Virtual unmanned aerial vehicle and real unmanned aerial vehicle fusion simulation method and system

Abstract

The application discloses a kind of virtual unmanned aerial vehicle and real installation unmanned aerial vehicle fusion simulation method and system, it is related to unmanned aerial vehicle modeling and simulation technical field, including: obtaining real installation unmanned aerial vehicle dynamics parameter information and setting on real installation unmanned aerial vehicle sensor module detection real installation unmanned aerial vehicle kinematics parameter information;Real installation unmanned aerial vehicle dynamics parameter and real installation unmanned aerial vehicle kinematics parameter are sent to simulation platform by communication module;According to real installation unmanned aerial vehicle dynamics parameter, virtual unmanned aerial vehicle model is established;According to real installation unmanned aerial vehicle kinematics parameter, sensor configuration is carried out to virtual unmanned aerial vehicle model;According to application scene, simulation environment is selected to realize virtual unmanned aerial vehicle fusion simulation.The application can provide higher simulation accuracy by accurate modeling and real sensor parameter configuration and multi-sensor fusion algorithm application, so that the flight behavior of virtual unmanned aerial vehicle is closer to real installation unmanned aerial vehicle, and the reliability of simulation result is enhanced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of unmanned aerial vehicle modeling and simulation, in particular to a virtual unmanned aerial vehicle and real unmanned aerial vehicle fusion simulation method and system. BACKGROUND

[0002] With the rapid development of unmanned aerial vehicle technology, unmanned aerial vehicle simulation has become an indispensable part of the design and verification of related algorithms and other processes. Currently, unmanned aerial vehicle simulation has achieved remarkable results in multiple fields such as unmanned aerial vehicle dynamics, obstacle avoidance technology, and cluster task planning, and has played an irreplaceable role.

[0003] Traditional unmanned aerial vehicle operation relies on actual flight testing, which not only has high costs and takes a long time, but also faces safety hazards and high risks in complex environments. Especially in complex environments, there are safety hazards; traditional unmanned aerial vehicle operation relies on actual flight testing, and existing unmanned aerial vehicle simulation systems usually cannot accurately simulate the complex environment and dynamic changes of the real world, resulting in a large difference between the simulation results and the actual performance. In addition, in the existing technology, the simulation of virtual unmanned aerial vehicles and the operation of real unmanned aerial vehicles often have significant differences, making it difficult to achieve effective cooperation. In unmanned aerial vehicle operation, data from different sensors need to be effectively fused to provide accurate environmental perception and flight status. Therefore, advanced data processing and fusion algorithms must be used to improve the efficiency and accuracy of data processing and ensure real-time performance.

[0004] In summary, unmanned aerial vehicle operation and simulation based on traditional methods still face some challenges. Existing simulation systems have limitations in simulating complex environments and dynamic changes in the real world, which may result in a large gap between simulation results and actual performance, affecting the effectiveness and reliability of algorithms. This is reflected in the following aspects:

[0005] 1. There are challenges in building accurate virtual models for real unmanned aerial vehicles, especially in complex dynamic behavior and sensor characteristic modeling, which often requires developers to invest a lot of time and resources;

[0006] 2. In the construction of high-precision virtual unmanned aerial vehicle models, traditional simulation systems often have difficulty accurately capturing complex dynamic behavior, and the parameter collection process of real unmanned aerial vehicles is tedious and easily affected by the environment, resulting in inaccurate data.

[0007] 3. In the rapid sensor model configuration, the configuration process is complex and time-consuming, affecting the simulation efficiency, and in dynamic environments, real-time calibration and configuration are difficult to achieve, resulting in data consistency problems.

[0008] 4、Data processing and fusion algorithm complexity, large amount of calculation, difficult to meet the real-time processing requirements, and when processing different sensor data, the adaptability is insufficient, which affects the fusion effect.

[0009] Therefore, there is an urgent need for a virtual unmanned aerial vehicle and a real unmanned aerial vehicle fusion simulation method, which builds a virtual unmanned aerial vehicle model according to the real unmanned aerial vehicle, and realizes the fusion and unified deployment of real resources and virtual resources in the simulation environment. SUMMARY

[0010] The present application provides a virtual unmanned aerial vehicle and a real unmanned aerial vehicle fusion simulation method and system, which can quickly and accurately establish a virtual unmanned aerial vehicle model according to the actual configuration of the real unmanned aerial vehicle, and can build a simulation environment according to the actual environment, and realize the fusion and unified deployment of real resources and virtual resources in the simulation environment with low cost, high real-time performance and strong reliability.

[0011] The technical scheme adopted by the present application to solve its technical problems is:

[0012] In a first aspect, the present application provides a virtual unmanned aerial vehicle and a real unmanned aerial vehicle fusion simulation method, comprising:

[0013] Obtaining the dynamic parameters of the real unmanned aerial vehicle and the kinematic parameters of the real unmanned aerial vehicle detected by the sensor module arranged on the real unmanned aerial vehicle;

[0014] Sending the dynamic parameters of the real unmanned aerial vehicle and the kinematic parameters of the real unmanned aerial vehicle to the simulation platform through the communication module;

[0015] Establishing a virtual unmanned aerial vehicle model according to the dynamic parameters of the real unmanned aerial vehicle;

[0016] Configuring sensors for the virtual unmanned aerial vehicle model according to the kinematic parameters of the real unmanned aerial vehicle;

[0017] According to the application scene, the simulation environment is selected to realize the virtual unmanned aerial vehicle fusion simulation.

