Unmanned aerial vehicle group path planning method and system based on environment analysis

Abstract

The invention provides an unmanned aerial vehicle group path planning method and system based on environment analysis, and relates to the technical field of unmanned aerial vehicle path planning. Unmanned aerial vehicle operation data are collected, unmanned aerial vehicle operation abnormity is analyzed based on the unmanned aerial vehicle operation data, an unmanned aerial vehicle group flight path is collected, and the flight distance in the unmanned aerial vehicle flight process is obtained; the method comprises the steps of screening out a flight risk interval based on a flight interval in a flight process of an unmanned aerial vehicle, collecting weather data, analyzing flight abnormity generated by the flight risk interval under the weather influence based on the weather data, and obtaining unmanned aerial vehicle group density in a set range when the unmanned aerial vehicle generates the flight abnormity based on an unmanned aerial vehicle group flight path. The flight influence of flight abnormity on the unmanned aerial vehicle group is analyzed based on the unmanned aerial vehicle group density, the optimal flight path of the unmanned aerial vehicle group is screened out based on the flight influence of the flight abnormity on the unmanned aerial vehicle group, potential flight risks such as out-of-control, course deviation and even crash of the unmanned aerial vehicles can be predicted, and therefore the flight safety is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle path planning, and particularly to a method and system for unmanned aerial vehicle swarm path planning based on environmental analysis. Background Art

[0002] Unmanned aerial vehicle path planning refers to the process of planning the best flight path from the starting point to the ending point according to the performance of the unmanned aerial vehicle, mission requirements, and environmental information, so as to achieve the mission goal. The purpose is to ensure that the unmanned aerial vehicle can complete the flight mission safely and efficiently, while reducing energy consumption and improving the mission success rate.

[0003] In related technologies, the analysis of environmental impact mostly judges whether the environment does not meet the flight safety conditions by calculating the wind speed, wind direction, and turbulence conditions on the candidate path. However, this judgment method only judges the severity of the environment and cannot combine the analysis of weather impact, unmanned aerial vehicle flight anomalies, and the flight state of the unmanned aerial vehicle swarm to analyze the flight impact on the unmanned aerial vehicle swarm, resulting in low prediction accuracy of risks during the flight process.

[0004] Therefore, it is an urgent problem for those skilled in the art to provide a method and system for unmanned aerial vehicle swarm path planning based on environmental analysis to solve the above problems. Summary of the Invention

[0005] The purpose of the present invention is to provide a method and system for unmanned aerial vehicle swarm path planning based on environmental analysis. The present invention comprehensively analyzes the flight impact of the unmanned aerial vehicle swarm by combining weather impact and operation anomaly impact, and can predict potential flight risks, such as unmanned aerial vehicle out of control, deviation from the route, or even crash, thereby improving flight safety.

[0006] Based on the above purpose, the technical solutions provided by the present invention are as follows: A method for unmanned aerial vehicle swarm path planning based on environmental analysis includes the following specific steps: S1. Collect unmanned aerial vehicle operation data and analyze unmanned aerial vehicle operation anomalies based on the unmanned aerial vehicle operation data; S2. Collect the flight paths of the unmanned aerial vehicle swarm, obtain the flight spacing during the flight of the unmanned aerial vehicle, and screen out the flight risk spacing based on the flight spacing during the flight of the unmanned aerial vehicle; S3. Collect weather data and analyze the flight anomalies generated by the flight risk spacing under the influence of weather based on the weather data; S4. Based on the flight paths of the unmanned aerial vehicle swarm, obtain the density of the unmanned aerial vehicle swarm within a set range when the unmanned aerial vehicle has a flight anomaly, and analyze the flight impact of the flight anomaly on the unmanned aerial vehicle swarm based on the density of the unmanned aerial vehicle swarm; S5. Screen out the best flight path of the unmanned aerial vehicle swarm based on the flight impact of the flight anomaly on the unmanned aerial vehicle swarm.

