Underforest autonomous obstacle avoidance unmanned aerial vehicle laser radar system

Abstract

The invention discloses an under-forest autonomous obstacle avoidance unmanned aerial vehicle laser radar system. An unmanned aerial vehicle platform is used for bearing all parts of the system and realizing a flight function; the sensor module is mounted on the unmanned aerial vehicle platform and is used for acquiring forest environment data by carrying a binocular camera and a laser radar; the central processing module is used for receiving the forest environment data, constructing a forest environment model, generating an original flight path based on the forest environment model, and automatically generating a detour path when an intersection point exists between the original flight path and a dangerous flight area formed by obstacles, so that obstacle avoidance flight of the unmanned aerial vehicle is realized; the flight control module is used for controlling the unmanned aerial vehicle to fly by adjusting motor rotation speed; the communication module is used for performing data interaction with a ground control station; the power management module is used for intelligently distributing power to each module of the system. According to the invention, safe and efficient flight and data acquisition of the unmanned aerial vehicle in the under-forest environment are realized, and the efficiency and reliability of forest resource investigation are significantly improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of unmanned aerial vehicle (UAV) navigation and obstacle avoidance, and in particular relates to an autonomous obstacle avoidance UAV laser radar system under a forest. Background Art

[0002] Forests play an irreplaceable role in carbon sequestration, biodiversity maintenance, water resource reserves, and ecological balance. With the development of ecological projects, the area of ​​forest protection and management in my country's forestry projects has gradually increased. However, the geographical locations of forest protection projects are often rugged and difficult to access, which greatly increases the workload, difficulty, and pressure on management personnel. There is an urgent need for a tool that can assist or replace manual low-altitude forest management.

[0003] The application of drone technology in forestry continues to expand and deepen. For forest fire monitoring, drones can quickly detect smoke and fire sources and transmit information in real time, assisting in developing firefighting plans. For pest control, drones can be used to efficiently spray pesticides. These applications provide valuable insights and a foundation for exploring application scenarios for the development of autonomous obstacle avoidance drone lidar systems under forests. Furthermore, continuous advancements in sensor, processor, and communication technologies are providing technical support for drone applications in a variety of fields.

[0004] Existing drones are easily affected by complex environments when flying under forests, and it is difficult for them to autonomously avoid obstacles and collect continuous and complete data, which cannot meet the refined needs of forest management in low-altitude forests.

[0005] In order to solve the above problems, it is urgent to propose an understory autonomous obstacle avoidance UAV lidar system to improve the efficiency and accuracy of forest resource surveys. Summary of the Invention

[0006] In order to solve the above technical problems, the present invention provides an under-forest autonomous obstacle avoidance UAV lidar system, specifically an under-forest autonomous obstacle avoidance UAV lidar system with integrated data collection above and below the forest, which can efficiently and safely complete the accurate perception and three-dimensional reconstruction of multi-level forest structure information, and provide comprehensive, continuous and digital data support for forest resource surveys.

[0007] The present invention proposes an autonomous obstacle avoidance UAV laser radar system under the forest, comprising: a UAV platform, a sensor module, a central processing module, a flight control module, a communication module and a power management module;

[0008] The UAV platform is used to carry the various components of the system and realize the flight function;

[0009] The sensor module is installed on the UAV platform and is used to collect forest environment data by carrying a binocular camera and a lidar;

[0010] The central processing module is connected to the sensor module and is used to receive forest environment data, build a forest environment model, and generate an original flight path based on the forest environment model. When the original flight path intersects with a dangerous flight area formed by an obstacle, a detour path is automatically generated to achieve obstacle avoidance flight of the UAV;

[0011] The flight control module is connected to the central processing module and is used to control the flight of the drone by adjusting the motor speed;

[0012] The communication module is connected to the flight control module and is used to exchange data with the ground control station;

[0013] The power management module is connected to the drone platform, sensor module, central processing module and communication module respectively, and is used to intelligently distribute power to various modules of the system.

