Basalt fiber unmanned aerial vehicle
By using basalt fiber materials and adjustment components on the drone, combined with environmental perception and flight path planning module, the problem that the drone cannot effectively avoid obstacles when performing flight missions is solved, achieving higher flight stability and Ability to avoid obstacles.
Patent Information
- Application Number
- CN202411930425.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-26
- Publication Date
- 2025-02-14
AI Technical Summary
Existing drones cannot effectively avoid static and dynamic obstacles when performing flight missions, resulting in high collision risks and affecting the safety and efficiency of flight missions.
The basalt fiber drone is adopted to optimize the structural shape and airflow distribution of the drone by setting adjustment components and fan blade components, and combine the environment perception module and flight path planning module to identify and plan obstacle avoidance paths in real time.
It significantly improves the flight stability and wind resistance of the drone, enhances obstacle avoidance capabilities and flight efficiency, and improves the safety and accuracy of mission execution.
Smart Images
Figure CN119429209A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of unmanned aerial vehicles (UAVs), and in particular to a basalt fiber UAV. Background Art
[0002] Unmanned Aerial Vehicle (UAV) is an aircraft that does not require direct control by a driver but is controlled by a remote control system or an autonomous control program. Basalt fiber is a high-performance material made by high-temperature melting of natural basalt rock. It has the characteristics of light weight, high strength, corrosion resistance, and high temperature resistance. Basalt fiber UAV is the product of the combination of traditional UAV and new material technology. The superior performance of basalt fiber is used to further improve the safety, reliability and adaptability of UAV.
[0003] When a drone is performing a flight mission, if it is unable to avoid static and dynamic obstacles, such as buildings, telephone poles, trees and other static obstacles, it is not only prone to collisions, causing damage to the drone or crashing. At the same time, due to the uncertainty and randomness of dynamic obstacles, the lack of obstacle avoidance capability will greatly increase the risk of collision, thereby threatening the safety of the flight mission, and further reducing the efficiency of mission execution and the ability to adapt to complex environments, significantly limiting the breadth and flexibility of its application scenarios.
[0004] Currently, no effective solution has been proposed for the problems in the related technologies. Summary of the invention
[0005] In view of the problems in the related technology, the present invention proposes a basalt fiber UAV to overcome the above-mentioned technical problems existing in the existing related technology.
[0006] To this end, the specific technical solution adopted by the present invention is as follows: A basalt fiber UAV comprises a basalt fiber UAV shell, a battery is arranged on the top of the basalt fiber UAV shell, support frames are evenly arranged around the basalt fiber UAV shell, an adjustment component is arranged on one side of the support frame, and a fan blade component is arranged on the side of the support frame away from the adjustment component; a mounting plate is arranged inside the basalt fiber UAV shell, a controller is arranged on the inner top of the mounting plate, and a sensor is arranged on the top of the basalt fiber UAV shell.
[0007] Furthermore, in order to be able to adjust the distance between the support frame and the basalt fiber UAV shell, the structural form of the UAV can be flexibly adjusted according to different requirements of the flight environment, and the overall volume of the UAV can be reduced for easy storage and carrying. At the same time, the telescopic adjustment between the support frame and the basalt fiber UAV shell can change the position between the fan blades, thereby improving the flight stability and wind resistance of the UAV. Mounting blocks are arranged at both ends of one side of the support frame, and a plurality of first protrusions are symmetrically arranged at both ends of the outer side of the support frame. A plurality of second protrusions are symmetrically arranged at both ends of the outer side of the support frame. The adjustment component includes a first mounting plate symmetrically arranged at both ends of the support frame and the mounting block, a second mounting plate is arranged on one side of the first mounting plate, a plurality of first connecting shafts are arranged between two groups of first mounting plates located in the same horizontal direction, and a plurality of second connecting shafts are arranged between two groups of second mounting plates located in the same horizontal direction. The first connecting shaft and the second connecting shaft are connected by a plurality of connecting rods, and the adjacent two groups of connecting rods are arranged in a cross-type, and a third connecting shaft is arranged at the intersection of the adjacent two groups of connecting rods; a slide groove matching one of the connecting rods is opened inside the first mounting plate.
[0008] Furthermore, in order to optimize the airflow distribution and reduce the interference between the rotors by setting the fan blade assembly on the outer side of the support frame and driving the fan blades to rotate by a motor, thereby improving the flight efficiency and stability of the UAV, the fan blade assembly includes a motor set on the outer side of the support frame, and fan blades are set on the top of the motor.
[0009] Furthermore, in order to provide higher precision and reliability for the fixation of the adjustment component through the cooperation of the limit hole and the limit column, effectively avoid the influence of vibration and displacement on the flight performance, and improve the structural stability of the UAV in a complex flight environment, a limit hole is opened inside the first protrusion, and a limit column matching the limit hole is arranged on the outside of the second protrusion.
