CN113110107A - Unmanned aerial vehicle flight control simulation system, device and storage medium - Google Patents
Unmanned aerial vehicle flight control simulation system, device and storage medium Download PDFInfo
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Abstract
The application discloses unmanned aerial vehicle flight control simulation system, equipment and storage medium belongs to electronic equipment technical field. The application provides an unmanned aerial vehicle flight control simulation system, can be in every cycle, based on the navigation data of control command and last cycle, through flight control module, the navigation module, sensor model, aircraft model and actuator model, carry out flight control's simulation to unmanned aerial vehicle, thereby realize the emulation to flight control system, because sensor model, aircraft model and actuator model are the model based on the software mode realization, the cost of carrying out flight control system simulation test has been reduced, and the operation model need not spend a large amount of time and prepares, the purpose of improvement simulation test efficiency has been realized.
Description
Technical Field
The application relates to the technical field of electronic equipment, in particular to an unmanned aerial vehicle flight control simulation system, unmanned aerial vehicle flight control simulation equipment and a storage medium.
Background
Along with the rapid development of electronic equipment technology, various unmanned aerial vehicles appear, and the flight control system is the important assurance that unmanned aerial vehicle flies safely, effectively accomplishes the task, for guaranteeing flight control system's reliability, often can test it through flight control simulation system. In the related art, a semi-physical simulation system is adopted to simulate a flight control system of an unmanned aerial vehicle, and a conventional semi-physical simulation system comprises a remote controller, a remote controller control receiver, a ground station, a flight controller, at least one unmanned aerial vehicle sensor, an actuating mechanism, a simulation computer, a rotary table, a Global Positioning System (GPS) simulation system, an atmospheric simulation system and a comprehensive display.
In the technology, the cost of equipment required for building the semi-physical simulation system is high, and the building process is complicated, so that the simulation test of the flight control system is high in cost and low in efficiency.
Disclosure of Invention
The embodiment of the application provides an unmanned aerial vehicle flight control simulation system, an unmanned aerial vehicle flight control simulation device and a storage medium, and the unmanned aerial vehicle flight control simulation system can simulate the flight control of an unmanned aerial vehicle in each period, so that the high-efficiency and low-cost unmanned aerial vehicle flight control system simulation test is realized. The technical scheme is as follows:
in one aspect, an unmanned aerial vehicle flight control simulation system is provided, and the unmanned aerial vehicle flight control simulation system includes: a flight control module, an actuator model, an airplane model, a sensor model and a navigation module,
the flight control module is used for sending actuator data of a second period to the actuator model based on a control command and navigation data of the unmanned aerial vehicle in a first period output by the navigation module, wherein the navigation data of the unmanned aerial vehicle in the first period comprises at least one of a first position, a first attitude and a first speed of the unmanned aerial vehicle, the actuator data is used for representing kinetic energy parameters required to be output for executing the control command, and the second period is the next period of the first period;
the actuator model is used for determining the operating parameters of the power element of the unmanned aerial vehicle based on the actuator data of the second period of the flight control module and sending the operating parameters to the airplane model;
the aircraft model is used for acquiring simulated state data of the unmanned aerial vehicle based on the operation parameters and sending the simulated state data to the sensor model, wherein the simulated state data comprises at least one of a calculated position, a calculated attitude, a calculated speed, a calculated acceleration and a calculated angular velocity of the unmanned aerial vehicle;
the sensor model is used for determining sensor reading of at least one sensor based on the simulation state data and sending the sensor reading to the navigation module;
the navigation module is configured to determine navigation data of the drone within the second period based on the sensor reading, the navigation data of the drone within the second period including at least one of a second position, a second attitude, and a second velocity of the drone.
In some embodiments, the actuator model is derived based on fitting real actuator data to real power parameters.
