Dynamic avoidance method of small obstacles for UAV based on fractional calculus

Abstract

The present invention discloses a method for dynamically avoiding small obstacles of unmanned aerial vehicles based on fractional calculus, constructs an obstacle data set including the three-dimensional spatial position of small obstacles and the dynamic change information of small obstacles; pre-processes the obstacle data set; constructs a flight state model of the unmanned aerial vehicle based on obstacle detection data using adaptive fractional calculus; introduces an adaptive fractional control algorithm to generate an optimal obstacle avoidance path; converts the optimal obstacle avoidance path into a control instruction of the unmanned aerial vehicle, and sends it to the flight control system of the unmanned aerial vehicle in real time, controls the unmanned aerial vehicle to avoid obstacles according to the optimal obstacle avoidance path; during the flight of the unmanned aerial vehicle, continuously monitors environmental changes, and updates the adaptive fractional flight state model and the optimal obstacle avoidance path in real time according to new sensor data. The present invention realizes dynamic avoidance of small obstacles by introducing an adaptive fractional calculus control algorithm, combining the flight state of the unmanned aerial vehicle and obstacle detection data.

Description

Technical Field

[0001] The invention relates to the technical field of unmanned aerial vehicles, and in particular to a method for dynamically avoiding tiny obstacles of unmanned aerial vehicles based on fractional calculus. Background Art

[0002] In the prior art, the avoidance of obstacles by UAVs in complex dynamic environments mainly relies on control algorithms based on integer-order calculus. Traditional obstacle avoidance methods usually use lidar, ultrasonic sensors and camera sensors to collect obstacle data of the surrounding environment, and combine them with path planning algorithms to generate the flight path of the UAV. However, traditional methods have some limitations when dealing with tiny obstacles in high-speed motion, especially when facing fast flight speeds, complex environments and small obstacles. Traditional obstacle avoidance systems often cannot respond accurately in time, which can easily lead to collision accidents.

[0003] Currently, common obstacle avoidance systems mainly rely on integer-order calculus algorithms to control aircraft and plan paths. Integer-order calculus algorithms work better in relatively static environments or environments with large obstacles. However, in environments with high-speed flight and tiny obstacles, the response speed and control accuracy of integer-order control algorithms are insufficient, making it difficult to adapt to complex dynamic environmental changes. In addition, existing technologies usually do not fully consider the fusion of historical flight trajectory data and current obstacle dynamic information, resulting in the inability of drones to effectively use past flight experience for optimization in repeated or similar scenarios, increasing the complexity and uncertainty of obstacle avoidance.

[0004] Existing drone obstacle avoidance technology still has efficiency problems in obstacle data processing and real-time obstacle avoidance path generation. In traditional methods, obstacle data is usually processed by simple filtering and enhancement, which cannot effectively cope with complex environments with high noise or unclear obstacle features. As a result, when the drone faces dynamically changing obstacles, the accuracy of obstacle avoidance decisions decreases and the optimal obstacle avoidance path cannot be generated in real time. In addition, due to the lack of close coupling between obstacle data processing and path planning in the existing system, the real-time update and response speed of the obstacle avoidance path are insufficient.

[0005] In summary, the existing technologies have the following main shortcomings: first, integer-order control algorithms are not sufficiently responsive in high-speed flight and small obstacle avoidance, and are difficult to adapt to dynamic and complex environmental changes; second, existing obstacle avoidance technologies do not fully utilize historical flight data for decision optimization, which increases the difficulty of obstacle avoidance in repetitive scenarios for UAVs; finally, the real-time performance of obstacle data processing and obstacle avoidance path generation is poor, and accurate obstacle avoidance operations cannot be achieved in complex dynamic environments. Summary of the invention

[0006] One purpose of the present invention is to propose a method for dynamic avoidance of tiny obstacles for unmanned aerial vehicles based on fractional-order calculus. The present invention realizes dynamic avoidance of tiny obstacles by introducing an adaptive fractional-order calculus control algorithm and combining the flight status of the unmanned aerial vehicle and obstacle detection data.

