WO2019227330A1 - 一种无人机的仿真方法及装置 - Google Patents
一种无人机的仿真方法及装置 Download PDFInfo
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- WO2019227330A1 WO2019227330A1 PCT/CN2018/088988 CN2018088988W WO2019227330A1 WO 2019227330 A1 WO2019227330 A1 WO 2019227330A1 CN 2018088988 W CN2018088988 W CN 2018088988W WO 2019227330 A1 WO2019227330 A1 WO 2019227330A1
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B17/00—Systems involving the use of models or simulators of said systems
- G05B17/02—Systems involving the use of models or simulators of said systems electric
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/44—Arrangements for executing specific programs
- G06F9/455—Emulation; Interpretation; Software simulation, e.g. virtualisation or emulation of application or operating system execution engines
Definitions
- the invention relates to the field of unmanned aerial vehicles, in particular to a method and a device for simulating unmanned aerial vehicles.
- the control terminal of the drone is generally equipped with a simulator.
- the simulator supports the simulation of the drone. Users can familiarize themselves with the basic functions of the drone and exercise the flying operation technology through the simulator. Test the drone's flight performance through the simulator function.
- Embodiments of the present invention provide a method and a device for simulating an unmanned aerial vehicle, so as to realize the simulation of an unmanned aerial vehicle configured with redundant sensors.
- a first aspect of the embodiments of the present invention provides a drone simulation method, including:
- the simulated flight state data output by the simulation model is transmitted to a flight controller, wherein the simulated flight state data is determined according to the simulated sensor data output by the redundant sensor model.
- a second aspect of the embodiments of the present invention provides a drone simulation device, including:
- Memory for storing executable instructions
- a processor configured to call the executable instructions stored in the memory to perform the following operations:
- the simulated flight state data output by the simulation model is transmitted to a flight controller, wherein the simulated flight state data is determined according to the simulated sensor data output by the redundant sensor model.
- a third aspect of the embodiments of the present invention provides a computer-readable storage medium that stores executable instructions that, when executed by one or more processors, implement the simulation method according to the first aspect.
- a fourth aspect of the embodiments of the present invention provides an unmanned aerial vehicle, including the simulation device according to the second aspect.
- a fifth aspect of the embodiments of the present invention provides a control terminal, which is communicatively connected with a flight controller of an unmanned aerial vehicle, and includes the simulation device according to the second aspect.
- a power signal output by the flight controller is obtained, and a simulation model of the UAV is operated according to the power signal, wherein the simulation model includes redundant data.
- the remaining sensor model transmits the simulated flight state data output by the simulation model to a flight controller to drive the simulation process to continue. In this way, simulation of unmanned aerial vehicles equipped with redundant sensors is realized to improve flight safety.
- FIG. 1 schematically illustrates a simulation principle of a drone according to an embodiment of the present invention.
- FIG. 2 schematically illustrates a block diagram of an embodiment in which a simulation device is located in a flight controller.
- FIG. 3 schematically illustrates a block diagram of an embodiment in which a simulation device is located at a control terminal.
- FIG. 4 schematically illustrates steps of a simulation method of a drone according to an embodiment of the present invention.
- FIG. 5 schematically illustrates a flowchart of adjusting a redundant sensor model according to an embodiment of the present invention.
- FIG. 6 schematically illustrates a block diagram of a simulation apparatus according to an embodiment of the present invention.
- FIG. 7 schematically illustrates the structure of a computer-readable medium of the present invention.
- a component when a component is called “fixed to” another component, it may be directly on another component or a centered component may exist. When a component is considered to be “connected” to another component, it can be directly connected to another component or a centered component may exist at the same time.
- more and more drones are configured with redundant sensors, that is, configured with multiple sensors of the same type, for example, drones are configured with multiple gyroscopes, multiple compasses, and multiple accelerometers.
- multiple satellite positioning devices One or more of multiple satellite positioning devices.
- multiple sensors of the same type are redundantly backed up to each other.
- the drone's flight controller obtains sensor data from a sensor. When this sensor works abnormally, the flight controller will perform a sensor switching operation, that is, the flight controller can switch to a redundant backup with the sensor. Another sensor from which sensor data is obtained.
- the flight controller may perform a selection operation from multiple sensors of the same type, that is, the flight controller may separately evaluate the operating states of multiple sensors of the same type, And select the sensor whose working condition meets the preset requirements, that is, the flight controller obtains sensor data from the sensor whose working condition meets the preset requirements.
- the flight controller can evaluate the measurement accuracy of multiple sensors of the same type separately, and the flight controller chooses to select the sensor with the highest measurement accuracy.
- the control terminal can have a built-in drone simulator.
- the simulator can support the simulation of drones. Users can familiarize themselves with the basic functions of drones and exercise flight operation techniques through the simulators. Developers of drones can Function tests the drone's flight performance.
- the control terminal may include one or more of a remote controller, a smart phone, a tablet computer, a laptop computer, a desktop computer, and a wearable device.
- the simulators in the prior art do not support the simulation of unmanned aerial vehicles configured with redundant sensors. In this way, it cannot be determined through simulation when the environmental factors change or the working state of the redundant sensors changes.
- embodiments of the present invention provide a simulation method and device for a drone, so as to implement simulation of a drone configured with redundant sensors.
- the simulation model of the drone includes at least a physical model 101 and a redundant sensor model 102 of the drone.
- the physical model 101 of the drone is a software module that represents the physical mode of the drone.
- the physical model 101 of the UAV receives the power signal, wherein the power signal may be a PWM signal, and the physical model 101 of the UAV responds to the received power signal and outputs the true value of the simulated flight state data, The value can represent the flying state of the drone after the influence of the dynamic signal on the drone model physics 101.
- the UAV physical model 101 includes one of a motor-propeller model, a dynamic model, a kinematics model, and an object model (the object model is used to characterize the physical structure of the UAV, such as power, structure, weight, electromechanical, etc.) Or more.
- the redundant sensor model 102 may include one or more types of sensor models, and each type of sensor model includes at least two sensor models, for example, 3 gyroscope models, 2 satellite positioning device models, 3 accelerometer models, and 3 compass models.
- the redundant sensor model 102 may be a software module. After the redundant sensor model 102 receives the true value of the simulated flight state data of the physical model 101 of the drone, each type of sensor model in the redundant sensor model 102 may be targeted at simulated flight The state data truth value outputs the corresponding analog sensor data.
- the simulated sensor data may be output to the fusion module 103, and the fusion module 103 may determine simulated flight state data according to the simulated sensor data, that is, the fusion module 103 may fuse the simulated sensor data Calculate to determine simulated flight status data.
- the fusion module 103 may be a software model.
- the simulation model of the UAV includes the fusion module 103.
- the simulated flight state data is simulated sensor data
- the simulated sensor data is directly determined as simulated flight state data without being fused.
- the real sensor data can be directly determined as the real flight status data.
- the controller 104 is an important component or software module of the flight controller. When the flight controller is in the simulation mode, it can receive simulated flight status data. In addition, the flight controller can receive the amount of joystick of the control terminal 105, where the amount of joystick is controlled by the control terminal 105 by detecting the user's drone. By operating the determined control instruction, the controller 104 can generate a power signal according to the simulated flight state data and / or the amount of the joystick. The physical model 101 of the drone can receive the power signal output by the controller 104. In this way, the unmanned person can be driven. The simulation of the machine continues.
- the controller 104 may receive the real flight status data, wherein the controller 104 may generate a power signal according to the real flight status data and / or the amount of the control lever output by the control terminal 105 ,
- the real power system of the drone may receive the power signal and perform corresponding operations.
- the power system may include one or more of an ESC, a motor, a propeller, and an engine.
- the control terminal 105 can obtain simulated flight status data, where the control terminal 105 includes an interactive device and the interactive device includes a display device, wherein the display device may be a display screen, a touch display screen, or an LED One or more of the display devices, the control terminal 105 may display the simulated flight status data on the display device, so that the user can know the flight status of the drone in the simulation mode through the display device.
- the control terminal 105 can obtain the real flight status data, and the control terminal 105 can display the simulated flight status data on the display device, so that the user can know the drone in the normal working mode through the display device. Flight status.
- the control terminal 105 may detect a parameter configuration operation of the user's redundant sensor model 102, and the parameter configuration operation may be a user's operation on multiple sensor models of the same type.
- the physical parameters of each model are set, wherein the parameters are initialization parameters.
- the parameters may include at least one of a redundancy parameter of a sensor model of the same type and an attribute parameter of the sensor model.
- the redundancy parameter of the sensor models of the same type is the number of multiple sensor models of the same type.
- the attribute parameters of the sensor model may be used to characterize any physical characteristics of the sensor model.
- the attribute parameters may include one of noise, delay, zero drift, temperature drift, non-linearity, and installation position. Or more.
- the control terminal 105 may determine a redundant sensor model parameter configuration instruction according to the detected parameter configuration operation, and the redundant sensor model parameter configuration instruction may be used to configure parameters of each of a plurality of sensor models of the same type.
- the control terminal 105 can detect the user's redundant sensor model adjustment operation and determine the redundancy based on the detected adjustment operation.
- the adjustment instruction is used to adjust the redundant sensor model 102.
- the adjustment instruction includes at least one of an attribute parameter adjustment instruction of the sensor model and a fault injection instruction of the sensor model.
