Unmanned aerial vehicle autonomous route planning flight control system
By designing the autonomous route planning flight control system of drone, using real-time environment and its own state perception and analysis, generating and optimizing routes and performing flight control, the problems of poor planning capabilities, high risks and poor resilience in traditional systems are solved, and more efficient and safe drone flight is achieved.
Patent Information
- Application Number
- CN202510220198.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-02-27
- Publication Date
- 2025-03-28
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
The existing UAV flight control system has shortcomings in planning capabilities, risk management and resilience, and cannot independently plan and optimize routes, and has limited ability to perceive and respond to environmental changes.
A drone autonomous route planning flight control system is designed. Through the control area determination module, environment perception data acquisition and processing module, its own status data acquisition and processing module, comprehensive data analysis module, flight mode evaluation module and flight control module, real-time perception and analysis of the environment and its own status are realized, and optimized routes are generated and flight control is performed.
The system can generate optimized routes in a short time, significantly reduce the need for manual intervention, improve planning efficiency and route quality, enhance the perception and response ability of environmental changes, and reduce the risk of collision and out of control.
Smart Images
Figure CN119717861A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of unmanned aerial vehicle control, and more specifically, to an unmanned aerial vehicle autonomous route planning flight control system. Background Art
[0002] With the rapid development of drone technology, its application scenarios are becoming increasingly extensive, and it has important prospects in aerial photography, agricultural plant protection, logistics distribution, and power inspection; therefore, it is particularly important to develop a drone flight control system that can autonomously plan, optimize and execute routes.
[0003] The traditional UAV flight control system includes a sensor module, a control module, a navigation module and a communication module; the sensor module includes an accelerometer, a gyroscope, a magnetometer and a barometer, which are used to measure the attitude, speed and altitude information of the UAV. These sensors provide the flight control system with necessary feedback data so that it can understand the status of the UAV; the main control module is responsible for processing sensor data, transmitting data and receiving instructions with the ground control station, and controlling the speed and direction of the UAV motor, thereby achieving direct control of the UAV's flight attitude; the navigation module ensures that the UAV can fly accurately according to the predetermined route.
[0004] However, in actual use, it still has some shortcomings, such as poor planning ability. Traditional UAV flight control systems mainly rely on manual route planning and flight control; lack of autonomous flight control, decision-making and management capabilities; high risk. The route planning of traditional systems is relatively single and limited. The ability to perceive obstacles on the flight path, subtle undulations in the terrain, and dynamic changes in meteorological conditions is almost absent, making it difficult to detect dangers in advance and respond effectively, greatly increasing the risk of collision and loss of control; poor adaptability. Once the traditional system sets a fixed route before takeoff, when encountering environmental changes in the air, it lacks an intelligent mechanism to automatically and quickly re-plan the route and can only rely on manual remote control intervention, making it difficult to cope with complex and changeable flight environments and mission requirements.
[0005] Therefore, there is an urgent need to provide a UAV autonomous route planning flight control system to solve the problems of poor planning ability, high risk and poor adaptability of existing UAV flight control systems. Summary of the invention
[0006] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a UAV autonomous route planning flight control system, which solves the problems raised in the above-mentioned background technology through the following scheme.