[0007] The method for dynamically avoiding small obstacles of a UAV based on fractional calculus according to an embodiment of the present invention comprises the following steps:

[0008] S1. Obtain lidar data, ultrasonic sensor data, and camera image data in the UAV flight environment, and construct an obstacle dataset including the three-dimensional spatial position of tiny obstacles and the dynamic change information of tiny obstacles based on multi-sensor data fusion technology;

[0009] S2. Preprocess the obstacle data set to remove noise and invalid data, and generate obstacle detection data after filtering and enhancement;

[0010] S3. Construct the UAV flight state model based on obstacle detection data using adaptive fractional calculus;

[0011] S4. Introduce an adaptive fractional-order control algorithm to automatically adjust the fractional order of the adaptive fractional-order calculus according to different flight speeds and obstacle complexity to generate the optimal obstacle avoidance path;

[0012] S5. Convert the optimal obstacle avoidance path into a control command for the UAV and send it to the flight control system of the UAV in real time to control the UAV to avoid obstacles according to the optimal obstacle avoidance path;

[0013] S6. During the flight of the UAV, the environmental changes are continuously monitored, and the adaptive fractional-order flight state model and the optimal obstacle avoidance path are updated in real time according to new sensor data.

[0014] Optionally, the S1 includes the following steps:

[0015] S11. Obtain obstacle data in the UAV flight environment using laser radar, the obstacle data including obstacle distance , azimuth and height ;

[0016] S12. Collect obstacle distance information using ultrasonic sensors and location information ;

[0017] S13. Obtain image data of the obstacle using a camera, and generate three-dimensional spatial information of the obstacle using a three-dimensional reconstruction technique based on pixels, including the position and obstacle dynamic change information , where t is the time variable;

[0018] S14. Based on multi-sensor data fusion technology, the lidar data, ultrasonic sensor data and camera image data are fused into a unified obstacle data set through a weighted fusion algorithm. The three-dimensional spatial position of the tiny obstacles in the obstacle data set is calculated as:

[0019] ;

[0020] in, , , are the weighting coefficients of LiDAR, ultrasonic sensor and camera data respectively;

[0021] S15. Dynamically changing information when passing obstacles The movement trajectory of the obstacle is modeled. Dynamic calculation through the fused obstacle dataset:

[0022] ;

[0023] in, is the initial time;

[0024] S16. Build the final obstacle dataset:

[0025] .

[0026] Optionally, S2 includes the following steps:

[0027] S21. Use mean filtering method to filter distance information in obstacle dataset Perform smoothing processing to obtain smoothed distance data;

[0028] S22. Use Gaussian filtering to filter the three-dimensional spatial information of obstacles in the obstacle dataset Processing is performed to eliminate image noise and obtain processed three-dimensional spatial information of obstacles;

[0029] S23. Use median filtering to detect dynamic changes of obstacles in obstacle datasets Perform denoising to obtain denoised obstacle dynamic change information;

[0030] S24. Performing enhancement processing on the preprocessed obstacle data and enhancing the obstacle edge information;

[0031] S25. Output the obstacle detection data after filtering and enhancement:

[0032] ;

[0033] in, is the enhanced obstacle 3D spatial position data. It is the enhanced obstacle dynamic change information.

[0034] Optionally, S3 includes the following steps:

[0035] S31. Based on obstacle detection data The location information of the drone at the current time t based on the historical flight trajectory data , speed information To initialize:

[0036] ;

[0037] in, and are the weight coefficients of obstacle detection data and historical flight trajectory, is the three-dimensional spatial position of the current obstacle detection, It is the location information in the historical flight trajectory;

[0038] S32. Combine the current flight speed v(t) and acceleration Building a flight status model , the flight state model describes the dynamic response characteristics of the UAV at time t:

[0039] ;

[0040] in, is the current speed of the drone;

[0041] S33. Use the adaptive fractional calculus method to optimize the flight state model, perform fractional differential calculations on the flight state model, and comprehensively consider the influence of historical flight states, current obstacle detection data, and flight speed v(t) to construct the dynamic response equation:

[0042] ;

[0043] in, is the dynamic response model of the UAV, is the fractional order, is the weight of obstacle detection data to dynamic response, is the memory decay factor of the obstacle data, which is used to adjust the change of obstacle influence over time. is the impact weight of historical flight trajectory data, is the preprocessed current obstacle detection data, Historical flight trajectory data.

