CN117492383B - Unmanned aerial vehicle automatic test system and method based on semi-physical simulation - Google Patents
Unmanned aerial vehicle automatic test system and method based on semi-physical simulation Download PDFInfo
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Abstract
The invention discloses an unmanned aerial vehicle automatic test system and method based on semi-physical simulation, wherein the system comprises a semi-physical simulation platform and a test module; the semi-physical simulation platform comprises a flight control module, an unmanned aerial vehicle motion simulator, a three-dimensional environment simulator and a ground control station; the flight control module is in communication connection with the unmanned aerial vehicle motion simulator and the three-dimensional environment simulator, and the three-dimensional environment simulator and the ground control station are both in communication connection with the unmanned aerial vehicle motion simulator; the unmanned aerial vehicle motion simulator comprises a complete machine fault model; the test module is in communication connection with the flight control module, the unmanned aerial vehicle motion simulator and the three-dimensional environment simulator and is used for injecting faults based on the fault test case library to perform fault tests and receiving fault test results. The semi-physical simulation technology can realize high-fidelity simulation, realize high-efficiency unmanned aerial vehicle automatic test, solve the problems of high cost, time and labor waste of a true mechanical experiment, and have great significance in unmanned aerial vehicle test and safety modeling.
Description
Technical Field
The invention relates to the technical field of unmanned aerial vehicle testing, in particular to an unmanned aerial vehicle automatic testing method and system based on semi-physical simulation.
Background
In the research of unmanned aerial vehicle safety test, fault-tolerant algorithm verification and other safety-critical tests carried out by the current method based on a true machine experiment are low-efficiency, high-cost and difficult to realize automatically, and cannot be evaluated comprehensively and formally. Therefore, there is an urgent need for an unmanned aerial vehicle testing scheme to realize low-cost, high-efficiency unmanned aerial vehicle automated testing.
Disclosure of Invention
The invention aims to provide an unmanned aerial vehicle automatic test system and method based on semi-physical simulation, so as to realize unmanned aerial vehicle automatic test with low cost and high efficiency.
In a first aspect, an unmanned aerial vehicle automatic test system based on semi-physical simulation is provided, which comprises a semi-physical simulation platform and a test module;
the semi-physical simulation platform comprises a flight control module, an unmanned aerial vehicle motion simulator, a three-dimensional environment simulator and a ground control station; the flight control module is in communication connection with the unmanned aerial vehicle motion simulator and the three-dimensional environment simulator, and the three-dimensional environment simulator and the ground control station are both in communication connection with the unmanned aerial vehicle motion simulator; the unmanned aerial vehicle motion simulator comprises a complete machine fault model, wherein the complete machine fault model is formed by combining a fault principle and designing fault parameters on the basis of the unmanned aerial vehicle motion model;
the test module is in communication connection with the flight control module, the unmanned plane motion simulator and the three-dimensional environment simulator and is used for injecting faults based on the fault test case library to perform fault tests and receiving fault test results.
Further, the complete machine fault model comprises a power system fault model, a load fault model and a sensor fault model. The power system fault model is formed by superposing disturbance fault coefficients in the model of each module of the power system; the load fault model is formed by superimposing a change in moment of inertia due to a mass change occurring on the body in the moment of inertia model; the sensor fault model is formed by superposing zero offset drift amount, scale factor change, constant offset change and white noise in each sensor model.
Further, the power system fault model includes:
a battery fault model, expressed as follows:
in the method, in the process of the invention,which is indicative of the voltage of the battery,representation ofThe capacitor is effectively discharged and the capacitor is effectively discharged,indicating the voltage at which the battery is fully charged,as a function of the fit of the battery,representing a battery failure coefficient;
an electric tuning fault model, which is represented as follows:
in the method, in the process of the invention,representing the equivalent average voltage of the electrical tone,representing the throttle input received by the electric motor,which is indicative of the voltage of the battery,representing an electric regulation fault coefficient;
a motor fault model, expressed as follows:
in the method, in the process of the invention,indicating the actual instantaneous rotational speed of the motor,for the motor response speed failure coefficient,indicating maximum fault responseThe coefficient of the,representing the dynamic response speed of the motor, s representing the complex frequency,representing the failure coefficient of the motor execution efficiency,is the desired steady state rotational speed;
a propeller failure model, expressed as follows:
wherein T represents the pulling force of the propeller, and M represents the torque of the propeller;indicating the coefficient of tension of the steel sheet,is the proportionality coefficient of the torque and the pulling force,indicating the failure coefficient of the pulling force.