[0014] Optionally, the sensor module further includes an air pressure sensor and an optical flow sensor;

[0015] The air pressure sensor is used to obtain air pressure data of the UAV's flight altitude;

[0016] The optical flow sensor is used to sense changes in ground texture when the drone is flying and provide relative displacement information.

[0017] Optionally, the central processing module includes a point cloud map construction unit;

[0018] The point cloud map construction unit is used to preprocess the raw point cloud data collected by the lidar to obtain pure point cloud data and extract local geometric features of the pure point cloud data; use the improved iterative closest point algorithm to align the current frame point cloud with the existing map, and optimize the pose estimation through multiple iterations until the convergence conditions are met; after the alignment is completed, the current frame point cloud is fused with the global map using a weighted averaging strategy, and the accumulated errors are corrected through loop detection and global optimization functions, and finally a three-dimensional point cloud map is constructed to provide data support for the central processing module.

[0019] Optionally, the central processing module further includes an environment modeling unit, an obstacle recognition unit and a path planning unit;

[0020] The environment modeling unit is used to collect forest environment data, obtain the location, shape and distance information of surrounding obstacles, and build a forest environment model;

[0021] The obstacle recognition unit is used to extract the boundary coordinates of the obstacle based on the forest environment model, remove the concave points to generate a convex polygon obstacle area, and expand the generated convex polygon obstacle area to form a dangerous flight area including a safety margin;

[0022] The path planning unit is used to generate an original flight path, and then determine whether the original flight path intersects with the boundary of the dangerous flight area based on the positional relationship between the current position of the drone and the target waypoint. If two intersections are detected, the original flight path is determined to be blocked, and an intermediate path point is automatically inserted into the original flight path to generate a detour path to achieve obstacle avoidance flight.

[0023] Optionally, the UAV platform further includes an automatic take-off unit and a return unit;

[0024] The automatic take-off unit is used to realize intelligent automatic take-off and autonomous obstacle avoidance flight of the UAV;

[0025] The return-to-home unit is used to automatically trigger the return-to-home mechanism when a predetermined flight mission is completed or when it is detected that the battery voltage is lower than a safety threshold.

[0026] The present invention also provides an operating method of an autonomous obstacle avoidance UAV laser radar system under a forest, based on the system, comprising:

[0027] Start the drone platform and activate the sensor module;

[0028] Collect forest environment data by using binocular cameras and lidar;

[0029] receiving the forest environment data and constructing a forest environment model;

[0030] Based on the forest environment model, an original flight path is generated. When the original flight path intersects with a dangerous flight area formed by an obstacle, a detour path is automatically generated, thereby achieving obstacle avoidance flight of the UAV.

[0031] Optionally, before receiving the forest environment data and constructing the forest environment model, the process further includes:

[0032] The raw point cloud data collected by the lidar is preprocessed to obtain pure point cloud data, and the local geometric features of the pure point cloud data are extracted; the improved iterative closest point algorithm is used to align the current frame point cloud with the existing map, and the pose estimation is optimized through multiple iterations until the convergence conditions are met; after the alignment is completed, the current frame point cloud is fused with the global map using a weighted averaging strategy, and the accumulated error is corrected through loop detection and global optimization functions, and finally a three-dimensional point cloud map is constructed to provide data support for building a forest environment model.

[0033] Optionally, based on the forest environment model, an original flight path is generated, and when the original flight path intersects with a dangerous flight area formed by an obstacle, a detour path is automatically generated, thereby achieving obstacle avoidance flight of the UAV, the process includes:

[0034] Based on the collected forest environment data, the location, shape and distance information of surrounding obstacles are obtained to build a forest environment model;

[0035] Based on the forest environment model, the boundary coordinates of obstacles are extracted, and concave points are removed to generate a convex polygon obstacle area. The generated convex polygon obstacle area is then expanded to form a dangerous flight area with a safety margin.

