CN114936455A - Unmanned aerial vehicle digital twin model construction method - Google Patents
Unmanned aerial vehicle digital twin model construction method Download PDFInfo
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Abstract
The invention discloses a method for constructing a digital twin model of an unmanned aerial vehicle, which belongs to the technical field of unmanned aerial vehicle modeling, wherein a design software is adopted to construct a digital twin three-dimensional geometric model of the unmanned aerial vehicle, so as to obtain a geometric twin mapping of a physical entity of the unmanned aerial vehicle, store the geometric twin mapping and introduce the geometric twin mapping into a phantom engine, and endow the physical attributes and behavior functional characteristics of the geometric model of the unmanned aerial vehicle; an unmanned aerial vehicle physical framework structure model is built based on a simulation tool, behavior characteristics of the unmanned aerial vehicle are reflected, a simulation actuator and a kinematics model are built based on the simulation tool according to the unmanned aerial vehicle physical framework structure model, and the unmanned aerial vehicle geometric model in the illusion engine is driven to move by feedback data of the simulation actuator and the kinematics model. The invention realizes the multi-dimensional mapping of the physical entity object in the aspects of geometric shape, physical property, behavior response and the like, and has better twin performance.
Description
Technical Field
The invention belongs to the technical field of unmanned aerial vehicle modeling, and particularly relates to a method for constructing a digital twin model of an unmanned aerial vehicle.
Background
Unmanned aerial vehicles, commonly referred to as drones, are aircraft that are maneuvered by ground equipment to perform flight missions, originally applied in the military field. With the rapid development of microsystems, artificial intelligence and sensing technology, quad-rotor unmanned aerial vehicle has also gained popularity, because it has advantages such as small, the quality is light, the disguise is strong, with low costs, simple structure, flexibility are good of size, its application is deepening and expanding constantly. Except basic aerial photography, agricultural plant protection, daily patrol and examine, police use fields such as security protection, four rotor unmanned aerial vehicle are in logistics distribution, and the emergency rescue field has also obtained new development. For example, the unmanned aerial vehicle carries a life detection instrument to sense vital signs by using the Doppler effect to search and rescue; when a fire disaster occurs at high altitude and high-rise buildings in a city or rescue tasks such as organizing mountains and sea, rescue can be carried out by using modes such as throwing life buoys and life saving ropes to trapped people or searching distress signals by using an unmanned aerial vehicle; and (3) adopting an unmanned aerial vehicle to transport articles such as virus samples, vaccines and medicines, or carrying a disinfectant to sterilize. Therefore, the unmanned aerial vehicle can be the essential high-tech medium-strength force in the field of future emergency rescue.
Although the functions and application fields of the unmanned aerial vehicle are more and more extensive, the environment faced during actual flight is increasingly complex, in the development process of the unmanned aerial vehicle, flight tests are difficult to be carried out in a real fire scene, a disaster area and a complex environment with large pedestrian flow, and a large amount of time period needs to be consumed. Therefore, the unmanned aerial vehicle performance test simulation platform aiming at the complex scene needs to be researched and developed urgently, and the unmanned aerial vehicle performance test simulation platform has profound significance for control algorithm optimization and complex application scene test flight.