Further, the load fault model is represented as follows:
where J represents the total moment of inertia,indicating a normal rotational variable, the rotational speed of the motor,representing a varying moment of inertia, whichIn,when the rotating mass center of the object rotates around the f axis, the moment of inertia of the object relative to the h axis is represented, and both f and h are x, y and z; such asRepresenting the centre of mass around the objectThe moment of inertia of the object about the axis of rotation as the axis rotates;representing the centre of mass around the objectWhen the shaft rotates, the object is perpendicular to the rotation shaftThe moment of inertia of the shaft;representing the centre of mass around the objectWhen the shaft rotates, the object is perpendicular to the rotation shaftThe moment of inertia of the shaft; other analogy;
the additional moment generated by the mass offset is expressed as:
in the method, in the process of the invention,representing the additional moment created by the mass offset,indicating the quality change occurring on the machine body,representation ofThe position in the body coordinate system,the gesture rotation matrix is represented and,indicating the gravitational acceleration.
Further, the sensor fault model includes:
a gyroscope fault model, represented as follows:
in the method, in the process of the invention,representing the actual angular rate of the body;representing the measured angular rate;all represent white noise;indicating zero offset drift amount of the gyroscope;representing a change in scale factor of the gyroscope; subscript i denotes a gyroscope serial number;representing a change in the bias of the gyroscope constant value;the impact coefficient of the gyroscope is changed;representation pairSeeking a derivative;
an accelerometer fault model, expressed as follows:
in the method, in the process of the invention,all represent white noise;representing the real acceleration of the machine body;representing the measured specific force;indicating the zero offset drift amount of the accelerometer;representing a change in the accelerometer scale factor; subscript ofRepresenting accelerometer serial numbers;representing gravitational acceleration;representing a lever arm vector;representing a gesture rotation matrix;representing a unit vector along the z-axis;representation pairSeeking a derivative;
a magnetometer fault model, represented as follows:
in the method, in the process of the invention,all represent white noise;representing magnetic field vectors under a geographic system;representing the measured magnetic field vector;representing a change in magnetometer scale factors;indicating the zero offset drift amount of the magnetometer; subscript ofRepresenting magnetometer number;representing a change in magnetometer constant bias;
a barometer fault model, expressed as follows:
in the method, in the process of the invention,representing the measured height of the barometer;representing white noise;representing a change in the barometer scale factor; subscript ofIndicating the barometer number;indicating a change in the constant bias of the barometer;indicating the zero offset drift amount of the barometer;representing the height of the machine body in geographic coordinates;
a GPS fault model, expressed as follows:
in the method, in the process of the invention,representing whiteNoise;representing a change in the GPS scale factor; subscript r denotes GPS sequence number;representing a change in the GPS constant bias;indicating the zero offset drift amount of the GPS;representing a true position signal;representing the measured position signal.
Further, an environmental fault model is also included, the environmental fault model including a wind disturbance fault model loaded in the unmanned aerial vehicle motion simulator or a three-dimensional environmental simulator and an obstacle model loaded in the three-dimensional environmental simulator.
Further, the wind disturbance fault model is represented as follows:
in the method, in the process of the invention,representing a total wind field value;representing an atmospheric turbulence wind field;representing a constant wind field;representing the tangential wind;indicating gusts;
wind disturbances can cause variations in the unmanned aerial vehicle force and moment, the force of the wind disturbance being described as follows:
in the method, in the process of the invention,representing the force generated by the wind disturbance;the air resistance coefficient of the machine body is represented;is the speed of the air relative to the machine body;is a gesture rotation matrix;representing the speed of the unmanned aerial vehicle under the machine body coordinate system;
the moment generated by wind disturbance is described as follows:
in the method, in the process of the invention,representing the moment of the wind disturbance generation pair;indicating that the point of action of wind is located on the bodyThe position in the coordinate system is determined,、、the components of the position in the x-axis, y-axis and z-axis of the machine body coordinate system are represented respectively.