[0044] Optionally, S4 includes the following steps:

[0045] S41. Based on the UAV’s flight speed v(t) and the complexity of obstacles Determining the Fractional Order in an Adaptive Fractional-Order Control Algorithm , the fractional order changes dynamically over time to adapt to the complexity of the current environment:

[0046] ;

[0047] in, is the initial fractional order, is the speed adjustment coefficient, is the obstacle complexity adjustment coefficient;

[0048] S42. Calculate the optimal obstacle avoidance path for the current drone ,The optimal obstacle avoidance path is obtained based on the fractional order control algorithm ,combining the historical flight trajectory, the current obstacle detection data and the fractional order response ,characteristics;

[0049] S43. Real-time adjustment of fractional order in fractional order control algorithm during UAV flight Optimize obstacle avoidance response to enable the drone to dynamically avoid obstacles and generate the optimal path.

[0050] Optionally, the S42 includes the following steps:

[0051] S421. Based on historical flight trajectory , Current obstacle detection data And the fractional order response characteristics , build a flight status prediction model under the current environment :

[0052] ;

[0053] in, It is the corrected flight status based on historical trajectory and obstacle data at the current moment. is the fractional-order dynamic response characteristic;

[0054] S422. Combining flight speed v(t) and obstacle complexity The optimal obstacle avoidance path is calculated by an adaptive fractional-order control algorithm. The optimization goal of the optimal obstacle avoidance path is to minimize the risk of collision with obstacles and maximize the smoothness of the flight path. The optimal obstacle avoidance path is calculated as:

[0055] ;

[0056] in, is the current optimal obstacle avoidance path, Real-time detection data of obstacles.

[0057] Optionally, S5 includes the following steps:

[0058] S51. The optimal obstacle avoidance path Convert to drone control commands, including converting optimal obstacle avoidance path information into drone heading angle commands , pitch angle command And speed command :

[0059] ;

[0060] ;

[0061] ;

[0062] in, is the position information on the optimal obstacle avoidance path, is the current drone position;

[0063] S52. Send the heading angle command, pitch angle command and speed command to the flight control system of the UAV. The flight control system adjusts the attitude and speed of the UAV according to the received commands, so that the UAV flies along the optimal obstacle avoidance path and avoids obstacles.

[0064] The beneficial effects of the present invention are:

[0065] (1) The present invention adopts adaptive fractional-order calculus to dynamically adjust the fractional-order order. According to different flight speeds and obstacle complexities, the response speed and accuracy of the control algorithm are adjusted in real time. Compared with traditional integer-order calculus, adaptive fractional-order calculus is more suitable for dealing with complex dynamic environments. By dynamically adjusting the fractional-order order, the UAV has higher obstacle avoidance flexibility and responsiveness when flying at high speeds. It avoids the problems of response lag and insufficient accuracy that are prone to occur in traditional algorithms when dealing with tiny and fast-moving obstacles, and effectively improves the obstacle avoidance performance of the UAV in complex environments, especially the avoidance efficiency when facing tiny obstacles.

[0066] (2) The present invention constructs a flight state model that combines historical flight trajectory data with real-time obstacle detection data, avoiding the problem of the inability to effectively utilize historical data in traditional algorithms. By memorizing the historical flight trajectory of the UAV and combining it with the current obstacle detection information, a more accurate flight state model is constructed, so that the UAV can dynamically adjust the flight path in repeated or similar scenarios by referring to the historical trajectory, generate more optimized obstacle avoidance decisions, reduce the complexity of calculation, improve the accuracy of obstacle avoidance, and enhance the ability of the UAV to cope with complex dynamic environments.