[0018] As a further technical scheme of the present application, the dynamic parameters of the real unmanned aerial vehicle include the mass of the real unmanned aerial vehicle, the diameter of the propeller, the temperature, the geometric torque, the maximum flight altitude, the number of unmanned aerial vehicle rotors, the capacity of the unmanned aerial vehicle battery, the minimum capacity of the unmanned aerial vehicle battery, the voltage of the battery, the resistance of the battery and the cross-sectional area of the unmanned aerial vehicle.

[0019] As a further technical scheme of the present application, the sensor module includes a laser radar, a magnetometer, a barometer, an inertial measurement unit (IMU), a global positioning system (GPS) and a distance sensor.

[0020] As a further technical scheme of the present application, the unmanned aerial vehicle dynamics parameters and the unmanned aerial vehicle kinematics parameters are sent to the simulation platform through the communication module; specifically, the communication module realizes the communication between the unmanned aerial vehicle and the simulation platform through the UDP / MAVLink protocol.

[0021] As a further technical scheme of the present application, the virtual unmanned aerial vehicle model is established according to the unmanned aerial vehicle dynamics parameters; specifically, a multi-rotor kinematics position dynamics model, an attitude dynamics model and a control efficiency model are established according to the unmanned aerial vehicle dynamics parameters.

[0022] The multi-rotor kinematics position dynamics model is:

[0023]

[0024] wherein, is the acceleration component of the unmanned aerial vehicle in the x, y and z directions of the ground coordinate system, g is the gravity acceleration, and is usually taken as 9.81 m / s 2 b is the lift (or thrust) generated in the body coordinate system, m is the mass of the unmanned aerial vehicle, θ, φ and ψ are the attitude angles of the unmanned aerial vehicle, representing the pitch angle, roll angle and yaw angle respectively;

[0025] The attitude dynamics model is:

[0026]

[0027] wherein, [τ x τ y τ z ] is the total torque of the unmanned aerial vehicle in the x, y and z directions; [I xx I yy I zz ] is the diagonal elements of the moment of inertia matrix of the unmanned aerial vehicle, representing the moment of inertia around the x, y and z axes respectively; is the angular acceleration component of the unmanned aerial vehicle in the body coordinate system;

[0028] The control efficiency model is:

[0029] The body lift f b and the moment τ acting on the body are obtained according to the rotational angular velocity of the rotor; the lift f b and the moment τ have the following relationship with the rotational angular velocity of the rotor:

[0030]

[0031] wherein, f b is the total thrust generated in the body coordinate system, [τ x τ​y τ z ] is the total torque of the unmanned aerial vehicle in the x, y, z directions, c T is the thrust coefficient, indicating the relationship between the thrust generated by each rotor and the input power; dc T is the change amount of the thrust coefficient; c M is the moment coefficient, indicating the relationship between the moment generated by the rotor and the input power; is the square of the angular velocity of the four rotors.

[0032] As a further technical solution of the application, the virtual unmanned aerial vehicle model is configured with sensors according to the actual sensor parameters of the installed unmanned aerial vehicle; specifically including:

[0033] The data output of the virtual sensor is generated according to the actual sensor parameters of the installed unmanned aerial vehicle;

[0034] The performance of the actual sensor under different flight states and environmental conditions is simulated in the simulation platform, and the sensor configuration of the virtual unmanned aerial vehicle is performed;

[0035] Noise and error are introduced in the data generation process according to the characteristics of the virtual environment to adjust the sensor configuration.

[0036] As a further technical solution of the application, the data output of the virtual sensor is generated according to the actual sensor parameters of the installed unmanned aerial vehicle; specifically including:

[0037] By setting the flight path of each unmanned aerial vehicle during simulation and setting the control algorithm used during flight, i.e. each virtual unmanned aerial vehicle has a path point and controller that can be individually set for each unmanned aerial vehicle, independent control of each unmanned aerial vehicle is achieved;

[0038] The control algorithm in the default mode is to make the unmanned aerial vehicle pass through each path point in turn at a fixed speed of 1m / s.

[0039] As a further technical solution of the application, the performance of the actual sensor under different flight states and environmental conditions is simulated in the simulation platform, and the sensor configuration of the virtual unmanned aerial vehicle is performed; specifically including: multi-sensor fusion is performed by adaptively and dynamically adjusting the sensor weights based on the extended Kalman filter algorithm, wherein the adaptive and dynamic adjustment of the sensor weights is based on the performance indicators of the installed sensors, including accuracy, response time, and stability of historical data for dynamic score calculation, and the dynamic score calculation formula is as follows,

[0040]

[0041] wherein Score i is the dynamic score of sensor i, used to dynamically adjust the weight of the sensor, the higher the score, the greater the weight; Accuracyi This refers to the accuracy of sensor i, indicating the correctness of the sensor in measurement or detection, usually expressed as a percentage (e.g., 0.95 represents 95% accuracy); MaxAccuracy refers to the highest accuracy among all sensors. ResponseTime i The response time of sensor i refers to the time required for the sensor to output a result from receiving a signal. MinResponseTime refers to the lowest response time among all sensors; Stability i The stability of sensor i is represented by the standard deviation; α, β, and γ are weighting coefficients that control the degree of influence of accuracy, response time, and stability on the dynamic score, respectively, and their values ​​are between 0 and 1.

[0042] As a further technical solution of the present invention, the multi-sensor fusion is performed by adaptively and dynamically adjusting sensor weights based on the extended Kalman filter algorithm; it also includes updating the sensor weights, and the entropy values ​​of each sensor in the sensor configuration module of the virtual drone are calculated as follows:

[0043]

[0044] Among them, H i The entropy value of sensor i represents the uncertainty of the sensor's output information; m refers to the total number of different states or categories output by sensor i, used to determine the number of states to be considered in the entropy calculation; P k The probability of sensor i outputting state k is represented by the frequency of state k occurring in all outputs, which is between 0 and 1; log(Pk) is the logarithm of the probability of state k, which reflects the information contribution of state k.