In some embodiments, the model aircraft comprises:
the attitude sub-model is used for acquiring an Euler angle of the unmanned aerial vehicle based on the propeller torque and the rotational inertia of the unmanned aerial vehicle, and the Euler angle is used for representing the calculated attitude of the unmanned aerial vehicle;
the speed submodel is used for acquiring the calculated speed of the unmanned aerial vehicle based on the Euler angle and the operation parameters;
and the position sub-model is used for acquiring the calculated position of the unmanned aerial vehicle based on the calculated speed.
In some embodiments, the attitude sub-model is configured to obtain a calculated angular velocity of the drone based on a propeller torque and a moment of inertia of the drone; based on the calculated angular velocity, the euler angle of the unmanned aerial vehicle is obtained.
In some embodiments, the velocity sub-model is configured to obtain a rotation matrix of the drone based on the euler angle, the rotation matrix being used to represent a calculated pose of the drone; acquiring the calculated acceleration of the unmanned aerial vehicle based on the rotation matrix and the operation parameters; based on the calculated acceleration, the calculated speed of the unmanned aerial vehicle is obtained.
In some embodiments, the location submodel is configured to obtain a wind speed during the second period based on the location of the drone during the first period; and acquiring the calculated position of the unmanned aerial vehicle based on the wind speed in the second period and the calculated speed.
In some embodiments, the sensor model is to determine accelerometer readings based on the calculated acceleration; determining a gyroscope reading based on the calculated angular velocity; based on the calculated attitude, magnetometer readings are determined.
In some embodiments, the drone flight control simulation system further includes: a remote controller, a receiver corresponding to the remote controller and a ground station,
the remote controller is used for sending the control instruction to the receiver;
the receiver is used for sending the received control instruction to the flight control module;
the ground station is used for sending the control command to the flight control module.
In one aspect, a computer device is provided that includes one or more processors and one or more memories having at least one computer program stored therein that is loaded and executed by the one or more processors to implement operations performed by the unmanned aerial vehicle flight control simulation system.
In one aspect, a computer-readable storage medium having at least one computer program stored therein is provided, the at least one computer program being loaded and executed by a processor to perform operations performed by the drone flight control simulation system.
In one aspect, a computer program product is provided that includes at least one computer program stored in a computer readable storage medium. The at least one computer program is read by a processor of the computer device from a computer-readable storage medium, and the at least one computer program is executed by the processor to cause the computer device to implement the operations performed by the drone flight control simulation system.
The application provides an unmanned aerial vehicle flight control simulation system, can be in every cycle, based on the navigation data of control command and last cycle, through flight control module, the navigation module, sensor model, aircraft model and actuator model, carry out flight control's simulation to unmanned aerial vehicle, thereby realize the emulation to flight control system, because sensor model, aircraft model and actuator model are the model based on the software mode realization, the cost of carrying out flight control system simulation test has been reduced, and the operation model need not spend a large amount of time and prepares, the purpose of improvement simulation test efficiency has been realized.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic structural diagram of a flight control simulation system of an unmanned aerial vehicle according to an embodiment of the present application;
fig. 2 is a schematic data flow diagram of a flight control simulation system of an unmanned aerial vehicle according to an embodiment of the present application;
FIG. 3 is a diagram illustrating a data fitting result provided by an embodiment of the present application;
FIG. 4 is a schematic structural diagram of a sensor model provided in an embodiment of the present application;
FIG. 5 is a flow chart of a model transformation provided by an embodiment of the present application;
fig. 6 is a schematic structural diagram of a computer device according to an embodiment of the present application.
Detailed Description
To make the purpose, technical solutions and advantages of the present application clearer, the following will describe embodiments of the present application in further detail with reference to the accompanying drawings, and it is obvious that the described embodiments are some, but not all embodiments of the present application. 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 application.
The terms "first," "second," and the like in this application are used for distinguishing between similar items and items that have substantially the same function or similar functionality, and it should be understood that "first," "second," and "nth" do not have any logical or temporal dependency or limitation on the number or order of execution.