[0007] Preferably, S1 includes the following specific steps: Collect the operation data of the UAV, where the operation data of the UAV includes attitude angle data, position data, and vibration signal data, and the attitude angle data includes pitch angle, roll angle, and yaw angle; Analyze the three-dimensional attitude deviation based on the pitch angle, roll angle, and yaw angle; Analyze the position drift distance based on the position data; Analyze the vibration energy based on the vibration signal data; Analyze the abnormal operation of the UAV based on the three-dimensional attitude deviation, position drift distance, and vibration energy.

[0008] Preferably, S2 includes the following specific steps: Collect the flight paths of the UAV swarm and synchronize the timestamps; Collect the UAV data, where the UAV data includes the UAV size, flight speed, and set obstacle avoidance response time; Obtain the flight safety distance based on the UAV size, flight speed, and set obstacle avoidance response time; Obtain the flight distance during the flight of the UAV based on the flight paths of the UAV swarm; Based on the comparison result of the flight distance and the flight safety distance, screen out the flight distances smaller than the flight safety distance and set them as flight risk distances.

[0009] Preferably, S3 includes the following specific steps: Collect the weather data, where the weather data includes wind speed and wind direction; Analyze the projected speed of the wind in the direction of the UAV connection line and the relative speed along the UAV connection line based on the wind speed and wind direction; Analyze the change value of the flight risk distance under the influence of the weather based on the projected speed of the wind in the direction of the UAV connection line and the relative speed along the UAV connection line; Analyze the flight anomaly value under the influence of the weather based on the change value of the flight risk distance and the abnormal operation of the UAV.

[0010] Preferably, S4 includes the following specific steps: Based on the flight paths of the UAV swarm, obtain the number of UAVs within the set range when a flight anomaly occurs for the UAV. Multiply the number of UAVs by the volume of a single UAV to obtain the total UAV volume, and divide the total UAV volume by the spatial volume of the set range to obtain the UAV swarm density; Obtain the average speed of the UAV swarm and the distance from the UAV with the flight anomaly; Analyze the flight influence value of the flight anomaly on the UAV swarm based on the UAV swarm density, the flight anomaly value under the influence of the weather, the average speed of the UAV swarm, and the distance from the UAV with the flight anomaly.

[0011] Preferably, S5 includes the following specific steps: Reverse order the flight impact values of flight anomalies on the UAV swarm, screen out the flight path corresponding to the smallest flight impact value of flight anomalies on the UAV swarm, and set it as the optimal flight path of the UAV swarm.

[0012] The UAV swarm path planning system based on environmental analysis includes: An operation anomaly analysis module, which is used to collect UAV operation data and analyze UAV operation anomalies based on the UAV operation data; A flight risk spacing screening module, which is used to collect the flight paths of the UAV swarm, obtain the flight spacing during the flight of the UAV, and screen out the flight risk spacing based on the flight spacing during the flight of the UAV; A flight anomaly analysis module, which is used to collect weather data and analyze the flight anomalies generated by the flight risk spacing under the influence of weather based on the weather data; A flight impact analysis module, which is used to obtain the UAV swarm density within a set range when the UAV has a flight anomaly based on the flight path of the UAV swarm, and analyze the flight impact of the flight anomaly on the UAV swarm based on the UAV swarm density; An optimal flight path screening module, which is used to screen out the optimal flight path of the UAV swarm based on the flight impact of the flight anomaly on the UAV swarm.

[0013] The UAV swarm path planning method provided by the present invention collects UAV operation data, analyzes UAV operation anomalies based on the UAV operation data, collects the flight paths of the UAV swarm, obtains the flight spacing during the flight of the UAV, screens out the flight risk spacing based on the flight spacing during the flight of the UAV, collects weather data, analyzes the flight anomalies generated by the flight risk spacing under the influence of weather based on the weather data, obtains the UAV swarm density within a set range when the UAV has a flight anomaly based on the flight path of the UAV swarm, analyzes the flight impact of the flight anomaly on the UAV swarm based on the UAV swarm density, and screens out the optimal flight path of the UAV swarm based on the flight impact of the flight anomaly on the UAV swarm.