The unmanned aerial vehicle simulation platform is continuously and iteratively replaced according to simulation requirements, the realized functions are different, and the simulation platform which is proposed at present mainly comprises a digital simulation platform and a semi-physical simulation platform. The digital simulation platform utilizes software to simulate various devices and environments, and the running process of the system does not contain any hardware real object. In 2018, the important significance of an unmanned aerial vehicle simulation platform on experimental teaching is considered, an open simulation platform facing course education is designed based on a Robot Operating System (ROS) such as Chenjin voice, the open simulation platform is suitable for various algorithms, but in order to improve the authenticity of data, the ROS environment required by a model is created by collecting environmental information in advance by using collecting equipment such as kinect, and the portability is poor. In 2020, a combined simulation environment is built based on Matlab and an Amesim platform, such as Takaki 29594c, and a flight control law and an airplane body model are respectively built for closed-loop simulation, although communication between platforms can be realized, the problem of poor real-time performance still exists. The hardware, such as an aircraft control core board or a related sensor, is added to the simulation system, and the hardware is connected to the hardware to acquire real data and replace part of simulation data. The dSPACE real-Time simulation system is developed by the German dSPACE company, and has strong calculation and code generation capabilities by connecting RTW (real Time workshop) toolboxes in MATLAB/Simulink for matching use, and the speed of real-Time simulation is greatly improved. A large amount of research and development work of unmanned aerial vehicle real-time simulation systems is conducted by various domestic colleges and universities and related research units based on real-time simulation equipment such as dSPACE, but the platforms are very expensive and have a long development period. Bin Hu and the like are combined with xPC and Simulink technologies to design a semi-physical simulation system of the unmanned aerial vehicle based on a rapid prototyping technology, so that the research and development period is shortened, but visual visualization of data cannot be realized due to lack of visual simulation. Garcia etc. has proposed rotor unmanned aerial vehicle and Gazebo emulation combined simulation platform and test method based on Pixhawk, and this platform has demonstrated simulation sensor and simulation model and has navigated and keep away the control effect of barrier to unmanned aerial vehicle under real condition, but the modeling approach adopts original mathematics modeling method, and the modeling means is single, leads to physical entity real-time operation operating mode and flight control model data to be the separation, and the information collection usability is low.
Disclosure of Invention
In order to solve the problems that the traditional unmanned aerial vehicle modeling method is single, the unmanned aerial vehicle digital model mapping is difficult to realize from multiple dimensions, the real-time data of a physical entity is separated from the model, and the information availability is low, the invention provides the unmanned aerial vehicle digital twin model construction method, the multi-dimensional mapping of the physical entity object in the aspects of geometric shape, physical attributes, behavior response and the like is realized, and the better twin performance is achieved.
The technical scheme adopted by the invention for solving the technical problem is as follows: a method for constructing an unmanned aerial vehicle digital twin model comprises the following steps: adopting design software to construct a digital twin three-dimensional geometric model of the unmanned aerial vehicle, obtaining a geometric twin mapping of a physical entity of the unmanned aerial vehicle, storing the geometric twin mapping and introducing the geometric twin mapping into a phantom engine, and endowing the geometric model of the unmanned aerial vehicle with physical attributes and behavior functional characteristics; an unmanned aerial vehicle physical framework structure model is built based on a simulation tool, behavior characteristics of the unmanned aerial vehicle are reflected, a simulation actuator and a kinematics model are built based on the simulation tool according to the unmanned aerial vehicle physical framework structure model, and the unmanned aerial vehicle geometric model in the illusion engine is driven to move by feedback data of the simulation actuator and the kinematics model.
As a further embodiment of the present invention, the drone physical frame structure model includes an actuator model, a kinematics/dynamics model, and a controller.
As a further embodiment of the present invention, the actuator model is represented by a first-order inertial element:
wherein: τ is a time constant, Ω represents the angular velocity generated by the brushless DC motor, and Ω 0 Representing the initial angular velocity of the brushless dc motor.
As a further embodiment of the invention, said kinematics/dynamics model comprises a rotor dynamics model of the unmanned aerial vehicle, a rigid body dynamics model, a rigid body kinematics model,
the unmanned rotor dynamics model is represented by the following equation:
wherein: t is i Represents the thrust generated by No. i brushless DC motor, omega i Representing the angular velocity generated by No. i brushless DC motor; k represents thrust force according to the rotation direction of the brushless DC motorFactor k 1 Or resistance factor k 2 Determined by the following formula:
wherein, C T Is the coefficient of thrust, C P Is the drag coefficient, ρ is the air density, and D is the propeller diameter.
As a further embodiment of the present invention, the rigid body dynamics model is represented as:
wherein, U 1 Indicating the total thrust produced by the propeller, U 2 Represents the total thrust, U, of the unmanned aerial vehicle during the implementation of the rolling motion 3 Total thrust, U, when representing unmanned aerial vehicle to implement pitching motion 4 Representing the total thrust when the drone achieves yawing motion.