Fig. 1 is a schematic structural diagram of a flight control simulation system of an unmanned aerial vehicle according to an embodiment of the present application, and referring to fig. 1, the flight control simulation system of the unmanned aerial vehicle includes a computer device 100, and the computer device 100 is capable of providing an embedded environment.
A flight controller is run on the computer apparatus 100, the flight controller including: flight control module 101, navigation module 102 and aircraft simulation model 103, this aircraft simulation model 103 includes: an actuator model 103A, an airplane model 103B, and a sensor model 103C.
In some embodiments, the drone flight control simulation system further includes: a remote control 104, a receiver 105 corresponding to the remote control, and a ground station 106. The remote controller 104 is configured to send a control instruction to the receiver 105, and the receiver 105 is configured to send the received control instruction to the flight control module 101. The ground station 106 may be a desktop computer, a notebook computer, or a mobile terminal, and is configured to send a control instruction to the flight control module 101 and receive status data sent by the flight control module 101.
In some embodiments, the flight control module 101 is also configured to transmit status data to the ground station 106.
It should be noted that the remote controller 104 and the ground station 106 may be the same hardware device or different hardware devices, which is not limited in this embodiment of the present invention.
Based on the structure shown in fig. 1, fig. 2 is a schematic data flow diagram of an unmanned aerial vehicle flight control simulation system provided in the embodiment of the present application, and the embodiment of the present application takes a first period and a second period as examples to describe in detail each functional module of the unmanned aerial vehicle flight control simulation system, and the second period is the next period of the first period.
In the first period, the description of each functional module in the flight control simulation system of the unmanned aerial vehicle is as follows:
in some embodiments, the flight control module 101 is configured to simulate flight control of the drone during the first period. Illustratively, the flight control module 101 is configured to send first actuator data to the actuator model 103A based on the first control command and the navigation data of the drone in the last period sent by the navigation module 102, where the navigation data of the drone in the last period includes at least one of a position, an attitude, and a speed of the drone in the last period, and the first actuator data is used to represent a kinetic energy parameter required to be output to execute the first control command.
In some embodiments, the actuator model 103A is used to simulate the power output of the drone during the first period. Illustratively, the actuator model 103A is configured to determine a first operating parameter of the power element of the drone based on the first actuator data sent by the flight control module 101, which is sent to the aircraft model 103B. Wherein the first actuator data includes a first Pulse Width Modulation (PWM) waveform and a first voltage, and the first operating parameter includes a first motor speed and a first propeller lift.
In some embodiments, the actuator model 103A is model data for implementing a law equation based on fitting real actuator data to real operating parameters, the actuator model 103A being capable of calculating the first operating parameter based on the first actuator data. The process of fitting the law formula includes: PWM waveforms with different frequencies and voltages with different values are input into a motor of a real unmanned aerial vehicle, a rotating speed sensor and a force sensor are adopted, the rotating speed of the motor and the propeller lift force of the unmanned aerial vehicle are measured, and therefore a plurality of real actuator data and a plurality of corresponding real operation parameters are obtained. And fitting the obtained real data based on the corresponding relation between the actuator data and the operation parameters to obtain a rule formula according with the corresponding relation.
For example, the correspondence includes any one of:
along with the rise of the frequency of the PWM waveform, the rotating speed of the motor and the lift force of the propeller are increased, and the increasing speed of the rotating speed of the motor and the lift force of the propeller is reduced;
as the voltage increases, the motor speed and the propeller lift increase.
It should be noted that the rule formula includes a plurality of fitting parameters, and the fitting parameters enable the rule formula to conform to the correspondence, and the values of the fitting parameters are not limited in this embodiment.
Fig. 3 is a schematic diagram of a data fitting result provided by an embodiment of the present application, as shown in fig. 3, which shows a fitting result of real actuator data and real propeller lift force, where a circle represents the real data and a gray plane represents the fitting result. The actuator model is established by collecting real data, and the consistency of the established actuator model and the real power model can be ensured.