[0014] Compared with the prior art, the present invention comprehensively analyzes the flight impact of the UAV swarm considering the influence of weather and operation anomalies, and can predict potential flight risks, such as UAV out of control, deviation from the flight path or even crashing, thereby improving flight safety.

[0015] The present invention also provides a UAV swarm path planning system based on environmental analysis. Since it belongs to the same technical concept as the method and solves the same technical problems, it should have the same beneficial effects, which will not be elaborated here. Description of the Drawings

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0017] Figure 1 Schematic diagram of the path planning method for a drone swarm based on environmental analysis provided by an embodiment of the present invention; Figure 2 Schematic diagram of the S1 process of the path planning method for a drone swarm based on environmental analysis provided by an embodiment of the present invention; Figure 3 Schematic diagram of the S2 process of the path planning method for a drone swarm based on environmental analysis provided by an embodiment of the present invention; Figure 4 Schematic diagram of the S3 process of the path planning method for a drone swarm based on environmental analysis provided by an embodiment of the present invention; Figure 5 Schematic diagram of the system structure of the path planning method for a drone swarm based on environmental analysis provided by an embodiment of the present invention. Detailed implementation manners

[0018] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art belong to the scope of protection of the present invention.

[0019] The embodiments of the present invention provide a path planning method and system for a drone swarm based on environmental analysis, mainly solving the technical problem that in the prior art, only the severity of the environment is judged, and the flight impact on the drone swarm cannot be analyzed in combination with weather effects, abnormal drone flights, and the flight state of the drone swarm, resulting in low prediction accuracy of risks during flight.

[0020] Please refer to Figure 1 , Figure 1 Schematic diagram of the path planning method for a drone swarm based on environmental analysis provided by an embodiment of the present invention; The path planning method for a drone swarm based on environmental analysis includes the following specific steps: S1. Collect drone operation data and analyze drone operation anomalies based on the drone operation data; Please refer to Figure 2 , Figure 2 Schematic diagram of the S1 process of the path planning method for a drone swarm based on environmental analysis provided by an embodiment of the present invention; In this embodiment, S1 includes the following specific steps: Collect the operating data of the drone. The operating data of the drone includes attitude angle data, position data, and vibration signal data. The attitude angle data includes pitch angle, roll angle, and yaw angle. Analyze the three-dimensional attitude deviation based on the pitch angle, roll angle, and yaw angle. When the present invention is specifically implemented, the three-dimensional attitude deviation can be obtained through the three-dimensional attitude deviation calculation formula. The three-dimensional attitude deviation calculation formula is: , where is the three-dimensional attitude deviation value, is the actual pitch angle, is the desired pitch angle. In this embodiment, the desired pitch angle is obtained from the pre-planned flight trajectory. A continuous and differentiable trajectory function is generated based on the flight trajectory, and the target position, speed, and acceleration at each moment are calculated. The desired attitude angle is generated according to the acceleration command. The following expected values can be obtained in the same way. is the pitch angle deviation, is the actual roll angle, is the desired roll angle, is the roll angle deviation, is the actual yaw angle, is the desired yaw angle, is the yaw angle deviation. Detecting the deviation between the current attitude and the desired attitude of the drone can monitor the attitude out-of-control caused by sensor failure or power imbalance.

[0021] Analyze the position drift distance based on the position data. When the present invention is specifically implemented, the position drift distance can be obtained through the position drift distance calculation formula. The position drift distance calculation formula is: , where is the position drift distance, is the actual abscissa, is the expected abscissa, is the actual ordinate, is the expected ordinate, is the actual height, is the expected height. Detecting the deviation distance between the actual position and the target trajectory of the drone can monitor power imbalance or navigation system failure.