[0010] Furthermore, the controller includes a data acquisition module, an environment perception module, a flight path planning module and a flight control module; The data acquisition module is used to collect the flight data of the UAV during flight using sensors, and pre-process the flight data to obtain flight optimization data; An environmental perception module is used to extract the flight image of the UAV from the flight optimization data and identify static obstacle features and dynamic obstacle features based on the flight image; The flight path planning module is used to set a static path based on the characteristics of static obstacles, track the motion trajectory of dynamic obstacles to obtain a dynamic path, perform path conflict detection on the static path and the dynamic path to obtain an executable path, and select the optimal flight path from the executable paths; The flight control module is used to detect whether the drone follows the optimal flight path, and when the drone deviates from the optimal flight path, the correction algorithm is used to correct the drone parameters so that the drone always remains on the optimal flight path.
[0011] Furthermore, the flight path planning module sets a static path based on the characteristics of static obstacles, tracks the motion trajectory of dynamic obstacles to obtain a dynamic path, performs path conflict detection on the static path and the dynamic path to obtain an executable path, and selects the optimal flight path from the executable path, including: Use 3D map generation technology to build a 3D environment model for UAV flight, mark the location of static obstacles in the 3D environment model, and use the global path planning algorithm to generate a static path that avoids static obstacles; The motion trajectories of dynamic obstacles are divided based on the characteristics of dynamic obstacles, and the motion modes of dynamic obstacles are obtained respectively, and the motion modes include uniform linear motion, uniform accelerated motion and curved motion; According to the uniform linear motion, uniform accelerated motion and curved motion of the dynamic obstacle, the future trajectory of the dynamic obstacle is predicted, and the dynamic path is marked in the 3D environment model based on the future trajectory of the dynamic obstacle; Evaluate the conflict probability between the static path and the dynamic path, and filter the conflicting paths between the static path and the dynamic path based on the conflict probability to obtain the executable path of the UAV; The multi-objective optimization algorithm is used to select the optimal flight path from the executable paths, and the optimal flight path is used to generate executable instructions and sent to the control center.
[0012] Furthermore, according to the uniform linear motion, uniform accelerated motion and curved motion of the dynamic obstacle, the future trajectory of the dynamic obstacle is predicted, and the dynamic path is marked in the three-dimensional environment model based on the future trajectory of the dynamic obstacle, including: The motion pattern of the dynamic obstacle at the current moment is input as input data into the predefined time series prediction model to obtain the initial trajectory of the dynamic obstacle; Obtain the uniform linear motion, uniform accelerated motion and curved motion of the UAV in the historical time period to generate a reference offset compensation function, and make a preliminary correction to the initial trajectory of the dynamic obstacle according to the reference offset compensation function; According to the static obstacle marking points in the 3D environment model and the predefined flight mission requirements, the initial trajectory of the dynamic obstacle is corrected by environmental constraints to obtain the future trajectory of the dynamic obstacle.
[0013] Furthermore, the conflict probability between the static path and the dynamic path is evaluated, and the conflicting paths between the static path and the dynamic path are filtered based on the conflict probability, and the executable paths of the drone are obtained, including: Extracting future trajectory prediction points of the future trajectory of the dynamic obstacle, performing uncertainty range analysis on the future trajectory prediction points, and generating an influence area of the dynamic obstacle based on the uncertainty range analysis result; The probability value of the impact area is calculated using Gaussian distribution, the time dimension is added to the three-dimensional map model, and a dynamic occupancy probability matrix is generated based on the probability value of the impact area; The dynamic occupancy probability matrix is used to cumulatively calculate the occupancy probability of the future trajectory prediction point at the current moment. If the influence areas of the dynamic obstacles are the same, the occupancy probabilities of the influence areas of the dynamic obstacles are superimposed. Discretize the static path into several static trajectory points, find the occupancy probability value of the static trajectory point in the dynamic occupancy probability matrix, and compare the occupancy probability value of the static trajectory point with a preset threshold; If the occupancy probability value is greater than the preset threshold, it means that there is a conflicting impact area between the static path and the dynamic path, and the impact area is filtered to obtain the executable path of the drone. Otherwise, it means that there is no conflicting impact area between the static path and the dynamic path.
[0014] Furthermore, the Gaussian distribution calculation formula is: ; In the formula, P occ ( x , y , z ) represents the target point ( x , y , z ) is in the affected area; ( x , y , z ) represents the coordinates of the current trajectory point of the dynamic obstacle in the three-dimensional environment model; ( μ x , μ y , μ z ) represents the coordinates of the future trajectory prediction point of the dynamic obstacle in the three-dimensional environment model; σ x , σ y , σ z Represents Gaussian distribution x , y and z The standard deviation in the direction; exp represents the exponential function.