In some embodiments, the airplane model 103B is used to simulate the actual state of motion of the drone during the first period. Illustratively, the aircraft model 103B is configured to obtain first simulated state data of the drone based on the first operating parameter sent by the actuator model 103A, send the first simulated state data to the sensor model 103C, the first simulated state data including at least one of a first calculated position, a first calculated attitude, a first calculated velocity, a first calculated acceleration, and a first calculated angular velocity of the drone.
In some embodiments, the airplane model 103B includes: a posture sub-model, a speed sub-model and a position sub-model.
In some embodiments, a pose sub-model is used to simulate the pose of the drone over a first period. Illustratively, this gesture submodel is used for obtaining this unmanned aerial vehicle's first calculation angular velocity based on this unmanned aerial vehicle's screw torque and inertia, based on this first calculation angular velocity, obtains this unmanned aerial vehicle's first euler angle, and this first euler angle is used for representing this unmanned aerial vehicle's first calculation gesture.
Wherein, the method for obtaining the calculated angular velocity of the unmanned aerial vehicle is shown as a formula (1),
Jω’＝M (1)
in formula (1), M ═ Mx，My，Mz]The propeller torques of the drone in the x-axis, y-axis, and z-axis directions are indicated, and J ═ Jx，Jy，Jz]The inertia moment of the drone in the x-axis, y-axis, and z-axis directions is represented, and ω ═ ωx，ωy，ωz]The first calculated angular velocity in the x-axis, y-axis, and z-axis directions is represented, and ω' represents a derivation operation for ω and also represents a first calculated angular acceleration of the drone.
It should be noted that, above-mentioned this unmanned aerial vehicle's screw torque and inertia all can obtain through experimental measurement.
Wherein, the method for obtaining the first Euler angle of the unmanned aerial vehicle is shown as formula (2),
θ’＝w (2)
in the formula (2), θ ═ θx，θy，θz]The first euler angles in the x-axis, y-axis, and z-axis directions are expressed, the euler angle in the x-axis direction is referred to as a roll angle (roll), the euler angle in the y-axis direction is referred to as a pitch angle (pitch), the euler angle in the z-axis direction is referred to as a heading angle (yaw), and θ' represents a derivation operation on θ, and may also represent a low calculated angular velocity of the drone.
In some embodiments, a speed sub-model is used to simulate the speed of the drone over a first period. Illustratively, the velocity sub-model is configured to obtain a first rotation matrix of the drone based on the first euler angle, the first rotation matrix being used to represent a first calculated pose of the drone; acquiring a first calculated acceleration of the unmanned aerial vehicle based on the first rotation matrix and the first operation parameter; based on the first calculated acceleration, a first calculated speed of the unmanned aerial vehicle is obtained.
The first euler angle and the first rotation matrix can be used for representing a first calculated attitude of the unmanned aerial vehicle, that is, the first euler angle and the first rotation matrix can be mutually converted, so that the first rotation matrix can be calculated based on the first euler angle, and calculation of a subsequent first calculated acceleration and determination of a sensor reading can be performed. The first rotation matrix may be a 3 × 3 matrix or a 1 × 4 vector. For example, if the first euler angle w is [0.5, 0.6, 0.7], the first rotation matrix is calculated as 3 × 3 as follows:
or, a vector of 1 x 4 as follows:
[0.8946，0.1238，0.3500，0.2487]
wherein, the method for obtaining the first calculated acceleration of the unmanned aerial vehicle is shown in formula (3),
in formula (3), v '═ v'x，v’y，v’z]The first calculated acceleration in the directions of the x-axis, the y-axis and the z-axis is shown, g represents the gravitational acceleration, and the value of e is 9.813＝[0，0，1]F denotes a first propeller lift, m denotes the mass of the drone, which can be obtained by experimental measurements, R denotes a first rotation matrix of 3 x 3.