[0022] Analyze the vibration energy based on the vibration signal data. When the present invention is specifically implemented, the vibration energy can be obtained through the vibration energy calculation formula. The vibration energy calculation formula is: , where is the vibration energy, is the complex amplitude after the acceleration signal undergoes Fourier transform (FFT). In this embodiment, three-axis acceleration is collected at a sampling rate greater than or equal to 100 Hz. The low-frequency motion components (such as the translational acceleration of the drone) are filtered out through high-pass filtering, and the high-frequency vibration signals are retained. To reduce spectral leakage, the signal is divided into multiple segments (for example, 1024 points per frame with an overlap rate of 50%). FFT is performed on each frame of the signal to obtain the complex spectrum. is the energy density of the signal at frequency p. is the lower frequency limit. is the upper frequency limit. and is obtained based on the fault characteristic frequency (such as the main frequency of propeller imbalance). Usually, 80% - 120% of the main vibration frequency is taken. If the motor speed is 3000 RPM, the rotational frequency is 50 Hz. For each rotation of each propeller of a quadcopter drone, 4 periodic vibration shocks (corresponding to 4 blades) will be generated due to imbalance. Therefore, the main vibration frequency is 200 Hz, the lower frequency limit can be 160 Hz, and the upper frequency limit can be 240 Hz. The method of quantifying the fault characteristic intensity by analyzing the frequency components of the vibration signal can monitor the high-frequency vibrations caused by periodic mechanical faults such as propeller imbalance and motor faults.

[0023] Analyze the abnormal operation of the drone based on three-dimensional attitude deviation, position drift distance, and vibration energy.

[0024] In the specific implementation of the present invention, the abnormal operation of the drone can be obtained through the drone abnormal operation calculation formula. The drone abnormal operation calculation formula is: , where is the drone abnormal operation value. is the three-dimensional attitude deviation threshold. When the drone is hovering or flying at a low speed, if the attitude angle deviation exceeds a certain range, it will cause unbalanced power distribution. According to the dynamics model of a multi-rotor drone, when the roll or pitch angle exceeds 10°, the horizontal component of the lift force increases significantly, which may lead to uncontrollable drift. Through drone flight experiments, it can be known that when the attitude angle deviation exceeds 5° - 8°, the time for the drone to correct its position increases significantly. When it exceeds 10°, more than 50% of the experimental drones show a tendency to lose control. Therefore, the three-dimensional attitude deviation threshold in this embodiment is set to 8°. is the position drift distance threshold. Perform hovering and cruising tasks multiple times in a windless environment, calculate the statistical distribution of the deviation, and obtain the mean value of about 0.09 m and the standard deviation of about 0.04 m through the flight deviation data. According to the three-standard-deviation principle, the position drift distance threshold in this embodiment is set to 0.21 m. is the vibration energy threshold. Through vibration table testing, when the propellers of a quadcopter drone are slightly unbalanced (mass eccentricity of 0.5 g), the vibration energy of the accelerometer is about 0.05 - 0.15 m 2 / s4 When the quadcopter drone has a serious imbalance in the propellers (mass eccentricity of 2 g), the vibration energy of the accelerometer is about 0.3 m 2 / s 4 Therefore, the vibration energy threshold in this embodiment is set to 0.15 m 2 / s 4 , is the influence weight of the three-dimensional attitude deviation, is the influence weight of the position drift distance, is the influence weight of the vibration energy, .

[0025] In the specific implementation of the present invention, the influence weights of the three-dimensional attitude deviation, the position drift distance, and the vibration energy are obtained through experiments by those skilled in the art. The obtaining steps are as follows: collect the operation data of several drones, obtain the abnormal operation of the drones based on the analysis of the three-dimensional attitude deviation, the position drift distance, and the vibration energy. The technicians first set the levels corresponding to the abnormal operation of the drones according to the operation data of the drones, and then analyze the abnormal operation levels of the drones. Import the abnormal operation of the drones calculated by the system and the abnormal operation levels judged by the technicians into the pre-trained fitting software for fitting to obtain the values of the influence weights of the three-dimensional attitude deviation, the position drift distance, and the vibration energy with the highest matching degree.