[0015] The beneficial effects of the present invention are: 1. The shell of the drone of the present invention is made of basalt fiber, which has the advantages of high strength, high temperature resistance, corrosion resistance, electromagnetic shielding and environmental protection, which significantly improves the performance of the drone. By setting an adjustment component, the distance between the support frame and the basalt fiber shell can be adjusted, so that the structural form of the drone can be flexibly adjusted according to the different needs of the flight environment, and the overall volume of the drone can be reduced, which is convenient for storage and carrying. At the same time, the telescopic adjustment between the support frame and the basalt fiber shell can change the position between the fan blades, thereby improving the flight stability and wind resistance of the drone.
[0016] 2. The present invention extracts UAV flight images through the combination of the environmental perception module and the flight path planning module, accurately identifies the characteristics of static and dynamic obstacles, realizes the scientific setting of static paths and real-time tracking of the motion trajectories of dynamic obstacles, thereby ensuring the comprehensiveness and flexibility of path planning, improving the obstacle avoidance capability and flight efficiency of UAVs in complex environments, and further improving the safety and accuracy of mission execution.
[0017] 3. The present invention performs path conflict detection on static paths and dynamic paths, screens out safe executable paths, and selects the optimal flight path from them, which can effectively avoid obstacles and potential collision risks and ensure flight safety. At the same time, it optimizes path planning to reduce energy consumption and flight time, improves the efficiency and reliability of UAV mission execution, and achieves efficient and safe flight goals in complex environments. BRIEF DESCRIPTION OF THE DRAWINGS
[0018] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.
[0019] Figure 1 is a schematic structural diagram of a basalt fiber UAV according to an embodiment of the present invention; Figure 2 is a schematic structural diagram of a basalt fiber UAV from another angle according to an embodiment of the present invention; Figure 3 is a schematic diagram of the structure of an adjustment component in a basalt fiber UAV according to an embodiment of the present invention; Figure 4 is a structural schematic diagram of another angle of the adjustment component in the basalt fiber drone according to an embodiment of the present invention; Figure 5 yes Figure 4 A partial enlarged view of the middle A; Figure 6It is a principle block diagram of a controller in a basalt fiber UAV according to an embodiment of the present invention.
[0020] In the figure: 1. Basalt fiber UAV shell; 2. Battery; 3. Support frame; 4. Adjustment assembly; 401. First mounting plate; 402. Second mounting plate; 403. First connecting shaft; 404. Second connecting shaft; 405. Connecting rod; 406. Third connecting shaft; 407. Slide; 5. Fan blade assembly; 501. Motor; 502. Fan blade; 6. Mounting plate; 7. Controller; 701. Data acquisition module; 702. Environment perception module; 703. Flight path planning module; 704. Flight control module; 8. Sensor; 9. Mounting block; 10. First protrusion; 1001. Limiting hole; 11. Second protrusion; 1101. Limiting column. DETAILED DESCRIPTION
[0021] To further illustrate each embodiment, the present invention provides drawings, which are part of the disclosure of the present invention and are mainly used to illustrate the embodiments and can be used in conjunction with the relevant descriptions in the specification to explain the operating principles of the embodiments. With reference to these contents, ordinary technicians in the field should be able to understand other possible implementations and advantages of the present invention. The components in the figures are not drawn to scale, and similar component symbols are generally used to represent similar components.
[0022] According to an embodiment of the present invention, a basalt fiber UAV is provided.
[0023] The present invention is further described with reference to the accompanying drawings and specific embodiments. Figure 1-Figure 5 As shown, the configuration method according to the embodiment of the present invention includes a basalt fiber UAV shell 1, a battery 2 is arranged on the top of the basalt fiber UAV shell 1, support frames 3 are evenly arranged around the basalt fiber UAV shell 1, an adjustment component 4 is arranged on one side of the support frame 3, and a fan blade component 5 is arranged on the side of the support frame 3 away from the adjustment component 4; a mounting plate 6 is arranged inside the basalt fiber UAV shell 1, a controller 7 is arranged on the inner top of the mounting plate 6, and a sensor 8 is arranged on the top of the basalt fiber UAV shell 1.
[0024] With the help of the above-mentioned scheme, the UAV shell of the present invention is made of basalt fiber material, which has the advantages of high strength, high temperature resistance, corrosion resistance, electromagnetic shielding and environmental protection, which significantly improves the performance of the UAV. By setting the adjustment component 4, the distance between the support frame 3 and the basalt fiber UAV shell 1 can be adjusted, so that the structural form of the UAV can be flexibly adjusted according to the different requirements of the flight environment.