[0036] Generate an original flight path. Based on the positional relationship between the drone's current position and the target waypoint, determine whether the original flight path intersects with the boundary of the dangerous flight area. If two intersections are detected, the original flight path is determined to be obstructed. Intermediate path points are automatically inserted into the original flight path to generate a detour path to achieve obstacle avoidance flight.

[0037] Compared with the prior art, the present invention has the following advantages and technical effects:

[0038] The present invention uses a drone platform to carry the various system components and realize the flight function, so that the entire system can move autonomously in a forest environment, providing a basis for collecting forest environment data. The sensor module is equipped with a binocular camera and a lidar to collect forest environment data, and obtains information such as the position, shape, and distance of obstacles from two dimensions: vision and laser ranging, providing rich and accurate data for subsequent processing. After receiving this data, the central processing module constructs a forest environment model and generates an original flight path. When it is detected that the original flight path has an intersection with the dangerous flight area formed by the obstacle, a detour path is automatically generated, thereby realizing the autonomous obstacle avoidance flight of the drone, improving the safety and reliability of the flight, and effectively avoiding the occurrence of collision accidents.

[0039] At the same time, the system can support remote route mission configuration, status monitoring and data feedback through real-time communication with the ground control station, and has autonomous return and low-battery protection mechanisms to ensure the safety and continuity of the operation process.

[0040] In addition, the power management module intelligently distributes power to each module of the system, ensuring that each component has a stable power supply during flight, avoiding system failures or flight interruptions due to insufficient power, improving the overall operating efficiency and stability of the system, extending the flight time of the drone, and enhancing its practicality in tasks such as forest resource surveys.

[0041] In summary, the forest autonomous obstacle avoidance UAV lidar system of the present invention has the capabilities of autonomous flight, intelligent obstacle avoidance, three-dimensional point cloud reconstruction and multi-level structure perception in both above-forest and under-forest environments. It breaks through the bottlenecks of fragmentation, low efficiency and high interference in traditional forest resource surveys, and provides efficient and reliable technical support for the construction of digital forestry and smart forest management systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The accompanying drawings, which constitute part of this application, are intended to provide a further understanding of this application. The exemplary embodiments and descriptions of this application are intended to explain this application and do not constitute an improper limitation on this application. In the accompanying drawings:

[0043] Figure 1 A schematic diagram of the rack structure of a system according to an embodiment of the present invention;

[0044] Figure 2 A diagram showing the connection relationship between components of a system according to an embodiment of the present invention;

[0045] Figure 3 Schematic diagram of three-dimensional point cloud data according to an embodiment of the present invention. DETAILED DESCRIPTION

[0046] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments in this application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0047] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0048] Example 1

[0049] Traditional forest resource surveys mostly rely on manual sample plot measurement methods, which are not only inefficient and time-consuming, but also difficult to operate in areas with dense forest stands or complex terrain. They often cannot take into account the continuous collection of forest canopy (above forest) and understory structures, resulting in data fragmentation and making it difficult to achieve overall modeling and accurate assessment of forest stand structure.

[0050] To address the above issues, this embodiment integrates a lidar system into a drone platform with autonomous obstacle avoidance capabilities. Combined with high-precision flight control and path planning algorithms, it proposes a new model for continuous collaborative flight and integrated data collection above and below the forest, breaking through the technical bottleneck of traditional methods that have difficulty covering the spatial structure under the forest canopy.

[0051] In this embodiment, a forest autonomous obstacle avoidance UAV lidar system is proposed, the overall structure of which is composed of a fuselage body, a power system, a sensor assembly, a flight control system and a landing gear. The fuselage body is made of lightweight and high-strength carbon fiber composite materials, which has excellent structural strength and lightweight characteristics. It can not only reduce the flight load, but also improve the ability to resist collisions in complex forest environments. The power system consists of a high-efficiency, low-noise propeller and a matching brushless motor and electronic regulator. It can accurately adjust the speed and thrust output according to the instructions of the flight control system, thereby achieving smooth takeoff, fine control and complex path flight, especially suitable for flight scenarios with limited space and dense obstacles under the forest. In addition, the landing gear design has good shock absorption and buffering performance, which can adapt to the uneven take-off and landing surface under complex terrain conditions under the forest, ensuring the safety of the flight take-off and landing process and the stability of the equipment.