[0026] S2. Collect the flight paths of the drone swarm, obtain the flight spacing during the flight of the drones, and screen out the flight risk spacing based on the flight spacing during the flight of the drones; Please refer to Figure 3 , Figure 3 which is the schematic flow chart of the S2 process of the drone swarm path planning method based on environmental analysis provided by the embodiment of the present invention; In this embodiment, S2 includes the following specific steps: Collect the flight paths of the drone swarm and synchronize the timestamps; In the specific implementation of the present invention, during the collaborative flight of the drone swarm, since there may be deviations in the timestamps of the data collected by each drone, it is necessary to align the trajectory data to a unified time axis through an interpolation method to accurately calculate the flight spacing. The specific formula is: , where is the no moment of the drone data collection, is the pe moment of the drone data collection, is the ne moment of the drone data collection, , is the three-dimensional coordinate of the drone at the no moment, is the three-dimensional coordinate of the drone at the pe moment, is the three-dimensional coordinate of the UAV at the ne moment. For example, the three-dimensional coordinate of the UAV at 0.1 s is (8, 15, 3), and the three-dimensional coordinate at 0.3 s is (14, 21, 5). Then the interpolation result of the UAV at 0.2 s is (11, 18, 4).

[0027] Collect UAV data, where the UAV data includes the UAV size, flight speed, and set obstacle avoidance response time; Obtain the flight safety distance based on the UAV size, flight speed, and set obstacle avoidance response time; In the specific implementation of the present invention, the flight safety distance can be obtained through the flight safety distance calculation formula. The flight safety distance calculation formula is: , where is the flight safety distance, is the UAV length, is the flight speed, is the set obstacle avoidance response time.

[0028] Obtain the flight distance during the UAV flight based on the flight path of the UAV swarm; In the specific implementation of the present invention, the flight distance can be obtained through the flight distance calculation formula. The flight distance calculation formula is: , where is the flight distance, is the abscissa of the UAV n at the t moment, is the abscissa of the UAV m closest to the UAV n at the t moment, is the ordinate of the UAV n at the t moment, is the ordinate of the UAV m closest to the UAV n at the t moment, is the altitude of the UAV n at the t moment, is the altitude of the UAV m closest to the UAV n at the t moment.

[0029] Based on the comparison result of the flight distance and the flight safety distance, screen out the flight distances less than the flight safety distance and set them as the flight risk distances.

[0030] S3. Collect weather data and analyze the flight anomalies caused by the flight risk distance under the influence of weather based on the weather data; Please refer to Figure 4 , Figure 4 is the schematic flow chart of the S3 process of the UAV swarm path planning method based on environmental analysis provided by the embodiment of the present invention; In this embodiment, S3 includes the following specific steps: Collect weather data, where the weather data includes wind speed and wind direction; Analyze the projected velocity of the wind in the direction of the UAV connection line and the relative velocity along the UAV connection line based on the wind speed and direction; In the specific implementation of the present invention, the projected velocity of the wind in the direction of the UAV connection line can be obtained through the projected velocity calculation formula, and the projected velocity calculation formula is: , where is the projected velocity of the wind in the direction of the UAV connection line, is the wind speed vector, and the wind speed vector can be obtained through the following calculation formula: , , , where is the wind speed, is the pitch angle of the wind direction, is the yaw angle of the wind direction, is the three-dimensional coordinates of UAV a, is the three-dimensional coordinates of UAV b, is the distance between UAV a and UAV b. When , the wind will cause the UAVs to move away from each other. When , the wind will cause the UAVs to approach each other.