[0045] The sensor weights are updated using a Bayesian method, with the following formula:

[0046] P(Weight i |Data)∝P(Data|Weight i )·P(Weight i );

[0047] Among them, P(Weight) i |Data) refers to the posterior probability of the weight of sensor i given the data; P(Data|Weight) i P(Weight) represents the probability of observing specific data when the weight of sensor i is assumed to be a certain value; i () refers to the prior probability of the weight of sensor i;

[0048] The final weight of the sensor is determined based on dynamic scoring, using the following formula:

[0049]

[0050] Wherein, Weighti refers to the maximum weight of sensor i; Scorei refers to the dynamic score of sensor i, reflecting the comprehensive performance of the sensor, the higher the score, the better the performance of the sensor, and the weight should be increased accordingly; Entropyi refers to the entropy value of sensor i; the higher the entropy value, the more uncertain the information output by the sensor, and the lower the reliability.

[0051] In a second aspect, the present application also provides a virtual unmanned aerial vehicle and real unmanned aerial vehicle fusion simulation system, comprising:

[0052] A real unmanned aerial vehicle parameter acquisition unit acquires real unmanned aerial vehicle dynamics parameter information and real unmanned aerial vehicle kinematics parameter information detected by a sensor module arranged on the real unmanned aerial vehicle;

[0053] A communication unit is used to send the real unmanned aerial vehicle dynamics parameters and the real unmanned aerial vehicle kinematics parameters to the simulation platform;

[0054] A virtual unmanned aerial vehicle modeling unit establishes a virtual unmanned aerial vehicle model according to the real unmanned aerial vehicle dynamics parameters;

[0055] A virtual unmanned aerial vehicle sensor configuration unit configures sensors for the virtual unmanned aerial vehicle model according to the real unmanned aerial vehicle kinematics parameters;

[0056] A virtual unmanned aerial vehicle simulation unit realizes virtual unmanned aerial vehicle fusion simulation according to the selected simulation environment of the application scenario.

[0057] Compared with the prior art, the present application has the following advantages:

[0058] The present application has significant advantages in the integration, accuracy, flexibility and user-friendliness of the unmanned aerial vehicle simulation system, and provides stronger support for the research and application of unmanned aerial vehicle algorithms. Among them, the present application has multiple unmanned aerial vehicle modeling, unmanned aerial vehicle real sensor configuration, flight control algorithm selection, simulation scene selection, communication configuration and flight log modules, which simplifies the development and test process of unmanned aerial vehicle algorithms and improves the overall efficiency of the system; at the same time, through accurate modeling and real sensor parameter configuration and multi-sensor fusion algorithm application, the present application can provide higher simulation accuracy, so that the flight behavior of the virtual unmanned aerial vehicle is closer to the real unmanned aerial vehicle, and the reliability of the simulation result is enhanced; further, the present application supports the selection of multiple control algorithms and simulation scenes, and users can flexibly configure according to specific needs, adapt to different application scenarios and task requirements, and has higher flexibility and scalability; in addition, the design of the communication module and the flight log module enables the system to process and record flight data in real time, discover and respond to abnormal situations in time, and improves the flight safety. BRIEF DESCRIPTION OF DRAWINGS

[0059] Figure 1 A virtual unmanned aerial vehicle and a real unmanned aerial vehicle fusion simulation method and system flow chart are proposed for the present application;

[0060] Figure 2 A specific embodiment structure diagram is proposed for the present application;

[0061] Figure 3 A general multi-rotor unmanned aerial vehicle modeling module parameter setting block diagram is proposed for the present application;

[0062] Figure 4 A virtual unmanned aerial vehicle sensor configuration module parameter setting block diagram is proposed for the present application;

[0063] Figure 5 A multi-sensor fusion step flow chart is proposed for the present application;

[0064] Figure 6 A virtual unmanned aerial vehicle and a real unmanned aerial vehicle fusion simulation system structure diagram is proposed for the present application. DETAILED DESCRIPTION

[0065] The specific embodiments of the present application will be described below in conjunction with the accompanying drawings and embodiments:

[0066] It should be noted that the structures, colors, proportions, sizes, etc. shown in the drawings attached to the present specification are only used to cooperate with the content disclosed in the specification, so that those skilled in the art can understand and read, and are not used to limit the implementation conditions of the present application. Any modification of structure, change of proportional relationship or adjustment of size, which does not affect the effect and purpose that can be achieved by the present application, should still fall within the scope of the technical content disclosed by the present application.

[0067] At the same time, the terms such as "up", "down", "left", "right", "middle" and "one" cited in the present specification are only for the convenience of clear description, and are not used to limit the scope of the present application. Change or adjustment of relative relationship, without substantial change of technical content, is also considered as the implementation scope of the present application.

[0068] Embodiment one

[0069] As shown in Figure 1 , the present application proposes a virtual unmanned aerial vehicle and a real unmanned aerial vehicle fusion simulation method, which comprises:

[0070] Step 101, obtaining the dynamic parameters of the real unmanned aerial vehicle and the kinematic parameters of the real unmanned aerial vehicle detected by the sensor module arranged on the real unmanned aerial vehicle;

[0071] Step 102, send the installed unmanned aerial vehicle dynamics parameters and the installed unmanned aerial vehicle kinematics parameters to the simulation platform through the communication module;

[0072] Step 103, establish a virtual unmanned aerial vehicle model according to the installed unmanned aerial vehicle dynamics parameters;

[0073] Step 104, configure sensors for the virtual unmanned aerial vehicle model according to the installed unmanned aerial vehicle kinematics parameters;

[0074] Step 105, realize virtual unmanned aerial vehicle fusion simulation according to the application scene selection simulation environment.