As a further embodiment of the present invention, the rigid body kinematics model is represented by a combination of translational acceleration and rotational acceleration, and is modeled as:
wherein:
translational acceleration in three axial directions; phi, theta and psi sequentially represent the roll angle, the pitch angle and the yaw angle of the unmanned aerial vehicle;
sequentially representing the roll angular speed, the pitch angular speed and the yaw angular speed of the unmanned aerial vehicle;
angular acceleration representing roll, pitch and yaw motions of unmanned aerial vehicle in sequenceDegree, m represents unmanned aerial vehicle mass, g represents gravitational acceleration, l represents length of horn, J r Representing the moment of inertia of a brushless DC motor, I x 、I y And I z Inertia about the x, y and z axes is described, respectively.
As a further embodiment of the present invention, the controller employs a cascade control structure.
As a further embodiment of the present invention, the simulation tool communicates with the phantom engine through middleware to complete the digital mapping of the behavior of the unmanned aerial vehicle, so as to give the unmanned aerial vehicle a dynamic behavior characteristic of the geometric model, that is, the feedback data of the simulation actuator and the kinematic model drives the geometric model of the unmanned aerial vehicle in the phantom engine to move; and a fish-eye camera module is additionally arranged in the middleware and used for sensing the surrounding environment and feeding back the environment to the digital twin model.
As a further embodiment of the invention, the simulation actuator and kinematic model comprises an actuator module, a kinematic and dynamic module, a three-dimensional virtual simulation environment module; the actuator module inputs a pulse width PWM command signal sent by the controller, unmanned aerial vehicle state information fed back by the kinematics and dynamics module in real time and three-dimensional environment information sensed in the three-dimensional virtual simulation environment module, and outputs the signals as tension and moment; the kinematics and dynamics module inputs a reset signal and the pulling force and moment given by the actuator module and outputs the state information of the unmanned aerial vehicle; the three-dimensional virtual simulation environment module inputs state information of the unmanned aerial vehicle, visually displays the state information of the unmanned aerial vehicle, and outputs the three-dimensional environment information sensed by the fisheye camera module.
As a further embodiment of the present invention, the drone status information includes drone attitude and location information.
The beneficial effects of the invention include: the problem that an existing unmanned aerial vehicle modeling method is single and information of an entity is separated from a model is solved, an unmanned aerial vehicle digital twin model is built based on a digital twin technology, multi-dimensional mapping of an unmanned aerial vehicle physical entity object in geometrical shape, physical properties, behavioral response and the like is achieved, and the unmanned aerial vehicle digital twin model has better twin performance.
Drawings
FIG. 1a is a quad-rotor F450 drone propeller geometry diagram;
FIG. 1b is a geometric model diagram of a quad-rotor F450 unmanned aerial vehicle;
FIG. 2 is a diagram of a physical frame structure model of the UAV;
FIG. 3 is a block diagram of a cascade control of the controller;
FIG. 4 is a diagram of a simulated actuator and a kinematic model;
FIG. 5 is a diagram of a middleware model;
FIG. 6 is a diagram of a simulation experiment platform;
FIG. 7 is a graph of actual and desired attitude angles in flight;
fig. 8 is a diagram of the motion state of the unmanned aerial vehicle at the moment of 40 seconds;
fig. 9 is an architecture diagram of a flight simulation platform system of the unmanned aerial vehicle in embodiment 1.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and it should be understood that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the description of the present invention, it should be noted that the terms "vertical", "horizontal", "inside", "outside", and the like indicate orientations or positional relationships based on those shown in the drawings, and are only for convenience of describing the present invention and simplifying the description, but do not indicate or imply that the referred device or component must have a specific orientation, be constructed in a specific orientation, and be operated, and thus, should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and "third" are used merely to distinguish one element from another, and are not to be construed as indicating or implying relative importance.
