CN-122018526-A - Unmanned aerial vehicle control simulation method, simulator and storage medium thereof

CN122018526ACN 122018526 ACN122018526 ACN 122018526ACN-122018526-A

Abstract

The embodiment of the application relates to the technical field of unmanned aerial vehicle simulation control, in particular to an unmanned aerial vehicle control simulation method, a simulator thereof and a storage medium. The application discloses an unmanned aerial vehicle control simulation method which is applied to an unmanned aerial vehicle control simulator and needs to acquire current physical state information and remote control input signals in real time, wherein flight control calculation is performed on the basis of the current physical state information and the remote control input signals to obtain driving control parameters, physical moment calculation is performed on the basis of inertia tensor and the driving control parameters to obtain rotating moment representation parameters, throttle input analysis is performed to obtain driving thrust representation parameters, air resistance analysis is performed to obtain air resistance representation parameters, and control simulation is performed on the basis of the three representation parameters to update the current physical state information and perform cyclic execution. The flight control calculation is coupled with the physical moment calculation, the throttle input analysis and the air resistance analysis to form a closed loop calculation architecture, so that the logic decoupling problem is solved, the simulation precision is improved, and the flight hand feeling of the real traversing machine is restored.

Inventors

CAI YIZE
LIU JUNHUI
Zhang Hekuan

Assignees

深圳市泽芯未来科技有限公司

Dates

Publication Date: 20260512
Application Date: 20260410

Claims (10)