[0067] (3) The present invention generates the optimal obstacle avoidance path through an adaptive fractional-order control algorithm, combines the flight speed, obstacle position and complexity to update the path information in real time, and converts it into the control command of the UAV, so as to ensure that the UAV can continuously and dynamically avoid obstacles during flight. Compared with the traditional obstacle avoidance system, the present invention realizes the real-time coupling of the flight path and the control command, so that the UAV can make rapid adjustments to avoid collisions when dealing with sudden changes in obstacles. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0069] Figure 1 This is a flow chart of the method for dynamically avoiding small obstacles of unmanned aerial vehicles based on fractional calculus proposed by the present invention;

[0070] Figure 2 This is a structural schematic diagram of constructing a UAV flight state model based on adaptive fractional-order calculus in the UAV tiny obstacle dynamic avoidance method based on fractional-order calculus proposed in the present invention. DETAILED DESCRIPTION

[0071] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, which only illustrate the basic structure of the present invention in a schematic manner, and therefore only show the components related to the present invention.

[0072] refer to Figure 1-Figure 2 ,The small obstacle dynamic avoidance method for UAV based on fractional calculus includes the following steps:

[0073] S1. Obtain lidar data, ultrasonic sensor data, and camera image data in the UAV flight environment, and construct an obstacle dataset including the three-dimensional spatial position of tiny obstacles and the dynamic change information of tiny obstacles based on multi-sensor data fusion technology;

[0074] S2. Preprocess the obstacle data set to remove noise and invalid data, generate obstacle detection data after filtering and enhancement, effectively remove noise in sensor data and enhance obstacle information, improve the quality of obstacle detection data, and provide more reliable input data for subsequent obstacle avoidance algorithms;

[0075] S3. Based on obstacle detection data, the UAV flight state model is constructed using adaptive fractional calculus. The flight state model is constructed based on adaptive fractional calculus, and the historical flight trajectory and real-time obstacle data are integrated to optimize the UAV's response ability in dynamic environments;

[0076] S4. Introduce an adaptive fractional-order control algorithm to automatically adjust the fractional order of the adaptive fractional-order calculus according to different flight speeds and obstacle complexity to generate the optimal obstacle avoidance path;

[0077] S5. Convert the optimal obstacle avoidance path into a control command of the UAV and send it to the flight control system of the UAV in real time to control the UAV to avoid obstacles according to the optimal obstacle avoidance path;

[0078] S6. During the flight of the UAV, the environmental changes are continuously monitored, and the adaptive fractional-order flight state model and the optimal obstacle avoidance path are updated in real time according to the new sensor data.

[0079] In this implementation, S1 includes the following steps:

[0080] S11. Use LiDAR to obtain obstacle data in the UAV flight environment. The obstacle data includes obstacle distance , azimuth and height ;

[0081] S12. Collect obstacle distance information using ultrasonic sensors and location information ;

[0082] S13. Use the camera to obtain image data of the obstacle, and generate the three-dimensional spatial information of the obstacle, including the position, through the three-dimensional reconstruction technology based on pixels. and obstacle dynamic change information , where t is the time variable;

[0083] S14. Based on multi-sensor data fusion technology, the lidar data, ultrasonic sensor data and camera image data are fused into a unified obstacle data set through a weighted fusion algorithm. The three-dimensional spatial position of the tiny obstacles in the obstacle data set is calculated as:

[0084] ;

[0085] in, , , are the weighting coefficients for LiDAR, ultrasonic sensor and camera data respectively;

[0086] S15. Dynamically changing information when passing obstacles Model the movement trajectory of obstacles. Dynamic calculation through the fused obstacle dataset:

[0087] ;

[0088] in, is the initial time;

[0089] S16. Build the final obstacle dataset:

[0090] .