In addition, the technical features involved in the different embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
Example 1
A method for constructing an unmanned aerial vehicle digital twin model aims to solve the problems that an unmanned aerial vehicle modeling method is single and entity and model information are separated, the unmanned aerial vehicle digital model construction method is researched from three aspects of geometry, physics and behavior, the unmanned aerial vehicle digital twin model is constructed based on a digital twin technology, a physical entity is represented in a digital mode, and multi-dimensional mapping of physical entity objects in the aspects of geometry, physical attributes, behavior response and the like is achieved.
1. Geometric model
In the embodiment, a four-rotor F450 unmanned aerial vehicle is used as an object, and a digital twin three-dimensional geometric model of the unmanned aerial vehicle is constructed by adopting CATIA software. Preferably, in order to solve the problem of modeling the curved surfaces of the propeller and the horn, a curved surface modeling technology is adopted to build a geometric model of the four-rotor propeller shown in fig. 1(a), the geometric model is consistent with physical entity objects in the aspects of appearance, size, shape and the like, similarly, model accessories such as a fuselage, a horn, a foot rest and the like are respectively built and are assembled according to the actual size and the geometric position relation, so that the geometric twin mapping of the physical entity of the four-rotor F450 unmanned aerial vehicle shown in fig. 1(b) is obtained, stored as an STL format file and introduced into a virtual Engine UE4(Unreal Engine 4), a new skeleton layer is set through blueprint types, the problem of attribute calibration is solved, and the physical attributes and behavior function characteristics of the geometric model F450 are endowed.
2. Physical mathematical model
To implement the behavioral mapping of the F450 model, the embodiment designs a physical framework structure model of the quad-rotor drone, which can reflect the behavioral characteristics of the drone, including an actuator model, a kinematics/dynamics model, and a controller model, as shown in fig. 2.
The unmanned aerial vehicle actuator mainly bears the effect that controller PWM signal turns into motor drive power, and its physics mathematical model can simplify to the inertia link of first order and show:
wherein: τ isTime constant, Ω represents the angular velocity generated by the brushless DC motor, Ω 0 Representing the initial angular velocity of the brushless dc motor.
The kinematics/dynamics model comprises an unmanned aerial vehicle rotor dynamics model, a rigid body dynamics model, and a rigid body kinematics model, wherein the unmanned aerial vehicle rotor dynamics model is represented by formula (2):
wherein: t is i Represents the thrust generated by No. i brushless DC motor, omega i Representing the angular speed generated by the No. i brushless direct current motor; k represents a thrust factor k according to the rotation direction of the brushless DC motor 1 Or resistance factor k 2 Determined by equation (3):
wherein, C T Is the coefficient of thrust, C P Is the drag coefficient, ρ is the air density, and D is the propeller diameter. In order to objectively reflect the behavior and design performance of the physical entity of the propeller, the present embodiment adopts F450 physical entity data as modeling data, as shown in table 1:
TABLE 1 Propeller characteristics Table
The thrust required by the ascending, rolling, pitching and yawing motions of the unmanned aerial vehicle is determined by the rotating speeds of the four brushless direct current motors, and the model can be modeled as follows:
wherein, U 1 Indicating the total thrust produced by the propeller, U 2 Represents the total thrust, U, of the unmanned aerial vehicle during the implementation of the rolling motion 3 Total thrust, U, when representing unmanned aerial vehicle to implement pitching motion 4 Representing the total thrust when the drone achieves yawing motion.
The four-rotor rigid body kinematics model is described by adopting a combination form of translational acceleration and rotational acceleration, and can be modeled as follows:
wherein:
translational acceleration in three axes; phi, theta, psi phi sequentially represent the roll angle, the pitch angle and the yaw angle of the unmanned aerial vehicle;
sequentially representing the roll angular speed, the pitch angular speed and the yaw angular speed of the unmanned aerial vehicle;
sequentially representing the angular acceleration of the rolling, pitching and yawing motions of the unmanned aerial vehicle, wherein m represents the mass of the unmanned aerial vehicle, g represents the gravity acceleration, l represents the length of the arm, and J r Representing the moment of inertia of a brushless DC motor, I x 、I y And I z Inertia about the x, y and z axes is described, respectively.