[0052] like Figure 1 A schematic diagram of the frame structure of the lidar system for a forest autonomous obstacle avoidance drone. This structure illustrates the drone's overall appearance and main components. It utilizes a four-layer integrated design, from top to bottom: sensor equipment layer, onboard computer layer, flight control and energy distribution layer, and battery layer. This vertically layered, modular design effectively integrates multiple core functional modules while significantly reducing the drone's overall size, enabling higher space utilization and system integration. This compact structural layout not only improves the drone's stability and anti-interference capabilities, but also enables greater maneuverability in complex forest environments, facilitating stable flight and obstacle avoidance in confined spaces such as undergrowth.

[0053] Figure 2 This is a component connection diagram of the understory autonomous obstacle avoidance UAV lidar system of this embodiment, which shows the connection relationship of the main hardware structures of each part of the UAV.

[0054] The system specifically includes: UAV platform, sensor module, central processing module, flight control module, communication module and power management module;

[0055] The UAV platform is used to carry the various components of the system and realize the flight function;

[0056] The sensor module is installed on the UAV platform and is used to collect forest environment data by carrying a binocular camera and a lidar;

[0057] The central processing module is connected to the sensor module and is used to receive forest environment data, build a forest environment model, and generate an original flight path based on the forest environment model. When the original flight path intersects with a dangerous flight area formed by an obstacle, a detour path is automatically generated to achieve obstacle avoidance flight of the UAV;

[0058] The flight control module is connected to the central processing module and is used to control the flight of the drone by adjusting the motor speed;

[0059] The communication module is connected to the flight control module and is used to conduct stable and efficient data exchange with the ground control station to achieve flight status monitoring, mission command issuance and telemetry data feedback, ensuring the system's remote control and real-time information transmission capabilities during operation;

[0060] The power management module is connected to the drone platform, sensor module, central processing module, and communication module, providing real-time monitoring and intelligent allocation of power to the entire system. This module continuously monitors key parameters such as battery voltage and current, dynamically adjusting the power supply strategy for each component to ensure stable operation of core components such as flight control, sensors, and radar, effectively improving system energy efficiency and flight safety.

[0061] The drone platform of this embodiment is feasible and adopts a lightweight carbon fiber structure design with a compact body and strong maneuverability, which is suitable for complex flights in forest environments. It is equipped with a variety of high-precision sensors, including lidar, binocular camera, ultrasonic module, barometric altimeter and IMU module, to ensure that the system has stable flight and high-precision perception capabilities in both open areas above the forest and highly obstructed areas under the forest. The lidar can achieve 360° scanning and obtain high-density point cloud data for real-time construction of three-dimensional maps and obstacle distribution information. The binocular camera provides stereoscopic visual information of the environment to assist in identifying obstacles and terrain features. The multi-sensor fusion algorithm integrates and processes the above information to ensure obstacle avoidance accuracy and flight safety.

[0062] It is feasible that the laser radar carried on the UAV platform is installed above the fuselage and is controlled by a drive system composed of a servo. The servo supports the radar to rotate freely at multiple angles, enabling multi-directional scanning. Combined with the autonomous flight path and attitude adjustment of the UAV, the system can efficiently and continuously collect data on the forest environment from multiple angles and perspectives. This structure significantly expands the scanning field of view and point cloud coverage density of the laser radar, and improves the perception ability of complex obstacle scenes under the forest. By combining multi-angle rotation scanning with maneuverable flight, the system can obtain high-resolution, high-integrity three-dimensional point cloud data, effectively adapting to the perception blind spots caused by terrain undulations, vegetation obstruction, etc. in the forest environment. Ultimately, all-round spatial perception and high-precision three-dimensional modeling of forest plots are achieved, providing comprehensive data support for subsequent forest resource surveys, target identification, and environmental monitoring.