- the adjustment instruction includes an attribute parameter adjustment instruction of the sensor model, and the attribute parameter adjustment instruction may be used to adjust an attribute parameter of one or more sensor models in a plurality of sensor models of the same type.
- the adjustment instruction includes a fault injection instruction for a sensor model, and the fault injection instruction is used to inject a fault for one or more sensor models of a plurality of sensor models of the same type.
- the redundant sensor model 102 may be adjusted. Specifically, one or more of a plurality of sensor models of the same type may be adjusted, where the adjustment includes adjusting the sensor model. Attribute parameters, and in some cases, the adjustment includes injecting a fault into the sensor model. Further, after adjusting the redundant sensor model 102, the simulation working state of the flight controller may be determined, that is, the response of the flight controller to the adjustment of the redundant sensor model 102 may be determined. In this way, It can be determined whether the flight controller has performed certain response operations for the adjustment of the redundant sensor model 102.
- the flight controller of the UAV performs a preset response operation. Specifically, when the redundant sensor model is adjusted, under normal circumstances, the flight controller should perform a preset response operation. Among them, when the flight controller is in the normal working mode, the flight controller obtains data from one of the redundant sensors. When this sensor works abnormally, the flight controller performs a sensor switching operation, that is, flight control. The sensor can be switched to another sensor that is redundantly backed up with this sensor.
- the flight controller When the flight controller is in simulation mode, when the flight controller controller 104 obtains simulated sensor data from a sensor model in the redundant sensor model 102, when this sensor model works abnormally, the flight controller should execute a preset The sensor model switching operation, that is, the flight controller can switch to another sensor model redundantly backed up with the sensor module, so that the controller 104 can obtain simulated sensor data from the another sensor model.
- the preset response operation is determined according to the adjustment instruction, and the preset response operation is different for different adjustment instructions.
- determining whether the flight controller of the drone performs a preset response operation may include: determining whether the flight controller of the drone performs At least one of a sensor model selection operation and a sensor model switching operation.
- the working state of the redundant sensor model 102 changes.
- the flight controller should perform pre-planning based on the change of the working state. Set operation. For example, after a fault is injected into the satellite positioning device model 1, under normal circumstances, the flight controller should perform a sensor model switching operation to switch to the satellite positioning device model 2. At this time, in order to confirm whether the flight controller works normally, the flight Whether the controller has performed a sensor model switching operation to switch to the satellite positioning device model 2.
- the simulation model may be built into the flight controller.
- the drone 200 includes a flight controller 2001.
- the simulation model 2002 may be built in the flight controller 2001.
- the flight controller 2001 When the flight controller 2001 is in a simulation mode, the flight controller 2001 may be controlled by flight.
- the internal data link of the controller or the external data link obtains the power signal generated by the controller, and runs the simulation model 2002 according to the power signal.
- the power signal is the power signal generated by the controller.
- the flight controller 2001 can transmit the simulated flight state data output by the simulation model 2002 to the controller in the flight controller 2001 through the internal data link or external data link of the flight controller. / Or
- the simulated flight state data generates a power signal, and the power signal is transmitted to the simulation model 2002 through an internal data link or an external data link of the flight controller 2001.
- the process of running the simulation model 2002 is as described above. Specifically, the physical model of the UAV outputs the true value of the simulated flight state data according to the dynamic signal, the redundant sensor model obtains the true value of the simulated flight state data and outputs the simulated sensor data, and the simulated flight state can be determined according to the simulated sensor data data.
- the flight controller 2001 may send the simulated flight state data to the control terminal 201, and the control terminal 201 may obtain the simulated flight state data and display the simulated flight state data on a display device.
- the flight controller 2001 may obtain a redundant sensor model parameter configuration instruction sent by the control terminal 201, and configure parameters of the redundant sensor model according to the configuration instruction.
- the flight controller 2001 may also obtain an adjustment instruction of the redundant sensor model sent by the control terminal 201, and adjust the redundant sensor model according to the adjustment instruction.
- the simulation working state of the flight controller 2001 is determined, and the specific process of determining the simulation working state is as described above.
- the flight controller 2001 may send the simulation work status to the control terminal 201 so that the control terminal 201 displays the simulation work status.
- the drone image 2012 can be displayed, which is used to display the current attitude, position, battery power and other information of the drone 200, but also other information in the environment where the drone 200 is currently located.
- Object images such as tree images 2011, building images 2013, etc., thereby facilitating the user to further operate the drone 200 according to the environment.
- the simulation model may be built into the control terminal.
- the control terminal 301 has a built-in simulation model 3011 of the drone.
- the control terminal 301 obtains a power signal from the flight controller 3001 through a wireless data link or a wired data link.
- the simulation model 3011 is run according to a power signal.
- the power signal is a power signal generated by a controller.
- the control terminal 301 can transmit the simulated flight state data output by the simulation model 3011 to the controller of the flight controller 3001 of the drone 300 through a wireless data link or a wired data link.
- the simulated flight state data generates a power signal
- the control terminal 301 receives the power signal and transmits the power signal to the simulation model 3011 to drive the simulation to continue.
- the process of running the simulation model 3011 is as described above. Specifically, the physical model of the UAV outputs the true value of the simulated flight state data according to the dynamic signal, the redundant sensor model obtains the true value of the simulated flight state data and outputs the simulated sensor data, and the simulated flight state can be determined according to the simulated sensor data data.
- control terminal 301 may acquire the simulated flight state data and display the simulated flight state data on a display device.
- the control terminal 301 may configure parameters of the redundant sensor model according to the configuration instruction.
- control terminal 301 may also adjust a redundant sensor model according to the adjustment instruction, and after adjusting the redundant sensor model, determine a simulation working state of the flight controller. Specifically, as described above, under normal circumstances, after adjusting the redundant sensor model, the flight controller performs a response operation to the change of the redundant sensor model, and the simulation working state of the flight controller changes.
- the flight controller may send the simulation work status to the control terminal 301 so that the control terminal 301 determines the simulation work status of the flight controller.
- the drone image 3012 can be displayed, which is used to display the current attitude, position, battery power, and other information of the drone 300, but also the current environment of the drone.
- Other object images such as a tree image 3011, a building image 3013, etc., thereby facilitating the user to further operate the drone according to the environment.
- FIG. 4 schematically illustrates steps of a method for simulating a drone according to an embodiment of the present invention. As shown in FIG. 4, the method includes:
- step S401 the flight controller of the UAV is in a simulation mode, and a power signal output by the flight controller is obtained.
- the execution subject of the simulation method is a simulation device of a drone, and further, the execution subject of the simulation method is a processor of the simulation device, wherein the processor may be one or more, the One or more work individually or in concert to implement the simulation method.
- the flight controller may include a simulation device; in some cases, the control terminal may include the simulation device.
- the drone's flight controller can have two modes: simulation mode and normal working mode.
- a control terminal in communication with the flight controller sends a simulation start instruction to the flight controller to instruct the flight controller to enter a simulation mode.
- the simulation device After the flight controller enters the simulation mode, the simulation device obtains the power signal output by the flight controller.
- the simulation device obtains the power signal output by the flight controller through an internal data link or an external data link;
- the control terminal includes a simulation device, the simulation device uses a wireless data link or a wired data link Obtain the power signal output by the flight controller. See the previous section for specific principles.
- the unmanned aerial vehicle includes a redundant sensor. Accordingly, in the simulation method of the unmanned aerial vehicle, the simulation instruction carries a redundant sensor model parameter configuration instruction.
- Step S402 Run a simulation model of the drone according to the power signal, wherein the simulation model includes a redundant sensor model, and wherein the redundant sensor model includes at least two sensor models of the same type.
- the simulation device has a built-in simulation model of the drone. After the simulation device obtains the power signal, it can run the simulation model according to the power signal, wherein the simulation model includes a redundant sensor model, and the simulation model is running. Data on simulated flight status can then be obtained.
- the simulation device obtains the power signal, it can run the simulation model according to the power signal, wherein the simulation model includes a redundant sensor model, and the simulation model is running. Data on simulated flight status can then be obtained.
- the redundant sensor model receives the true value output by the UAV physical model and performs simulation.
- the redundant sensor model may include one or more of at least two satellite positioning (GPS) device models, at least two compass models, at least two accelerometer models, and at least two gyroscope models, This allows analog sensor data to be determined in real time, including acceleration, position information, angular velocity and / or linear velocity.
- the redundant sensor model may include an air pressure sensor model, a magnetic field sensor model, an altitude sensor model, or other sensor models in addition to the aforementioned satellite positioning device model, compass model, accelerometer model, and gyroscope model.
- the number of which sensor model or sensor models is redundant may be selected according to actual needs, and the attribute parameters of each sensor model of the same type of sensor model should be different.
- the specific principle please refer to the previous section, which will not be repeated here.
- step S403 the simulated flight state data output by the simulation model is transmitted to a flight controller, wherein the simulated flight state data is determined according to the simulated sensor data output by the redundant sensor model.
- the simulated flight state data may be obtained by merging the simulated sensor data.
- the simulated flight state data may be simulated sensor data output by a redundant sensor model.
- the simulation method further includes: acquiring a redundant sensor model parameter configuration instruction; and configuring parameters of the redundant sensor model of the UAV according to the parameter configuration instruction.