Obtaining a first calculated speed of the unmanned aerial vehicleThe method comprises the following steps: integrating the calculated acceleration v' to obtain a first calculated speed v, v ═ v [ v ]x，vy，vz]First calculation speeds in the x-axis, y-axis, and z-axis directions are shown.
In some embodiments, a position sub-model is used to simulate the position of the drone during a first period. Illustratively, the position sub-model is configured to obtain a first wind speed based on a calculated position of the drone during a previous period; based on the first wind speed and the first calculated speed, a first calculated position of the drone is obtained.
Wherein, the method for obtaining the first wind speed is shown in formula (4),
vwind＝(v0+p’zk)e1 (4)
in the formula (4), vwindRepresenting a first wind speed, v0Represents the constant wind speed in the x-axis direction required when developing the flight control system, and optionally the constant wind speed is 10m/s, p'zIn the last period, the position of the unmanned aerial vehicle in the z-axis direction, namely the height of the unmanned aerial vehicle, k is a height coefficient, and optionally, the value range of k is greater than or equal to 0.1 and less than or equal to 0.5, and e1＝[1，0，0]。
It should be noted that, if the first period is an initial period, the method for obtaining the first wind speed is as shown in equation (5),
vwind＝(v0+hk)e1 (5)
in equation (5), h represents the preset initial height of the drone.
Wherein, the method for obtaining the calculated position of the unmanned aerial vehicle is shown in formula (6),
p’＝v+vwind (6)
in formula (6), p ═ px，py，pz]The calculated positions of the unmanned aerial vehicle on the x axis, the y axis and the z axis are represented, and p' represents the derivation operation on p.
Through above-mentioned gesture submodel, speed submodel and position submodel, can obtain this unmanned aerial vehicle's first simulation state data, this first simulation state data include first calculation position, first calculation gesture, first computational speed, first calculation acceleration and first calculation angular velocity at least one.
In some embodiments, the sensor model 103C is used to simulate real sensors of the drone. Illustratively, the sensor model 103C is configured to determine a first sensor reading of at least one sensor based on the first simulated state data sent by the aircraft model 103B, which is sent to the navigation module 102.
For example, if the sensor model 103C is an inertial sensor unit (IMU) model, the sensor model 103C is configured to determine a first accelerometer reading based on a first calculated acceleration, determine a first gyroscope reading based on a first calculated angular velocity, and determine a first magnetometer reading based on a first rotation matrix. The IMU model can be implemented based on a sensor fusion and tracking toolkit (sensor fusion and tracking toolbox) in Matlab software, as shown in FIG. 4, the IMU model can output a corresponding first sensor reading based on first simulation state data, and the IMU model can be set in parameters of the model based on requirements of different scenes.
For another example, if the sensor model 103C is a GPS model, the sensor model 103C is used to determine a first GPS reading based on a first calculated position. The GPS model can be realized based on a sensor fusion and tracking tool box in Matlab software, and the type of the GPS can be set in the parameters of the model based on the requirements of different scenes.
In some embodiments, the navigation module 102 is configured to simulate fine guidance of the drone during the first period. Illustratively, the navigation module 102 is configured to determine first navigation data based on the first sensor readings sent by the sensor model 103C, send the first navigation data to the flight control module 101, the first navigation data including at least one of a first position, a first attitude, and a first velocity of the drone.
For example, if the first sensor readings include readings of the IMU model and readings of the GPS model, the navigation module 102 is configured to fuse the readings of the IMU model and the readings of the GPS model based on a fusion algorithm, such as a kalman filter algorithm, to obtain first navigation data, which is used to indicate an accurate position, attitude, and speed of the drone during the first period.