Further, the obstacle model is built through a three-dimensional environment simulator and is used for calling the obstacle model to simulate a specific task scene at a preset fault time during testing.
Further, the fault test performed by the test module includes:
initializing: the method comprises communication connection initialization, fault parameter initialization, fault test case initialization and three-dimensional scene initialization;
resetting: according to the test requirement, the unmanned aerial vehicle is in a normal flight state immediately before the test fault;
fault injection: selecting an interested fault module according to the test requirement, and sending a fault parameter value of a fault test case to a corresponding fault model;
use case assessment: and judging flight test results after different fault parameter injection by collecting the motion state of the unmanned aerial vehicle after fault injection.
In a second aspect, an unmanned aerial vehicle automatic test method based on semi-physical simulation is provided, which comprises the following steps:
s1: building an unmanned aerial vehicle semi-physical simulation platform, which specifically comprises the following steps:
s1.1: building a complete machine fault model and an environment fault model; the complete machine fault model is formed by combining a fault principle on the basis of an unmanned plane motion model and designing fault parameters; the environment fault model comprises a wind disturbance fault model and an obstacle model; the complete machine fault model comprises a power system fault model, a load fault model and a sensor fault model. The power system fault model is formed by superposing disturbance fault coefficients in the model of each module of the power system; the load fault model is formed by superimposing a change in moment of inertia due to a mass change occurring on the body in the moment of inertia model; the sensor fault model is formed by superposing zero offset drift amount, scale factor change, constant offset change and white noise in each sensor model;
s1.2: generating a dynamic link library according to the built complete machine fault model and the environment fault model, and dynamically loading during simulation;
s1.3: building an unmanned aerial vehicle motion simulator, a three-dimensional environment simulator and a ground control station; the three-dimensional environment simulator and the ground control station are all in communication connection with the unmanned aerial vehicle motion simulator; the unmanned plane motion simulator and the three-dimensional environment simulator are all in communication connection with the flight control module to form a semi-physical simulation closed loop; during simulation, the complete machine fault model is used for being loaded to the unmanned plane motion simulator, the wind disturbance fault model is used for being loaded to the unmanned plane motion simulator or the three-dimensional environment simulator, and the obstacle model is used for being loaded to the three-dimensional environment simulator;
s2: the automatic test method specifically comprises the following steps:
s2.1: initializing, including communication connection initialization, fault parameter initialization, fault test case initialization and three-dimensional scene initialization;
s2.2: resetting, and enabling the unmanned aerial vehicle to be in a normal flight state immediately before a test fault according to a test requirement;
s2.3: fault injection, namely selecting an interested fault module according to test requirements, and sending fault parameter values of fault test cases to corresponding fault models;
s2.4: use case assessment: and judging flight test results after different fault parameter injection by collecting the motion state of the unmanned aerial vehicle after fault injection.
The invention provides an unmanned aerial vehicle automatic test system and method based on semi-physical simulation, which can realize high-fidelity simulation by adopting the semi-physical simulation technology, realize high-efficiency unmanned aerial vehicle automatic test, solve the problems of high cost, time and labor waste of a true mechanical experiment and have great significance in unmanned aerial vehicle test and safety modeling.
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In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic diagram of an unmanned aerial vehicle automatic test system based on semi-physical simulation provided by an embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be described in detail below. It will be apparent that the described embodiments are only some, but not all, embodiments of the invention. All other embodiments, based on the examples herein, which are within the scope of the invention as defined by the claims, will be within the scope of the invention as defined by the claims.