[0025] In one embodiment, for the above-mentioned support frame 3, mounting blocks 9 are provided at both ends of one side of the support frame 3, a plurality of first protrusions 10 are symmetrically provided at both ends of the outside of the support frame 3, and a plurality of second protrusions 11 are symmetrically provided at both ends of the outside of the support frame 3. The adjustment component 4 includes a first mounting plate 401 symmetrically provided at both ends of the support frame 3 and the mounting block 9, a second mounting plate 402 is provided on one side of the first mounting plate 401, a plurality of first connecting shafts 403 are provided between the two groups of first mounting plates 401 located in the same horizontal direction, a plurality of second connecting shafts 404 are provided between the two groups of second mounting plates 402 located in the same horizontal direction, and the first connecting shaft 403 and the second connecting shaft 404 is connected by a plurality of connecting rods 405, two adjacent groups of connecting rods 405 are cross-arranged, and a third connecting shaft 406 is arranged at the intersection of the two adjacent groups of connecting rods 405; a slide groove 407 matched with one of the connecting rods 405 is provided inside the first mounting plate 401, so that the distance between the support frame 3 and the basalt fiber UAV shell 1 can be adjusted, and the structural form of the UAV can be flexibly adjusted according to different requirements of the flight environment, and the overall volume of the UAV can be reduced, which is convenient for storage and carrying. At the same time, the telescopic adjustment between the support frame 3 and the basalt fiber UAV shell 1 can change the position between the fan blades 502, thereby improving the flight stability and wind resistance of the UAV.
[0026] The working principle of the adjustment component 4 is as follows: in actual application, the driving source of the adjustment component 4 can be a hydraulic cylinder, which drives one of the connecting rods 405 to move up and down in the slide groove 407 through the output end of the hydraulic cylinder, and through the coordinated use of the first connecting shaft 403, the second connecting shaft 404 and the third connecting shaft 406, the movement of the current connecting rod 405 drives the movement of the remaining cross-arranged connecting rods 405, thereby realizing the angle adjustment of the connecting rod 405, so as to adjust the distance between the support frame 3 and the basalt fiber UAV shell 1. During the adjustment of the support frame 3, the limit column 1101 moves in and out of the limit hole 1001, thereby providing higher accuracy and reliability for the fixation of the adjustment component 4, and then flexibly adapting to the needs of different flight environments, and meeting the use requirements of different scenarios such as space limitation, high stability or load changes.
[0027] In one embodiment, for the above-mentioned fan blade assembly 5, the fan blade assembly 5 includes a motor 501 arranged on the outer side of the support frame 3, and a fan blade 502 is arranged on the top of the motor 501. By setting the fan blade assembly 5 on the outer side of the support frame 3 and driving the fan blade 502 to rotate by the motor 501, the airflow distribution can be optimized and the interference between the rotors can be reduced, thereby improving the flight efficiency and stability of the UAV.
[0028] In one embodiment, for the above-mentioned first protrusion 10, a limiting hole 1001 is opened inside the first protrusion 10, and a limiting column 1101 matching with the limiting hole 1001 is arranged outside the second protrusion 11, so that the matching of the limiting hole 1001 and the limiting column 1101 provides higher precision and reliability for the fixation of the adjustment component 4, effectively avoids the influence of vibration and displacement on the flight performance, and improves the structural stability of the UAV in a complex flight environment.
[0029] like Figure 6 As shown, in one embodiment, for the above-mentioned controller 7, the controller 7 includes a data acquisition module 701, an environment perception module 702, a flight path planning module 703 and a flight control module 704; The data acquisition module 701 is used to collect the flight data of the UAV during flight using the sensor 8, and pre-process the flight data to obtain flight optimization data.
[0030] It should be noted that flight optimization data includes the drone’s position information, speed information, flight attitude angles (such as pitch angle, roll angle, heading angle), altitude information, environmental data collected by sensors (such as lidar point cloud, infrared image, visible light image), and real-time status information of the power system (such as motor speed, battery power), etc.
[0031] The environment perception module 702 is used to extract the flight image of the UAV from the flight optimization data, and identify the static obstacle features and dynamic obstacle features based on the flight image.
[0032] It should be noted that the method of identifying static obstacle features and dynamic obstacle features based on flight images is to extract features and semantic segmentation of flight images, combined with machine learning or deep learning algorithms, to classify objects in flight images into static or dynamic categories. For example, the shape and motion trend of objects in the image are analyzed through edge detection, feature point tracking or optical flow method. Stationary objects (such as buildings and trees) are marked as static obstacles, while objects with continuous displacement characteristics (such as vehicles and pedestrians) are marked as dynamic obstacles. The motion trajectory of dynamic obstacles is further predicted to provide input for path planning.
[0033] The flight path planning module 703 is used to set a static path based on the characteristics of static obstacles, track the motion trajectory of dynamic obstacles to obtain a dynamic path, perform path conflict detection on the static path and the dynamic path to obtain an executable path, and select the optimal flight path from the executable paths.
[0034] The flight path planning module 703 sets a static path based on the characteristics of static obstacles, tracks the motion trajectory of dynamic obstacles to obtain a dynamic path, performs path conflict detection on the static path and the dynamic path to obtain an executable path, and selects the optimal flight path from the executable path, including: The three-dimensional map generation technology is used to build a three-dimensional environment model for UAV flight, and the positions of static obstacles are marked in the three-dimensional environment model. The global path planning algorithm is used to generate a static path that avoids static obstacles.