In order to increase the response speed of the system and reduce the influence of the hysteresis of the excessive integration on the response performance of the system, the controller of the embodiment adopts a cascade control structure as shown in fig. 3. The attitude adjustment is accelerated through the control of the outer ring proportion P, the inner ring uses PID (proportion integration differentiation) to adjust and eliminate the phenomena of oscillation and overshoot in the flight of the quad-rotor unmanned aerial vehicle, and the robustness of the system is improved.
F450 unmanned aerial vehicle behavior digital mapping
According to the physical framework structure model of the quad-rotor unmanned aerial vehicle, simulation actuators and kinematic models shown in the figure 4 are built on the basis of Simulink by using an Aerospace Block and a UAV Toolbox, wherein the simulation actuators and kinematic models comprise an actuator module, a kinematic and dynamic module and a three-dimensional virtual simulation environment module.
In order to endow the geometric model of the F450 drone with dynamic behavior characteristics, in this embodiment, a middleware as shown in fig. 5 is designed based on C + + dynamic DLL communication, so as to solve the problem of a communication interface between Simulink and the UE4, and implement digital mapping of the behavior of the F450 drone, that is, the geometric model of the F450 drone in the UE4 is driven to move by feedback data of the simulation actuator and the kinematic model in fig. 4.
In order to complete the immersive interaction between the digital model and the virtual environment in the unmanned aerial vehicle simulated flight simulation experiment, a fisheye camera module is additionally arranged in the middleware and used for sensing the surrounding environment and feeding back the sensed surrounding environment to the twin model, so that the interaction between the unmanned aerial vehicle and the three-dimensional environment is realized.
The actuator module inputs a pulse width PWM command signal sent by the controller, unmanned aerial vehicle state information fed back by the kinematics and dynamics module in real time and three-dimensional environment information sensed in the three-dimensional virtual simulation environment module, and outputs the signals as tension and moment;
the kinematics and dynamics module inputs a reset signal and the pulling force and moment given by the actuator module and outputs the state information of the unmanned aerial vehicle;
the three-dimensional virtual simulation environment module inputs state information of the unmanned aerial vehicle, visually displays the state information of the unmanned aerial vehicle and outputs three-dimensional environment information sensed by the fisheye camera module;
in the above embodiment, the unmanned aerial vehicle status information includes unmanned aerial vehicle attitude and position information.
The embodiment is applied to an unmanned aerial vehicle flight simulation platform, and the unmanned aerial vehicle flight simulation platform comprises a physical layer, a virtual layer, a transmission layer and a service layer, wherein the physical layer, the virtual layer, the transmission layer and the service layer are displayed and interacted from initial physical entity data acquisition to final service layer, and the display and interaction are shown in fig. 9.
The physical layer is the basis of the whole platform and is also the only source for acquiring real sensing data, and information such as Pixhawk sensing posture and position is adopted in the embodiment and transmitted to the virtual layer in real time. The virtual layer is a core part of the whole platform and is composed of the unmanned aerial vehicle digital twin model constructed by the embodiment and the three-dimensional virtual environment model, so that accurate modeling of the physical entity and reproduction of a real scene are realized. The transmission layer is a bridge and a link between the physical layer and the virtual layer, and realizes data bidirectional transmission by adopting a Socket communication scheme through a transmission network and a defined data interface. The service layer is a man-machine interaction interface, and the system control and running state reproduction functions are realized by utilizing the ground station.
Example 2
This example presents experimental and performance analysis protocols.
1. Experimental Environment and configuration
In order to verify the effectiveness of the simulation platform, an interactive test experiment and a flight attitude control experiment of the unmanned aerial vehicle digital twin model in a virtual scene are performed. As shown in fig. 6, the simulation experiment platform is composed of a Pixhawk autopilot, a digital twin model of the unmanned aerial vehicle, a three-dimensional virtual environment digital mapping model (UE4 twin three-dimensional virtual scene), a remote controller of the unmanned aerial vehicle F450, and a receiver. The PC configuration for operating the unmanned aerial vehicle digital twin model and the three-dimensional virtual scene is as follows: intel core i58300H processor, 16G memory, NVIDIA GeForce GTX 1060 display card.