1. A method for simulating unmanned aerial vehicle operation, which is applied to an unmanned aerial vehicle operation simulator, the method comprising: acquiring current physical state information of a target unmanned aerial vehicle model in real time in the unmanned aerial vehicle control simulator, and acquiring a remote control input signal; Performing flight control calculation based on the remote control input signals and the current physical state information to obtain driving control parameters of the target unmanned aerial vehicle model corresponding to each rotating shaft; Performing physical moment calculation based on the corresponding preset inertia tensor and the driving control parameter of the target unmanned aerial vehicle model to obtain a rotation moment characterization parameter of the target unmanned aerial vehicle model; performing throttle input analysis based on the remote control input signal to obtain a driving thrust characterization parameter of the target unmanned aerial vehicle model; Carrying out air resistance analysis based on the current physical state information to obtain an air resistance characterization parameter of the target unmanned aerial vehicle model; and performing control simulation on the target unmanned aerial vehicle model based on the rotation moment representation parameter, the driving thrust representation parameter and the air resistance representation parameter so as to update the current physical state information of the target unmanned aerial vehicle, and returning to execute the acquisition of the remote control input signal until the current physical state information triggers a preset control simulation termination condition.
2. The unmanned aerial vehicle manipulation simulation method according to claim 1, wherein a world coordinate system is a coordinate system defined in the unmanned aerial vehicle manipulation simulator with respect to a simulation scene, and further comprising, before the performing flight control calculation based on the remote control input signal and the current physical state information, obtaining driving control parameters of the target unmanned aerial vehicle model corresponding to each rotation axis: Extracting a current angular velocity corresponding to the world coordinate system from the current physical state information; And converting the current angular velocity into a body coordinate system to determine the local angular velocity of the body corresponding to the body coordinate system, wherein the body coordinate system is a coordinate system defined in the unmanned aerial vehicle control simulator relative to the target unmanned aerial vehicle model, and each coordinate axis of the body coordinate system corresponds to each rotation axis of the target unmanned aerial vehicle model respectively.
3. The unmanned aerial vehicle control simulation method according to claim 1, wherein the step of analyzing accelerator input based on the remote control input signal to obtain a driving thrust characterization parameter of the target unmanned aerial vehicle model comprises the steps of: Obtaining a thrust maximum value corresponding to the target unmanned aerial vehicle model; performing throttle input extraction on the remote control input signal to obtain original throttle input data; Carrying out linearization compensation processing on the input data of the original throttle to obtain a compensated throttle control quantity; performing basic thrust calculation based on the compensated throttle control quantity and the thrust maximum value to obtain a basic thrust characterization parameter; Performing antigravity compensation processing on the original throttle input data to obtain antigravity compensation characterization parameters; And performing thrust synthesis based on the basic thrust characterization parameter and the antigravity compensation characterization parameter to obtain the driving thrust characterization parameter.
4. The unmanned aerial vehicle maneuver simulation method of claim 3, wherein the performing the antigravity compensation process on the raw throttle input data comprises: Analyzing the change rate of the original throttle input data to determine throttle change rate characterization parameters; Filtering the throttle change rate characterization parameter to filter high-frequency noise and obtain a filtered change rate characterization parameter; And calculating compensation quantity based on the filtered change rate characterization parameter and a preset compensation gain to obtain the antigravity compensation characterization parameter.
5. The unmanned aerial vehicle control simulation method according to claim 1, wherein the air resistance analysis is performed based on the current physical state information to obtain an air resistance characterization parameter of the target unmanned aerial vehicle model, and the method comprises the following steps: Extracting the linear speed of the machine body corresponding to the target unmanned aerial vehicle model from the current physical state information; and carrying out nonlinear resistance calculation on the engine body linear speed to determine an air resistance representation parameter of the target unmanned aerial vehicle model, wherein the nonlinear resistance calculation comprises the step of determining the magnitude and the direction of the air resistance representation parameter based on the product relation between the model value of the engine body linear speed and the engine body linear speed.
6. The unmanned aerial vehicle manipulation simulation method according to claim 1, wherein the performing manipulation simulation on the target unmanned aerial vehicle model to update the current physical state information of the target unmanned aerial vehicle comprises: performing flight mode trigger detection based on the current physical state information to determine a current flight mode of the target unmanned aerial vehicle model; Based on the current flight mode, determining a corresponding target control simulation strategy from a plurality of preset control simulation strategies; Based on the target manipulation simulation strategy, differential torque application is performed on the rotation torque characterization parameter, the driving thrust characterization parameter and the air resistance characterization parameter so as to update the current physical state information.
7. The unmanned aerial vehicle maneuver simulation method of claim 6, wherein the performing flight mode trigger detection based on the current physical state information to determine the current flight mode of the target unmanned aerial vehicle model comprises: detecting a current flight state of the target unmanned aerial vehicle model based on the current physical state information; in response to detecting that the current flight state is a falling inversion state, determining the current flight mode as a roll-over recovery mode; Based on the target manipulation simulation strategy, differential torque application is performed on the rotation torque characterization parameter, the driving thrust characterization parameter and the air resistance characterization parameter to update the current physical state information, including: Based on the target manipulation simulation strategy corresponding to the rollover recovery mode, executing the following operations: acquiring the length of a paddle arm and the maximum thrust value corresponding to the target unmanned aerial vehicle model; Performing single-axis locking and dead zone processing on the remote control input signal to obtain a turning positive control instruction; Calculating a turning moment based on the turning return control instruction, the length of the paddle arm and the maximum thrust value to obtain a turning recovery moment; and driving the target unmanned aerial vehicle model to turn over and return to the right based on the turning-over restoring moment so as to update the current physical state information.
8. The unmanned aerial vehicle maneuver simulation method of claim 6, wherein the performing flight mode trigger detection based on the current physical state information to determine the current flight mode of the target unmanned aerial vehicle model further comprises: in response to detecting that the current flight state of the target unmanned aerial vehicle model is an impending crash state, determining the current flight mode as a crash recovery mode; Based on the target manipulation simulation strategy, differential torque application is performed on the rotation torque characterization parameter, the driving thrust characterization parameter and the air resistance characterization parameter to update the current physical state information, including: Based on the target control simulation strategy corresponding to the crash recovery mode, extracting the current angular speed of the target unmanned aerial vehicle model from the current physical state information; calculating damping moment based on the current angular speed and a preset damping deceleration rate to obtain crash damping moment; and applying the crash damping moment to the target unmanned aerial vehicle model to restrain the rotation movement of the target unmanned aerial vehicle model and update the current physical state information.
9. A unmanned aerial vehicle steering simulator, comprising a memory, a processor, the memory storing a computer program, the processor implementing the unmanned aerial vehicle steering simulation method of any of claims 1 to 8 when executing the computer program.
10. A computer-readable storage medium, characterized in that the storage medium stores a program that is executed by a processor to implement the unmanned aerial vehicle manipulation simulation method according to any one of claims 1 to 8.