[0035] It should be noted that the sensor 8 is used to collect the three-dimensional spatial data of the UAV flight area, and the collected data is denoised, filtered and registered through point cloud processing technology to generate a complete three-dimensional map model; in the three-dimensional environment model, the position and shape characteristics of the static obstacles are marked by the object detection and segmentation algorithm, and the range of the obstacles is recorded in the form of spatial coordinates; based on the three-dimensional environment model and the marked static obstacle information, a global path planning algorithm (such as A* algorithm, Dijkstra algorithm or RRT algorithm) is used to calculate the optimal path from the starting point to the target point. The algorithm will comprehensively consider factors such as the spatial distribution of obstacles, aircraft performance and path length to ensure that the path can avoid static obstacles; the planned static path is projected into the three-dimensional environment model in the form of a three-dimensional trajectory for the UAV to track and execute during flight, and the path can be updated according to real-time feedback to optimize flight safety and efficiency.
[0036] The motion trajectories of dynamic obstacles are divided based on the characteristics of dynamic obstacles, and the motion modes of dynamic obstacles are obtained respectively, and the motion modes include uniform linear motion, uniform accelerated motion and curved motion.
[0037] According to the uniform linear motion, uniformly accelerated motion and curved motion of the dynamic obstacle, the future trajectory of the dynamic obstacle is predicted, and the dynamic path is marked in the three-dimensional environment model based on the future trajectory of the dynamic obstacle.
[0038] Among them, predicting the future trajectory of the dynamic obstacle according to the uniform linear motion, uniform accelerated motion and curved motion of the dynamic obstacle, and marking the dynamic path in the three-dimensional environment model based on the future trajectory of the dynamic obstacle includes: The motion pattern of the dynamic obstacle at the current moment is input into the predefined time series prediction model as input data to obtain the initial trajectory of the dynamic obstacle.
[0039] The uniform linear motion, uniformly accelerated motion and curved motion of the UAV in the historical time period are obtained to generate a reference offset compensation function, and the initial trajectory of the dynamic obstacle is preliminarily corrected according to the reference offset compensation function.
[0040] According to the static obstacle marking points in the 3D environment model and the predefined flight mission requirements, the initial trajectory of the dynamic obstacle is corrected by environmental constraints to obtain the future trajectory of the dynamic obstacle, and the dynamic path is marked in the 3D environment model based on the future trajectory of the dynamic obstacle.
[0041] It should be noted that the following further describes the prediction of the future trajectory of a dynamic obstacle based on the uniform linear motion, uniform accelerated motion, and curved motion of the dynamic obstacle, and the marking of a dynamic path in the three-dimensional environment model based on the future trajectory of the dynamic obstacle: Step 1: Generate initial trajectory of dynamic obstacles: Input data: The position, speed, and motion pattern (such as straight lines, curves, or irregular motion) of dynamic obstacles are collected in real time through sensors (such as radars and cameras).
[0042] Time series prediction model: Use a predefined time series prediction model (such as LSTM, GRU, or Kalman filter) to take the motion pattern of the dynamic obstacle as input data, combined with the historical motion trajectory, to predict the initial trajectory of the dynamic obstacle in the future.
[0043] Output: The initial trajectory of the dynamic obstacle as a time series of spatial coordinates, representing its possible position at future moments.
[0044] Step 2: Generation of reference offset compensation function: Historical data acquisition: Collect the flight data of the UAV in the past period of time, including the trajectory and deviation of uniform linear motion, uniformly accelerated motion, and curved motion.
[0045] Benchmark compensation function fitting: Based on historical data, regression analysis or interpolation algorithm is used to fit the benchmark offset compensation function to compensate for deviations in the motion trajectory.
[0046] For example, the compensation function for uniform linear motion may be a linear offset function; the compensation function for uniform accelerated motion may be a quadratic offset function; and the compensation function for curved motion may be a cubic spline or a sine function.
[0047] Output: A baseline offset compensation function that adjusts for potential errors in dynamic obstacle trajectories.
[0048] Step 3: Correct the initial trajectory by benchmark offset compensation: Apply compensation function: Apply the reference offset compensation function of the corresponding motion mode to correct the spatial coordinates of each time point in the initial trajectory of the dynamic obstacle.
[0049] Correction process: Select the corresponding compensation function according to the movement mode of the dynamic obstacle (such as straight line, curve).
[0050] Output result: The dynamic obstacle trajectory corrected based on the historical deviation is obtained.
[0051] Step 4: Correct trajectory based on environmental constraints: Static obstacle information acquisition: Extract the annotation points of static obstacles and their spatial range from the 3D environment model.
[0052] Environmental constraint correction: Intersection detection is performed on the corrected dynamic obstacle trajectory and the spatial range of static obstacles. Offset adjustment is performed on trajectory points that may overlap with static obstacles to ensure that the corrected trajectory avoids static obstacles. For example, a path optimization algorithm (such as A* algorithm, RRT) can be used to replan the trajectory points.
[0053] Mission requirement constraints: Based on predefined mission requirements (such as flight altitude, heading deviation limit, etc.), the trajectory is further adjusted to meet the mission requirements.
