modeling module is electrically connected to the input end of the static path planning unit through a data bus, providing three-dimensional terrain data including elevation, obstruction, slope, etc. On the other hand, the output end of the cooperative scheduling unit in the task configuration and scheduling module is electrically connected to the input end of the cooperative path planning unit through a task link, providing scheduling information such as task division, unmanned aerial vehicle allocation, and time window.

[0030] Firstly, the static path planning unit extracts the heading angle based on the survey area boundary and the DEM terrain model using the minimum circumscribed rectangle method, and performs preliminary strip cutting type flight path laying. To cope with the complexity of the terrain, the unit further calls its built-in simulated annealing algorithm submodule to adaptively optimize the flight path spacing according to the elevation fluctuation and obstruction characteristics, control the lateral overlap degree to keep it within a specified range, improve the survey area coverage rate and reduce redundant image overlap, thereby improving the utilization rate of flight path number and data quality.

[0031] Subsequently, the planning data is output from the static path planning unit to the dynamic path optimization unit, which integrates multi-source dynamic environmental information including wind speed, pedestrian and vehicle flow density, temporary obstacles, etc., and combines with the adaptive genetic algorithm or large neighborhood search (ALNS) algorithm submodule to insert an environmental response adjustment mechanism in the flight path sequence. The system reorders the flight path, transfers the task and slides the time according to the soft time window constraint, to ensure that the flight path still has the feasibility and priority control ability under the real-time environmental change.

[0032] For multiple UAVs cooperative task, the cooperative path planning unit synthesizes static and dynamic path calculation results to build a multi-task and multi-path joint optimization model; the unit uses a distributed cooperative optimization algorithm game theory strategy to optimize the task allocation and path scheduling among multiple UAVs, avoiding the problems of route overlap, flight conflict or long waiting time; the system can dynamically evaluate the multi-UAV task load according to the remaining range of UAV, power consumption model, path complexity, etc., and generate the optimal multi-UAV cooperative path set.

[0033] Finally, the overall path planning result is quantitatively evaluated by the path evaluation unit, which forms a scoring model by comprehensively weighting multiple indicators such as shortest distance, complete coverage, least number of turns, and lowest path overlap rate, and optimizes different path combination schemes; the optimal result will be packaged as a flight task plan and output to the flight control instruction execution unit in the UAV flight control module through electrical connection for subsequent task execution phase.

[0034] Four, flight and data acquisition phase In the flight and data acquisition phase, the task path planning result is output from the path evaluation unit in the path planning module and transmitted to the flight control instruction execution unit in the UAV flight control module through electrical connection; the flight control instruction execution unit analyzes the task instruction package and automatically schedules the UAV to start the flight process.

[0035] The flight control instruction execution unit is electrically connected to the attitude and height perception unit for real-time reception of attitude, height and environmental change signals from the IMU (inertial measurement unit) and laser range finder feedback, realizing flight attitude adjustment and altitude dynamic correction; if a large fluctuation area is encountered during flight, the system realizes real-time adjustment of flight height through the laser range finder unit to ensure constant working distance, thereby improving data consistency.

[0036] When performing the surveying task, the task execution interface unit sends a synchronous control signal to activate the core perception sub-unit in the surveying perception module: The image acquisition unit starts the multispectral camera assembly and captures multi-channel images such as visible light and near-infrared light at preset intervals; The GNSS receiving unit synchronously records the precise geographic coordinates and time stamp at the time of each image shooting; the geographic coordinates include latitude, longitude and elevation; The laser range finder unit periodically emits laser pulses to obtain distance information and sparse point cloud data of ground targets; The above three types of raw data are synchronously sent to the image registration and time synchronization unit, which performs preliminary fusion and matching in space and time dimensions to ensure one-to-one correspondence between images and location information.

[0037] All sensor data is converged in the data buffer unit of the data buffer and transmission module through the communication bus in the UAV body. The buffer unit adopts a ring queue structure, stores the current frame data in real time, and marks the frame number, ensuring data sequence and integrity.

[0038] In this phase, the flight control module and the surveying and mapping perception module achieve high coordination among multiple units: the flight control system controls the flight path and rhythm, the surveying and mapping component perceives the environment and synchronously collects data, and the data buffer mechanism serves as a connecting hub, receiving multi-source data and smoothly outputting to the next phase.

[0039] V. Data transmission and buffer stage In the data buffer and transmission stage, all surveying and mapping data collected during flight, including multispectral images, GNSS positioning information, and laser ranging data, are packaged by the image registration and time synchronization unit in the surveying and mapping perception module and transmitted to the data buffer unit in the data buffer and transmission module through the data bus.