[0031] The relative velocity along the UAV connection line can be obtained through the relative velocity calculation formula, and the relative velocity calculation formula is: , where is the relative velocity along the UAV connection line, is the ground speed vector of UAV a, is the ground speed vector of UAV b. The airspeed vector of the UAV is collected, and the ground speed vector is obtained by adding the wind speed vector and the airspeed vector of the UAV.

[0032] Analyze the change value of the flight risk spacing under the influence of weather based on the projected velocity of the wind in the direction of the UAV connection line and the relative velocity along the UAV connection line; In the specific implementation of the present invention, the change value of the flight risk spacing under the influence of weather can be obtained through the change value calculation formula of the flight risk spacing, and the change value calculation formula of the flight risk spacing is: , where is the change value of the flight risk spacing, is the duration for which UAVs a and b maintain the same flight risk spacing in the pre-set flight route.

[0033] Analyze the flight anomaly value under the influence of weather based on the change value of the flight risk spacing and the abnormal operation of the UAV.

[0034] In the specific implementation of the present invention, the flight anomaly value under the influence of weather can be obtained through the flight anomaly value calculation formula under the influence of weather, and the flight anomaly value calculation formula under the influence of weather is: , where is the flight outlier affected by weather, is the operation outlier of UAV a, is the operation outlier of UAV b.

[0035] S4. When a flight anomaly occurs to a UAV based on the flight path of the UAV swarm, obtain the density of the UAV swarm within a set range, and analyze the impact of the flight anomaly on the flight of the UAV swarm based on the density of the UAV swarm; In this embodiment, S4 includes the following specific steps: When a flight anomaly occurs to a UAV based on the flight path of the UAV swarm, obtain the number of UAVs within a set range, obtain the total volume of the UAVs by multiplying the number of UAVs by the volume of a single UAV, and obtain the density of the UAV swarm by dividing the total volume of the UAVs by the spatial volume of the set range; Obtain the average speed of the UAV swarm and the distance from the UAV with the flight anomaly; Analyze the flight impact value of the flight anomaly on the UAV swarm based on the density of the UAV swarm, the flight outlier affected by weather, the average speed of the UAV swarm, and the distance from the UAV with the flight anomaly.

[0036] When the present invention is specifically implemented, the flight impact value of the flight anomaly on the UAV swarm can be obtained through the flight impact value calculation formula. The flight impact value calculation formula is: , where, is the flight impact value, is the flight direction of the flight anomaly UAV is the density of the UAV swarm at the angle, is the average speed of the UAV swarm, is the set safe flight speed of the UAV, is the flight direction of the flight anomaly UAV is the number of UAVs within the set range of the angle, is the operation outlier of the k-th UAV, is the distance between the k-th UAV and the UAV with the flight anomaly, is the angle integration constant. Through this formula, analyze the impact of the abnormal UAV on the UAV swarm in each direction. Through the flight impact value formula, the impact degree of the flight anomaly on the UAV swarm can be calculated and evaluated, helping to detect and handle abnormal situations in a timely manner and avoid chain reactions.

[0037] S5. Screen out the optimal flight path of the UAV swarm based on the impact of the flight anomaly on the flight of the UAV swarm.

[0038] In this embodiment, S5 includes the following specific steps: Reverse-sort the flight impact values of flight anomalies on the UAV swarm, filter out the flight path corresponding to the minimum flight impact value of flight anomalies on the UAV swarm, and set it as the optimal flight path of the UAV swarm.