[0075] In the embodiment of the application, according to the actual configuration of the installed unmanned aerial vehicle, the model of the virtual unmanned aerial vehicle is quickly and accurately established, the simulation environment can be built according to the actual environment, and low-cost / high real-time and strong-reliability virtual unmanned aerial vehicle and installed unmanned aerial vehicle fusion simulation is realized.

[0076] Referring to Figure 2 and Figure 3 , the installed unmanned aerial vehicle dynamics parameters are the dynamics parameters of the installed unmanned aerial vehicle, including: installed unmanned aerial vehicle mass, propeller diameter, temperature, geometric torque, maximum flight altitude, unmanned aerial vehicle rotor number, unmanned aerial vehicle battery capacity, unmanned aerial vehicle battery minimum capacity, battery voltage, battery resistance and unmanned aerial vehicle cross-sectional area parameters. The dynamics characteristics and kinematics model of the real unmanned aerial vehicle are modeled by mathematical equations, the dynamics equation is converted into a state space form, the Euler angle is used to describe the attitude change of the unmanned aerial vehicle, and the motion equation and control efficiency model are established in combination with the change of position and speed, so that the flight behavior and response characteristics are accurately simulated in the simulation environment.

[0077] In the embodiment of the application, the installed unmanned aerial vehicle dynamics parameters and the installed unmanned aerial vehicle kinematics parameters are sent to the simulation platform through the communication module; specifically, the communication module realizes the communication between the installed unmanned aerial vehicle and the simulation platform through the UDP / MAVLink protocol.

[0078] In this embodiment, the scene selection module on the simulation platform is used to select the virtual unmanned aerial vehicle scene, and a plurality of preset simulation scenes are provided, such as urban environment, mountain terrain, rain, fog, sunlight weather conditions, etc., and the user can select according to the actual demand. Further, this embodiment also supports the creation of custom scenes, allowing users to design unique flight environments according to specific task requirements. By reasonably selecting and configuring the simulation scene, the pertinence and effectiveness of the simulation experiment can be effectively improved, and real test conditions are provided for performance evaluation and algorithm verification of the unmanned aerial vehicle.

[0079] The virtual unmanned aerial vehicle model is established according to the actual unmanned aerial vehicle dynamics parameters; specifically, a multi-rotor kinematics position dynamics model, an attitude dynamics model and a control efficiency model are established according to the actual unmanned aerial vehicle dynamics parameters.

[0080] The multi-rotor kinematics position dynamics model is:

[0081]

[0082] wherein, is the acceleration component of the unmanned aerial vehicle in the x, y and z directions of the ground coordinate system, g is the gravity acceleration, and is usually taken as 9.81 m / s 2 b is the lift (or thrust) generated in the body coordinate system, m is the mass of the unmanned aerial vehicle, and θ, φ and ψ are the attitude angles of the unmanned aerial vehicle, representing the pitch angle, roll angle and yaw angle respectively.

[0083] The attitude dynamics model is:

[0084]

[0085] wherein, x y z is the total torque of the unmanned aerial vehicle in the x, y and z directions; and xx yy zz is the diagonal element of the rotational inertia matrix of the unmanned aerial vehicle, representing the rotational inertia around the x, y and z axes respectively. is the angular acceleration component of the unmanned aerial vehicle in the body coordinate system.

[0086] The control efficiency model is:

[0087] The body lift f b and the moment τ acting on the body are obtained according to the rotational angular velocity of the rotor; the lift f b and the moment τ have the following relationship with the rotational angular velocity of the rotor:

[0088]

[0089] wherein, f b is the total thrust generated in the body coordinate system, [τ x y z ] is the total torque of the unmanned aerial vehicle in the x, y and z directions, c T is the thrust coefficient, representing the relationship between the thrust generated by each rotor and the input power; dc T is the change amount of the thrust coefficient; and c M ​​​​​​​is the torque coefficient, representing the relationship between the torque generated by the rotor and the input power; is the square of the rotational angular velocity of the four rotors.

[0090] In the embodiment, the input comprehensively considers the mass of the multi-rotor unmanned aerial vehicle, the temperature and altitude of the flight environment, the diameter of the propeller, the geometric torque, and the battery-related performance parameters and other factors, and through a series of performance formula calculations, the maximum load, the maximum flight speed and the maximum flight distance of the unmanned aerial vehicle are calculated in the forward flight mode, or the maximum endurance time of the unmanned aerial vehicle and the propeller speed in the hovering state are obtained in the hovering mode, so as to quickly build a general virtual unmanned aerial vehicle model, so that the unmanned aerial vehicle model in the simulation platform is consistent with the actual unmanned aerial vehicle model in parameters. Through the simulation platform, multiple unmanned aerial vehicles with different parameters can be set and added.

[0091] In the embodiment, the kinematic parameters of the actual unmanned aerial vehicle detected by the sensor module arranged on the actual unmanned aerial vehicle are converted into sensor configuration parameters of the virtual unmanned aerial vehicle, wherein the sensor module comprises a laser radar, a magnetometer, a barometer, an inertial measurement unit (IMU), a global positioning system (GPS) and a distance sensor. The corresponding virtual sensor can be selected based on the sensor configuration of the actual unmanned aerial vehicle, and parameter mapping is performed. For example, if a GPS is used in the actual unmanned aerial vehicle, a GPS can be configured on the virtual unmanned aerial vehicle, and parameters such as horizontal / vertical accuracy, update frequency, etc. are set.