[0063] Figure 3This diagram illustrates the 3D point cloud data generated by the autonomous obstacle avoidance UAV lidar system in the forest after completing a forest flight mission. This diagram shows the spatial data collected by the drone through real-time lidar scanning during flight in the forest environment, and the resulting high-density 3D point cloud model constructed after the mission. This point cloud data clearly reflects environmental information such as the forest topography, tree distribution, and obstacle morphology, providing accurate basic data support for subsequent forest stand structure analysis, terrain reconstruction, and resource assessment.

[0064] Optionally, the sensor module also includes an air pressure sensor and an optical flow sensor; the air pressure sensor is used to obtain air pressure data at the drone's flight altitude; the optical flow sensor is used to sense changes in ground texture during drone flight and provide relative displacement information; the two work together to improve altitude control accuracy and flight stability during low-altitude flight.

[0065] The central processing module may include a point cloud map construction unit. Within the point cloud map construction unit, raw point cloud data of the environment space is collected using a lidar system. After collection, the raw point cloud is preprocessed using various denoising algorithms, such as statistical filtering and radius filtering, to identify and remove noise points and outliers that deviate from the normal point cloud distribution, thereby obtaining relatively pure and stable basic point cloud data.

[0066] Then, in the feature extraction phase, the system analyzes the point cloud data based on local geometric features. By calculating parameters such as the normal vector distribution and curvature change of the point cloud neighborhood, it accurately extracts structural information such as planar features and edge features. These features not only improve the accuracy of the point cloud structure representation, but also provide stable feature support for subsequent registration algorithms.

[0067] During the point cloud registration phase, a modified iterative closest point (ICP) algorithm is employed. This algorithm first establishes a preliminary correspondence between the current frame's point cloud and an existing map (local or global) based on geometric features and calculates an initial pose transformation estimate. Subsequently, multiple iterations are performed, re-matching corresponding point pairs and updating the pose estimate with each iteration until convergence criteria are met, such as when the pose change is less than a threshold or the error is minimized.

[0068] After registration is complete, the current frame's point cloud is fused with the global map using the optimized pose transformation. During the fusion process, the system considers the overlap and spatial distribution of point clouds, using fusion strategies such as weighted averaging to ensure map consistency and continuity, avoiding gaps, overlapping errors, or redundant data.

[0069] The system also integrates loop detection and global optimization capabilities. When a drone re-enters an area it previously traversed, it identifies potential loops through feature matching and pose comparison. Once a loop is detected, a global map optimization process is immediately triggered, adjusting the pose and topology of the point cloud in the affected local area. This corrects drift caused by accumulated errors and significantly improves the overall accuracy and consistency of the point cloud map.

[0070] Through the above process, a high-precision, high-resolution, and error-controllable three-dimensional point cloud map is finally constructed, providing stable and reliable spatial data support for subsequent tasks such as environmental perception, positioning navigation, path planning, and parameter extraction.

[0071] As another possible implementation, this embodiment combines LiDAR and IMU input data, processes the radar point cloud through preprocessing and state estimation algorithms (including forward and backward steps), projects it into the world coordinate system, and updates the environment map in real time. During the map construction process, an incremental kd-tree structure is used to dynamically manage the point cloud data, achieving efficient modeling and continuous optimization of the environment.

[0072] It is feasible that the central processing module further includes an environment modeling unit, an obstacle recognition unit and a path planning unit;

[0073] The environmental modeling unit is used to collect forest environmental data, obtain the position, shape and distance information of surrounding obstacles, and construct a forest environmental model; the obstacle identification unit is used to extract the boundary coordinates of obstacles based on the forest environmental model, remove concave points to generate a convex polygon obstacle area, and expand the generated convex polygon obstacle area to form a dangerous flight area including a safety margin; the path planning unit is used to generate an original flight path based on the A* algorithm, and then determine whether the original flight path has an intersection with the boundary of the dangerous flight area based on the positional relationship between the current position of the drone and the target waypoint. If two intersections are detected, the original flight path is determined to be blocked, and an intermediate path point is automatically inserted into the original flight path to generate a detour path to achieve obstacle avoidance flight.