[0040] The data buffer unit is used for real-time temporary storage of multi-source data generated by sensors. Its structure has a high-bandwidth input channel and a ring buffer mechanism, which can ensure queuing of high-speed collected data within a short time and prevent data frame loss caused by communication delay or system response jitter. In addition, the buffer unit also has frame number indexing and timestamp calibration functions, facilitating subsequent data reconstruction and synchronous processing.

[0041] When the buffer data reaches the threshold at the specified period or buffer area, it is automatically pushed to the wireless communication interface unit. This unit encodes the buffer data packet in real time through the data transmission module carried by the UAV and sends it to the wireless receiving unit in the ground control and result processing module in the form of wireless signal. A stable data path can be established between the two ends through a conventional telemetry channel, 4G / 5G network, or dedicated frequency band communication link.

[0042] When the survey area environment is complex or the distance between the UAV and the ground station exceeds the limit, the system automatically dispatches the edge relay unit in the data buffer and transmission module. This relay unit, as a mobile communication node, is deployed at the boundary of the operation area. It receives data sent by the UAV and forwards it to the ground terminal, effectively expanding the communication radius and alleviating the problem of blind area interference.

[0043] To enhance system robustness, a breakpoint resume control unit is embedded in the wireless communication path to record data transmission status and interruption frame number. When communication anomalies (such as signal loss or bandwidth drop) occur, the buffer unit suspends transmission. After the link is restored, the breakpoint resume control unit instructs the buffer module to resume transmission from the interruption position, ensuring the integrity of the surveying and mapping data. The ground platform also has the ability to receive breakpoint markers and request retransmission, improving end-to-end data integrity and security.

[0044] Six, image processing and output stage In the image processing and output stage, the ground control platform receives the surveying and mapping data packet transmitted from the unmanned aerial vehicle end via the wireless link. The data is first decoded by the wireless receiving unit in the ground control and result processing module, the original data stream is restored, and then transmitted to the image preprocessing unit.

[0045] The image preprocessing unit corrects the image distortion caused by lens distortion, heading angle offset or uneven illumination by matching the GNSS timestamp and position information carried in the data frame header with the image data. The output end of the unit is connected to the image stitching and modeling unit through the internal data channel for further generation of visual surveying and mapping results.

[0046] In the image stitching and modeling unit, the system first extracts image feature points using the image overlap area, calls the image registration algorithm to align multiple images, and generates a complete orthophoto map. Then, the laser ranging data and high-precision GNSS positioning information are introduced to correct the image elevation and enhance the spatial positioning. The system fuses optical images and dense point clouds to reconstruct the three-dimensional terrain model and digital surface model (DSM) of the target area through modeling algorithms, supporting detailed restoration of ground object height, building outline and undulating topography.

[0047] After processing, the result data is output to the result export unit, which supports exporting surveying and mapping results in multiple formats, such as GeoTIFF format orthophoto map, DEM / DSM elevation model, three-dimensional point cloud data (LAS, OBJ format), DXF or Shapefile vector map, etc. The system supports exporting data to local storage devices, cloud platforms or synchronizing to GIS system interfaces according to user needs.

[0048] To realize the quality control of the results, the system also sets up a result evaluation unit, which is electrically connected with the three-dimensional modeling unit in the terrain modeling module, compares the differences between the surveying and mapping results and the modeling estimated data, and the evaluation indicators include image registration error, elevation deviation, point cloud density, etc. The evaluation report is automatically generated through the platform and can be fed back to the path planning module for subsequent route model optimization.