[0054] Output: Future trajectory of dynamic obstacles corrected by environmental constraints.
[0055] Among them, the conflict probability between the static path and the dynamic path is evaluated, and the conflicting paths between the static path and the dynamic path are filtered based on the conflict probability, and the executable paths of the drone are obtained, including: The future trajectory prediction points of the future trajectory of the dynamic obstacle are extracted, the uncertainty range analysis is performed on the future trajectory prediction points, and the influence area of the dynamic obstacle is generated based on the uncertainty range analysis result.
[0056] The probability value of the impact area is calculated using Gaussian distribution, the time dimension is added to the three-dimensional map model, and a dynamic occupancy probability matrix is generated based on the probability value of the impact area.
[0057] Among them, the Gaussian distribution calculation formula is: ; In the formula, P occ x , y , z Indicates the target point x , y , z Probability value of being in the affected area; x , y , z Represents the coordinates of the current trajectory point of the dynamic obstacle in the three-dimensional environment model; ( μ x , μ y , μ z ) represents the coordinates of the future trajectory prediction point of the dynamic obstacle in the three-dimensional environment model; σ x , σ y , σ z Respectively represent the Gaussian distribution in x , y and z The standard deviation in the direction; exp represents the exponential function.
[0058] The dynamic occupancy probability matrix is used to cumulatively calculate the occupancy probability of the future trajectory prediction point at the current moment. If the influence areas of the dynamic obstacles are the same, the occupancy probabilities of the influence areas of the dynamic obstacles are superimposed. Discretize the static path into several static trajectory points, find the occupancy probability value of the static trajectory point in the dynamic occupancy probability matrix, and compare the occupancy probability value of the static trajectory point with a preset threshold; If the occupancy probability value is greater than the preset threshold, it means that there is a conflicting impact area between the static path and the dynamic path, and the impact area is filtered to obtain the executable path of the drone. Otherwise, it means that there is no conflicting impact area between the static path and the dynamic path.
[0059] It should be noted that based on the future trajectory point positions of dynamic obstacles, the influence area of each trajectory point on the space is accumulated in the dynamic occupancy probability matrix using its Gaussian distribution or other probability distribution models. The accumulation of occupancy probability follows the superposition principle, that is, if the influence areas of multiple dynamic obstacles overlap, the occupancy probabilities of the overlapping areas are weighted and superimposed to form the final dynamic occupancy probability matrix.
[0060] The static path is discretized into several static trajectory points. The corresponding occupancy probability value of each static trajectory point in the dynamic occupancy probability matrix is found and compared with the preset occupancy probability threshold point by point.
[0061] If the occupancy probability value of a static trajectory point is greater than the preset threshold, it is determined that the point is within the influence range of the dynamic obstacle, and there is a conflict between the static path and the dynamic path in this area; for the conflicting influence area, the static path is adjusted or the conflicting trajectory points are eliminated through the path optimization algorithm, and re-planned, and the executable path of the drone is obtained after filtering out the conflicting area.
[0062] If the occupancy probability values of all static trajectory points are lower than the preset threshold, it means that there is no conflicting impact area between the static path and the dynamic path, and it can be directly used as the execution path of the drone.
[0063] The multi-objective optimization algorithm is used to select the optimal flight path from the executable paths, and the optimal flight path is used to generate executable instructions and sent to the control center.
[0064] The flight control module 704 is used to detect whether the drone follows the optimal flight path, and when the drone deviates from the optimal flight path, use a correction algorithm to correct the drone parameters so that the drone always remains on the optimal flight path.
[0065] It should be noted that the optimization objective function is set according to the flight mission requirements of the UAV. Common objectives include shortest path length, lowest energy consumption, shortest flight time, highest obstacle avoidance safety, etc. A comprehensive objective function is formed by assigning weights to each objective.
[0066] The executable path is represented as a discrete sequence of path points, the attributes of each path (such as length, time, energy consumption, etc.) are extracted, and the path attributes are optimized and calculated in combination with a multi-objective optimization algorithm (such as NSGA-II, MOEA / D or Pareto optimization algorithm), and the optimal flight path that meets the requirements of the comprehensive objective function is output.
[0067] Based on the optimal flight path, the flight instruction set of the UAV is generated, including speed, direction, waypoint coordinates, etc., and the instruction set is encoded into a standard control protocol format (such as MAVLink) and sent to the control center of the UAV through the communication module; the Pareto optimal solution is quickly searched in the feasible solution space through the optimization algorithm, and the weights and constraints of the optimization objectives are adjusted in real time according to the UAV mission scenario, so that the generated flight path meets the mission requirements and has environmental adaptability.
[0068] In order to facilitate understanding of the above technical solutions of the present invention, the working principle or operation mode of the present invention in the actual process is described in detail below.