[0039] Please refer to Figure 5 , Figure 5 which is a schematic structural diagram of a UAV swarm path planning system based on environmental analysis provided by an embodiment of the present invention; A UAV swarm path planning system based on environmental analysis includes: An operation anomaly analysis module, configured to collect UAV operation data and analyze UAV operation anomalies based on the UAV operation data; A flight risk distance screening module, configured to collect the flight paths of the UAV swarm, obtain the flight distances during the UAV flight, and screen out the flight risk distances based on the flight distances during the UAV flight; A flight anomaly analysis module, configured to collect weather data and analyze flight anomalies generated by flight risk distances under the influence of weather based on the weather data; A flight impact analysis module, configured to obtain the UAV swarm density within a set range when the UAV generates flight anomalies based on the UAV swarm flight path, and analyze the flight impact of flight anomalies on the UAV swarm based on the UAV swarm density; An optimal flight path screening module, configured to screen out the optimal flight path of the UAV swarm based on the flight impact of flight anomalies on the UAV swarm.

[0040] In this embodiment, an operation anomaly analysis module, a flight risk distance screening module, a flight anomaly analysis module, a flight impact analysis module, and an optimal flight path screening module are provided in the UAV swarm path planning system based on environmental analysis. The functions of each module in the system correspond to the steps in the method, which will not be elaborated here.

[0041] In the embodiments provided by the present invention, it should be understood that the disclosed methods and systems can be implemented in other ways. The system embodiments described above are only illustrative. For example, the division of modules is only a logical function division. In actual implementation, there may be other division methods, such as: multiple modules or components can be combined, or can be integrated into another system, or some features can be ignored, or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed with each other can be through some interfaces, indirect coupling or communication connection of devices or modules, and can be electrical, mechanical, or other forms.

[0042] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions according to the embodiments of the present invention are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from a website, computer, server, or data center to another website, computer, server, or data center via a wired network or / and a wireless network. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or a data center that includes one or more collections of available media. The available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. The semiconductor media can be a solid-state drive.

[0043] Those of ordinary skill in the art will realize that the units and algorithm steps of the examples described in connection with the embodiments disclosed in the present invention can be implemented in electronic hardware, or in a combination of computer software and electronic hardware. Whether these functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. A professional technician can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0044] It should be understood that in the present invention, if the terms "system", "device", "unit", and / or "module" are used, they are only a method for distinguishing different components, elements, parts, portions, or assemblies at different levels. However, if other words can achieve the same purpose, the term can be replaced by other expressions.

[0045] As shown in the present invention and the claims, unless the context clearly indicates an exceptional situation, words such as "a", "an", "one", and / or "the" are not specifically singular and can also include the plural. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of the steps and elements that have been clearly identified, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements. An element defined by the statement "comprising one..." does not exclude the existence of another identical element in the process, method, commodity, or device that includes the element.

[0046] If a flow chart is used in the present invention, the flow chart is used to illustrate the operations performed by the system according to the embodiments of the present invention. It should be understood that the previous or subsequent operations do not necessarily need to be precisely executed in sequence. On the contrary, the steps can be processed in reverse order or simultaneously. At the same time, other operations can also be added to these processes, or one or more steps can be removed from these processes.

[0047] The above has introduced in detail the method and system for path planning of an unmanned aerial vehicle swarm based on environmental analysis provided by the present invention. The above description of the disclosed embodiments enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather will conform to the broadest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for path planning of a drone swarm based on environmental analysis, characterized in that, It includes the following specific steps: S1. Collect the operation data of the UAV, and analyze the abnormal operation of the UAV based on the operation data of the UAV; S2. Collect the flight paths of the UAV swarm, obtain the flight spacing during the flight of the UAV, and screen out the flight risk spacing based on the flight spacing during the flight of the UAV; S3. Collect the weather data, and analyze the flight anomalies caused by the weather influence on the flight risk spacing based on the weather data; S4. Based on the flight paths of the UAV swarm, obtain the density of the UAV swarm within a set range when the UAV has a flight anomaly, and analyze the flight impact of the flight anomaly on the UAV swarm based on the density of the UAV swarm; S5. Screen out the optimal flight path of the UAV swarm based on the flight impact of the flight anomaly on the UAV swarm.