[0049] The above only describes the preferred embodiments of the present application and is not intended to limit the present application. For those skilled in the art, the present application has various modifications and changes. Any modification, equivalent replacement, improvement, etc. within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. An integrated system for autonomous path planning and high-precision mapping of unmanned aerial vehicles, characterized by: include: A terrain modeling module is used to acquire remote sensing images and generate a digital elevation model and a three-dimensional terrain model of the target area. The terrain modeling module includes a remote sensing image access unit, an image interpretation unit, a DEM generation unit, and a three-dimensional modeling unit; The task configuration and scheduling module is used to receive surveying and mapping task requirements, divide survey area tasks, and perform UAV scheduling and allocation. The task configuration and scheduling module includes a task input unit, a task decomposition unit, a collaborative scheduling unit, and a soft time window control unit; a path planning module, electrically connected to the terrain modeling module and the task configuration and scheduling module, for generating an optimized flight path based on three-dimensional terrain data and task allocation information, the path planning module comprising a static path planning unit, a dynamic path optimization unit, a collaborative path planning unit, and a path evaluation unit; A UAV flight control module is electrically connected to the path planning module and is used to control the UAV to automatically perform flight missions according to the flight path instructions. The UAV flight control module includes a flight control instruction execution unit, an attitude and altitude perception unit, and a mission execution interface unit; A surveying and mapping perception module, electrically connected to the flight control module, for performing image acquisition, position recording, and elevation measurement during flight. The surveying and mapping perception module includes a laser rangefinder unit, a GNSS receiving unit, an image acquisition unit, and an image registration and time synchronization unit; A data caching and transmission module is electrically connected to the surveying and mapping perception module and is used to cache, encode and wirelessly transmit the collected data. The data caching and transmission module includes a data caching unit, a wireless communication interface unit, a breakpoint resume control unit and an edge relay unit. The ground control and results processing module is connected to the wireless communication interface unit and is used to receive surveying and mapping data and complete image processing, results modeling and output. The module includes an image preprocessing unit, an image stitching and modeling unit, a results export unit and a results evaluation unit.

2. The integrated system for autonomous path planning and high-precision mapping of unmanned aerial vehicles according to claim 1, characterized in that: The image interpretation unit is used to extract topographic features, object boundaries and shadow areas in the image, and input the extracted structured interpretation data into the DEM generation unit through the data channel for subsequent elevation modeling and three-dimensional reconstruction.

3. The integrated system for autonomous path planning and high-precision mapping of unmanned aerial vehicles according to claim 1, characterized in that: The static path planning unit uses the minimum circumscribed rectangle method to perform geometric fitting on the survey area boundary, extracts the main heading angle as a reference for the route direction, and adopts a simulated annealing algorithm to globally optimize the flight strip spacing and route arrangement positions under the elevation constraint and survey area coverage index, ensuring that the flight strip overlap rate remains within the preset range and reducing excessive route overlap caused by sudden terrain changes.

4. The integrated system for autonomous path planning and high-precision mapping of unmanned aerial vehicles according to claim 1, characterized in that: The dynamic path optimization unit constructs a multi-constraint route adjustment model based on real-time meteorological information, pedestrian and vehicle flow data in the measurement area, and dynamic environmental factors of temporary obstacles. It introduces an adaptive genetic algorithm to optimize the sequence of drone path nodes, and combines it with a large neighborhood search algorithm to perform perturbation and repair operations on the generated path, thereby achieving real-time reconstruction and emergency avoidance of the path under sudden environmental changes.

5. The integrated system for autonomous path planning and high-precision mapping of unmanned aerial vehicles according to claim 1, characterized in that: The collaborative path planning unit constructs a multi-UAV task allocation game model, combines the UAV load capacity, remaining power, range distance and task priority parameters, and adopts an allocation strategy based on the game equilibrium solution to avoid the intersection of multiple UAV flight paths, overlapping coverage of measurement areas and waste of resources.

6. The UAV autonomous path planning and high-precision mapping integrated system according to claim 1, characterized in that: The image acquisition unit is composed of a multispectral imaging device with adjustable viewing angle and multi-band shooting capability. It is connected to the GNSS receiving unit and the image registration and time synchronization unit via a high-speed bus to form a synchronous trigger mechanism to ensure that each frame of the captured image is matched one-to-one with the corresponding position and time data.

7. The integrated system for autonomous path planning and high-precision mapping of unmanned aerial vehicles according to claim 1, characterized in that: The data cache unit adopts a dual buffer structure of a high-bandwidth input channel and a ring buffer mechanism, and is equipped with a frame index table and a timestamp recording mechanism. It can identify image frames and corresponding position data in the order of acquisition, and record the transmitted frame number and data segment through the breakpoint resumption control unit when communication is abnormal. After communication is restored, the cache position is relocated and data transmission is resumed from the interrupted frame to avoid repeated transmission and data omission.

8. The integrated system for autonomous path planning and high-precision mapping of unmanned aerial vehicles according to claim 1, characterized in that: The result evaluation unit is used to perform spatial registration and comparison between the orthophoto images in the surveying and mapping results and the three-dimensional terrain model generated in the modeling phase, calculate the image overlap error, elevation deviation and ground feature boundary offset, and feed back the evaluation results to the path planning module through the data return link for route adjustment and task secondary deployment.

Images