- the simulation device may obtain a redundant sensor model parameter configuration instruction, wherein the redundant sensor model parameter configuration instruction is used for parameter configuration of one or more of a plurality of sensor models of the same type in the redundant sensor model.
- the simulation device receives a parameter configuration instruction sent by the control terminal, and the simulation device may configure parameters of the one or more sensor models according to the parameter configuration instruction.
- the control terminal includes a simulation device
- the control terminal detects a parameter configuration operation of the user, and determines a parameter configuration instruction according to the parameter configuration operation.
- the simulation device obtains the parameter configuration instruction and configures the parameter according to the parameter configuration instruction. Parameters for one or more sensor models. See the previous section for specific principles.
- step S402 may also be performed. Including steps S4021 to S4023.
- step S4021 an adjustment instruction of the redundant sensor model is obtained.
- the adjustment instruction of the redundant sensor model is sent by the user to the flight controller through the control terminal based on the attribute parameters adjusted by the user or the type of injection failure.
- the adjustment instruction includes an operation code and an operand.
- the operation code determines an attribute parameter or a type of an injection fault.
- the operand determines an operation object, that is, each sensor model in the redundant sensor model.
- the adjustment instruction includes at least one of an attribute parameter adjustment instruction of the sensor model and a fault injection instruction of the sensor model.
- the attribute parameter adjustment instruction and the fault injection instruction of the sensor model also include an operation code and an operand.
- the operation code of the attribute parameter adjustment instruction determines the attribute parameter, and the operand determines each sensor model in the redundant sensor model; the sensor model
- the operation code of the fault injection instruction determines the type of injection fault, and the operand determines each sensor model in the redundant sensor model.
- the attribute parameter adjustment instruction is used to adjust one or more of the attribute parameters of the sensor model, that is, noise, delay, zero drift, temperature drift, non-linearity, and installation position;
- the fault injection instruction is used to adjust the injection into the sensor model.
- Faults, faults include one or more of data stuck and disconnected.
- the specific operation objects of the attribute parameter adjustment instruction and the fault injection instruction can be selected according to actual conditions.
- step S4022 the redundant sensor model is adjusted according to the adjustment instruction.
- the simulation device may adjust the attribute parameters of the built-in simulation model. For example, the simulation device may adjust the installation position of one or more sensor models in the redundant sensor model. . As another example, a simulation device injects a fault into one or more sensors in a redundant sensor model.
- step S4023 after adjusting the redundant sensor model, a simulation working state of the flight controller of the UAV is determined.
- the simulation device receives an adjustment instruction of the redundant sensor model sent by the control terminal, and the simulation device may adjust attributes and fault injection of the one or more sensor models according to the adjustment instruction.
- the control terminal includes a simulation device
- the control terminal detects a user's adjustment operation and determines an adjustment instruction according to the adjustment operation.
- the simulation device obtains the adjustment instruction and adjusts the one or more sensors according to the adjustment instruction. Model attribute parameters and fault injection. See the previous section for specific principles.
- the simulation working state of the flight controller refers to whether the flight controller of the UAV performs a preset response operation, that is, whether the flight controller of the UAV performs at least one of a sensor model selection operation and a sensor model switching operation.
- the preset response operation is determined according to the adjustment instruction. For example, when the adjustment instruction is an attribute parameter adjustment instruction, the preset response operation may be a sensor model selection operation. When the adjustment instruction is a fault injection instruction, the preset response operation may be a sensor model switching operation.
- simulation model may be built in the flight controller or in a control terminal (mobile smart device or computer) that is communicatively connected with the flight controller, and may be selected according to the needs of the user.
- the simulation device 600 includes: a memory 601 for storing executable instructions; and a processor 602 for executing the memory 601.
- the executable instructions stored in FIG. 4 are shown in detail.
- the simulation method specifically includes: obtaining a power signal output by the flight controller when the flight controller of the UAV is in a simulation mode; and operating the unmanned according to the power signal.
- a simulation model of the aircraft wherein the simulation model includes a redundant sensor model, wherein the redundant sensor model includes at least two sensor models of the same type; and transmitting the simulated flight state data output by the simulation model to The flight controller, wherein the simulated flight state data is determined according to the simulated sensor data output by the redundant sensor model.
- the memory 601 may be a non-volatile or volatile readable storage medium, such as an electrically erasable and programmable read-only memory (EEPROM), a flash memory, and / or a hard disk drive.
- EEPROM electrically erasable and programmable read-only memory
- the readable storage medium includes a computer program including code / computer-readable instructions that, when executed by the processor 602, enables a hardware structure and / or a device including the hardware structure to execute, for example, the above described in conjunction with FIG. 4 Process and any distortions.
- the processor 602 may be a single CPU (Central Processing Unit), but may also include two or more processing units.
- the processor may include a general-purpose microprocessor, an instruction set processor and / or an associated chipset and / or a special-purpose microprocessor (eg, an application-specific integrated circuit (ASIC)).
- ASIC application-specific integrated circuit
- the redundant sensor model includes one or more of at least two satellite positioning device models, at least two compass models, at least two accelerometer models, and at least two gyroscope models. Species.
- the method further includes: acquiring a redundant sensor model parameter configuration instruction; and configuring parameters of the redundant sensor model of the UAV according to the parameter configuration instruction.
- the parameters include at least one of redundancy parameters of sensor models of the same type and attribute parameters of the sensor models.
- the at least two sensor models of the same type include at least a first sensor model and a second sensor model of the same type as the first sensor model, wherein attribute parameters of the first sensor model are different from Attribute parameters of the second sensor model.
- the method further includes: obtaining an adjustment instruction of the redundant sensor model; adjusting the redundant sensor model according to the adjustment instruction; after adjusting the redundant sensor model To determine the simulation working status of the drone's flight controller.
- the adjustment instruction includes at least one of an attribute parameter adjustment instruction of the sensor model and a fault injection instruction of the sensor model.
- the attribute parameters of the sensor model include one or more of noise, delay, zero drift, temperature drift, non-linearity, and installation position.
- determining the simulation working state of the flight controller of the UAV includes: after adjusting the redundant sensor model, Determines whether the drone's flight controller performs a preset response operation.
- the preset response operation is determined according to the adjustment instruction.
- determining whether the flight controller of the UAV performs a preset response operation includes: determining whether the flight controller of the UAV performs At least one of a sensor model selection operation and a sensor model switching operation.
- the simulation model is built into the flight controller. At this time, the simulation model receives parameter configuration instructions and adjustment instructions sent by the control terminal, configures parameters of the sensor model, and adjusts attributes and fault injection of the sensor model. See the previous section for specific principles.
- the simulation model is built in a control terminal that is communicatively connected with a flight controller.
- the control terminal detects the parameter configuration operation and adjustment operation of the user, and determines the parameter configuration instruction and adjustment instruction according to the parameter configuration operation and adjustment operation.
- the simulation model obtains and configures the sensor model based on the parameter configuration instruction and adjustment instruction. Parameters, and adjusting properties and fault injection of the one or more sensor models. See the previous section for specific principles.
- a computer-readable storage medium may be any medium capable of containing, storing, transmitting, propagating, or transmitting instructions.
- computer-readable storage media may include, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, devices, or propagation media.
- Specific examples of computer-readable storage media include: magnetic storage devices such as magnetic tapes or hard disk drives (HDD); optical storage devices such as optical disks (CD-ROM); memories such as random access memory (RAM) or flash memory; and / or Wired / wireless communication link.
- the computer-readable medium stores executable instructions.
- the one or more processors can be caused to execute the instructions shown in FIG. 4.
- a simulation method which specifically includes: obtaining a power signal output by the flight controller when the UAV's flight controller is in a simulation mode; and running a simulation model of the UAV according to the power signal, wherein the simulation model A redundant sensor model is included, wherein the redundant sensor model includes at least two sensor models of the same type; and the simulated flight state data output by the simulation model is transmitted to a flight controller, wherein the simulated flight state The data is determined based on the simulated sensor data output by the redundant sensor model.
- the redundant sensor model includes one or more of at least two satellite positioning device models, at least two compass models, at least two accelerometer models, and at least two gyroscope models. Species.
- the method further includes: acquiring a redundant sensor model parameter configuration instruction; and configuring parameters of the redundant sensor model of the UAV according to the parameter configuration instruction.
- the parameters include at least one of redundancy parameters of sensor models of the same type and attribute parameters of the sensor models.
- the at least two sensor models of the same type include at least a first sensor model and a second sensor model of the same type as the first sensor model, wherein attribute parameters of the first sensor model are different from Attribute parameters of the second sensor model.
- the method further includes: obtaining an adjustment instruction of the redundant sensor model; adjusting the redundant sensor model according to the adjustment instruction; after adjusting the redundant sensor model To determine the simulation working status of the drone's flight controller.
- the adjustment instruction includes at least one of an attribute parameter adjustment instruction of the sensor model and a fault injection instruction of the sensor model.
- the attribute parameters of the sensor model include one or more of noise, delay, zero drift, temperature drift, non-linearity, and installation position.
- determining the simulation working state of the flight controller of the UAV includes: after adjusting the redundant sensor model, Determines whether the drone's flight controller performs a preset response operation.
- the preset response operation is determined according to the adjustment instruction.
- determining whether the flight controller of the UAV performs a preset response operation includes: determining whether the flight controller of the UAV performs At least one of a sensor model selection operation and a sensor model switching operation.