[0007] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: a UAV autonomous route planning flight control system, comprising: a control area determination module, an environment perception data acquisition module, an environment perception data processing module, an own state data acquisition module, an own state data processing module, a comprehensive data analysis module, a flight mode evaluation module, a flight control module and a data interaction transmission module; Control area determination module: used to determine the target drone data as the target control area, and then divide it into sub-areas according to the equal time division method, and number each sub-area in the target control area as 1, 2, ..., i, ..., n in sequence; Environmental perception data acquisition module: used to obtain environmental perception data within the flight area of the target UAV and transmit the environmental perception data to the environmental perception data processing module; Environmental perception data processing module: used to analyze and process the environmental perception data collected by the environmental perception data collection module to obtain the obstacle feature data influence coefficient value, the meteorological environment data influence coefficient value and the airspace interference data influence coefficient value, and transmit them to the comprehensive analysis module; Self-status data acquisition module: used to obtain the self-status data of the target UAV and transmit the self-status data to the self-status data processing module; The self-state data processing module is used to analyze and process the self-state data collected by the self-state data collection module to obtain the flight attitude data influence coefficient value and the power data influence coefficient value, and transmit them to the comprehensive analysis module; Comprehensive data analysis module: used to import the obstacle characteristic data influence coefficient value, meteorological environment data influence coefficient value, airspace interference data influence coefficient value, flight attitude data influence coefficient value and power data influence coefficient value obtained by the environment perception data processing module and the self-state data processing module into the flight mode rationality index mathematical model, obtain the flight mode rationality index value, and transmit it to the flight mode evaluation module; Flight mode evaluation module: used to compare the flight mode rationality index value obtained by the comprehensive data analysis module with the preset flight mode rationality index value, calculate the difference between the flight mode rationality index value and the preset flight mode rationality index value, and when the difference is less than the preset difference, transmit the judgment result and the target UAV data to the data interaction transmission module; when the difference is greater than the preset difference, transmit the judgment result and the target UAV data to the flight control module; Flight control module: used to adjust the flight mode to the next standard according to the judgment result and the target UAV data, then control the flight mode and generate a new optimized route, and then transmit the judgment result, target UAV data and the new optimized route to the data interaction transmission module; Data interaction and transmission module: used to transmit the obtained judgment results, target drone data and optimized routes to the user information terminal, providing reference data for users to make adjustment measures.
[0008] Preferably, the environmental perception data includes obstacle feature data influencing parameters, meteorological environment data influencing parameters and airspace interference data influencing parameters; The obstacle characteristic data influencing parameters include the relative distance of the obstacle, denoted as Δd; the relative height of the obstacle, denoted as Δh; the relative moving speed of the obstacle, denoted as Δv; the geometric diameter of the obstacle equivalent sphere, denoted as D; the meteorological environment data influencing parameters include the environmental relative wind speed, denoted as V; the environmental relative wind direction angle, denoted as θ; the environmental air pressure, denoted as P; the environmental humidity, denoted as H; the environmental light intensity, denoted as L; the airspace interference data includes the electromagnetic radiation intensity, denoted as E; the signal interference frequency, denoted as f; the interference signal density, denoted as ρ; the radio frequency interference intensity, denoted as I; and the frequency band occupancy rate, denoted as B.
[0009] Preferably, the environmental perception data processing module includes an obstacle feature data influence coefficient calculation unit, a meteorological environment data influence coefficient calculation unit and an airspace interference data influence coefficient calculation unit.
[0010] Preferably, the obstacle feature data influence coefficient calculation unit is used to import the obstacle feature data influence parameters into the obstacle feature data influence coefficient mathematical model to obtain the obstacle feature data influence coefficient value; the meteorological environment data influence coefficient calculation unit is used to import the meteorological environment data influence parameters into the meteorological environment data influence coefficient mathematical model to obtain the meteorological environment data influence coefficient value; the airspace interference data influence coefficient calculation unit is used to import the airspace interference data influence parameters into the airspace interference data influence coefficient mathematical model to obtain the airspace interference data influence coefficient value.
[0011] Preferably, the obstacle characteristic data influence coefficient mathematical model is specifically: , the mathematical model of the meteorological environment data influence coefficient is as follows: , the mathematical model of the spatial interference data influence coefficient is as follows: , where Δd i Indicates the relative distance of obstacles in the i-th time period; Δd min Indicates the minimum allowable relative distance of obstacles; Δh i Indicates the relative height of the obstacle in the i-th time period; Δh min Indicates the minimum allowable relative height of the obstacle; Δv i represents the relative moving speed of the obstacle in the i-th time period; D i V represents the geometric diameter of the equivalent sphere of the obstacle in the i-th time period; irepresents the relative wind speed in the i-th time period; θ i represents the relative wind direction angle of the environment in the i-th time period; P i represents the ambient air pressure in the i-th time period; P0 represents the standard atmospheric pressure; Indicates the allowable value of standard ambient air pressure; H i represents the ambient humidity in the i-th time period; L i represents the ambient light intensity in the i-th time period; E i represents the electromagnetic radiation intensity in the i-th time period; f i represents the signal interference frequency in the i-th time period; f0 represents the standard value of the signal interference frequency; f max Indicates the maximum permissible signal interference frequency; f min Represents the minimum permissible signal interference frequency; ρ i represents the interference signal density in the i-th time period; I i Indicates the radio frequency interference intensity in the i-th time period; I min Indicates the minimum permissible radio frequency interference intensity; B i Indicates the frequency band occupancy rate in the i-th time period.