In some embodiments, the drone flight control simulation system further includes: a remote control 104, a receiver 105 corresponding to the remote control, and a ground station 106. The remote controller 104 is configured to send a first control instruction to the receiver 105, the receiver 105 is configured to send the received first control instruction to the flight control module 101, the ground station 106 is configured to receive first state data, and send the first control instruction to the flight control module 101, where the first state data is navigation data of the unmanned aerial vehicle in a previous period.
In some embodiments, the flight module 101 is further configured to report the first status data of the drone to the ground station 106.
In the second period, the description of each functional module in the flight control simulation system of the unmanned aerial vehicle is as follows:
in some embodiments, the flight control module 101 is configured to simulate flight control of the drone during the second period. Illustratively, the flight control module 101 is configured to send second actuator data to the actuator model 103A based on a second control command and the first navigation data sent by the navigation module 102, the second actuator data being indicative of a kinetic energy parameter required to be output to execute the second control command.
In some embodiments, the actuator model 103A is used to simulate the power output of the drone during the second period. Illustratively, the actuator model 103A is configured to determine a second operating parameter of the power element of the drone based on the second actuator data sent by the flight control module 101, which is sent to the aircraft model 103B. Wherein the second actuator data includes a second PWM waveform and a second voltage, and the second operating parameter includes a second motor speed and a second propeller lift. The method for determining the second operating parameter is the same as that in the first period, and is not described herein again.
In some embodiments, the model aircraft 103B is used to simulate the actual state of motion of the drone during the second period. Illustratively, the aircraft model 103B is configured to obtain second simulated state data of the drone based on the second operating parameter sent by the actuator model 103A, and send the second simulated state data to the sensor model 103C, the second simulated state data including at least one of a second calculated position, a second calculated attitude, a second calculated velocity, a second calculated acceleration, and a second calculated angular velocity of the drone.
In some embodiments, the airplane model 103B includes:
and the attitude sub-model is used for simulating the attitude of the unmanned aerial vehicle in the second period. Illustratively, the attitude sub-model is configured to obtain a second calculated angular velocity of the drone based on a propeller torque and a moment of inertia of the drone; based on the second calculated angular velocity, a second euler angle of the drone is obtained, the second euler angle being used to represent a second calculated attitude of the drone.
And the speed sub-model is used for simulating the speed of the unmanned aerial vehicle in the second period. Illustratively, the velocity sub-model is configured to obtain a second rotation matrix of the drone based on the second euler angle, the second rotation matrix being configured to represent a second calculated pose of the drone; acquiring a second calculated acceleration of the unmanned aerial vehicle based on a second rotation matrix and the second operation parameter; and acquiring a second calculated speed of the unmanned aerial vehicle based on the second calculated acceleration.
And the position sub-model is used for simulating the position of the unmanned aerial vehicle in the second period. Illustratively, the location submodel is to obtain a second wind speed based on the first calculated location; and acquiring a second calculated position of the unmanned aerial vehicle based on the second wind speed and the second calculated speed.
The method for acquiring the corresponding data by each submodel is the same as the method in the first period, and is not described herein again.
Through above-mentioned gesture submodel, speed submodel and position submodel, can obtain this unmanned aerial vehicle's second simulation state data, this second simulation state data includes that the second calculates position, the second calculates gesture, the second calculates speed, the second calculates acceleration and the second calculates at least one of angular velocity.
In some embodiments, the sensor model 103C is used to simulate real sensors of the drone. Illustratively, the sensor model 103C is configured to determine a second sensor reading of at least one sensor based on the second simulated state data sent by the aircraft model 103B, which is sent to the navigation module 102.
For example, if the sensor model 103C is an IMU model, the sensor model 103C is configured to determine a second accelerometer reading based on a second calculated acceleration, determine a second gyroscope reading based on a second calculated angular velocity, and determine a second magnetometer reading based on a second rotation matrix.
For another example, if the sensor model 103C is a GPS model, the sensor model 103C is used to determine a second GPS reading based on a second calculated position.