- the simulation model is built into the flight controller.
- the simulation model is built in a control terminal that is communicatively connected with a flight controller.
- an embodiment of the present invention further provides a drone, which includes the aforementioned simulation device for the drone for simulation; the embodiment of the present invention also provides A control terminal is provided, which includes the aforementioned UAV simulation device for simulation.
- the simulation model is built into the flight controller, and the simulation model receives parameter configuration instructions and adjustment instructions sent by the control terminal, configures parameters of the sensor model, and adjusts the sensor model. Properties and fault injection. See the previous section for specific principles.
- the simulation model is built in the control terminal that is communicatively connected with the flight controller, and the control terminal detects a user's parameter configuration operation and adjustment operation, and determines according to the parameter configuration operation and adjustment operation.
- the parameter configuration instruction and the adjustment instruction, the simulation model obtains and configures the parameters of the sensor model according to the parameter configuration instruction and the adjustment instruction, and adjusts the attributes and fault injection of the one or more sensor models. See the previous section for specific principles.
- the embodiment of the present invention can simulate the operation of a redundant sensor without adding a hardware entity by using a simulation model including a redundant sensor model, and at the same time, an adjustment function is added to verify whether the redundant UAV model can Executing predetermined processing logic, thereby improving the function and performance of the drone in the case of redundancy, and improving flight safety.
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Abstract
一种无人机的仿真方法,包括:在无人机的飞行控制器处于仿真模式中,获取飞行控制器输出的动力信号;根据动力信号运行无人机的仿真模型,其中,仿真模型中包括冗余传感器模型(102),其中,冗余传感器模型(102)中包括至少两个相同类型的传感器模型;将仿真模型输出的模拟飞行状态数据传输至飞行控制器,其中,模拟飞行状态数据是根据冗余传感器模型(102)输出的模拟传感器数据确定的。还涉及一种无人机的仿真装置(600),其包括:存储器(601),用于存储可执行指令;处理器(602),用于执行存储器(601)中存储的可执行指令,以执行前述无人机的仿真方法。
Description
本发明涉及无人机领域,尤其涉及一种无人机的仿真方法及装置。
目前,无人机的控制终端一般会配置模拟器,模拟器支持对无人机的仿真,用户可以通过模拟器熟悉无人机的基本功能、锻炼飞行操作技术等,无人机的开发人员可以通过模拟器功能测试无人机的飞行性能。
现有技术中,为了保证无人机的飞行安全性,越来越多的无人机配置冗余传感器。然而,现有的模拟器无法对配置有冗余传感器的无人机进行仿真,这样会降低无人机的飞行安全性。
发明内容
本发明实施例提供一种无人机的仿真方法及装置,以实现对配置有冗余传感器的无人机的仿真。
本发明实施例第一方面提供了一种无人机的仿真方法,包括:
在无人机的飞行控制器处于仿真模式中,获取飞行控制器输出的动力信号;
根据所述动力信号运行无人机的仿真模型,其中,所述仿真模型中包括冗余传感器模型,其中,所述冗余传感器模型中包括至少两个相同类型的传感器模型;
将所述仿真模型输出的模拟飞行状态数据传输至飞行控制器,其中,所述模拟飞行状态数据是根据冗余传感器模型输出的模拟传感器数据确定的。
本发明实施例第二方面提供了一种无人机的仿真装置,包括:
存储器,用于存储可执行指令;
处理器,用于调用所述存储器中存储的所述可执行指令,以执行如下操作:
在无人机的飞行控制器处于仿真模式中,获取飞行控制器输出的动力信号;
根据所述动力信号运行无人机的仿真模型,其中,所述仿真模型中包 括冗余传感器模型,其中,所述冗余传感器模型中包括至少两个相同类型的传感器模型;
将所述仿真模型输出的模拟飞行状态数据传输至飞行控制器,其中,所述模拟飞行状态数据是根据冗余传感器模型输出的模拟传感器数据确定的。
本发明实施例第三方面提供了一种计算机可读存储介质,其存储有可执行指令,所述可执行指令在由一个或多个处理器执行时实现如第一方面所述的仿真方法。
本发明实施例第四方面提供了一种无人机,包括如第二方面所述的仿真装置。
本发明实施例第五方面提供了一种控制终端,与无人机的飞行控制器通信连接,包括如第二方面所述的仿真装置。
本发明实施例中,当无人机的飞行控制器处于仿真模式中,获取飞行控制器输出的动力信号,根据所述动力信号运行无人机的仿真模型,其中,所述仿真模型中包括冗余传感器模型,将所述仿真模型输出的模拟飞行状态数据传输至飞行控制器以驱动仿真过程的持续进行。通过这种方式,实现了对配置有冗余传感器的无人机进行仿真,提高飞行安全性。
附图是用来提供对本发明的进一步理解,并且构成说明书的一部分,与下面的具体实施方式一起用于解释本发明,但并不构成对本发明的限制。在附图中:
图1示意性示出了根据本发明实施例的无人机的仿真原理的示意图。
图2示意性示出了仿真装置位于飞行控制器的实施例的框图。
图3示意性示出了仿真装置位于控制终端的实施例的框图。
图4示意性示出了本发明实施例的无人机的仿真方法的步骤示意图。
图5示意性示出了本发明实施例的对冗余传感器模型进行调整的流程示意图。
图6示意性示出了本发明实施例的仿真装置的框图。
图7示意性示出了本发明计算机可读介质的结构示意图。
下面将结合本发明实施例中的附图,对本发明实施例中的技术方案进行清楚地描述,显然,所描述的实施例仅仅是本发明一部分实施例,而不是全部的实施例。基于本发明中的实施例,本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例,都属于本发明保护的范围。
需要说明的是,当组件被称为“固定于”另一个组件,它可以直接在另一个组件上或者也可以存在居中的组件。当一个组件被认为是“连接”另一个组件,它可以是直接连接到另一个组件或者可能同时存在居中组件。
除非另有定义,本文所使用的所有的技术和科学术语与属于本发明的技术领域的技术人员通常理解的含义相同。本文中在本发明的说明书中所使用的术语只是为了描述具体的实施例的目的,不是旨在于限制本发明。本文所使用的术语“及/或”包括一个或多个相关的所列项目的任意的和所有的组合。
下面结合附图,对本发明的一些实施方式作详细说明。在不冲突的情况下,下述的实施例及实施例中的特征可以相互组合。