[0012] Preferably, the self-state data includes flight attitude data influencing parameters and power data influencing parameters; The flight attitude data influencing parameters include the UAV roll angle, denoted as r; the UAV pitch angle, denoted as p; the UAV heading angle, denoted as y; the UAV acceleration, denoted as a; the power data influencing parameters include propeller thrust, denoted as F; the motor real-time speed, denoted as ω; the propeller torque, denoted as t; and the UAV flight speed, denoted as v.
[0013] Preferably, the self-state data processing module includes a flight attitude data influence coefficient calculation unit and a power data influence coefficient calculation unit.
[0014] Preferably, the flight attitude data influence coefficient calculation unit is used to import the flight attitude data influence parameters into the flight attitude data influence coefficient mathematical model to obtain the flight attitude data influence coefficient value; the power data influence coefficient calculation unit is used to import the power data influence parameters into the power data influence coefficient mathematical model to obtain the power data influence coefficient value.
[0015] Preferably, the flight attitude data influence coefficient mathematical model is specifically: , the mathematical model of the power data influence coefficient is as follows: , where r i represents the rolling angle of the UAV in the i-th time period, r i-1 represents the rolling angle of the drone in the i-1th time period, r maxIndicates the maximum allowable roll angle of the drone, p i represents the pitch angle of the drone in the i-th time period, p i-1 represents the pitch angle of the drone in the i-1th time period, p max Indicates the maximum allowable pitch angle of the drone, y i represents the heading angle of the drone in the i-th time period, y i-1 represents the heading angle of the drone in the i-1th time period, y max represents the maximum allowable heading angle of the drone, y0 represents the initial heading angle, a i represents the acceleration of the drone in the i-th time period, a max Indicates the maximum operating acceleration of the drone, F i represents the propeller thrust in the i-th time period, ω i Indicates the real-time speed of the motor in the i-th time period, t i represents the propeller torque in the i-th time period, v i Represents the flight speed of the drone in the i-th time period.
[0016] Preferably, the flight mode rationality index mathematical model is specifically: .
[0017] Technical effects and advantages of the present invention: Through intelligent analysis and processing of environmental perception data and self-status data, the present invention can generate optimized routes and perform route planning, flight control, and decision-making based on data analysis results in a short period of time, greatly reducing the need for manual intervention and significantly improving planning efficiency and route quality; The present invention can collect environmental perception data in real time, and has excellent perception capabilities for obstacles on the flight path, subtle ups and downs of the terrain, and dynamic changes in meteorological conditions. It can detect dangers in advance and make timely and effective responses, significantly reducing the risk of collision and loss of control. The present invention has an intelligent mechanism for automatically and rapidly re-planning the route by performing real-time detection of its own status data. It does not need to rely too much on manual remote control intervention and can easily cope with complex and changeable flight environments and mission requirements. BRIEF DESCRIPTION OF THE DRAWINGS
[0018] Figure 1 It is a schematic diagram of the overall structure of the present invention. DETAILED DESCRIPTION
[0019] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.
[0020] As attached Figure 1 The autonomous route planning flight control system of an unmanned aerial vehicle shown in the figure includes a control area determination module, an environmental perception data acquisition module, an environmental perception data processing module, an own state data acquisition module, an own state data processing module, a comprehensive data analysis module, a flight mode evaluation module, a flight control module and a data interaction transmission module.
[0021] The output end of the control area determination module is telecommunication connected to the input end of the environment perception data acquisition module; the output end of the environment perception data acquisition module is telecommunication connected to the input end of the environment perception data processing module; the output end of the environment perception data processing module is telecommunication connected to the input end of the comprehensive data analysis module; the output end of the control area determination module is telecommunication connected to the input end of the self-state data acquisition module; the output end of the self-state data acquisition module is telecommunication connected to the input end of the self-state data processing module; the output end of the self-state data processing module is telecommunication connected to the input end of the comprehensive data analysis module; the output end of the comprehensive data analysis module is telecommunication connected to the input end of the flight mode assessment module; the output end of the flight mode assessment module is telecommunication connected to the input end of the flight control module; the output end of the flight mode assessment module is telecommunication connected to the input end of the data interaction transmission module; the output end of the flight control module is telecommunication connected to the input end of the data interaction transmission module.