The invention aims to solve the problems of low efficiency, high cost and difficulty in realizing automatic test in the test of the prior art of the real machine experiment, adopts a semi-physical simulation technology to construct an unmanned aerial vehicle automatic test system and method, and considers trusted simulation hardware, trusted simulation software and a trusted simulation model in order to ensure that the simulation result is reliable enough when the semi-physical simulation platform is constructed. For trusted simulation hardware, a high-performance real-time computer is used for simulating a motion model of the unmanned aerial vehicle, and a rendering engine is operated by a high-performance display card to realize high-fidelity simulation of a three-dimensional environment; for trusted simulation software, a model-based design method is adopted to ensure the standardization and reliability of model programming; and building a high-precision motion model for the trusted simulation model to simulate the real motion of the unmanned aerial vehicle. The platform can develop and verify a top-layer visual algorithm, simulate multi-agent cluster formation, make intelligent decisions and the like on the basis of high reliability, and can meet the requirements of various application scenes and safety verification. The diversity of platform functions can meet the safety requirements of various scenes, and the scheme of the invention is specifically described below.
As shown in fig. 1, the embodiment of the invention provides an unmanned aerial vehicle automatic test system based on semi-physical simulation, which comprises a semi-physical simulation platform and a test module;
the semi-physical simulation platform comprises a flight control module, an unmanned aerial vehicle motion simulator, a three-dimensional environment simulator and a ground control station; the flight control module is in communication connection with the unmanned aerial vehicle motion simulator and the three-dimensional environment simulator, and the three-dimensional environment simulator and the ground control station are both in communication connection with the unmanned aerial vehicle motion simulator; the unmanned aerial vehicle motion simulator comprises a complete machine fault model, wherein the complete machine fault model is formed by combining a fault principle and designing fault parameters on the basis of the unmanned aerial vehicle motion model;
the test module is in communication connection with the flight control module, the unmanned plane motion simulator and the three-dimensional environment simulator and is used for injecting faults based on the fault test case library to perform fault tests and receiving fault test results.
Specifically, the flight control module comprises a self-driving instrument and an onboard computer connected with the self-driving instrument, is responsible for being connected with the simulation computer, receives control instructions from the test module, sensor data of the unmanned aerial vehicle motion simulator and visual data of the three-dimensional environment simulator, and responds to the gestures. The unmanned aerial vehicle motion simulator, the three-dimensional environment simulator, the ground control station and the test module are built in the simulation computer. The unmanned aerial vehicle motion simulator loads the complete machine fault model to perform motion simulation, a fault injection interface is reserved for fault testing, and the communication forwarding module of the unmanned aerial vehicle motion simulator sends pose data of the unmanned aerial vehicle to the local network through UDP. Building a three-dimensional scene model of the unmanned aerial vehicle through a three-dimensional environment simulator, receiving pose data of the unmanned aerial vehicle in a local network in real time for three-dimensional display, wherein visual data of the three-dimensional environment simulator can be used for visual perception and control test of an onboard computer and is used for realizing a top layer control function with a flight control module; the three-dimensional environment simulator can build different flight scene models, and supports the switching of flight scenes, the switching of flight viewing angles and the displaying of flight data and flight tracks. The ground control station is mainly used for carrying out sensor calibration, initialization and parameter adjustment on the self-driving instrument before flying; meanwhile, the unmanned aerial vehicle flight state receiving and real-time control command sending functions are supported. The test module designs a fault test case library according to the requirements of a user, circularly injects the fault cases into the semi-physical simulation platform for automatic simulation test, and the simulation test process is displayed in real time through the three-dimensional environment simulator and evaluates the results of the test data according to the motion state of the unmanned aerial vehicle.
The unmanned aerial vehicle system mainly comprises a body subsystem and a sensor subsystem. The method comprises the steps of continuously decomposing each subsystem from top to bottom by adopting a model-based idea, accurately establishing a model of each subsystem, and finally topologically connecting the decomposed subsystems from bottom to top to obtain a comprehensive model of the whole system. On the basis, the fault parameters are designed by combining the fault principle, so that a complete machine fault model is obtained.