[0074] As a specific implementation, a drone navigates a complex understory environment, surrounded by various natural obstacles such as trees and shrubs. The drone uses a variety of sensors to perceive its surroundings in real time. A lidar (lidar) continuously emits laser beams, scanning the surrounding area to obtain information on the distance, outline, and spatial distribution of obstacles. A binocular vision camera assists in identifying irregularly shaped obstacles or complex terrain features under the forest, such as fallen tree trunks, exposed roots, or gaps in underbrush. An onboard computer fuses the multi-source information from the lidar and vision systems, analyzing the flight environment in real time, dynamically constructing a local map, and using an obstacle avoidance algorithm to plan a safe, feasible, and optimized flight path. Based on the path planning results, the flight control system promptly adjusts the drone's flight attitude and heading, enabling the drone to flexibly avoid obstacles in the understory, such as circumventing dense tree trunks or avoiding sudden, low shrubs. This ensures safe and autonomous flight in the complex and confined spaces of the forest.

[0075] Furthermore, the central processing module includes an intelligent algorithm module for improving obstacle avoidance efficiency and accuracy. The algorithm module uses environmental data collected by the lidar and multi-sensor information fusion technology to perform real-time obstacle identification, path planning and dynamic adjustment, thereby achieving efficient and accurate obstacle avoidance flight.

[0076] As a specific implementation method, the central processing module of this embodiment is mainly responsible for executing flight attitude control and path tracking instructions, receiving and processing real-time data from multiple sensors (such as lidar, binocular camera, inertial measurement unit, etc.), and achieving safe flight in complex environments with the support of the obstacle avoidance algorithm running on the onboard computer.

[0077] The core process of the obstacle avoidance algorithm is as follows:

[0078] First, the environmental perception module creates a high-precision model of the forest environment the drone is currently in. Based on a two-dimensional or three-dimensional grid map, the flight area is divided into several small cells (grids), and each grid is assigned an attribute label, such as a traversable area, an obstacle area, or an area with specific restrictions, thereby constructing a complete environmental representation model.

[0079] Subsequently, the corresponding coordinate points are determined on the map based on the drone's starting and target locations. The path planning phase uses heuristic search strategies such as the modified A* algorithm, combined with a well-designed cost evaluation function. This algorithm considers the actual cost from the starting point to the current node and the estimated cost from the current node to the target node, gradually expanding the nodes to ultimately generate a feasible path that avoids obstacles.

[0080] After path planning is completed, the algorithm further optimizes the path quality. Common methods include path smoothing and corner optimization, aiming to reduce sharp turns and redundant sections in the path and improve flight efficiency and stability.

[0081] To enhance dynamic obstacle avoidance, the system sets anchor points along the path and, in conjunction with a repulsive field mechanism, generates an offset vector away from obstacles. Combined with the Dynamic Window Algorithm (DWA), the system adjusts the search window in real time based on current environmental perception data and the drone's motion state, selecting the optimal trajectory within the constraints. This allows the drone to adapt to the complex and ever-changing dynamic obstacle environment under the forest, achieving efficient and safe autonomous obstacle avoidance flight.

[0082] Optionally, the drone platform further includes an automatic take-off unit and a return-home unit; the automatic take-off unit is used to realize intelligent automatic take-off and autonomous obstacle avoidance flight of the drone; the return-home unit is used to automatically trigger the return-home mechanism when completing a predetermined flight mission or detecting that the battery voltage is lower than a safety threshold.