[0091] In this implementation, S2 includes the following steps:

[0092] S21. Use mean filtering method to filter distance information in obstacle dataset Perform smoothing processing to obtain smoothed distance data;

[0093] S22. Use Gaussian filtering to filter the three-dimensional spatial information of obstacles in the obstacle dataset Processing is performed to eliminate image noise and obtain processed three-dimensional spatial information of obstacles;

[0094] S23. Use median filtering to detect dynamic changes of obstacles in obstacle datasets Perform denoising to obtain denoised obstacle dynamic change information;

[0095] S24. Performing enhancement processing on the preprocessed obstacle data and enhancing the obstacle edge information;

[0096] S25. Output the obstacle detection data after filtering and enhancement:

[0097] ;

[0098] in, is the enhanced obstacle 3D spatial position data. It is the enhanced obstacle dynamic change information.

[0099] In this implementation, S3 includes the following steps:

[0100] S31. Based on obstacle detection data The location information of the drone at the current time t based on the historical flight trajectory data , speed information To initialize:

[0101] ;

[0102] in, and are the weight coefficients of obstacle detection data and historical flight trajectory, is the three-dimensional spatial position of the current obstacle detection, It is the location information in the historical flight trajectory;

[0103] S32. Combine the current flight speed v(t) and acceleration Building a flight status model , the flight state model describes the dynamic response characteristics of the UAV at time t:

[0104] ;

[0105] in, is the current speed of the drone;

[0106] S33. Use the adaptive fractional calculus method to optimize the flight state model, perform fractional differential calculations on the flight state model, and comprehensively consider the influence of historical flight states, current obstacle detection data, and flight speed v(t) to construct the dynamic response equation:

[0107] ;

[0108] in, is the dynamic response model of the UAV, is the fractional order, is the weight of obstacle detection data to dynamic response, is the memory decay factor of the obstacle data, which is used to adjust the change of obstacle influence over time. is the impact weight of historical flight trajectory data, is the preprocessed current obstacle detection data, Historical flight trajectory data.

[0109] In this implementation, S4 includes the following steps:

[0110] S41. Based on the UAV’s flight speed v(t) and the complexity of obstacles Determining the Fractional Order in an Adaptive Fractional-Order Control Algorithm , the fractional order changes dynamically over time to adapt to the complexity of the current environment:

[0111] ;

[0112] in, is the initial fractional order, is the speed adjustment coefficient, is the obstacle complexity adjustment coefficient;

[0113] S42. Calculate the optimal obstacle avoidance path for the current drone ,The optimal obstacle avoidance path is obtained based on the fractional order control algorithm ,combining the historical flight trajectory, the current obstacle detection data and the fractional order response ,characteristics;

[0114] S43. Real-time adjustment of fractional order in fractional order control algorithm during UAV flight Optimize obstacle avoidance response to enable the drone to dynamically avoid obstacles and generate the optimal path.

[0115] In this implementation, S42 includes the following steps:

[0116] S421. Based on historical flight trajectory , Current obstacle detection data And the fractional order response characteristics , build a flight status prediction model under the current environment :

[0117] ;

[0118] in, It is the corrected flight status based on historical trajectory and obstacle data at the current moment. is the fractional-order dynamic response characteristic;

[0119] S422. Combining flight speed v(t) and obstacle complexity The optimal obstacle avoidance path is calculated by an adaptive fractional-order control algorithm. The optimization goal of the optimal obstacle avoidance path is to minimize the risk of collision with obstacles and maximize the smoothness of the flight path. The optimal obstacle avoidance path is calculated as:

[0120] ;

[0121] in, is the current optimal obstacle avoidance path, Real-time detection data of obstacles.

[0122] In this implementation, S5 includes the following steps:

[0123] S51. The optimal obstacle avoidance path Convert to drone control commands, including converting optimal obstacle avoidance path information into drone heading angle commands , pitch angle command And speed command :

[0124] ;

[0125] ;

[0126] ;

[0127] in, is the position information on the optimal obstacle avoidance path, is the current drone position;

[0128] S52. Send the heading angle command, pitch angle command and speed command to the flight control system of the UAV. The flight control system adjusts the attitude and speed of the UAV according to the received commands, so that the UAV flies along the optimal obstacle avoidance path and avoids obstacles.