Description

Unmanned aerial vehicle control simulation method, simulator and storage medium thereof Technical Field The embodiment of the application relates to the technical field of unmanned aerial vehicle simulation control, in particular to an unmanned aerial vehicle control simulation method, a simulator thereof and a storage medium. Background The traversing machine is widely applied to the fields of racing games, video and aerial photography, special operation and the like by virtue of the characteristics of high thrust-weight ratio and high maneuverability. Because the traversing machine has high operation difficulty, operators can master the control skills through a large amount of training, the real machine training becomes a core cultivation mode, but the real machine training has limitation due to the hardware characteristics of the traversing machine, a high-fidelity physical simulation training tool becomes an industry just needed, and the traversing machine simulation flight technology becomes an important direction in the unmanned plane field. An ideal traversing machine simulator has the core requirement of accurately restoring physical characteristics of the real world in a virtual environment, including aerodynamic interference and motor dynamic response, especially flight control algorithm logic, and the physical simulation precision of the simulator becomes a core index for measuring the performance of the simulator. The current mainstream traversing machine simulator in the market simplifies the real flight control and physical model of the unmanned plane, so as to realize the rapid development of the simulator and meet the basic flight training requirement. However, in the related scheme, the problems of logic decoupling are common in the process of simulating flight control and physical feedback, so that the simulation accuracy is greatly reduced, and the real flight hand feeling of the traversing machine cannot be restored. Disclosure of Invention The embodiment of the application aims to at least solve one of the technical problems existing in the prior art. Therefore, the embodiment of the application provides an unmanned aerial vehicle control simulation method, a simulator and a storage medium thereof, which can improve the logic decoupling problem in the process of simulating flight control and physical feedback, so that the simulation precision is improved, and the method is beneficial to restoring the real flight hand feeling of a traversing machine. According to a first aspect of the embodiment of the application, the unmanned aerial vehicle control simulation method is applied to an unmanned aerial vehicle control simulator, and comprises the following steps: acquiring current physical state information of a target unmanned aerial vehicle model in real time in the unmanned aerial vehicle control simulator, and acquiring a remote control input signal; Performing flight control calculation based on the remote control input signals and the current physical state information to obtain driving control parameters of the target unmanned aerial vehicle model corresponding to each rotating shaft; Performing physical moment calculation based on the corresponding preset inertia tensor and the driving control parameter of the target unmanned aerial vehicle model to obtain a rotation moment characterization parameter of the target unmanned aerial vehicle model; performing throttle input analysis based on the remote control input signal to obtain a driving thrust characterization parameter of the target unmanned aerial vehicle model; Carrying out air resistance analysis based on the current physical state information to obtain an air resistance characterization parameter of the target unmanned aerial vehicle model; and performing control simulation on the target unmanned aerial vehicle model based on the rotation moment representation parameter, the driving thrust representation parameter and the air resistance representation parameter so as to update the current physical state information of the target unmanned aerial vehicle, and returning to execute the acquisition of the remote control input signal until the current physical state information triggers a preset control simulation termination condition. According to some embodiments of the present application, the world coordinate system is a coordinate system defined in the unmanned aerial vehicle control simulator with respect to a simulation scene, and before performing flight control calculation based on the remote control input signal and the current physical state information, obtaining driving control parameters of the target unmanned aerial vehicle model corresponding to each rotation axis, the method further includes: Extracting a current angular velocity corresponding to the world coordinate system from the current physical state information; And converting the current angular velocity into a body coordinate system to determine the local