In some embodiments, the navigation module 102 is configured to simulate fine guidance of the drone during the second period. Illustratively, the navigation module 102 is configured to determine second navigation data based on second sensor readings sent by the sensor model, send the second navigation data to the flight control module, the second navigation data including at least one of a second position, a second attitude, and a second velocity of the drone.
For example, if the second sensor readings include readings of the IMU model and readings of the GPS model, the navigation module 102 is configured to fuse the readings of the IMU model and the readings of the GPS model based on a fusion algorithm, such as a kalman filter algorithm, to obtain second navigation data, where the second navigation data is used to indicate an accurate position, attitude, and speed of the drone during the second period.
In some embodiments, the drone flight control simulation system further includes: a remote control 104, a receiver 105 corresponding to the remote control, and a ground station 106. The remote controller 104 is configured to send a second control instruction to the receiver 105, the receiver 105 is configured to send the received second control instruction to the flight control module 101, the ground station 106 is configured to receive second state data, and send the second control instruction to the flight control module 101, where the second state data is the first navigation data.
In some embodiments, the flight module 101 is further configured to report the second status data of the drone to the ground station 106.
It should be noted that the aircraft simulation model is a model implemented based on Matlab language, and as shown in fig. 5, it is necessary to convert a relevant code of the aircraft simulation model into a code of C language or C + + language based on a code generator (code generator) tool in Matlab software, and then deploy the aircraft simulation model on an aircraft controller to implement the above functions.
The utility model provides an unmanned aerial vehicle flight control simulation system, can be in every cycle, based on the navigation data of control command and last cycle, through flight control module, navigation module, sensor model, aircraft model and actuator model, carry out flight control's simulation to unmanned aerial vehicle, thereby realize the emulation to flight control system, because sensor model, aircraft model and actuator model are the model based on the software mode realizes, the cost of carrying out flight control system simulation test has been reduced, and the operation model need not spend a large amount of time to prepare, the purpose of improvement simulation test efficiency has been realized.
Fig. 6 is a schematic structural diagram of a computer device 600 according to an embodiment of the present application, where the computer device 600 may generate a relatively large difference due to different configurations or performances, and may include one or more processors (CPUs) 601 and one or more memories 602, where at least one program code is stored in the one or more memories 602, and is loaded and executed by the one or more processors 601 to implement the operations performed by the flight control simulation system for unmanned aerial vehicles. Certainly, the computer device 600 may further have components such as a wired or wireless network interface, a keyboard, and an input/output interface, so as to perform input and output, and the computer device 600 may further include other components for implementing device functions, which are not described herein again.
In an exemplary embodiment, a computer-readable storage medium, such as a memory including at least one program code executable by a processor, to implement the operations performed by the drone flight control simulation system is also provided. For example, the computer-readable storage medium may be a read-only memory (ROM), a Random Access Memory (RAM), a compact disc-read-only memory (CD-ROM), a magnetic tape, a floppy disk, an optical data storage device, and the like.
In an exemplary embodiment, a computer program product is also provided, the computer program product comprising at least one computer program, the at least one computer program being stored in a computer readable storage medium. The processor of the computer device reads the at least one computer program from the computer-readable storage medium, and the processor executes the at least one computer program, so that the computer device realizes the operations performed by the flight control simulation system of the unmanned aerial vehicle.
It will be understood by those skilled in the art that all or part of the steps for implementing the above embodiments may be implemented by hardware, or may be implemented by a program instructing relevant hardware, and the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, etc.
The above description is only exemplary of the present application and should not be taken as limiting, as any modification, equivalent replacement, or improvement made within the spirit and principle of the present application should be included in the protection scope of the present application.