[0083] As a specific implementation, the communication module of this embodiment is used to implement two-way data communication with a ground control station. During flight, the drone can transmit its flight status information (including position coordinates, flight altitude, speed, attitude, etc.) and data collected by various sensors (such as lidar point cloud data, camera images, ultrasonic detection information, etc.) to the ground control station in real time, allowing operators to monitor flight operations and environmental perception status in real time.

[0084] At the same time, the ground control station can issue a variety of control instructions to the UAV, including but not limited to route setting, flight parameter adjustment, mission mode switching, and emergency return commands, ensuring that the UAV has good operational flexibility and safety in complex environments such as under the forest.

[0085] The drone also includes an energy management system, which is used to stabilize the voltage, balance the load and dynamically distribute the electric energy output by the battery to ensure the normal power supply of the drone flight control system, airborne computing platform, lidar sensor and other electronic equipment, and improve the energy utilization efficiency and operational stability of the overall system.

[0086] Example 2

[0087] This embodiment also provides an operating method for an autonomous obstacle avoidance UAV lidar system under a forest, based on the system, including:

[0088] Start the drone platform and activate the sensor module;

[0089] Collect forest environment data by using binocular cameras and lidar;

[0090] receiving the forest environment data and constructing a forest environment model;

[0091] Based on the forest environment model, an original flight path is generated. When the original flight path intersects with a dangerous flight area formed by an obstacle, a detour path is automatically generated, thereby achieving obstacle avoidance flight of the UAV.

[0092] It is feasible that, before receiving the forest environment data and constructing the forest environment model, the method further includes:

[0093] The raw point cloud data collected by the lidar is preprocessed to obtain pure point cloud data, and the local geometric features of the pure point cloud data are extracted; the improved iterative closest point algorithm is used to align the current frame point cloud with the existing map, and the pose estimation is optimized through multiple iterations until the convergence conditions are met; after the alignment is completed, the current frame point cloud is fused with the global map using a weighted averaging strategy, and the accumulated error is corrected through loop detection and global optimization functions, and finally a three-dimensional point cloud map is constructed to provide data support for building a forest environment model.

[0094] The process of generating an original flight path based on the forest environment model and automatically generating a detour path when the original flight path intersects a dangerous flight area formed by an obstacle, thereby achieving obstacle avoidance flight of the UAV, may include:

[0095] Based on the collected forest environment data, the location, shape and distance information of surrounding obstacles are obtained to build a forest environment model;

[0096] Based on the forest environment model, the boundary coordinates of obstacles are extracted, and concave points are removed to generate a convex polygon obstacle area. The generated convex polygon obstacle area is then expanded to form a dangerous flight area with a safety margin.

[0097] Generate an original flight path. Based on the positional relationship between the drone's current position and the target waypoint, determine whether the original flight path intersects with the boundary of the dangerous flight area. If two intersections are detected, the original flight path is determined to be obstructed. Intermediate path points are automatically inserted into the original flight path to generate a detour path to achieve obstacle avoidance flight.

[0098] The above are merely preferred embodiments of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A forest autonomous obstacle avoidance UAV laser radar system, characterized by: include: UAV platform, sensor module, central processing module, flight control module, communication module and power management module; The UAV platform is used to carry the various components of the system and realize the flight function; The sensor module is installed on the UAV platform and is used to collect forest environment data by carrying a binocular camera and a lidar; The central processing module is connected to the sensor module and is used to receive forest environment data, build a forest environment model, and generate an original flight path based on the forest environment model. When the original flight path intersects with a dangerous flight area formed by an obstacle, a detour path is automatically generated to achieve obstacle avoidance flight of the UAV; The flight control module is connected to the central processing module and is used to control the flight of the drone by adjusting the motor speed; The communication module is connected to the flight control module and is used to exchange data with the ground control station; The power management module is connected to the drone platform, sensor module, central processing module and communication module respectively, and is used to intelligently distribute power to various modules of the system.

2. The system according to claim 1, wherein: The sensor module also includes an air pressure sensor and an optical flow sensor; The air pressure sensor is used to obtain air pressure data of the UAV's flight altitude; The optical flow sensor is used to sense changes in ground texture when the drone is flying and provide relative displacement information.