[0022] The control area determination module is used to determine the target UAV data as a target control area, and then divide it into sub-areas according to an equal time division method, and number each sub-area in the target control area as 1, 2, ..., i, ..., n in sequence.
[0023] The environmental perception data acquisition module is used to obtain environmental perception data within the flight area of the target UAV and transmit the environmental perception data to the environmental perception data processing module.
[0024] In this embodiment, it should be specifically explained that the environmental perception data includes obstacle feature data influencing parameters, meteorological environment data influencing parameters and airspace interference data influencing parameters.
[0025] In this embodiment, it is specifically necessary to explain that the obstacle characteristic data influencing parameters include the relative distance of the obstacle, denoted as Δd; the relative height of the obstacle, denoted as Δh; the relative moving speed of the obstacle, denoted as Δv; the geometric diameter of the obstacle equivalent sphere, denoted as D; the meteorological environment data influencing parameters include the relative wind speed of the environment, denoted as V; the relative wind direction angle of the environment, denoted as θ; the ambient air pressure, denoted as P; the ambient humidity, denoted as H; the ambient light intensity, denoted as L; the airspace interference data includes the electromagnetic radiation intensity, denoted as E; the signal interference frequency, denoted as f; the interference signal density, denoted as ρ; the radio frequency interference intensity, denoted as I; and the frequency band occupancy rate, denoted as B.
[0026] The environmental perception data processing module is used to analyze and process the environmental perception data collected by the environmental perception data acquisition module to obtain the obstacle feature data influence coefficient value, the meteorological environment data influence coefficient value and the airspace interference data influence coefficient value, and transmit them to the comprehensive analysis module.
[0027] In this embodiment, it should be specifically explained that the environment perception data processing module includes an obstacle feature data influence coefficient calculation unit, a meteorological environment data influence coefficient calculation unit and an airspace interference data influence coefficient calculation unit.
[0028] In this embodiment, it is specifically necessary to explain that the obstacle feature data influence coefficient calculation unit is used to import the obstacle feature data influence parameters into the obstacle feature data influence coefficient mathematical model to obtain the obstacle feature data influence coefficient value; the meteorological environment data influence coefficient calculation unit is used to import the meteorological environment data influence parameters into the meteorological environment data influence coefficient mathematical model to obtain the meteorological environment data influence coefficient value; the airspace interference data influence coefficient calculation unit is used to import the airspace interference data influence parameters into the airspace interference data influence coefficient mathematical model to obtain the airspace interference data influence coefficient value.
[0029] In this embodiment, it should be specifically explained that the mathematical model of the obstacle characteristic data influence coefficient is specifically: , the mathematical model of the meteorological environment data influence coefficient is as follows: , the mathematical model of the spatial interference data influence coefficient is as follows: , where Δd i Indicates the relative distance of obstacles in the i-th time period; Δd min Indicates the minimum allowable relative distance of obstacles; Δh i Indicates the relative height of the obstacle in the i-th time period; Δh min Indicates the minimum allowable relative height of the obstacle; Δv i represents the relative moving speed of the obstacle in the i-th time period; D i V represents the geometric diameter of the equivalent sphere of the obstacle in the i-th time period; irepresents the relative wind speed in the i-th time period; θ i represents the relative wind direction angle of the environment in the i-th time period; P i represents the ambient air pressure in the i-th time period; P0 represents the standard atmospheric pressure; Indicates the allowable value of standard ambient air pressure; H i represents the ambient humidity in the i-th time period; L i represents the ambient light intensity in the i-th time period; E i represents the electromagnetic radiation intensity in the i-th time period; f i represents the signal interference frequency in the i-th time period; f0 represents the standard value of the signal interference frequency; f max Indicates the maximum permissible signal interference frequency; f min Represents the minimum permissible signal interference frequency; ρ i represents the interference signal density in the i-th time period; I i Indicates the radio frequency interference intensity in the i-th time period; I min Indicates the minimum permissible radio frequency interference intensity; B i Indicates the frequency band occupancy rate in the i-th time period.