目前,为了保证飞行安全性,越来越多的无人机配置有冗余传感器,即配置有多个相同类型的传感器,例如无人机配置多个陀螺仪、多个指南针、多个加速度计、多个卫星定位设备中的一种或多种。在实际应用中,多个相同类型的传感器相互冗余备份。在正常工作状态中,无人机的飞行控制器从一个传感器获取传感器数据,当这个传感器工作异常时,飞行控制器会执行传感器切换操作,即飞行控制器可以切换到一个与该传感器冗余备份的另一个传感器,从所述另一个传感器获取传感器数据。再例如,在某些情况中,在正常工作状态中,飞行控制器可以从多个相同类型的传感器执行选择操作,即飞行控制器可以对多个相同类型的传感器的工作状态进行分别进行评估,并选择工作状态符合预设要求的传感器,即飞行控制器从工作状态符合预设要求的传感器获取传感器数据。例如,飞行控制器可以对多个相同类型的传感器的测量精度进行分别进行评估,飞行控制器选择选择测量精度最高的传感器。
控制终端可以内置无人机的模拟器,模拟器可以支持对无人机的仿真,用户可以通过模拟器熟悉无人机的基本功能、锻炼飞行操作技术等, 无人机的开发人员可以通过模拟器功能测试无人机的飞行性能。其中,控制终端可以包括遥控器、智能手机、平板电脑、膝上型电脑、台式电脑、穿戴式设备中的一种或多种。然而,现有技术中的模拟器不支持对配置有冗余传感器的无人机进行仿真,这样,不能通过仿真来确定当环境因素改变或者冗余传感器的工作状态改变时,无人飞行控制器是否针对环境因素的改变或者冗余传感器的工作状态的改变执行响应操作,例如是否针对环境因素的改变或者冗余传感器的工作状态的改变执行预设操作,例如前述的传感器切换操作或者传感器选择操作等。针对这个问题,本发明实施例提供一种无人机的仿真方法和装置,以实现对配置有冗余传感器的无人机的仿真。
针对配置有冗余传感器的无人机的仿真,下面将详细介绍仿真原理。当无人机处于仿真模式时,具体地,当无人机的飞行控制器处于仿真模式时,无人机的仿真是基于无人机的仿真模型来实现的。参见图1,其中,无人机的仿真模型至少包括无人机的物理模型101和冗余传感器模型102。其中,无人机的物理模型101是一个软件模块,表征无人机的物理模态。无人机的物理模型101接收动力信号,其中,所述动力信号可以为PWM信号,无人机的物理模型101对接收到的动力信号进行响应输出模拟飞行状态数据真值,模拟飞行状态数据真值可以表征动力信号对无人机模型物理101的影响之后无人机的飞行状态。无人机的物理模型101包括电机-螺旋桨模型、动力学模型、运动学模型、对象模型(该对象模型用于表征无人机的物理结构,如动力、结构、重量、机电等)中的一种或多种。
冗余传感器模型102可以包括一个或者多个类型的传感器模型,每个类型的传感器模型包括至少两个传感器模型,例如,3个陀螺仪模型、2个卫星定位设备模型、3个加速度计模型和3个指南针模型。冗余传感器模型102可以为软件模块,冗余传感器模型102在接收到无人机的物理模型101的模拟飞行状态数据真值之后,冗余传感器模型102中每一个类型的传感器模型可以针对模拟飞行状态数据真值输出对应的模拟传感器数据。
进一步地,当飞行控制器处于仿真模式时,所述模拟传感器数据可 以被输出到融合模块103,融合模块103可以根据模拟传感器数据确定模拟飞行状态数据,即融合模块103可以对模拟传感器数据进行融合计算以确定模拟飞行状态数据。在某些情况中,所述融合模块103可以为软件模型,另外,在某些情况中,所述无人机的仿真模型包括融合模块103。当飞行控制器处于正常工作模式下时,无人机的真实飞行状态数据是根据真实冗余传感器***输出的真实传感器数据确定的。进一步地,融合模块103接收真实的冗余传感器***输出的真实传感器数据,并且将所述真实传感器数据融合以确定真实飞行状态数据。
在某些实施例中,当飞行控制器处于仿真模式时,所述模拟飞行状态数据为模拟传感器数据,所述模拟传感器数据不经过融合,直接将模拟传感器数据确定为模拟飞行状态数据。对应地,当飞行控制器处于正常工作模式下时,可以将真实传感器数据直接确定为真实飞行状态数据。
控制器104是飞行控制器的一个重要部件或者软件模块。当飞行控制器处于仿真模式时,可以接收模拟飞行状态数据,另外,飞行控制器可以接收控制终端105的控制杆量,其中,所述控制杆量为控制终端105通过检测用户的无人机控制操作确定的控制指令,控制器104可以根据模拟飞行状态数据和/或控制杆量产生动力信号,无人机的物理模型101可以接收控制器104输出的动力信号,通过这样方式,可以驱动无人机的仿真的持续进行。当飞行控制器处于正常工作模式下时,控制器104可以接收所述真实飞行状态数据,其中,所述控制器104可以根据真实飞行状态数据和/或控制终端105输出的控制杆量产生动力信号,无人机的真实动力***可以接收所述动力信号并执行对应的操作。其中,所述动力***可以包括电调、电机、螺旋桨、发动机中的一个或多个。
当飞行控制器处于仿真模式时,控制终端105可以获取模拟飞行状态数据,其中,控制终端105包括交互装置,交互装置包括显示装置,其中,所述显示装置可以为显示屏、触摸显示屏、LED显示装置中的一种或多种,控制终端105可以在显示装置上显示所述模拟飞行状态数据,这样用户可以通过显示装置了解仿真模式中无人机的飞行状态。当飞行控制器处于正常工作模式时,控制终端105可以获取真实飞行状态数据,控制终端105可以在显示装置上显示所述模拟飞行状态数据,这 样用户可以通过显示装置了解正常工作模式中无人机的飞行状态。
在飞行控制器处于仿真模式之前或者在仿真的模式的过程中,控制终端105可以检测用户的冗余传感器模型102的参数配置操作,所述参数配置操作可以是用户对多个相同类型的传感器模型中每一个模型的物理参数进行设置的操作,其中,所述参数为初始化参数。其中,所述参数可以包括相同类型的传感器模型的冗余度参数、传感器模型的属性参数中的至少一种。其中,所述相同类型的传感器模型的冗余度参数为多个相同类型的传感器模型的数量。另外,所述传感器模型的属性参数可以用于表征传感器模型的任何物理特征,其中,例如,所述属性参数可以包括噪声、延迟、零漂、温漂、非线性度、安装位置中的一种或多种。
控制终端105可以根据检测到的参数配置操作确定冗余传感器模型参数配置指令,所述冗余传感器模型参数配置指令可以用于对多个相同类型的传感器模型中的每一个的参数进行配置。
另外,在仿真的过程中,用户可以调整多个相同类型的传感器模型中的一个或多个模型,控制终端105可以检测用户的冗余传感器模型调整操作,并根据检测到的调整操作确定冗余传感器模型的调整指令。所述调整指令用于调整冗余传感器模型102。所述调整指令包括所述传感器模型的属性参数调节指令、传感器模型的故障注入指令中的至少一种。在某些情况中,所述调整指令包括所述传感器模型的属性参数调节指令,所述属性参数调整指令可以用于对多个相同类型的传感器模型中一个或多个传感器模型的属性参数进行调整。在某些情况中,所述调整指令包括传感器模型的故障注入指令,所述故障注入指令用于为多个相同类型的传感器模型中一个或多个传感器模型注入故障。
在获取到所述调整指令之后,可以对冗余传感器模型102进行调整,具体地,可以对多个相同类型的传感器模型中的一个或多个进行调整,其中,所述调整包括调整传感器模型的属性参数,在某些情况中,所述调整包括为传感器模型注入故障。进一步地,可以在对所述冗余传感器模型102进行调整之后,确定飞行控制器的仿真工作状态,即确定所述飞行控制器针对所述冗余传感器模型102的调整的响应,通过这种方式 可以确定飞行控制器是否针对冗余传感器模型102的调整执行了某些响应操作。
进一步地,可以确定无人机的飞行控制器是否执行预设的响应操作。具体地,当对冗余传感器模型进行调整时,在正常情况下,飞行控制器应该执行预设的响应操作。其中,如前所述,当飞行控制器处于正常工作模式时,飞行控制器从冗余传感器中的一个传感器获取数据,当这个传感器工作异常时,飞行控制器会执行传感器切换操作,即飞行控制器可以切换到一个与该传感器冗余备份的另一个传感器。则在飞行控制器处于仿真模式时,飞行控制器的控制器104从冗余传感器模型102中的一个传感器模型获取模拟传感器数据时,当这个传感器模型工作异常时,飞行控制器应该执行预设的传感器模型切换操作,即飞行控制器可以切换到一个与该传感器模块冗余备份的另一个传感器模型,这样控制器104可以从所述另一个传感器模型获取模拟传感器数据。
可以理解的是,所述预设的响应操作是根据所述调整指令确定的,针对不同的调整指令,所述预设的响应操作是不同的。
在某些实施例中,所述在对所述冗余传感器模型102进行调整之后,确定无人机的飞行控制器是否执行预设的响应操作可以包括:确定无人机的飞行控制器是否执行传感器模型选择操作、传感器模型切换操作中的至少一种。具体地,如前所述,当冗余传感器模型102中的一个或者多个模型调整之后,冗余传感器模型102的工作状态发生变换,正常情况下,飞行控制器应该根据工作状态的变化执行预设的操作。例如,当对卫星定位设备模型1注入故障之后,正常情况下,飞行控制器应该执行传感器模型切换操作以切换到卫星定位设备模型2,此时,为了确认飞行控制器是否正常工作,可以确定飞行控制器是否执行了传感器模型切换操作以切换到卫星定位设备模型2。
在某些实施例中,所述仿真模型可以内置在飞行控制器中。如图2所示,无人机200包括飞行控制器2001,其中,所述仿真模型2002可以内置在飞行控制器2001内,在飞行控制器2001处于仿真模式时,飞行控制器2001可以通过飞行控制器内部数据链路或者外部数据链路获取控制器产生的动力信号,并根据动力信号运行所述仿真模型2002, 如前所述,所述动力信号为控制器产生的动力信号。飞行控制器2001可以通过飞行控制器内部数据链路或者外部数据链路将仿真模型2002输出的模拟飞行状态数据传输至飞行控制器2001中的控制器,控制器根据控制终端201的控制杆量和/或所述模拟飞行状态数据产生动力信号,并将所述动力信号通过飞行控制器2001的内部数据链路或者外部数据链路传输至所述仿真模型2002。