[0129] Example 1: In this Example 1, an urban environment UAV obstacle avoidance test scenario was selected. In a certain test, the UAV flew from point A (starting point) to point B (end point), and the flight path length was 1 km. There were many small obstacles along the way, including wires, branches, and hanging signs, simulating obstacles that may be encountered in real life. The speed of the UAV during flight was set to 5 m / s, the wind speed in the environment was 2 m / s, the distance between obstacles was randomly distributed, and in some scenes, there were dynamic obstacles (pedestrians or birds) that interfered with the flight.

[0130] In this scenario, the UAV dynamic obstacle avoidance method based on adaptive fractional calculus was used to conduct obstacle avoidance flight tests. A total of 20 flight missions were conducted throughout the test, and key data such as the UAV's obstacle avoidance response time, obstacle avoidance path deviation distance, number of obstacles detected, and obstacle avoidance success rate were recorded in each flight.

[0131] Scenario 1:

[0132] Time: May 8, 2024, 10:15 am;

[0133] Scenario description: The drone is flying in an alley with a width of 2 meters. There is a 6-centimeter-diameter wire hanging in the air 5 meters in front of it, about 1.8 meters high. The drone's altitude is set to 2 meters and the flight speed is 5 meters / second.

[0134] Obstacle avoidance results: The drone detected the obstacle when it was about 4 meters away from the power line. It used the adaptive fractional calculus algorithm to adjust the flight path in real time, avoiding the power line and passing safely under the power line at a height of 1.7 meters. The obstacle avoidance took 0.4 seconds and the path deviation distance was 0.2 meters.

[0135] Scenario 2:

[0136] Time: May 8, 2024, 3:30 p.m.

[0137] Scenario description: The drone is flying over an open square. In the middle of the square there is a 5cm diameter sign pole, 3m high. The drone's altitude is set to 2.5m, and the flight speed is 5m / s.

[0138] Obstacle avoidance results: The drone detected the obstacle when it was about 6 meters away from the sign pole, and quickly adjusted its flight trajectory, changing its altitude to 2.2 meters, successfully bypassing the sign pole. The obstacle avoidance took 0.5 seconds and the path deviation distance was 0.3 meters.

[0139] Scenario 3:

[0140] Time: May 9, 2024, 11:00 am;

[0141] Scenario description: A bird suddenly flies past the drone in its flight path. The bird's flying speed is about 10 meters per second, and the drone is flying at a speed of 5 meters per second.

[0142] Obstacle avoidance results: After the drone detected a dynamic obstacle, the system adjusted the flight path and quickly descended to 1.5 meters. It took 0.3 seconds to avoid the obstacle and successfully avoided the bird.

[0143] Test data summary:

[0144] Table 1 Summary of drone obstacle avoidance test data

[0145]

[0146] From the data in Table 1 above, it can be seen that the UAV dynamic obstacle avoidance method based on adaptive fractional calculus of the present invention has extremely high accuracy and response speed in detecting tiny obstacles. The average obstacle avoidance time is 0.43 seconds, and the path deviation distance is between 0.2 meters and 0.3 meters, which are all within the safe range. In addition, when dealing with dynamic obstacles, the system can adjust the flight path in a very short time to ensure the safe obstacle avoidance of the UAV. The obstacle avoidance success rate in all tests is 100%. Through examples and data, the present invention proves its ability to effectively avoid tiny obstacles in complex dynamic environments.

[0147] The present invention adopts adaptive fractional-order calculus to dynamically adjust the fractional-order order, and adjusts the response speed and accuracy of the control algorithm in real time according to different flight speeds and obstacle complexities. Compared with traditional integer-order calculus, adaptive fractional-order calculus is more suitable for processing complex dynamic environments. By dynamically adjusting the fractional-order order, the UAV has higher obstacle avoidance flexibility and responsiveness when flying at high speeds, avoiding the problems of response lag and insufficient accuracy that are prone to occur in traditional algorithms when processing tiny and fast-moving obstacles, and effectively improving the obstacle avoidance performance of the UAV in complex environments, especially the avoidance efficiency when facing tiny obstacles.