2. Digital twin flight attitude performance test
In order to verify the attitude tracking performance of the unmanned aerial vehicle digital twin body, the roll angle error, the pitch angle error and the yaw angle error between the expected attitude and the actual attitude are analyzed respectively, and the result is shown in fig. 7. Lifting height of the refueling door is only 0-20 seconds, posture transformation is not carried out, and a pitch angle, a roll angle and a yaw angle are all 0 degree; the attitude angle is randomly adjusted for multiple times in the flying process of 20-120 seconds, the actual pitch angle, the actual roll angle and the actual yaw angle change along with the expected attitude curve, the expected attitude angle can be achieved within 1s, the overshoot is less than 2%, and the steady-state error is almost avoided.
In the test process, the motion state of the unmanned aerial vehicle at the moment of 40 seconds in fig. 7 is selected as a reference, and the real-time motion state display effect at the moment is verified in the three-dimensional virtual scene, wherein the roll angle is 15 degrees, the pitch angle is 8 degrees, the yaw angle is 20 degrees, and the test is shown in fig. 8.
The result shows that the digital twin model of the quad-rotor unmanned aerial vehicle can well follow the attitude control instruction of the remote controller, the virtual simulation attitude is consistent with the change of an expected attitude curve, the information of the physical entity is consistent with that of the twin model, the information utilization rate is high, the attitude transformation can be carried out in real time in a three-dimensional environment, and the effectiveness of the digital twin model of the unmanned aerial vehicle is verified.
Example 3
The method for constructing a three-dimensional virtual environment model according to embodiment 1:
the establishment of the virtual world capable of mapping the physical world is a foundation for unmanned aerial vehicle visual simulation, and therefore the twin virtual model of the real flying scene is established in the embodiment. The most important construction element of the virtual scene is a building, a simulation object is constructed by using 3ds Max, and although a real effect can be obtained, the modeling speed is too slow. Therefore, the embodiment provides a multivariate fusion three-dimensional visual virtual scene modeling method, which is divided into landmark buildings (such as libraries and stadiums) and non-landmark buildings (such as dormitory buildings and teaching buildings) according to the building characteristic attribute similarity normalization principle. Carrying out fine modeling on the landmark building by using 3ds Max, extracting a vector grid of the building from a remote sensing image, carrying out large-range modeling on the non-landmark building by using City Engine software, and carrying out integrated multi-element fusion on the models of the 3ds Max and the City Engine in UE 4.
1. Refined modeling
Firstly, performing fine modeling on important buildings in a scene, taking an N campus library as an example, importing CAD data of the important buildings into a 3ds Max, and after the processes of capturing, extruding, chamfering and inserting, using a rectangular tool to outline the edge of a wall body. Secondly, according to the height and the structure of the top surface of the building, different materials and texture elements are added in different areas of the model, so that the texture and the fidelity of the main body of the building are improved.
In order to reduce redundancy and complexity of the model and improve the running speed of the model, the embodiment provides a curved surface quantity optimization method. In the modeling process, invisible inner surfaces in splicing areas of connected buildings are removed, the generation of invalid curved surfaces is reduced, and redundancy caused by massive copying of structured models is avoided. For horizontal and vertical structures, the number of Boolean operations is minimized, reducing the complexity of the model. In order to reduce the number of models to the maximum extent, fine threshold values in the model modifier are optimized, and the operation speed of the models is improved on the basis of guaranteeing the authenticity of buildings.
2. Large scale modeling
The large-range modeling mainly solves the problem of non-landmark building, tree and road modeling, and in order to improve the modeling speed, the City Engine is used for large-range vector data modeling based on the rule method. In the traditional vector data extraction method of the oblique photography technology based on the building roof, the modeling deviation can be caused by the problems of building inclination, displacement and loss. In the embodiment, the building bottom is used as a standard, the FAME-Net network is used for training the aviation remote sensing image building data set, the extraction deviation existing in the traditional method is avoided, and the vector data of the building is extracted.