[0030] The self-state data acquisition module is used to obtain the self-state data of the target UAV and transmit the self-state data to the self-state data processing module.
[0031] In this embodiment, it should be specifically explained that the self-state data includes flight attitude data influencing parameters and power data influencing parameters.
[0032] In this embodiment, it should be specifically explained that the flight attitude data influencing parameters include the UAV roll angle, denoted as r; the UAV pitch angle, denoted as p; the UAV heading angle, denoted as y; the UAV acceleration, denoted as a; the power data influencing parameters include propeller thrust, denoted as F; the real-time motor speed, denoted as ω; the propeller torque, denoted as t; and the UAV flight speed, denoted as v.
[0033] The self-state data processing module is used to analyze and process the self-state data collected by the self-state data collection module to obtain the flight attitude data influence coefficient value and the power data influence coefficient value, and transmit them to the comprehensive analysis module.
[0034] In this embodiment, it should be specifically explained that the self-state data processing module includes a flight attitude data influence coefficient calculation unit and a power data influence coefficient calculation unit.
[0035] In this embodiment, it is specifically necessary to explain that the flight attitude data influence coefficient calculation unit is used to import the flight attitude data influence parameters into the flight attitude data influence coefficient mathematical model to obtain the flight attitude data influence coefficient value; the power data influence coefficient calculation unit is used to import the power data influence parameters into the power data influence coefficient mathematical model to obtain the power data influence coefficient value.
[0036] In this embodiment, it should be specifically explained that the flight attitude data influence coefficient mathematical model is specifically: , the mathematical model of the power data influence coefficient is as follows: , where r i represents the rolling angle of the UAV in the i-th time period, r i-1 represents the rolling angle of the drone in the i-1th time period, r max Indicates the maximum allowable roll angle of the drone, p i represents the pitch angle of the drone in the i-th time period, p i-1 represents the pitch angle of the drone in the i-1th time period, p max Indicates the maximum allowable pitch angle of the drone, y i represents the heading angle of the drone in the i-th time period, y i-1 represents the heading angle of the drone in the i-1th time period, y max represents the maximum allowable heading angle of the drone, y0 represents the initial heading angle, a i represents the acceleration of the drone in the i-th time period, a max Indicates the maximum operating acceleration of the drone, F i represents the propeller thrust in the i-th time period, ω i Indicates the real-time speed of the motor in the i-th time period, t i represents the propeller torque in the i-th time period, v i Represents the flight speed of the drone in the i-th time period.
[0037] The comprehensive data analysis module is used to import the obstacle feature data influence coefficient value, meteorological environment data influence coefficient value, airspace interference data influence coefficient value, flight attitude data influence coefficient value and power data influence coefficient value obtained by the environmental perception data processing module and the self-state data processing module into the flight mode rationality index mathematical model, obtain the flight mode rationality index value, and transmit it to the flight mode evaluation module.
[0038] In this embodiment, it should be specifically explained that the mathematical model of the flight mode rationality index is specifically: .
[0039] The flight mode evaluation module is used to compare the flight mode rationality index value obtained by the comprehensive data analysis module with the preset flight mode rationality index value, calculate the difference between the flight mode rationality index value and the preset flight mode rationality index value, and when the difference value is less than the preset difference value, transmit the judgment result and the target UAV data to the data interaction transmission module; when the difference value is greater than the preset difference value, transmit the judgment result and the target UAV data to the flight control module.
[0040] In this embodiment, it should be specifically explained that the preset flight mode rationality index value is the minimum value of the historical flight mode rationality index values, and the preset difference value is the average of the difference values between the historical flight mode rationality index value and the preset flight mode rationality index value.
[0041] The flight control module is used to adjust the flight mode to the next standard according to the judgment result and the target drone data, and then control the flight mode and generate a new optimized route, and then transmit the judgment result, the target drone data and the new optimized route to the data interaction transmission module.
[0042] The data interaction transmission module is used to transmit the obtained judgment results, target drone data and optimized routes to the user information terminal, providing reference data for the user to make adjustment measures.