所述运行所述仿真模型2002的过程如前所述。具体地,无人机的物理模型根据所述动力信号输出模拟飞行状态数据真值,冗余传感器模型获取所述模拟飞行状态数据真值并输出模拟传感器数据,根据模拟传感器数据可以确定模拟飞行状态数据。
另外,飞行控制器2001可以将模拟飞行状态数据发送给控制终端201,控制终端201可以获取所述模拟飞行状态数据并在显示装置上显示所述模拟飞行状态数据。飞行控制器2001可以获取控制终端201发送的冗余传感器模型参数配置指令,并根据所述配置指令对所述冗余传感器模型的参数进行配置。另外,飞行控制器2001还可以获取控制终端201发送的冗余传感器模型的调整指令,根据所述调整指令对冗余传感器模型进行调整。另外,对所述冗余传感器模型进行调整之后,确定飞行控制器2001的仿真工作状态,确定仿真工作状态的具体过程如前所述。所述飞行控制器2001可以将所述仿真工作状态发送给控制终端201以便于控制终端201显示所述仿真工作状态。
在显示装置中的交互界面中,不仅可以显示出无人机图像2012,用于显示无人机200当前的姿态、位置、电池电量等信息,还可以显示无人机200当前所在环境中的其他物体图像,例如树木图像2011、建筑物图像2013等,由此,方便用户根据环境对无人机200进行进一步地操作。
在某些实施例中,所述仿真模型可以内置在控制终端中。如图3所示,控制终端301内置无人机的仿真模型3011,在飞行控制器3001处于仿真模式时,控制终端301通过无线数据链路或者有线数据链路从飞行控制器3001获取动力信号,根据动力信号运行所述仿真模型3011,如前所述,所述动力信号为控制器产生的动力信号。控制终端301可以 通过无线数据链或者有线数据链路将仿真模型3011输出的模拟飞行状态数据传输至无人机300的飞行控制器3001的控制器,控制器根据控制终端301的控制杆量和/或所述模拟飞行状态数据产生动力信号,控制终端301接收所述动力信号,并将所述动力信号传输至所述仿真模型3011以驱动仿真的持续进行。
所述运行所述仿真模型3011的过程如前所述。具体地,无人机的物理模型根据所述动力信号输出模拟飞行状态数据真值,冗余传感器模型获取所述模拟飞行状态数据真值并输出模拟传感器数据,根据模拟传感器数据可以确定模拟飞行状态数据。
另外,控制终端301可以获取所述模拟飞行状态数据并在显示装置上显示所述模拟飞行状态数据。控制终端301可以根据所述配置指令对所述冗余传感器模型的参数进行配置。另外,所述控制终端301还可以据根据所述调整指令对冗余传感器模型调整,对所述冗余传感器模型进行调整之后,确定飞行控制器的仿真工作状态。具体地,如前所述,正常情况下,在对冗余传感器模型进行调整之后,飞行控制器会针对冗余传感器模型的变化执行响应操作,飞行控制器的仿真工作状态会发生变化。所述飞行控制器可以将所述仿真工作状态发送给控制终端301以便于控制终端301确定飞行控制器的述仿真工作状态。
此外,在显示装置中的交互界面中,不仅可以显示出无人机图像3012,用于显示无人机300当前的姿态、位置、电池电量等信息,还可以显示无人机当前所在环境中的其他物体图像,例如树木图像3011、建筑物图像3013等,由此,方便用户根据环境对无人机进行进一步地操作。
本发明实施例提供了一种无人机的仿真方法,图4示意性示出了根据本发明实施例的无人机的仿真方法的步骤示意图,如图4所示,该方法包括:
步骤S401,无人机的飞行控制器处于仿真模式中,获取飞行控制器输出的动力信号。
具体地,所述仿真方法的执行主体为无人机的仿真装置,进一步地,所述仿真方法的执行主体为仿真装置的处理器,其中,所述处理器可以为 一个或多个,所述一个或多个单独或者协同地工作以实现所述仿真方法。在某些情况中,飞行控制器可以包括仿真装置;在某些情况中,控制终端可以包括所述仿真装置。
无人机的飞行控制器可以有两种模式:仿真模式和正常工作模式。与该飞行控制器通信连接的控制终端向飞行控制器发送仿真开始指令,以指示飞行控制器进入仿真模式。
在飞行控制器进入仿真模式后,仿真装置获取飞行控制器输出的动力信号。当飞行控制器包括仿真装置时,仿真装置通过内部数据链路或者外部数据链路获取飞行控制器输出的动力信号;当控制终端包括仿真装置时,仿真装置通过无线数据链路或者有线数据链路获取飞行控制器输出的动力信号。具体原理请参见前述部分。
在本发明实施例中,无人机包括冗余传感器,相应地,在无人机的仿真方法中,仿真指令中携带有冗余传感器模型参数配置指令。
步骤S402,根据所述动力信号运行无人机的仿真模型,其中,所述仿真模型中包括冗余传感器模型,其中,所述冗余传感器模型中包括至少两个相同类型的传感器模型。
具体地,仿真装置内置无人机的仿真模型,仿真装置在获取到动力信号之后,可以根据动力信号运行所述仿真模型,其中,所述仿真模型包括冗余传感器模型,在运行所述仿真模型之后可以获取模拟飞行状态数据。其中,根据所述动力信号运行无人机的仿真模型的原理和过程请参见前述部分,在这里不再赘述。
根据本发明实施例,冗余传感器模型接收无人机物理模型输出的真值,进行仿真。可以理解的是,该冗余传感器模型可以包括至少两个卫星定位(GPS)设备模型、至少两个指南针模型、至少两个加速度计模型、至少两个陀螺仪模型中的一种或多种,如此便可实时确定模拟传感器数据,包括加速度、位置信息、角速度和/或线速度等。此外,冗余传感器模型除了之前提到的卫星定位设备模型、指南针模型、加速度计模型和陀螺仪模型,还可以包括气压传感器模型、磁场传感器模型和高度传感器模型或者其他传感器模型。
在本发明实施例中,可以根据实际需要,选择哪种或者哪几种传感器 模型的数量是多余度的,且相同类型的传感器模型的各传感器模型的属性参数应该各不相同。具体原理请参见前述部分,在此不再赘述。
步骤S403,将所述仿真模型输出的模拟飞行状态数据传输至飞行控制器,其中,所述模拟飞行状态数据是根据冗余传感器模型输出的模拟传感器数据确定的。
在本发明实施例中,模拟飞行状态数据可以是由模拟传感器数据经过融合后得到的,在某些情况中,模拟飞行状态数据可以为冗余传感器模型输出的模拟传感器数据。
可选地,所述仿真方法还包括:获取冗余传感器模型参数配置指令;根据所述参数配置指令对所述无人机的冗余传感器模型的参数进行配置。具体地,仿真装置可以获取冗余传感器模型参数配置指令,其中,所述冗余传感器模型参数配置指令用于对冗余传感器模型中多个相同类型的传感器模型中的一个或多个进行参数配置。当所述飞行控制器包括仿真装置时,仿真装置接收控制终端发送的参数配置指令,仿真装置可以根据所述参数配置指令来配置所述一个或多个传感器模型的参数。当所述控制终端包括仿真装置时,控制终端检测用户的参数配置操作,并根据所述参数配置操作确定参数配置指令,仿真装置获取所述参数配置指令并根据所述参数配置指令来配置所述一个或多个传感器模型的参数。具体原理请参见前述部分。
另外,为了进一步检验冗余无人机模型是否能够执行预定的处理逻辑,本发明还增加了对传感器模型的调整,如图5所示,在本发明的一些实施例中,步骤S402中还可以包括步骤S4021~S4023。
在步骤S4021中,获取冗余传感器模型的调整指令。
冗余传感器模型的调整指令是用户根据需要调整的属性参数或者注入故障的类型,通过控制终端发送给飞行控制器的。该调整指令包括操作码和操作数,操作码决定属性参数或者注入故障的类型,操作数决定操作对象即冗余传感器模型中的各传感器模型。
该调整指令包括传感器模型的属性参数调节指令、传感器模型的故障注入指令中的至少一种。同理,属性参数调节指令和传感器模型的故障注入指令也包括操作码和操作数,同理属性参数调节指令的操作码决定属性 参数,操作数决定冗余传感器模型中的各传感器模型;传感器模型的故障注入指令的操作码决定注入故障的类型,操作数决定冗余传感器模型中的各传感器模型。
其中,属性参数调节指令用于调整传感器模型的属性参数即噪声、延迟、零漂、温漂、非线性度、安装位置中的一种或多种;故障注入指令用于调节向传感器模型中注入故障,故障包括数据卡死、断开连接中的一种或者多种。其中,属性参数调节指令和故障注入指令的具体操作对象可以根据实际情况进行选择。
在步骤S4022中,根据所述调整指令对所述冗余传感器模型进行调整。
例如,当所述调节指令为属性参数调节指令时,仿真装置可以对内置的仿真模型的属性参数来进行调节,例如,仿真装置对冗余传感器模型中一个或多个传感器模型的安装位置进行调节。再例如,仿真装置对冗余传感器模型中一个或多个传感器注入故障。
在步骤S4023中,在对所述冗余传感器模型进行调整之后,确定无人机的飞行控制器的仿真工作状态。
当所述飞行控制器包括仿真装置时,仿真装置接收控制终端发送的冗余传感器模型的调整指令,仿真装置可以根据所述调整指令来调整所述一个或多个传感器模型的属性和故障注入。当所述控制终端包括仿真装置时,控制终端检测用户的调整操作,并根据所述调整操作确定调整指令,仿真装置获取所述调整指令并根据所述调整指令来调整所述一个或多个传感器模型的属性参数和故障注入。具体原理请参见前述部分。
飞行控制器的仿真工作状态指无人机的飞行控制器是否执行预设的响应操作,即无人机的飞行控制器是否执行传感器模型选择操作、传感器模型切换操作中的至少一种。其中,预设的响应操作是根据所述调整指令确定的,例如,所述调整指令为属性参数调节指令时,所述预设的响应操作可以为传感器模型选择操作。当所述调整指令为故障注入指令时,所述预设的响应操作可以为传感器模型切换操作。
还需说明的是,仿真模型可以内置在所述飞行控制器中,也可以内置在与飞行控制器通信连接的控制终端(移动智能设备或者计算机)中,可以根据用户的需求进行选择。