The complete machine fault model can be summarized to comprise a power system fault model, a load fault model and a sensor fault model; the power system fault model is formed by superposing disturbance fault coefficients in the model of each module of the power system; the load fault model is formed by superimposing a change in moment of inertia due to a mass change occurring on the body in the moment of inertia model; the sensor fault model is formed by superposing zero offset drift amount, scale factor change, constant offset change and white noise in each sensor model. The faults can finally influence the movement of the unmanned aerial vehicle by changing the force and the moment of the airplane, so that the whole faults are integrated by building a rigid body kinematics model of the unmanned aerial vehicle, and the rigid body kinematics model of the unmanned aerial vehicle is expressed as follows:
in the method, in the process of the invention,andrepresenting the position and velocity respectively in the world coordinate system,for the speed in the machine body coordinate system,for the gesture rotation matrix, superscript T denotes a transpose,is the weight of the unmanned aerial vehicle,in an inertial coordinate systemThe direction vector of the axis is set,for the total lift generated by all rotors of the unmanned aerial vehicle,for the angular velocity of the machine body, q represents a quaternion,the acceleration of the gravity is that,for the acceleration of the body,is the moment generated by the lifting force of the propeller on the engine body shaft,respectively represent、、The moment of force on the shaft is such that,the rotational inertia of the unmanned aerial vehicle is represented,the moment of the spinning top is represented,respectively representing moments generated in roll, pitch, yaw directions.、、、And appear below、、、、Midpoint (middle point) ""all means derivative.
Failure of the power system (motor, electric motor, battery, propeller) can affectAndthe method comprises the steps of carrying out a first treatment on the surface of the Load faults can affect、Andthe method comprises the steps of carrying out a first treatment on the surface of the The sensor faults are different from the above, and mainly comprise the changes of scale factors and constant value offsets, IMU data impact and random walk noise variance.
Specifically, the power system fault model includes:
the battery fault model, which can obtain the battery voltage according to the effective discharge capacitance, is expressed as follows:
in the method, in the process of the invention,which is indicative of the voltage of the battery,indicating that the effective discharge capacity is to be achieved,indicating the voltage at which the battery is fully charged,as a function of the fit of the battery,representing a battery failure coefficient;
an electric tuning fault model, the equivalent average voltage of which is expressed as follows:
in the method, in the process of the invention,representing the equivalent average voltage of the electrical tone,representing the throttle input received by the electric motor,which is indicative of the voltage of the battery,representing an electric regulation fault coefficient;
the motor fault model simplifies the dynamic process into a first-order inertia link, and the actual instantaneous rotating speed of the motor is expressed as follows:
in the method, in the process of the invention,indicating the actual instantaneous rotational speed of the motor,for the motor response speed failure coefficient,indicating the maximum failure response coefficient,representing the dynamic response speed of the motor, s representing the complex frequency,representing the failure coefficient of the motor execution efficiency,is the desired steady state rotational speed;
the tensile force and moment of the propeller fault model are expressed as follows:
wherein T represents the pulling force of the propeller, and M represents the torque of the propeller;indicating the coefficient of tension of the steel sheet,is the proportionality coefficient of the torque and the pulling force,indicating the failure coefficient of the pulling force.
Load faults include, but are not limited to, fault conditions caused by mass changes occurring on the machine body, and in this embodiment, the load fault model is represented as follows:
where J represents the total moment of inertia,indicating a normal rotational variable, the rotational speed of the motor,representing a varying moment of inertia, whereinWhen the rotating mass center of the object rotates around the f axis, the moment of inertia of the object relative to the h axis is represented, and both f and h are x, y and z; such asRepresenting the centre of mass around the objectThe moment of inertia of the object about the axis of rotation as the axis rotates;representing the centre of mass around the objectWhen the shaft rotates, the object is perpendicular to the rotation shaftThe moment of inertia of the shaft;representing the centre of mass around the objectWhen the shaft rotates, the object is perpendicular to the rotation shaftThe moment of inertia of the shaft; other analogy;
the additional moment generated by the mass offset is expressed as:
in the method, in the process of the invention,representing the additional moment created by the mass offset,indicating the quality change occurring on the machine body,representation ofThe position in the body coordinate system,the gesture rotation matrix is represented and,indicating the gravitational acceleration.