[0043] Secondly: In the drawings of the embodiments disclosed in the present invention, only the structures related to the embodiments disclosed in the present invention are involved, and other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of the present invention can be combined with each other; Finally: 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 principles of the present invention should be included in the protection scope of the present invention.
Claims
1. An autonomous route planning flight control system for unmanned aerial vehicles, characterized in that: include: Control area determination module, environment perception data acquisition module, environment perception data processing module, self-state data acquisition module, self-state data processing module, comprehensive data analysis module, flight mode evaluation module, flight control module and data interaction transmission module; Control area determination module: used to determine the target drone data as the target control area, and then divide it into sub-areas according to the equal time division method, and number each sub-area in the target control area as 1, 2, ..., i, ..., n in sequence; Environmental perception data acquisition module: used to obtain environmental perception data within the flight area of the target UAV and transmit the environmental perception data to the environmental perception data processing module; Environmental perception data processing module: used to analyze and process the environmental perception data collected by the environmental perception data collection module to obtain the obstacle feature data influence coefficient value, the meteorological environment data influence coefficient value and the airspace interference data influence coefficient value, and transmit them to the comprehensive analysis module; Self-status data acquisition module: used to obtain the self-status data of the target UAV and transmit the self-status data to the self-status data processing module; The self-state data processing module is used to analyze and process the self-state data collected by the self-state data collection module to obtain the flight attitude data influence coefficient value and the power data influence coefficient value, and transmit them to the comprehensive analysis module; Comprehensive data analysis module: used to import the obstacle characteristic data influence coefficient value, meteorological environment data influence coefficient value, airspace interference data influence coefficient value, flight attitude data influence coefficient value and power data influence coefficient value obtained by the environment perception data processing module and the self-state data processing module into the flight mode rationality index mathematical model, obtain the flight mode rationality index value, and transmit it to the flight mode evaluation module; Flight mode evaluation module: used to compare the flight mode rationality index value obtained by the comprehensive data analysis module with the preset flight mode rationality index value, calculate the difference between the flight mode rationality index value and the preset flight mode rationality index value, and when the difference is less than the preset difference, transmit the judgment result and the target UAV data to the data interaction transmission module; when the difference is greater than the preset difference, transmit the judgment result and the target UAV data to the flight control module; Flight control module: used to adjust the flight mode to the next standard according to the judgment result and the target UAV data, then control the flight mode and generate a new optimized route, and then transmit the judgment result, target UAV data and the new optimized route to the data interaction transmission module; Data interaction and transmission module: used to transmit the obtained judgment results, target drone data and optimized routes to the user information terminal, providing reference data for users to make adjustment measures.
2. The autonomous route planning flight control system for unmanned aerial vehicles according to claim 1, characterized in that: The environmental perception data includes obstacle feature data influencing parameters, meteorological environment data influencing parameters and airspace interference data influencing parameters; The obstacle characteristic data influencing parameters include the relative distance of the obstacle, denoted as Δd; The relative height of the obstacle is denoted as Δh; the relative moving speed of the obstacle is denoted as Δv; the geometric diameter of the obstacle equivalent sphere is denoted as D; the influencing parameters of the meteorological environment data include the relative wind speed of the environment, denoted as V; the relative wind direction angle of the environment, denoted as θ; the ambient air pressure, denoted as P; the ambient humidity, denoted as H; the ambient light intensity, denoted as L; the airspace interference data includes the electromagnetic radiation intensity, denoted as E; the signal interference frequency, denoted as f; the interference signal density, denoted as ρ; The radio frequency interference intensity is denoted as I; the frequency band occupancy rate is denoted as B.
3. The autonomous route planning flight control system for unmanned aerial vehicles according to claim 1, characterized in that: The environmental perception data processing module includes an obstacle feature data influence coefficient calculation unit, a meteorological environment data influence coefficient calculation unit and an airspace interference data influence coefficient calculation unit.
4. The autonomous route planning flight control system for unmanned aerial vehicles according to claim 3 is characterized by: The obstacle feature data influence coefficient calculation unit is used to import the obstacle feature data influence parameters into the obstacle feature data influence coefficient mathematical model to obtain the obstacle feature data influence coefficient value; the meteorological environment data influence coefficient calculation unit is used to import the meteorological environment data influence parameters into the meteorological environment data influence coefficient mathematical model to obtain the meteorological environment data influence coefficient value; the airspace interference data influence coefficient calculation unit is used to import the airspace interference data influence parameters into the airspace interference data influence coefficient mathematical model to obtain the airspace interference data influence coefficient value.