2. The method for path planning of a drone swarm based on environmental analysis according to claim 1, wherein The S1 includes the following specific steps: Collect the operation data of the UAV. The operation data of the UAV includes attitude angle data, position data, and vibration signal data. The attitude angle data includes pitch angle, roll angle, and yaw angle; Analyze the three-dimensional attitude deviation based on the pitch angle, roll angle, and yaw angle; Analyze the position drift distance based on the position data; Analyze the vibration energy based on the vibration signal data; Analyze the abnormal operation of the UAV based on the three-dimensional attitude deviation, position drift distance, and vibration energy.

3. The method for path planning of a drone swarm based on environmental analysis according to claim 2, wherein The S2 includes the following specific steps: Collect the flight paths of the UAV swarm and synchronize the timestamps; Collect the UAV data. The UAV data includes the UAV size, flight speed, and set obstacle avoidance response time; Obtain the flight safety spacing based on the UAV size, flight speed, and set obstacle avoidance response time; Obtain the flight spacing during the flight of the UAV based on the flight paths of the UAV swarm; Based on the comparison result of the flight spacing and the flight safety spacing, screen out the flight spacing smaller than the flight safety spacing and set it as the flight risk spacing.

4. The method for path planning of a drone swarm based on environmental analysis according to claim 3, wherein, The S3 includes the following specific steps: Collect the weather data. The weather data includes wind speed and wind direction; Analyze the projected speed of the wind in the direction of the UAV connection line and the relative speed along the UAV connection line based on the wind speed and wind direction; Analyze the change value of the flight risk spacing under the weather influence based on the projected speed of the wind in the direction of the UAV connection line and the relative speed along the UAV connection line; Analyze the flight anomaly value under the weather influence based on the change value of the flight risk spacing and the abnormal operation of the UAV.

5. The method for path planning of a drone swarm based on environmental analysis according to claim 4, characterized in that, The S4 includes the following specific steps: Based on the flight paths of the UAV swarm, obtain the number of UAVs within a set range when the UAV has a flight anomaly. Multiply the number of UAVs by the volume of a single UAV to obtain the total volume of the UAVs. Divide the total volume of the UAVs by the spatial volume of the set range to obtain the density of the UAV swarm; Obtain the average speed of the UAV swarm and the distance from the UAV with the flight anomaly; Analyze the flight impact value of the flight anomaly on the UAV swarm based on the density of the UAV swarm, the flight anomaly value under the weather influence, the average speed of the UAV swarm, and the distance from the UAV with the flight anomaly.

6. The method for path planning of a drone swarm based on environmental analysis according to claim 5, wherein The S5 includes the following specific steps: Sort the flight impact values of the flight anomaly on the UAV swarm in reverse order, screen out the flight path corresponding to the smallest flight impact value of the flight anomaly on the UAV swarm, and set it as the optimal flight path of the UAV swarm.

7. An unmanned aerial vehicle swarm path planning system based on environmental analysis, which is used to implement the unmanned aerial vehicle swarm path planning method based on environmental analysis according to any one of claims 1-6, characterized in that, It includes: An operation anomaly analysis module, which is used to collect the operation data of the UAV and analyze the operation anomalies of the UAV based on the operation data of the UAV; A flight risk distance screening module, which is used to collect the flight paths of the UAV swarm, obtain the flight distances during the flight of the UAV, and screen out the flight risk distances based on the flight distances during the flight of the UAV; A flight anomaly analysis module, which is used to collect weather data and analyze the flight anomalies caused by the flight risk distances under the influence of weather based on the weather data; A flight impact analysis module, which is used to obtain the density of the UAV swarm within a set range when the UAV has a flight anomaly based on the flight paths of the UAV swarm, and analyze the flight impact of the flight anomaly on the UAV swarm based on the density of the UAV swarm; An optimal flight path screening module, which is used to screen out the optimal flight path of the UAV swarm based on the flight impact of the flight anomaly on the UAV swarm.

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