[0092] In the embodiment, each sensor can be configured, including the type, enabled state, various parameters and default values of each sensor. The specific configuration is as shown in Figure 4 .

[0093] In the embodiment, the sensor configuration of the virtual unmanned aerial vehicle model is performed according to the kinematic parameters of the actual unmanned aerial vehicle; specifically, data output of the virtual sensor is generated according to the actual sensor parameters of the actual unmanned aerial vehicle; these data simulate the performance of the actual sensor under different flight states and environmental conditions, the sensor performance of the virtual unmanned aerial vehicle is ensured to be consistent with that of the actual unmanned aerial vehicle through the sensor configuration, the sensor configuration is adjusted according to the virtual environment characteristics through real parameter mapping, noise and errors are introduced in the data generation process, so that the output of the virtual sensor is closer to the performance of the actual sensor, the virtual unmanned aerial vehicle is ensured to have similar characteristics to the actual unmanned aerial vehicle in the simulation environment, and the authenticity and reliability of the simulation unmanned aerial vehicle and the simulation environment are improved.

[0094] In the embodiment, the data output of the virtual sensor is generated according to the actual sensor parameters of the actual unmanned aerial vehicle; specifically,

[0095] The flight path of each unmanned aerial vehicle in simulation is set, and the control algorithm used in flight is set, that is, each virtual unmanned aerial vehicle has a path point and a controller which can be set for each unmanned aerial vehicle independently, so as to realize independent control of each unmanned aerial vehicle.

[0096] The control algorithm in the default mode is to make the unmanned aerial vehicle pass through each path point in turn at a fixed speed of 1 m / s. By providing a programming interface of Simulink and C++, a user-defined control algorithm can be accessed.

[0097] In this embodiment, the north-east coordinate system is used, and the positive direction of the z-axis points to the positive direction, so that the z-axis coordinate data in the unmanned aerial vehicle track point are all negative numbers. In addition, the coordinates of each path point of the unmanned aerial vehicle represent the relative position of the path point and the initial point of the unmanned aerial vehicle, for example, (0, 5, -6) indicates that the path point is located at a position 5 meters east and 6 meters above the initial position. The communication module is used to realize data transmission and information interaction between the actual unmanned aerial vehicle, the ground control station, the virtual unmanned aerial vehicle and related equipment.

[0098] The initial flight state of the virtual unmanned aerial vehicle in the simulation environment is the real flight state of the unmanned aerial vehicle obtained by fusing multiple sensors of the actual unmanned aerial vehicle, including position, speed and attitude. In the actual unmanned aerial vehicle, multiple sensors are used to obtain flight state information; the simulation platform obtains real sensor data in real time on the basis of time synchronization; the initialization state vector includes position, speed and attitude; the extended Kalman filtering method based on dynamic weight adjustment is applied to real-time state estimation of the unmanned aerial vehicle. For example, the accurate position of the unmanned aerial vehicle is obtained by fusing the data of GPS and IMU. GPS provides global positioning, while IMU provides relative motion information; the more accurate speed estimation is obtained by fusing the acceleration data of IMU and the speed information of GPS; the attitude of the unmanned aerial vehicle is represented by four Euler angles in combination with the data of the angular velocity of IMU and the magnetometer. After multi-sensor fusion, the real flight state of the unmanned aerial vehicle is output, including position, speed and attitude; these information will be used as the initial flight state of the simulation unmanned aerial vehicle.

[0099] In the embodiment of the application, the performance of the actual sensor under different flight states and environmental conditions is simulated in the simulation platform to configure the sensor of the virtual unmanned aerial vehicle; specifically, the multi-sensor fusion is performed by adaptively and dynamically adjusting the sensor weight based on the extended Kalman filtering algorithm, wherein the adaptively and dynamically adjusting the sensor weight is based on the performance indicators of the actual sensor, including accuracy, response time and stability of historical data to dynamically calculate the dynamic score, and the dynamic score calculation formula is as follows,

[0100]

[0101] Score = (Accuracy + Response Time + Stability) / 3 iis the dynamic score of sensor i, used to dynamically adjust the weight of the sensor, the higher the score, the greater the weight; Accuracy i is the accuracy of sensor i, indicating the correctness of the sensor when measuring or detecting, usually expressed in percentage (such as 0.95 indicating 95% accuracy); MaxAccuracy refers to the highest accuracy among all sensors. ResponseTime i refers to the response time of sensor i, indicating the time required for the sensor to output results from receiving signals, MinResponseTime refers to the minimum response time among all sensors; Stability i refers to the stability of sensor i, expressed in standard deviation; α, β, γ refer to weight coefficients, respectively controlling the influence of accuracy, response time and stability on dynamic score, taking values between 0-1.

[0102] Through adaptive dynamic adjustment of sensor weight based on extended Kalman filter algorithm for multi-sensor fusion; also includes: updating the sensor weight, the entropy value of each sensor in the sensor configuration module of the virtual unmanned aerial vehicle is calculated as follows:

[0103]

[0104] where H i refers to the entropy value of sensor i, indicating the uncertainty of sensor output information; m refers to the total number of different states or categories output by sensor i, used to determine the number of states considered in entropy calculation; P k refers to the probability of sensor i output state k, indicating the frequency of state k appearing in all outputs, taking values between 0-1; log(Pk) refers to the logarithmic value of state k probability, reflecting the information contribution of state k;

[0105] The weight of the sensor is updated by Bayesian method, the specific formula is as follows:

[0106] P(Weight i |Data)∝P(Data|Weight i )·P(Weight i );

[0107] where P(Weight i |Data) refers to the posterior probability of sensor i weight given the data; P(Data|Weight i ) represents the probability of observing specific data assuming the weight of sensor i is a certain value; P(Weight i ) refers to the prior probability of sensor i weight;

[0108] Based on the dynamic score, the maximum weight of the sensor is determined, and the specific formula is as follows:

[0109]

[0110] Where Weighti refers to the maximum weight of sensor i; Scorei refers to the dynamic score of sensor i, reflecting the comprehensive performance of the sensor, the higher the score, the better the performance of the sensor, and the weight should be increased accordingly; Entropyi refers to the entropy value of sensor i; The higher the entropy value, the more uncertain the information output by the sensor, and the lower the reliability.