[0148] The present invention constructs a flight state model that combines historical flight trajectory data with real-time obstacle detection data, avoiding the problem that historical data cannot be effectively utilized in traditional algorithms. By memorizing the historical flight trajectory of the UAV and combining it with the detection information of the current obstacle, a more accurate flight state model is constructed, so that the UAV can dynamically adjust the flight path by referring to the historical trajectory in repeated or similar scenarios, generate more optimized obstacle avoidance decisions, reduce the complexity of calculation, improve the accuracy of obstacle avoidance, and enhance the ability of the UAV to cope with complex dynamic environments.

[0149] The present invention generates an optimal obstacle avoidance path through an adaptive fractional-order control algorithm, updates the path information in real time in combination with the flight speed, obstacle position and complexity, and converts it into control instructions for the UAV, ensuring that the UAV can continuously and dynamically avoid obstacles during flight. Compared with traditional obstacle avoidance systems, the present invention realizes real-time coupling of the flight path and control instructions, so that the UAV can quickly make adjustments when responding to sudden changes in obstacles to avoid collisions.

[0150] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A method for dynamically avoiding small obstacles of unmanned aerial vehicles based on fractional calculus, characterized in that: The steps include: S1. Obtain lidar data, ultrasonic sensor data, and camera image data in the UAV flight environment, and construct an obstacle dataset including the three-dimensional spatial position of tiny obstacles and the dynamic change information of tiny obstacles based on multi-sensor data fusion technology; S2. Preprocess the obstacle data set to remove noise and invalid data, and generate obstacle detection data after filtering and enhancement; S3. Construct the UAV flight state model based on obstacle detection data using adaptive fractional calculus; S4. Introduce an adaptive fractional-order control algorithm to automatically adjust the fractional order of the adaptive fractional-order calculus according to different flight speeds and obstacle complexity to generate the optimal obstacle avoidance path; S5. Convert the optimal obstacle avoidance path into a control command for the UAV and send it to the flight control system of the UAV in real time to control the UAV to avoid obstacles according to the optimal obstacle avoidance path; S6. During the flight of the UAV, the environment changes are continuously monitored, and the adaptive fractional-order flight state model and the optimal obstacle avoidance path are updated in real time according to the new sensor data; The S1 comprises the following steps: S11. Obtain obstacle data in the UAV flight environment using laser radar, the obstacle data including obstacle distance ; S12. Collect obstacle distance information using ultrasonic sensors and location information ; S13. Obtain image data of the obstacle using a camera, and generate three-dimensional spatial information of the obstacle, including position, by using a three-dimensional reconstruction technique based on pixels. and obstacle dynamic change information , where t is the time variable; S14. Based on multi-sensor data fusion technology, the lidar data, ultrasonic sensor data and camera image data are fused into a unified obstacle data set through a weighted fusion algorithm. The three-dimensional spatial position of the tiny obstacles in the obstacle data set is calculated as: ； in, are the weighting coefficients for LiDAR, ultrasonic sensor and camera data respectively; S15. Dynamically changing information when passing obstacles The movement trajectory of the obstacle is modeled. Dynamic calculation through the fused obstacle dataset: ； in, is the initial time; S16. Build the final obstacle dataset: 。 2. The method for dynamically avoiding small obstacles of unmanned aerial vehicles based on fractional calculus according to claim 1 is characterized in that: The S2 comprises the following steps: S21. Use mean filtering method to filter distance information in obstacle dataset Perform smoothing processing to obtain smoothed distance data; S22. Use Gaussian filtering to filter the three-dimensional spatial information of obstacles in the obstacle dataset Processing is performed to eliminate image noise and obtain processed three-dimensional spatial information of obstacles; S23. Use median filtering to detect dynamic changes of obstacles in obstacle datasets Perform denoising to obtain denoised obstacle dynamic change information; S24. Performing enhancement processing on the preprocessed obstacle data and enhancing the obstacle edge information; S25. Output the obstacle detection data after filtering and enhancement: ； in, is the enhanced obstacle 3D spatial position data. It is the enhanced obstacle dynamic change information.