本发明另一实施例提供了一种无人机的仿真装置,如图6所示,该仿真装置600包括:存储器601,用于存储可执行指令;处理器602,用于执行所述存储器601中存储的所述可执行指令,详见图4,该仿真方法具体包括:在无人机的飞行控制器处于仿真模式中,获取飞行控制器输出的动力信号;根据所述动力信号运行无人机的仿真模型,其中,所述仿真模型中包括冗余传感器模型,其中,所述冗余传感器模型中包括至少两个相同类型的传感器模型;将所述仿真模型输出的模拟飞行状态数据传输至飞行控制器,其中,所述模拟飞行状态数据是根据冗余传感器模型输出的模拟传感器数据确定的。
存储器601可以是非易失性或易失性可读存储介质,例如是电可擦除可编程只读存储器(EEPROM)、闪存、和/或硬盘驱动器。可读存储介质包括计算机程序,该计算机程序包括代码/计算机可读指令,其在由处理器602执行时使得硬件结构和/或包括硬件结构在内的设备可以执行例如上面结合图4所描述的流程及其任何变形。
处理器602可以是单个CPU(中央处理单元),但也可以包括两个或更多个处理单元。例如,处理器可以包括通用微处理器、指令集处理器和/或相关芯片组和/或专用微处理器(例如,专用集成电路(ASIC))。
在本发明的一些实施例中,所述冗余传感器模型中包括至少两个卫星定位设备模型、至少两个指南针模型、至少两个加速度计模型、至少两个陀螺仪模型中的一种或多种。
在本发明的一些实施例中,所述方法还包括:获取冗余传感器模型参数配置指令;根据所述参数配置指令对所述无人机的冗余传感器模型的参数进行配置。
在本发明的一些实施例中,所述参数包括:相同类型的传感器模型的冗余度参数、所述传感器模型的属性参数中的至少一种。
在本发明的一些实施例中,所述至少两个相同类型的传感器模型至少包括第一传感器模型和与第一传感器模型相同类型的第二传感器模型,其中,第一传感器模型的属性参数不同于第二传感器模型的属性参数。
在本发明的一些实施例中,所述方法还包括:获取冗余传感器模型的调整指令;根据所述调整指令对所述冗余传感器模型进行调整;在对所述 冗余传感器模型进行调整之后,确定无人机的飞行控制器的仿真工作状态。
在本发明的一些实施例中,所述调整指令包括所述传感器模型的属性参数调节指令、传感器模型的故障注入指令中的至少一种。
在本发明的一些实施例中,所述传感器模型的属性参数包括噪声、延迟、零漂、温漂、非线性度、安装位置中的一种或多种。
在本发明的一些实施例中,所述在对所述冗余传感器模型进行调整之后,确定无人机的飞行控制器的仿真工作状态,包括:在对所述冗余传感器模型进行调整之后,确定无人机的飞行控制器是否执行预设的响应操作。
在本发明的一些实施例中,所述预设的响应操作是根据所述调整指令确定的。
在本发明的一些实施例中,所述在对所述冗余传感器模型进行调整之后,确定无人机的飞行控制器是否执行预设的响应操作包括:确定无人机的飞行控制器是否执行传感器模型选择操作、传感器模型切换操作中的至少一种。
在本发明的一些实施例中,所述仿真模型内置在所述飞行控制器中。此时,该仿真模型接收控制终端发送的参数配置指令和调整指令,配置传感器模型的参数、以及调整传感器模型的属性和故障注入。具体原理请参见前述部分。
在本发明的一些实施例中,所述仿真模型内置在与飞行控制器通信连接的控制终端中。此时,控制终端检测用户的参数配置操作和调整操作,并根据所述参数配置操作和调整操作确定参数配置指令和调整指令,仿真模型获取并根据参数配置指令和调整指令,来配置传感器模型的参数、以及调整所述一个或多个传感器模型的属性和故障注入。具体原理请参见前述部分。
作为另一方面,本发明还提供了一种计算机可读介质,该计算机可读存储介质可供指令执行***(例如,一个或多个处理器)使用或者结合指令执行***使用。在本发明实施例的上下文中,计算机可读存储介质可以是能够包含、存储、传送、传播或传输指令的任意介质。例如,计算机可读存储介质可以包括但不限于电、磁、光、电磁、红外或半导体***、装置、器件或传播介质。计算机可读存储介质的具体示例包括:磁存储装置, 如磁带或硬盘(HDD);光存储装置,如光盘(CD-ROM);存储器,如随机存取存储器(RAM)或闪存;和/或有线/无线通信链路。
如图7所示,所述计算机可读介质存储有可执行指令,所述可执行指令在由一个或多个处理器执行时,可以使所述一个或多个处理器执行图4所示的仿真方法,该仿真方法具体包括:在无人机的飞行控制器处于仿真模式中,获取飞行控制器输出的动力信号;根据所述动力信号运行无人机的仿真模型,其中,所述仿真模型中包括冗余传感器模型,其中,所述冗余传感器模型中包括至少两个相同类型的传感器模型;将所述仿真模型输出的模拟飞行状态数据传输至飞行控制器,其中,所述模拟飞行状态数据是根据冗余传感器模型输出的模拟传感器数据确定的。
在本发明的一些实施例中,所述冗余传感器模型中包括至少两个卫星定位设备模型、至少两个指南针模型、至少两个加速度计模型、至少两个陀螺仪模型中的一种或多种。
在本发明的一些实施例中,所述方法还包括:获取冗余传感器模型参数配置指令;根据所述参数配置指令对所述无人机的冗余传感器模型的参数进行配置。
在本发明的一些实施例中,所述参数包括:相同类型的传感器模型的冗余度参数、所述传感器模型的属性参数中的至少一种。
在本发明的一些实施例中,所述至少两个相同类型的传感器模型至少包括第一传感器模型和与第一传感器模型相同类型的第二传感器模型,其中,第一传感器模型的属性参数不同于第二传感器模型的属性参数。
在本发明的一些实施例中,所述方法还包括:获取冗余传感器模型的调整指令;根据所述调整指令对所述冗余传感器模型进行调整;在对所述冗余传感器模型进行调整之后,确定无人机的飞行控制器的仿真工作状态。
在本发明的一些实施例中,所述调整指令包括所述传感器模型的属性参数调节指令、传感器模型的故障注入指令中的至少一种。
在本发明的一些实施例中,所述传感器模型的属性参数包括噪声、延迟、零漂、温漂、非线性度、安装位置中的一种或多种。
在本发明的一些实施例中,所述在对所述冗余传感器模型进行调整之后,确定无人机的飞行控制器的仿真工作状态,包括:在对所述冗余传感 器模型进行调整之后,确定无人机的飞行控制器是否执行预设的响应操作。
在本发明的一些实施例中,所述预设的响应操作是根据所述调整指令确定的。
在本发明的一些实施例中,所述在对所述冗余传感器模型进行调整之后,确定无人机的飞行控制器是否执行预设的响应操作包括:确定无人机的飞行控制器是否执行传感器模型选择操作、传感器模型切换操作中的至少一种。
在本发明的一些实施例中,所述仿真模型内置在所述飞行控制器中。
在本发明的一些实施例中,所述仿真模型内置在与飞行控制器通信连接的控制终端中。
根据仿真模型位于飞行控制器中或者与飞行控制器通信连接的控制终端中,本发明实施例还提供了一种无人机,包括前述的无人机的仿真装置进行仿真;本发明实施例还提供了一种控制终端,包括前述的无人机的仿真装置进行仿真。
对于本发明实施例提供的无人机,所述仿真模型内置在所述飞行控制器中,该仿真模型接收控制终端发送的参数配置指令和调整指令,配置传感器模型的参数、以及调整传感器模型的属性和故障注入。具体原理请参见前述部分。
对于本发明实施例提供的控制终端,所述仿真模型内置在与飞行控制器通信连接的控制终端中,控制终端检测用户的参数配置操作和调整操作,并根据所述参数配置操作和调整操作确定参数配置指令和调整指令,仿真模型获取并根据参数配置指令和调整指令,来配置传感器模型的参数、以及调整所述一个或多个传感器模型的属性和故障注入。具体原理请参见前述部分。
综上,本发明实施例通过包括冗余传感器模型的仿真模型,可在不增加硬件实体的情况下,模拟冗余传感器的运行,同时还加入调节功能,从而检验冗余无人机模型是否能够执行预定的处理逻辑,由此改进无人机在冗余情况下的功能和性能,提高飞行安全性。
以上对本发明的实施例进行了描述。但是,这些实施例仅仅是为了说明的目的,而并非为了限制本发明的范围。尽管在以上分别描述了各实施 例,但是这并不意味着各个实施例中的措施不能有利地结合使用。本发明的范围由所附权利要求及其等同物限定。不脱离本发明的范围,本领域技术人员可以做出多种替代和修改,这些替代和修改都应落在本发明的范围之内。
Claims (29)
- 一种无人机的仿真方法,其特征在于,包括:在无人机的飞行控制器处于仿真模式中,获取飞行控制器输出的动力信号;根据所述动力信号运行无人机的仿真模型,其中,所述仿真模型中包括冗余传感器模型,其中,所述冗余传感器模型中包括至少两个相同类型的传感器模型;将所述仿真模型输出的模拟飞行状态数据传输至飞行控制器,其中,所述模拟飞行状态数据是根据冗余传感器模型输出的模拟传感器数据确定的。
- 根据权利要求1所述的方法,其特征在于,所述冗余传感器模型中包括至少两个卫星定位设备模型、至少两个指南针模型、至少两个加速度计模型、至少两个陀螺仪模型中的一种或多种。
- 根据权利要求1所述的方法,其特征在于,所述方法还包括:获取冗余传感器模型参数配置指令;根据所述参数配置指令对所述无人机的冗余传感器模型的参数进行配置。
- 根据权利要求3所述的方法,其特征在于,所述参数包括:相同类型的传感器模型的冗余度参数、所述传感器模型的属性参数中的至少一种。
- 根据权利要求4所述的方法,其特征在于,所述至少两个相同类型的传感器模型至少包括第一传感器模型和与第一传感器模型相同类型的第二传感器模型,其中,第一传感器模型的属性参数不同于第二传感器模型的属性参数。
- 根据权利要求1所述的方法,其特征在于,所述方法还包括:获取冗余传感器模型的调整指令;根据所述调整指令对所述冗余传感器模型进行调整;在对所述冗余传感器模型进行调整之后,确定无人机的飞行控制器的仿真工作状态。
- 根据权利要求6所述的方法,其特征在于,所述调整指令包括所述传感器模型的属性参数调节指令、传感器模型的故障注入指令中的至少一种。
- 根据权利要求7所述的方法,其特征在于,所述传感器模型的属性参数包括噪声、延迟、零漂、温漂、非线性度、安装位置中的一种或多种。