5. The autonomous route planning flight control system for unmanned aerial vehicles according to claim 4, characterized in that: The mathematical model of the obstacle characteristic data influence coefficient is specifically: , the mathematical model of the meteorological environment data influence coefficient is as follows: , the mathematical model of the spatial interference data influence coefficient is as follows: , where Δd i Indicates the relative distance of obstacles in the i-th time period; Δd min Indicates the minimum allowable relative distance of obstacles; Δh i Indicates the relative height of the obstacle in the i-th time period; Δh min Indicates the minimum allowed relative height of obstacles; Δv i represents the relative moving speed of the obstacle in the i-th time period; D i V represents the geometric diameter of the equivalent sphere of the obstacle in the i-th time period; i represents the relative wind speed in the i-th time period; θ i represents the relative wind direction angle of the environment in the i-th time period; P i represents the ambient air pressure in the i-th time period; P0 represents the standard atmospheric pressure; Indicates the allowable value of standard ambient air pressure; H i represents the ambient humidity in the i-th time period; L i represents the ambient light intensity in the i-th time period; E i represents the electromagnetic radiation intensity in the i-th time period; f i represents the signal interference frequency in the i-th time period; f0 represents the standard value of the signal interference frequency; f max Indicates the maximum permissible signal interference frequency; f min Represents the minimum permissible signal interference frequency; ρ i represents the interference signal density in the i-th time period; I i Indicates the radio frequency interference intensity in the i-th time period; I min Indicates the minimum permissible radio frequency interference intensity; B i Indicates the frequency band occupancy rate in the i-th time period.
6. The autonomous route planning flight control system for unmanned aerial vehicles according to claim 1, characterized in that: The self-state data includes flight attitude data influencing parameters and power data influencing parameters; The flight attitude data influencing parameters include the drone roll angle, denoted as r; the drone pitch angle, denoted as p; the drone heading angle, denoted as y; the drone acceleration, denoted as a; the power data influencing parameters include the propeller thrust, denoted as F; the motor real-time speed, denoted as ω; The propeller torque is denoted as t; the flight speed of the drone is denoted as v.
7. The UAV autonomous route planning flight control system according to claim 1, characterized in that: The self-state data processing module includes a flight attitude data influence coefficient calculation unit and a power data influence coefficient calculation unit.
8. The autonomous route planning flight control system for unmanned aerial vehicles according to claim 7, characterized in that: The flight attitude data influence coefficient calculation unit is used to import the flight attitude data influence parameters into the flight attitude data influence coefficient mathematical model to obtain the flight attitude data influence coefficient value; the power data influence coefficient calculation unit is used to import the power data influence parameters into the power data influence coefficient mathematical model to obtain the power data influence coefficient value.
9. The autonomous route planning flight control system for unmanned aerial vehicles according to claim 8, characterized in that: The mathematical model of the flight attitude data influence coefficient is specifically: , the mathematical model of the power data influence coefficient is as follows: , where r i represents the rolling angle of the UAV in the i-th time period, r i-1 represents the rolling angle of the drone in the i-1th time period, r max Indicates the maximum allowable roll angle of the drone, p i represents the pitch angle of the drone in the i-th time period, p i-1 represents the pitch angle of the drone in the i-1th time period, p max Indicates the maximum allowable pitch angle of the drone, y i represents the heading angle of the drone in the i-th time period, y i-1 represents the heading angle of the drone in the i-1th time period, y max represents the maximum allowable heading angle of the drone, y0 represents the initial heading angle, a i represents the acceleration of the drone in the i-th time period, a max Indicates the maximum operating acceleration of the drone, F i represents the propeller thrust in the i-th time period, ω i Indicates the real-time speed of the motor in the i-th time period, t i represents the propeller torque in the i-th time period, v i Represents the flight speed of the drone in the i-th time period.
10. The UAV autonomous route planning flight control system according to claim 1, characterized in that: The mathematical model of the flight mode rationality index is specifically: .