[0069] In practical application, the basalt fiber UAV provided by the present invention is supported by a lightweight and high-strength basalt fiber UAV shell 1 as the main body, and the battery 2 installed on the top provides power for the whole system. The support frame 3 evenly distributed around the support frame 3 adjusts the distance between the support frame 3 and the basalt fiber UAV shell 1 through the adjustment component 4 to adapt to different flight requirements, and drives the fan blade component 5 through the controller 7 to provide flight thrust; at the same time, the controller includes a data acquisition module 701 for real-time collection of flight data; an environmental perception module 702 for identifying static and dynamic obstacle features; a flight path planning module 703 for generating a dynamic obstacle avoidance path based on perception data and task requirements; and a flight control module 704 for executing the planned path and accurately controlling the movement of the UAV. The sensor 8 set on the top further enhances the environmental data collection capability, provides perception guarantee for the flight of the UAV in a complex environment, and realizes efficient, safe and flexible autonomous flight capabilities as a whole.
[0070] In summary, with the aid of the above technical solution of the present invention, the shell of the UAV of the present invention is made of basalt fiber material, which has the advantages of high strength, high temperature resistance, corrosion resistance, electromagnetic shielding and environmental protection, and significantly improves the performance of the UAV. By setting the adjustment component 4, the distance between the support frame 3 and the basalt fiber UAV shell 1 can be adjusted, so that the structural form of the UAV can be flexibly adjusted according to the different needs of the flight environment, and the overall volume of the UAV can be reduced, which is convenient for storage and carrying. At the same time, the telescopic adjustment between the support frame 3 and the basalt fiber UAV shell 1 can change the position between the fan blades 502, thereby improving the flight stability and wind resistance of the UAV; the present invention uses the environmental perception module 702 and the flight path planning module 70 3 is combined to extract UAV flight images, accurately identify the characteristics of static and dynamic obstacles, realize the scientific setting of static paths and real-time tracking of dynamic obstacle motion trajectories, thereby ensuring the comprehensiveness and flexibility of path planning, improving the obstacle avoidance capability and flight efficiency of UAVs in complex environments, and thus improving the safety and accuracy of task execution; the present invention performs path conflict detection on static paths and dynamic paths, screens out safe executable paths, and selects the optimal flight path therefrom, which can effectively avoid obstacles and potential collision risks, ensure flight safety, and optimize path planning to reduce energy consumption and flight time, improve the efficiency and reliability of UAV task execution, and achieve efficient and safe flight goals in complex environments.
[0071] In the present invention, unless otherwise clearly stipulated and limited, the terms such as "installation", "setting", "connection", "fixation" and "screw-on" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral one; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium; it can be the internal connection of two elements or the interaction relationship between two elements. Unless otherwise clearly defined, ordinary technicians in this field can understand the specific meanings of the above terms in the present invention according to the specific circumstances.
[0072] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims
1. A basalt fiber UAV, comprising a basalt fiber UAV shell (1), characterized in that: A battery (2) is arranged on the top of the basalt fiber UAV shell (1), support frames (3) are evenly arranged around the basalt fiber UAV shell (1), an adjustment component (4) is arranged on one side of the support frame (3), and a fan blade component (5) is arranged on the side of the support frame (3) away from the adjustment component (4); A mounting plate (6) is arranged inside the basalt fiber UAV housing (1), a controller (7) is arranged on the inner top of the mounting plate (6), and a sensor (8) is arranged on the top of the basalt fiber UAV housing (1).
2. The basalt fiber UAV according to claim 1, characterized in that: Mounting blocks (9) are arranged at both ends of one side of the support frame (3), a plurality of first protrusions (10) are symmetrically arranged at both ends of the outside of the support frame (3), and a plurality of second protrusions (11) are symmetrically arranged at both ends of the outside of the support frame (3).
3. The basalt fiber UAV according to claim 2, characterized in that: The adjustment assembly (4) comprises a first mounting plate (401) symmetrically arranged at both ends of the support frame (3) and the mounting block (9); a second mounting plate (402) is arranged on one side of the first mounting plate (401); a plurality of first connecting shafts (403) are arranged between two groups of the first mounting plates (401) located in the same horizontal direction; a plurality of second connecting shafts (404) are arranged between two groups of the second mounting plates (402) located in the same horizontal direction; the first connecting shafts (403) and the second connecting shafts (404) are connected via a plurality of connecting rods (405); two adjacent groups of the connecting rods (405) are arranged in a cross-type manner, and a third connecting shaft (406) is arranged at the cross position of the two adjacent groups of the connecting rods (405); and a sliding groove (407) matching with one of the connecting rods (405) is provided inside the first mounting plate (401).
4. The basalt fiber UAV according to claim 1, characterized in that: The fan blade assembly (5) comprises a motor (501) arranged on one side of the outside of the support frame (3), and a fan blade (502) is arranged on the top of the motor (501).
5. The basalt fiber UAV according to claim 3, characterized in that: A limiting hole (1001) is provided inside the first protrusion (10), and a limiting column (1101) matching with the limiting hole (1001) is provided outside the second protrusion (11).