- 根据权利要求6所述的方法,其特征在于,所述在对所述冗余传感器模型进行调整之后,确定无人机的飞行控制器的仿真工作状态,包括:在对所述冗余传感器模型进行调整之后,确定无人机的飞行控制器是否执行预设的响应操作。
- 根据权利要求9所述的方法,其特征在于,所述预设的响应操作是根据所述调整指令确定的。
- 根据权利要求9或10所述的方法,其特征在于,所述在对所述冗余传感器模型进行调整之后,确定无人机的飞行控制器是否执行预设的响应操作包括:确定无人机的飞行控制器是否执行传感器模型选择操作、传感器模型切换操作中的至少一种。
- 根据权利要求1所述的方法,其特征在于,所述仿真模型内置在所述飞行控制器中。
- 根据权利要求1所述的方法,其特征在于,所述仿真模型内置在与飞行控制器通信连接的控制终端中。
- 一种无人机的仿真装置,其特征在于,包括:存储器,用于存储可执行指令;处理器,用于执行所述存储器中存储的所述可执行指令,以执行如下操作:在无人机的飞行控制器处于仿真模式中,获取飞行控制器输出的动力信号;根据所述动力信号运行无人机的仿真模型,其中,所述仿真模型中包括冗余传感器模型,其中,所述冗余传感器模型中包括至少两个相同类型的传感器模型;将所述仿真模型输出的模拟飞行状态数据传输至飞行控制器,其中,所述模拟飞行状态数据是根据冗余传感器模型输出的模拟传感器数据确定的。
- 根据权利要求14所述的装置,其特征在于,所述冗余传感器模型中包括至少两个卫星定位设备模型、至少两个指南针模型、至少两个加速度计模型、至少两个陀螺仪模型中的一种或多种。
- 根据权利要求14所述的装置,其特征在于,所述处理器还用于:获取冗余传感器模型参数配置指令;根据所述参数配置指令对所述无人机的冗余传感器模型的参数进行配置。
- 根据权利要求16所述的装置,其特征在于,所述参数包括:相同类型的传感器模型的冗余度参数、所述传感器模型的属性参数中的至少一种。
- 根据权利要求17所述的装置,其特征在于,所述至少两个相同类型的传感器模型至少包括第一传感器模型和与第一传感器模型相同类型的第二传感器模型,其中,第一传感器模型的属性参数不同于第二传感器模型的属性参数。
- 根据权利要求14所述的装置,其特征在于,所述处理器还用于:获取冗余传感器模型的调整指令;根据所述调整指令对所述冗余传感器模型进行调整;在对所述冗余传感器模型进行调整之后,确定无人机的飞行控制器的仿真工作状态。
- 根据权利要求19所述的装置,其特征在于,所述调整指令包括所述传感器模型的属性参数调节指令、传感器模型的故障注入指令中的至少一种。
- 根据权利要求20所述的装置,其特征在于,所述传感器模型的属性参数包括噪声、延迟、零漂、温漂、非线性度、安装位置中的一种或多种。
- 根据权利要求19所述的装置,其特征在于,所述处理器在对所述冗余传感器模型进行调整之后,确定无人机的飞行控制器的仿真工作状 态时,具体用于:在对所述冗余传感器模型进行调整之后,确定无人机的飞行控制器是否执行预设的响应操作。
- 根据权利要求22所述的装置,其特征在于,所述预设的响应操作是根据所述调整指令确定的。
- 根据权利要求22或23所述的装置,其特征在于,所述处理器在在对所述冗余传感器模型进行调整之后,确定无人机的飞行控制器是否执行预设的响应操作时,具体用于:确定无人机的飞行控制器是否执行传感器模型选择操作、传感器模型切换操作中的至少一种。
- 根据权利要求14所述的装置,其特征在于,所述仿真模型内置在所述飞行控制器中。
- 根据权利要求14所述的装置,其特征在于,所述仿真模型内置在与飞行控制器通信连接的控制终端中。
- 一种计算机可读存储介质,其特征在于,其存储有可执行指令,所述可执行指令在由一个或多个处理器执行时,可以使所述一个或多个处理器执行如权利要求1至13中任一项权利要求所述的无人机的仿真方法进行仿真。
- 一种无人机,其特征在于,包括如权利要求14至26中任一项权利要求所述的无人机的仿真装置。
- 一种控制终端,与无人机的飞行控制器通信连接,其特征在于,包括如权利要求14至26中任一项权利要求所述的无人机的仿真装置。
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Citations (8)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20070164168A1 (en) * | 2006-01-17 | 2007-07-19 | Hirvonen Jukka M | System and method for an integrated backup control system |
CN101482753A (zh) * | 2009-02-11 | 2009-07-15 | 北京华力创通科技股份有限公司 | 一种多余度飞控计算机实时仿真装置及*** |
CN103941592A (zh) * | 2013-01-22 | 2014-07-23 | 中国科学院沈阳自动化研究所 | 飞行机器人动力学模型的在线建模方法 |
CN106508011B (zh) * | 2010-12-28 | 2014-08-06 | 成都飞机工业(集团)有限责任公司 | 分布式传感器仿真*** |
CN105974494A (zh) * | 2016-07-14 | 2016-09-28 | 无锡信大气象传感网科技有限公司 | 一种基于双余度固定翼无人机的无线电探空*** |
WO2017045141A1 (en) * | 2015-09-16 | 2017-03-23 | SZ DJI Technology Co., Ltd. | Method and apparatus for operating mobile platform |
CN107168297A (zh) * | 2017-07-03 | 2017-09-15 | 电子科技大学 | 一种飞行控制计算机的可靠性验证方法及平台 |
CN107479368A (zh) * | 2017-06-30 | 2017-12-15 | 北京百度网讯科技有限公司 | 一种基于人工智能的训练无人机控制模型的方法及*** |
Family Cites Families (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN101858748B (zh) * | 2010-05-28 | 2012-08-29 | 南京航空航天大学 | 高空长航无人机的多传感器容错自主导航方法 |
CN104749949A (zh) * | 2015-03-19 | 2015-07-01 | 南京航空航天大学 | 一种基于PowerPC和x86的混合三余度无人机飞控计算机及内核设计方法 |
CN105182997B (zh) * | 2015-09-15 | 2018-06-26 | 北京航空航天大学 | 一种基于电磁仿真的无人机规划航路评估方法 |
CN105867414B (zh) * | 2016-04-18 | 2018-08-07 | 浙江大学 | 一种多传感器冗余备份的无人机飞行控制*** |
CN106767777A (zh) * | 2016-11-16 | 2017-05-31 | 江苏科技大学 | 一种新型水下机器人冗余捷联惯导装置 |
-
2018
- 2018-05-30 CN CN201880014874.9A patent/CN110383186A/zh active Pending
- 2018-05-30 WO PCT/CN2018/088988 patent/WO2019227330A1/zh active Application Filing
Patent Citations (8)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20070164168A1 (en) * | 2006-01-17 | 2007-07-19 | Hirvonen Jukka M | System and method for an integrated backup control system |
CN101482753A (zh) * | 2009-02-11 | 2009-07-15 | 北京华力创通科技股份有限公司 | 一种多余度飞控计算机实时仿真装置及*** |
CN106508011B (zh) * | 2010-12-28 | 2014-08-06 | 成都飞机工业(集团)有限责任公司 | 分布式传感器仿真*** |
CN103941592A (zh) * | 2013-01-22 | 2014-07-23 | 中国科学院沈阳自动化研究所 | 飞行机器人动力学模型的在线建模方法 |
WO2017045141A1 (en) * | 2015-09-16 | 2017-03-23 | SZ DJI Technology Co., Ltd. | Method and apparatus for operating mobile platform |
CN105974494A (zh) * | 2016-07-14 | 2016-09-28 | 无锡信大气象传感网科技有限公司 | 一种基于双余度固定翼无人机的无线电探空*** |
CN107479368A (zh) * | 2017-06-30 | 2017-12-15 | 北京百度网讯科技有限公司 | 一种基于人工智能的训练无人机控制模型的方法及*** |
CN107168297A (zh) * | 2017-07-03 | 2017-09-15 | 电子科技大学 | 一种飞行控制计算机的可靠性验证方法及平台 |
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