The cga (computer Generated architecture) rule is the core of building a large-scale modeling method, mainly focusing on modeling speed and efficiency, and omitting part of detail information of a building. Therefore, the modeling rules are compiled according to the structure type, the floor height and the roof color of the building, the building of the corresponding type is rapidly generated in a large batch, the building details are related to the constraint force of the rules, and the more the rules are, the more the model details are depicted.
In this embodiment, taking an N campus scenario as an example, in order to establish a CGA rule, it is necessary to find out a relationship between the number of floors of a building and the height, measure the height and the number of floors of some buildings in a research area, and draw table 2.
TABLE 2 building height and number of floors
The relationship between building height and floor number was obtained by fitting according to table 2, as shown below:
H＝3.46N+0.69(6)
the building is specifically split into small structural components, large-range rule construction is carried out according to the building structure, the height of a floor and the color, and texture mapping is carried out on doors, windows, roofs and outer walls of the building by using a mapping function.
In addition, in addition to the building model, the flower, grass, tree, street lamp and road parts are established using existing rules to generate a three-dimensional virtual scene for the campus N.
3. Model multivariate fusion
In order to improve the immersion and the interactivity of the simulation, DEM (digital Elevation map) digital Elevation data is imported into the UE4 to carry out rugged terrain design. In the embodiment, the GDEMV2 elevation data set is used as an original data source, but the running speed of a subsequent virtual scene is influenced by massive DEM data, and meanwhile, the data size describing a flat terrain and a complex area is different, so that the original DEM data needs to be processed. Therefore, the embodiment performs interpolation and noise reduction processing on the original data, and improves the data utilization rate. Then, the terrain data is imported into a Global Mapper for three-dimensional expansion to obtain a terrain file in hfz format. Then, according to the width and height of the elevation map, the resolution and the data range are set in the World Machine to obtain a height map file compatible with the RAW16 format of the UE 4. In order to perform multi-element fusion of scenes in UE4, a height map in a RAW16 format is imported, materials corresponding to remote sensing images are selected to create a concave-convex real three-dimensional terrain, buildings constructed by 3ds Max and type Engine are imported into UE4 according to the same proportion and placed on the created three-dimensional terrain, collision setting is added among different objects in order to solve dynamic interaction among models, and conversion of scenes from two-dimensional static state to three-dimensional dynamic state is achieved.
Example 4
Virtual scene immersion and interaction performance testing:
in order to carry out immersion and interactive test of a virtual scene, the digital model of the unmanned aerial vehicle is operated in the constructed N campus portal scene to carry out autonomous flight, and as the construction of the three-dimensional virtual scene is real scene twin mapping, mountains and terrain are consistent with real environment, elements such as trees, red flags and the like in the scene can move along with wind, the whole scene has better immersion. When the unmanned aerial vehicle is in the virtual scene and perceives the obstacle, due to collision setting in the scene, the action of avoiding the obstacle is executed at that time, the unmanned aerial vehicle has better interactivity with the environment, and technical support can be provided for performance tests such as three-dimensional survey, obstacle avoidance and the like.
It should be understood that the above examples are only for clarity of illustration and are not intended to limit the embodiments. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. And obvious variations or modifications therefrom are within the scope of the invention.
Claims (10)
1. A method for constructing a digital twin model of an unmanned aerial vehicle is characterized by comprising the following steps: adopting design software to construct a digital twin three-dimensional geometric model of the unmanned aerial vehicle, obtaining a geometric twin mapping of a physical entity of the unmanned aerial vehicle, storing the geometric twin mapping and introducing the geometric twin mapping into a phantom engine, and endowing the geometric model of the unmanned aerial vehicle with physical attributes and behavior functional characteristics; an unmanned aerial vehicle physical framework structure model is built based on a simulation tool, behavior characteristics of the unmanned aerial vehicle are reflected, a simulation actuator and a kinematics model are built based on the simulation tool according to the unmanned aerial vehicle physical framework structure model, and the unmanned aerial vehicle geometric model in the illusion engine is driven to move by feedback data of the simulation actuator and the kinematics model.