[0111] In the simulation running process, the performance change of the sensor is monitored in real time, and the weight is dynamically adjusted according to the feedback.

[0112] In this embodiment, M5 obtains real-time operation data of the unmanned aerial vehicle through the ground control station, and the multi-sensor fusion step is as shown in Figure 5 The steps are as follows: initialize the state vector x = [position, velocity, attitude (Euler angle )]

[0113] Initialize the covariance matrix P

[0114] Initialize sensor data: such as laser radar data L, IMU data I, GPS data G, and ranging sensor data D

[0115] Set the time step dt

[0116] Set the process noise covariance matrix Q and the measurement noise covariance matrix R

[0117] Set the initial weight of the sensor w_L, w_I, w_G, w_D

[0118] S1. Start the loop phase:

[0119] a. Update the state according to the control input (such as acceleration a and angular velocity ω):

[0120] x' = f(x, control input)

[0121] Where x' refers to the updated state vector, which contains new position, velocity and attitude information; f(x, control input) is a state transition function that describes how to update the state according to the current state and control input.

[0122] Position update: x'{pos} = x{pos} + v_{x}*dt + 0.5*a_{x}*dt^2

[0123] y'{pos} = x{pos} + v_{y}*dt + 0.5*a_{y}*dt^2

[0124] z'{pos} = x{pos} + v_{z}*dt + 0.5*a_{z}*dt^2

[0125] where x'{pos}, y'{pos}, z'{pos} are the updated position coordinates, representing the position in x, y, z axes respectively; x{pos}, y{pos}, z{pos} are the position coordinates in the current state; v_{x}, v_{y}, v_{z} are the components of the current speed of the UAV in x, y, z axes; dt is the time step, representing the time interval of state update; a_{x}, a_{y}, a_{z} are the acceleration components of the UAV in x, y, z axes.

[0126] Velocity update:

[0127] v'{x} = v{x} + a_{x}*dt

[0128] v'{y} = v{y} + a_{y}*dt

[0129] v'{z} = v{z} + a_{z}*dt

[0130] where v'{x}, v'{y}, v'{z} are the updated velocity components, representing the velocity in x, y, z axes respectively;

[0131] Attitude update (Euler angles):

[0132]

[0133] θ' = θ + q*dt

[0134] ψ' = ψ + r*dt

[0135] where θ', ψ' are the updated attitude (Euler angles), representing the roll angle, pitch angle, and yaw angle respectively; θ, ψ are the attitude (Euler angles) in the current state; p, q, r are the angular velocities around x, y, z axes respectively.

[0136] b. Update the covariance matrix:

[0137] P' = F*P*F^T + Q

[0138] where P' is the updated covariance matrix, F is the state transition matrix, Q is the process noise covariance matrix, and F can be obtained by linearizing the state transition equation.

[0139] S2. Update step phase:

[0140] a. Obtain multiple sensor data (illustrated with four sensors as an example):

[0141] L = Obtain LiDAR data()

[0142] I = Obtain IMU data()

[0143] G = Obtain GPS data()

[0144] D = Obtain Range Sensor data()

[0145] where L is LiDAR data, I is IMU data, G is GPS data, and D is Range Sensor data.

[0146] b. Calculate measurement and measurement matrix for each sensor:

[0147] z_L = h_L(x)

[0148] z_I = h_I(x)

[0149] z_G = h_G(x)

[0150] z_D = h_D(x)

[0151] where z_L, z_I, z_G, and z_D represent the measurements of LiDAR, IMU, GPS, and Range Sensor, respectively; h_L(x), h_I(x), h_G(x), and h_D(x) are measurement model functions that describe how to calculate the corresponding measurements from the state vector x.

[0152] c. Calculate measurement errors:

[0153] y_L = L - z_L

[0154] y_I = I - z_I

[0155] y_G = G - z_G

[0156] y_D = D - z_D

[0157] where y_L, y_I, y_G, and y_D represent the measurement errors of LiDAR, IMU, GPS, and Range Sensor, respectively.

[0158] d. Dynamically adjust sensor weights:

[0159] w_L = 1 / (1 + |y_L|)

[0160] w_I = 1 / (1 + |y_I|)

[0161] w_G = 1 / (1 + |y_G|)

[0162] w_D = 1 / (1+|y_D|)

[0163] where w_L, w_I, w_G and w_D represent the weights of lidar, IMU, GPS and ranging sensor respectively.

[0164] e. Calculate the measurement matrix H:

[0165] f. Calculate the Kalman gain:

[0166] K = P'*H^T*(H*P'*H^T+R)^(-1)

[0167] where H is the measurement matrix and R is the measurement noise covariance matrix.

[0168] g. Update the state vector and covariance matrix:

[0169] x = x' + K*(w_L*y_L + w_I*y_I + w_G*y_G + w_D*y_D)

[0170] P = (I-K*H)*P'

[0171] S3. Output the current state, output (x, P)

[0172] where x is the current state vector of the UAV and P is the current covariance matrix

[0173] After the multi-sensor fusion step, more accurate UAV state data, including position, velocity and attitude, are continuously obtained and sent to the simulation platform through the UDP protocol.