Sensor failures can be generalized as delays, breaks, misalignment, noise of the data. Specifically, the sensor failure model includes:
the gyroscope faults can be summarized as: the scale factor, constant bias, data impulse, random walk noise, temperature drift, and data delay, in this embodiment, the gyroscope fault model is expressed as follows:
in the method, in the process of the invention,representing the actual angular rate of the body;representing the measured angular rate;all represent white noise;indicating zero offset drift amount of the gyroscope;representing a change in scale factor of the gyroscope; subscript i denotes a gyroscope serial number;representing a change in the bias of the gyroscope constant value;white noise variation for gyroscopes;the impact coefficient of the gyroscope is changed;
accelerometer fault descriptions can be generalized as: the scale factor, constant bias, data impulse, random walk noise variation, in this embodiment the accelerometer fault model is represented as follows:
in the method, in the process of the invention,all represent white noise;representing the real acceleration of the machine body;representing the measured specific force;indicating the zero offset drift amount of the accelerometer;representing a change in the accelerometer scale factor; subscript ofRepresenting accelerometer serial numbers;representing gravitational acceleration;representing a lever arm vector;representing gesture rotationConverting a matrix;representing a unit vector along the z-axis;
magnetometer fault descriptions can be generalized to scale factor, constant bias, random walk noise variations, in this embodiment, the magnetometer fault model is represented as follows:
in the method, in the process of the invention,all represent white noise;representing magnetic field vectors under a geographic system;representing the measured magnetic field vector;representing a change in magnetometer scale factors;indicating the zero offset drift amount of the magnetometer; subscript ofRepresenting magnetometer number;representing a change in magnetometer constant bias;
the barometer fault description can be generalized to scale factor, constant bias, random walk noise variation, in this embodiment, the barometer fault model is expressed as follows:
in the method, in the process of the invention,representing the measured height of the barometer;representing white noise;representing a change in the barometer scale factor; subscript ofIndicating the barometer number;indicating a change in the constant bias of the barometer;indicating the zero offset drift amount of the barometer;representing the height of the machine body in geographic coordinates;
the GPS fault description can be generalized to the scale factor, the variation of random walk noise, and in this embodiment, the GPS fault model is represented as follows:
in the method, in the process of the invention,representing white noise;representing a change in the GPS scale factor; subscript r denotes GPS sequence number;representing a change in the GPS constant bias;indicating the zero offset drift amount of the GPS;representing a true position signal;representing the measured position signal.
The fault injection interfaces are reserved in all the fault models in the complete machine fault model so as to achieve the function of realizing fault injection by changing fault parameters. The complete machine fault model can realize fault simulation and injection of power system faults, load faults and sensor faults, and realize testing of the power system faults, the load faults and the sensor faults.
However, in actual testing, not only the requirement of the complete machine fault test of the unmanned aerial vehicle exists, but also the environment change needs to be tested under many task scenes, and in order to cope with the tests under the multi-task and multi-scene requirements, in some preferred embodiments of the invention, an environment fault model is also arranged. The environment fault model comprises a wind disturbance fault model and an obstacle model, the wind disturbance fault model is loaded in the unmanned plane motion simulator or the three-dimensional environment simulator, and the wind disturbance fault can affect the rigid body kinematics model、Andthe method comprises the steps of carrying out a first treatment on the surface of the The obstacle model loadingIn the three-dimensional environment simulator.
In particular, wind disturbance faults are among the most prevalent in extreme environments. Wind disturbances can be divided into a superposition of several wind farms. Generally, it can be classified into atmospheric turbulence, constant wind, gust, wind shear, so wind disturbance faults include turbulent wind faults, constant wind faults, gust faults, and wind shear faults. The wind disturbance fault model is represented as follows:
in the method, in the process of the invention,representing a total wind field value;representing an atmospheric turbulence wind field;representing a constant wind field;representing the tangential wind;indicating gusts;
wind disturbance can cause the variation of unmanned aerial vehicle force and moment, thereby cause the variation of complete machine motion gesture. The force of the wind disturbance is described as follows:
in the method, in the process of the invention,representing the force generated by the wind disturbance;the air resistance coefficient of the machine body is represented;is the speed of the air relative to the machine body;is a gesture rotation matrix;representing the speed of the unmanned aerial vehicle under the machine body coordinate system;
the moment generated by wind disturbance is described as follows:
in the method, in the process of the invention,representing the moment generated by wind disturbance;indicating the position of the wind action point under the machine body coordinate system,、、the components of the position in the x-axis, y-axis and z-axis of the machine body coordinate system are represented respectively.