2. The unmanned aerial vehicle digital twin model construction method as claimed in claim 1, wherein the unmanned aerial vehicle physical frame structure model comprises an actuator model, a kinematics/dynamics model and a controller.
3. The method for constructing the unmanned aerial vehicle digital twin model according to claim 2, wherein the actuator model is represented by a first-order inertia link:
wherein: τ is a time constant, Ω represents the angular velocity generated by the brushless DC motor, and Ω 0 Representing the initial angular velocity of the brushless dc motor.
4. The method of claim 2, wherein the kinematics/dynamics model comprises a rotor dynamics model of the UAV, a rigid dynamics model, a rigid kinematics model,
the model of unmanned rotor dynamics is represented by the following equation:
wherein: t is i Represents the thrust generated by No. i brushless DC motor, omega i Representing the angular speed generated by the No. i brushless direct current motor; k represents a thrust factor k according to the rotation direction of the brushless DC motor 1 Or resistance factor k 2 Determined by the following formula:
wherein, C T Is the coefficient of thrust, C P Is the drag coefficient, ρ is the air density, and D is the propeller diameter.
5. The unmanned aerial vehicle digital twin model construction method of claim 4, wherein the rigid body dynamics model is expressed as:
wherein, U 1 Indicating the total thrust produced by the propeller, U 2 Total thrust, U, representing the rolling motion of the drone 3 Means of noTotal thrust U when pitch motion is implemented by man-machine 4 Representing the total thrust when the drone achieves yawing motion.
6. The unmanned aerial vehicle digital twin model construction method according to claim 4, wherein the rigid body kinematics model is expressed by a combination form of translational acceleration and rotational acceleration, and is modeled as:
wherein:
translational acceleration in three axial directions; phi, theta, psi phi sequentially represent the roll angle, the pitch angle and the yaw angle of the unmanned aerial vehicle;
sequentially representing the roll angular speed, the pitch angular speed and the yaw angular speed of the unmanned aerial vehicle;
sequentially representing the angular acceleration of the rolling, pitching and yawing motions of the unmanned aerial vehicle, wherein m represents the mass of the unmanned aerial vehicle, g represents the gravity acceleration, l represents the length of the arm, and J r Representing the moment of inertia of a brushless DC motor, I x 、I y And I z Inertia about the x, y and z axes is described, respectively.
7. The unmanned aerial vehicle digital twin model building method as claimed in claim 2, wherein the controller adopts a cascade control structure.
8. The unmanned aerial vehicle digital twin model building method according to claim 2, wherein the simulation tool communicates with the phantom engine through middleware to perform unmanned aerial vehicle behavior digital mapping so as to endow the unmanned aerial vehicle with a geometric model with dynamic behavior characteristics, that is, feedback data of a simulation actuator and a kinematic model drives the unmanned aerial vehicle geometric model in the phantom engine to move; and a fish-eye camera module is additionally arranged in the middleware and used for sensing the surrounding environment and feeding back the environment to the digital twin model.
9. The unmanned aerial vehicle digital twin model construction method of claim 8, wherein the simulation actuator and kinematic model comprises an actuator module, a kinematic and dynamic module, and a three-dimensional virtual simulation environment module; the actuator module inputs a pulse width PWM command signal sent by the controller, unmanned aerial vehicle state information fed back by the kinematics and dynamics module in real time and three-dimensional environment information sensed in the three-dimensional virtual simulation environment module, and outputs the signals as tension and moment; the kinematics and dynamics module inputs a reset signal and the pulling force and moment given by the actuator module and outputs the state information of the unmanned aerial vehicle; the three-dimensional virtual simulation environment module inputs state information of the unmanned aerial vehicle, visually displays the state information of the unmanned aerial vehicle, and outputs the three-dimensional environment information sensed by the fisheye camera module.
10. The method as claimed in claim 9, wherein the drone status information includes drone attitude and position information.
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