[0174] Figure 5 The dynamic weight adjustment in the above formula is to dynamically adjust the weight according to the measurement error of each sensor to improve the accuracy of fusion. The weighted fusion during state update uses the weight of the sensor to weight the measurement error when updating the state vector, ensuring more reliable state estimation.

[0175] The position, velocity and attitude of the virtual UAV obtained after the control algorithm calculation on the simulation platform can also be sent to the real UAV through the ground control station through the UDP / MAVLink protocol, thereby realizing data transmission and information interaction between the real UAV and the virtual UAV.

[0176] The embodiment can record and store various data generated by the virtual UAV during flight. The data in this embodiment include flight state, sensor readings, communication information and environmental parameters, etc., and are stored in JSON or CSV format for subsequent analysis and processing and other system integration.

[0177] The unmanned aerial vehicle simulation system has significant advantages in integration, accuracy, flexibility and user-friendliness, and provides stronger support for the research and application of unmanned aerial vehicle algorithms. The unmanned aerial vehicle simulation system has multiple unmanned aerial vehicle modeling, unmanned aerial vehicle sensor configuration, flight control algorithm selection, simulation scene selection, communication configuration and flight log modules, simplifies the development and test process of the unmanned aerial vehicle algorithm, and improves the overall efficiency of the system. At the same time, through accurate modeling and real sensor parameter configuration and multi-sensor fusion algorithm application, the unmanned aerial vehicle simulation system can provide higher simulation accuracy, so that the flight behavior of the virtual unmanned aerial vehicle is closer to the actual unmanned aerial vehicle, and the reliability of the simulation result is enhanced. Furthermore, the unmanned aerial vehicle simulation system supports selection of multiple control algorithms and simulation scenes, and users can flexibly configure according to specific needs, adapt to different application scenarios and task requirements, has higher flexibility and scalability, and the design of the communication module and the flight log module enables the system to process and record flight data in real time, discover and respond to abnormal situations in time, and improves flight safety.

[0178] Embodiment two

[0179] Reference Figure 6 The unmanned aerial vehicle simulation system also provides a virtual unmanned aerial vehicle and actual unmanned aerial vehicle fusion simulation system, which comprises:

[0180] An actual unmanned aerial vehicle parameter acquisition unit 201 acquires actual unmanned aerial vehicle dynamics parameter information and actual unmanned aerial vehicle kinematics parameter information detected by a sensor module arranged on the actual unmanned aerial vehicle;

[0181] A communication unit 202 is used to send the actual unmanned aerial vehicle dynamics parameter and the actual unmanned aerial vehicle kinematics parameter to a simulation platform;

[0182] A virtual unmanned aerial vehicle modeling unit 203 establishes a virtual unmanned aerial vehicle model according to the actual unmanned aerial vehicle dynamics parameter;

[0183] A virtual unmanned aerial vehicle sensor configuration unit 204 configures sensors for the virtual unmanned aerial vehicle model according to the actual unmanned aerial vehicle kinematics parameter;

[0184] A virtual unmanned aerial vehicle simulation unit 205 realizes virtual unmanned aerial vehicle fusion simulation according to a selected simulation environment.

[0185] The actual unmanned aerial vehicle parameter acquisition unit and the communication unit are specifically carried on the actual unmanned aerial vehicle, and the virtual unmanned aerial vehicle modeling unit, the virtual unmanned aerial vehicle sensor configuration unit and the virtual unmanned aerial vehicle simulation unit are arranged on the simulation platform, so as to realize virtual unmanned aerial vehicle modeling / sensor configuration and simulation simulation through the simulation platform receiving the actual unmanned aerial vehicle parameter.

[0186] The various changes and specific examples of the method in the foregoing embodiments are also applicable to the virtual unmanned aerial vehicle and actual unmanned aerial vehicle fusion simulation system of the present embodiment. Through the foregoing detailed description of the virtual unmanned aerial vehicle and actual unmanned aerial vehicle fusion simulation method, those skilled in the art can clearly understand the virtual unmanned aerial vehicle and actual unmanned aerial vehicle fusion simulation system in the present embodiment. Therefore, in order to make the description simple, it will not be described in detail here.

[0187] In addition, it should be understood that, although the present specification is described in terms of embodiments, not every embodiment contains only one independent technical solution, and the description of the specification is only for the sake of clarity, and those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can be combined appropriately to form other embodiments that those skilled in the art can understand.

Claims

1. A simulation method for fusing virtual drones and physical drones, characterized in that, include: Acquire dynamic parameter information of the actual UAV and kinematic parameter information of the actual UAV detected by the sensor module set on the actual UAV; The dynamic parameters and kinematic parameters of the actual UAV are sent to the simulation platform through the communication module; A virtual drone model is established based on the dynamic parameters of the actual drone. Configure sensors for the virtual drone model based on the kinematic parameters of the actual drone; Select a simulation environment based on the application scenario to achieve virtual drone fusion simulation; The process of establishing a virtual UAV model based on the dynamic parameters of the actual UAV includes: establishing a multi-rotor kinematic position dynamics model, attitude dynamics model, and control efficiency model based on the dynamic parameters of the actual UAV. The kinematic position dynamics model of the multirotor is as follows: ； ； in, Let g be the acceleration components of the UAV in the x, y, z directions of the ground coordinate system, and g be the acceleration due to gravity. f b Let m be the lift generated in the body coordinate system, and m be the mass of the UAV. θ、 ψ For the attitude angles of the UAV, denoted as pitch angle, roll angle and yaw angle respectively; The attitude dynamics model is as follows: ； in, This represents the total torque of the UAV in the x, y, and z directions; These are the diagonal elements of the UAV's moment of inertia matrix, representing the moments of inertia about the x, y, and z axes, respectively. This represents the angular acceleration components of the UAV in the body coordinate system. The control efficiency model is as follows: The lift f of the airframe is derived from the rotational angular velocity of the rotor. b and the torque τ acting on the body; lift f b The torque τ is related to the rotor's rotational angular velocity as follows: ； Among them, f b The total thrust generated in the body coordinate system. Let be the total torque of the UAV in the x, y, and z directions. is the thrust coefficient, representing the relationship between the thrust generated by each rotor and the input power; This represents the change in the thrust coefficient; This is the torque coefficient, representing the relationship between the torque generated by the rotor and the input power; The square of the rotational angular velocity of the four rotors.