3. The system according to claim 1, wherein: The central processing module includes a point cloud map construction unit; The point cloud map construction unit is used to preprocess the raw point cloud data collected by the lidar to obtain clean point cloud data and extract local geometric features of the clean point cloud data; use the improved iterative closest point algorithm to align the current frame point cloud with the existing map, and optimize the pose estimation through multiple iterations until the convergence condition is met; After the registration is completed, the weighted average strategy is used to fuse the current frame point cloud with the global map, and the accumulated error is corrected through loop detection and global optimization functions. Finally, a three-dimensional point cloud map is constructed to provide data support for the central processing module.

4. The system according to claim 1, wherein: The central processing module also includes an environment modeling unit, an obstacle recognition unit and a path planning unit; The environment modeling unit is used to collect forest environment data, obtain the location, shape and distance information of surrounding obstacles, and build a forest environment model; The obstacle recognition unit is used to extract the boundary coordinates of the obstacle based on the forest environment model, remove the concave points to generate a convex polygon obstacle area, and expand the generated convex polygon obstacle area to form a dangerous flight area including a safety margin; The path planning unit is used to generate an original flight path, and then determine whether the original flight path intersects with the boundary of the dangerous flight area based on the positional relationship between the current position of the drone and the target waypoint. If two intersections are detected, the original flight path is determined to be blocked, and an intermediate path point is automatically inserted into the original flight path to generate a detour path to achieve obstacle avoidance flight.

5. The system according to claim 1, wherein: The UAV platform also includes an automatic take-off unit and a return unit; The automatic take-off unit is used to realize intelligent automatic take-off and autonomous obstacle avoidance flight of the UAV; The return-to-home unit is used to automatically trigger the return-to-home mechanism when a predetermined flight mission is completed or when it is detected that the battery voltage is lower than a safety threshold.

6. A method for operating an autonomous obstacle avoidance UAV laser radar system under a forest, characterized in that: The system according to any one of claims 1 to 5, comprising: Start the drone platform and activate the sensor module; Collect forest environment data by using binocular cameras and lidar; receiving the forest environment data and constructing a forest environment model; Based on the forest environment model, an original flight path is generated. When the original flight path intersects with a dangerous flight area formed by an obstacle, a detour path is automatically generated, thereby achieving obstacle avoidance flight of the UAV.

7. The operating method according to claim 6, characterized in that: Before receiving the forest environment data and building the forest environment model, the method further includes: The raw point cloud data collected by the lidar is preprocessed to obtain pure point cloud data, and the local geometric features of the pure point cloud data are extracted; the improved iterative closest point algorithm is used to align the current frame point cloud with the existing map, and the pose estimation is optimized through multiple iterations until the convergence conditions are met; after the alignment is completed, the current frame point cloud is fused with the global map using a weighted averaging strategy, and the accumulated error is corrected through loop detection and global optimization functions, and finally a three-dimensional point cloud map is constructed to provide data support for building a forest environment model.

8. The operating method according to claim 6, characterized in that: The process of generating an original flight path based on the forest environment model and automatically generating a detour path when the original flight path intersects with a dangerous flight area formed by an obstacle, thereby achieving obstacle avoidance flight of the UAV, includes: Based on the collected forest environment data, the location, shape and distance information of surrounding obstacles are obtained to build a forest environment model; Based on the forest environment model, the boundary coordinates of obstacles are extracted, and concave points are removed to generate a convex polygon obstacle area. The generated convex polygon obstacle area is then expanded to form a dangerous flight area with a safety margin. Generate an original flight path. Based on the positional relationship between the drone's current position and the target waypoint, determine whether the original flight path intersects with the boundary of the dangerous flight area. If two intersections are detected, the original flight path is determined to be obstructed. Intermediate path points are automatically inserted into the original flight path to generate a detour path to achieve obstacle avoidance flight.

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