Claims (10)
1. An unmanned aerial vehicle flight control simulation system, comprising: a flight control module, an actuator model, an airplane model, a sensor model and a navigation module,
the flight control module is used for sending actuator data of a second period to the actuator model based on a control instruction and navigation data of the unmanned aerial vehicle in a first period output by the navigation module, wherein the navigation data of the unmanned aerial vehicle in the first period comprises at least one of a first position, a first posture and a first speed of the unmanned aerial vehicle, the actuator data is used for representing kinetic energy parameters required to be output for executing the control instruction, and the second period is the next period of the first period;
the actuator model is used for determining the operating parameters of the power element of the unmanned aerial vehicle based on the actuator data of the second period of the flight control module and sending the operating parameters to the airplane model;
the aircraft model is used for acquiring simulated state data of the unmanned aerial vehicle based on the operation parameters and sending the simulated state data to the sensor model, wherein the simulated state data comprises at least one of a calculated position, a calculated attitude, a calculated speed, a calculated acceleration and a calculated angular velocity of the unmanned aerial vehicle;
the sensor model is used for determining the sensor reading of at least one sensor based on the simulation state data and sending the sensor reading to the navigation module;
the navigation module is configured to determine, based on the sensor readings, navigation data of the drone over the second period, the navigation data of the drone over the second period including at least one of a second position, a second attitude, and a second speed of the drone.
2. The unmanned aerial vehicle flight control simulation system of claim 1, wherein the actuator model is derived based on fitting real actuator data to real power parameters.
3. The unmanned aerial vehicle flight control simulation system of claim 1, wherein the aircraft model comprises:
the attitude sub-model is used for acquiring an Euler angle of the unmanned aerial vehicle based on the propeller torque and the rotational inertia of the unmanned aerial vehicle, and the Euler angle is used for representing the calculated attitude of the unmanned aerial vehicle;
the speed submodel is used for acquiring the calculated speed of the unmanned aerial vehicle based on the Euler angle and the operation parameters;
and the position sub-model is used for acquiring the calculated position of the unmanned aerial vehicle based on the calculated speed.
4. The unmanned aerial vehicle flight control simulation system of claim 3, wherein the attitude sub-model is configured to obtain a calculated angular velocity of the unmanned aerial vehicle based on a propeller torque and a moment of inertia of the unmanned aerial vehicle; and acquiring the Euler angle of the unmanned aerial vehicle based on the calculated angular velocity.
5. The UAV flight control simulation system of claim 3, wherein the velocity sub-model is configured to obtain a rotation matrix of the UAV based on the Euler angles, the rotation matrix being used to represent a calculated attitude of the UAV; acquiring the calculated acceleration of the unmanned aerial vehicle based on the rotation matrix and the operation parameters; and acquiring the calculated speed of the unmanned aerial vehicle based on the calculated acceleration.
6. The drone flight control simulation system of claim 3, wherein the location submodel is to obtain a wind speed during the second period based on the location of the drone during the first period; and acquiring the calculated position of the unmanned aerial vehicle based on the wind speed in the second period and the calculated speed.
7. The unmanned aerial vehicle flight control simulation system of claim 1, wherein the sensor model is to determine accelerometer readings based on the calculated acceleration; determining a gyroscope reading based on the calculated angular velocity; based on the calculated attitude, magnetometer readings are determined.
8. The unmanned aerial vehicle flight control simulation system of claim 1, further comprising: a remote controller, a receiver corresponding to the remote controller and a ground station,
the remote controller is used for sending the control instruction to the receiver;
the receiver is used for sending the received control instruction to the flight control module;
and the ground station is used for sending the control instruction to the flight control module.
9. A computer device, characterized in that the computer device comprises one or more processors and one or more memories, in which at least one computer program is stored, which is loaded and executed by the one or more processors to carry out the operations performed by the unmanned aerial vehicle flight control simulation system according to any one of claims 1 to 8.
10. A computer-readable storage medium, having at least one computer program stored therein, the at least one computer program being loaded and executed by a processor to perform the operations performed by the drone flight control simulation system of any one of claims 1 to 8.
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