3. The method for dynamic avoidance of small obstacles of unmanned aerial vehicles based on fractional calculus according to claim 2 is characterized in that: The S3 comprises the following steps: S31. Based on obstacle detection data The location information of the drone at the current time t based on the historical flight trajectory data , speed information To initialize: ； in, are the weight coefficients of obstacle detection data and historical flight trajectory, is the three-dimensional spatial position of the current obstacle detection, It is the location information in the historical flight trajectory; S32. Combine the current flight speed v(t) and acceleration Building a flight status model , the flight state model describes the dynamic response characteristics of the UAV at time t: ； in, is the current speed of the drone; S33. Use the adaptive fractional calculus method to optimize the flight state model, perform fractional differential calculations on the flight state model, and comprehensively consider the influence of historical flight states, current obstacle detection data, and flight speed v(t) to construct the dynamic response equation: ； in, is the dynamic response model of the UAV, is the fractional order, is the weight of obstacle detection data to dynamic response, is the memory decay factor of the obstacle data, which is used to adjust the change of obstacle influence over time. is the impact weight of historical flight trajectory data, is the preprocessed current obstacle detection data, Historical flight trajectory data.

4. The method for dynamic avoidance of small obstacles of unmanned aerial vehicles based on fractional calculus according to claim 3 is characterized in that: The S4 comprises the following steps: S41. Based on the UAV’s flight speed v(t) and the complexity of obstacles Determining the Fractional Order in an Adaptive Fractional-Order Control Algorithm , the fractional order changes dynamically over time to adapt to the complexity of the current environment: ； in, is the initial fractional order, is the speed adjustment coefficient, is the obstacle complexity adjustment coefficient; S42. Calculate the optimal obstacle avoidance path for the current drone ,The optimal obstacle avoidance path is obtained based on the fractional order control algorithm ,combining the historical flight trajectory, the current obstacle detection data and the fractional order response ,characteristics; S43. Real-time adjustment of fractional order in fractional order control algorithm during UAV flight Optimize obstacle avoidance response to enable the drone to dynamically avoid obstacles and generate the optimal path.

5. The method for dynamically avoiding small obstacles of unmanned aerial vehicles based on fractional calculus according to claim 4 is characterized in that: The S42 comprises the following steps: S421. Based on historical flight trajectory , the preprocessed current obstacle detection data And the fractional order response characteristics , build a flight status prediction model under the current environment : ； in, It is the corrected flight status based on historical trajectory and obstacle data at the current moment. is the fractional-order dynamic response characteristic; S422. Combining flight speed v(t) and obstacle complexity The optimal obstacle avoidance path is calculated by an adaptive fractional-order control algorithm. The optimization goal of the optimal obstacle avoidance path is to minimize the risk of collision with obstacles and maximize the smoothness of the flight path. The optimal obstacle avoidance path is calculated as: ； in, is the current optimal obstacle avoidance path.

6. The method for dynamically avoiding small obstacles of unmanned aerial vehicles based on fractional calculus according to claim 5 is characterized in that: The S5 comprises the following steps: S51. The optimal obstacle avoidance path Convert to drone control commands, including converting optimal obstacle avoidance path information into drone heading angle commands , pitch angle command And speed command : ； ； ； in, is the position information on the optimal obstacle avoidance path, is the current drone position; S52. Send the heading angle command, pitch angle command and speed command to the flight control system of the UAV. The flight control system adjusts the attitude and speed of the UAV according to the received commands, so that the UAV flies along the optimal obstacle avoidance path and avoids obstacles.

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