6. The basalt fiber UAV according to claim 1, characterized in that: The controller (7) comprises a data acquisition module (701), an environment perception module (702), a flight path planning module (703) and a flight control module (704); The data acquisition module (701) is used to collect flight data of the UAV during flight using the sensor (8), and to pre-process the flight data to obtain flight optimization data; An environment perception module (702) is used to extract a flight image of the UAV from the flight optimization data, and identify static obstacle features and dynamic obstacle features based on the flight image; A flight path planning module (703) is used to set a static path based on the characteristics of static obstacles, track the motion trajectory of dynamic obstacles to obtain a dynamic path, perform path conflict detection on the static path and the dynamic path to obtain an executable path, and select the optimal flight path from the executable paths; The flight control module (704) is used to detect whether the drone follows the optimal flight path, and when the drone deviates from the optimal flight path, use a correction algorithm to correct the drone parameters so that the drone always remains on the optimal flight path.
7. The basalt fiber UAV according to claim 6, characterized in that: The flight path planning module (703) sets a static path based on the characteristics of static obstacles, tracks the motion trajectory of dynamic obstacles to obtain a dynamic path, performs path conflict detection on the static path and the dynamic path to obtain an executable path, and selects the optimal flight path from the executable path, including: Use 3D map generation technology to build a 3D environment model for UAV flight, mark the location of static obstacles in the 3D environment model, and use the global path planning algorithm to generate a static path that avoids static obstacles; The motion trajectories of the dynamic obstacles are divided based on the characteristics of the dynamic obstacles, and the motion modes of the dynamic obstacles are obtained respectively, and the motion modes include uniform linear motion, uniform accelerated motion and curved motion; According to the uniform linear motion, uniform accelerated motion and curved motion of the dynamic obstacle, the future trajectory of the dynamic obstacle is predicted, and the dynamic path is marked in the 3D environment model based on the future trajectory of the dynamic obstacle; Evaluate the conflict probability between the static path and the dynamic path, and filter the conflicting paths between the static path and the dynamic path based on the conflict probability to obtain the executable path of the UAV; The multi-objective optimization algorithm is used to select the optimal flight path from the executable paths, and the optimal flight path is used to generate executable instructions and sent to the control center.
8. The basalt fiber UAV according to claim 7, characterized in that: The predicting of the future trajectory of the dynamic obstacle according to the uniform linear motion, uniform accelerated motion and curved motion of the dynamic obstacle, and marking the dynamic path in the three-dimensional environment model based on the future trajectory of the dynamic obstacle includes: The motion pattern of the dynamic obstacle at the current moment is input as input data into the predefined time series prediction model to obtain the initial trajectory of the dynamic obstacle; Obtain the uniform linear motion, uniform accelerated motion and curved motion of the UAV in the historical time period to generate a reference offset compensation function, and make a preliminary correction to the initial trajectory of the dynamic obstacle according to the reference offset compensation function; According to the static obstacle marking points in the 3D environment model and the predefined flight mission requirements, the initial trajectory of the dynamic obstacle is corrected by environmental constraints to obtain the future trajectory of the dynamic obstacle.
9. The basalt fiber UAV according to claim 8, characterized in that: The step of evaluating the conflict probability between the static path and the dynamic path, and filtering the conflicting paths between the static path and the dynamic path based on the conflict probability to obtain the executable path of the drone includes: Extracting future trajectory prediction points of the future trajectory of the dynamic obstacle, performing uncertainty range analysis on the future trajectory prediction points, and generating an influence area of the dynamic obstacle based on the uncertainty range analysis result; The probability value of the impact area is calculated using Gaussian distribution, the time dimension is added to the three-dimensional map model, and a dynamic occupancy probability matrix is generated based on the probability value of the impact area; The dynamic occupancy probability matrix is used to cumulatively calculate the occupancy probability of the future trajectory prediction point at the current moment. If the influence areas of the dynamic obstacles are the same, the occupancy probabilities of the influence areas of the dynamic obstacles are superimposed. Discretize the static path into several static trajectory points, find the occupancy probability value of the static trajectory point in the dynamic occupancy probability matrix, and compare the occupancy probability value of the static trajectory point with a preset threshold; If the occupancy probability value is greater than the preset threshold, it means that there is a conflicting impact area between the static path and the dynamic path, and the impact area is filtered to obtain the executable path of the drone. Otherwise, it means that there is no conflicting impact area between the static path and the dynamic path.
10. The basalt fiber UAV according to claim 9, characterized in that: The Gaussian distribution calculation formula is: ； In the formula, P occ ( x , y , z ) represents the target point ( x , y , z ) is in the affected area; ( x , y , z ) represents the coordinates of the current trajectory point of the dynamic obstacle in the three-dimensional environment model; ( μ x , μ y , μ z ) represents the coordinates of the future trajectory prediction point of the dynamic obstacle in the three-dimensional environment model; σ x , σ y , σ z Respectively represent the Gaussian distribution in x , y and z The standard deviation in the direction; exp represents the exponential function.