2. The method for fusion simulation of virtual drones and physical drones according to claim 1, characterized in that, The actual UAV dynamic parameters include: actual UAV mass, propeller diameter, temperature, geometric torque, maximum flight altitude, number of UAV rotors, UAV battery capacity, minimum UAV battery capacity, battery voltage, battery resistance, and UAV cross-sectional area parameters.

3. The method for fusion simulation of virtual drones and physical drones according to claim 1, characterized in that, The sensor module includes: lidar, magnetometer, barometer, inertial measurement unit, global positioning system, and distance sensor.

4. The method for fusion simulation of virtual drones and physical drones according to claim 1, characterized in that, The process involves transmitting the dynamic parameters and kinematic parameters of the actual UAV to the simulation platform via the communication module. Specifically, the communication module enables communication between the actual UAV and the simulation platform via the UDP / MAVLink protocol.

5. The virtual drone and real drone fusion simulation method according to claim 1, characterized in that, The process of configuring sensors for the virtual drone model based on the kinematic parameters of the actual drone includes: Generate virtual sensor data output based on the actual sensor parameters of the installed drone; The simulation platform simulates the performance of actual sensors under different flight states and environmental conditions, enabling the configuration of sensors for virtual drones. Based on the characteristics of the virtual environment, noise and errors are introduced during the data generation process to adjust the sensor configuration.

6. The virtual drone and physical drone fusion simulation method according to claim 5, characterized in that, The process of generating virtual sensor data output based on the actual sensor parameters of the installed UAV specifically includes: By setting the flight path of each drone during simulation and setting the control algorithm used during flight, each virtual drone has a path point and controller that can be set individually, thus enabling independent control of each drone. The default control algorithm causes the drone to pass through each path point sequentially at a fixed speed of 1 m / s.

7. The method for fusion simulation of virtual drones and physical drones according to claim 5, characterized in that, The process involves simulating the performance of actual sensors under different flight states and environmental conditions in a simulation platform to configure the sensors of a virtual UAV. Specifically, this includes: adaptively and dynamically adjusting sensor weights based on an extended Kalman filter algorithm for multi-sensor fusion. The adaptive dynamic adjustment of sensor weights is based on the performance indicators of the actual sensors, including accuracy, response time, and the stability of historical data, and is dynamically scored. The dynamic scoring calculation formula is as follows. ； in, It is a sensor i The dynamic score is used to dynamically adjust the weight of the sensor; the higher the score, the greater the weight. It is a sensor i The accuracy rate indicates the correctness of the sensor during measurement or detection; MaxAccuracy This refers to the highest accuracy rate among all sensors. Sensor i The response time represents the time required for a sensor to output a result from receiving a signal; MinResponseTime This refers to the lowest response time among all sensors; Stability i Sensor i The stability of is expressed by standard deviation; α, β, γ These refer to the weighting coefficients, which respectively control the degree of influence of accuracy, response time, and stability on dynamic scoring.

8. The method for fusion simulation of virtual drones and physical drones according to claim 7, characterized in that, The method of adaptively and dynamically adjusting sensor weights based on the extended Kalman filter algorithm for multi-sensor fusion also includes updating the sensor weights. The entropy values ​​of each sensor in the sensor configuration module of the virtual drone are calculated as follows: in, H i The entropy value of sensor i represents the uncertainty of the sensor's output information; m Sensor i The total number of different states or categories output is used to determine the number of states that need to be considered in entropy calculation; P k Sensor i Output status k The probability of state among all outputs. k The frequency of occurrence is between 0 and 1; log(Pk) State k The logarithm of probability reflects the state. k Information contribution; The sensor weights are updated using a Bayesian method, with the following formula: in, This refers to the sensor's response to given data. i The posterior probability of the weights; This represents the probability of observing data when the weight of sensor i is assumed to be a certain value; Sensor i Prior probabilities of weights; The final weight of the sensor is determined based on dynamic scoring, using the following formula: ； in, Sensor i The final weight; Sensor i The dynamic score reflects the overall performance of the sensor. The higher the score, the better the sensor's performance, and the weight should be increased accordingly. Sensor i The entropy value; the higher the entropy value, the more uncertain the information output by the sensor, and the lower the reliability.

9. A simulation system integrating virtual and physical drones, characterized in that, The virtual drone and physical drone fusion simulation method as described in any one of claims 1-8 includes: The actual drone parameter acquisition unit acquires the dynamic parameters of the actual drone and the kinematic parameters of the actual drone detected by the sensor modules set on the actual drone; The communication unit is used to send the dynamic parameters and kinematic parameters of the actual UAV to the simulation platform; The virtual drone modeling unit establishes a virtual drone model based on the dynamic parameters of the actual drone. The virtual drone sensor configuration unit configures the sensors of the virtual drone model based on the kinematic parameters of the actual drone. The virtual drone simulation unit selects a simulation environment based on the application scenario to achieve virtual drone fusion simulation.

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