And for the real-time obstacle, building an obstacle model through a three-dimensional environment simulator, and calling the obstacle model at a preset fault time to simulate a specific task scene during testing.
The fault test performed by the test module comprises the following steps:
initializing: the method comprises communication connection initialization, fault parameter initialization, fault test case initialization and three-dimensional scene initialization; the communication connection is initialized to realize the communication connection work of the motion simulator, the three-dimensional engine, the ground control station software and the self-driving instrument; initializing fault parameters to initialize the fault parameters for subsequent fault injection; initializing the cases into test cases required by initializing according to fault characteristics and forming a test case library for automatic test; and initializing the three-dimensional scene, namely constructing a simulated scene model by using a three-dimensional engine, and initializing simulated three-dimensional scene information.
Resetting: according to the test requirement, the unmanned aerial vehicle is in a normal flight state immediately before the test fault; the reset task mainly comprises the step of sending a control instruction to the self-driving instrument through the test module, so that the unmanned aerial vehicle is in a specific flight state, and the flight state is formulated according to different test tasks and test scenes.
Fault injection: and the fault injection is used as a part of the fault tolerance safety test of the unmanned aerial vehicle, the interested fault module is selected according to different test scenes and requirements, and the fault parameter value of the fault test case is sent to the corresponding fault model through UDP.
Use case assessment: and judging flight test results after different fault parameter injection by collecting the motion state of the unmanned aerial vehicle in the three-dimensional environment simulator after fault injection, and collecting the test results of each time to form a test result library.
The unmanned aerial vehicle automatic test system based on the semi-physical simulation provided by the embodiment can realize high-fidelity simulation by adopting the semi-physical simulation technology, realize efficient unmanned aerial vehicle automatic test, solve the problems of high cost, time and labor waste of a true mechanical experiment, and have great significance in unmanned aerial vehicle test and safety modeling.
It should be noted that, for the simulation test scenario of the unmanned aerial vehicle cluster, a plurality of unmanned aerial vehicle motion simulators can be set on one simulation computer, and the simulation computer is connected with a plurality of flight control modules so as to simulate a plurality of unmanned aerial vehicles; of course, if the number of unmanned aerial vehicle clusters is large, a plurality of simulation computers can be used for networking, so that simultaneous simulation of multiple unmanned aerial vehicles is realized.
The embodiment of the invention also provides an unmanned aerial vehicle automatic test method based on semi-physical simulation, which comprises the following steps:
s1: building an unmanned aerial vehicle semi-physical simulation platform, which specifically comprises the following steps:
s1.1: building a complete machine fault model and an environment fault model; the complete machine fault model is formed by combining a fault principle on the basis of an unmanned plane motion model and designing fault parameters; the environment fault model comprises a wind disturbance fault model and an obstacle model; the specific construction of the complete machine fault model and the environment fault model is referred to the foregoing embodiments, and will not be described herein again;
s1.2: generating a dynamic link library according to the built complete machine fault model and the environment fault model, and dynamically loading during simulation; during automatic test, different fault parameters are sent to corresponding fault models through UDP, so that different situations can be simulated, and an efficient test function is realized;
s1.3: building an unmanned aerial vehicle motion simulator, a three-dimensional environment simulator and a ground control station; the three-dimensional environment simulator and the ground control station are all in communication connection with the unmanned aerial vehicle motion simulator; the unmanned plane motion simulator and the three-dimensional environment simulator are all in communication connection with the flight control module to form a semi-physical simulation closed loop; during simulation, the complete machine fault model is used for being loaded to the unmanned plane motion simulator, the wind disturbance fault model is used for being loaded to the unmanned plane motion simulator or the three-dimensional environment simulator, and the obstacle model is used for being loaded to the three-dimensional environment simulator;
s2: the automatic test method specifically comprises the following steps:
s2.1: initializing, including communication connection initialization, fault parameter initialization, fault test case initialization and three-dimensional scene initialization;
s2.2: resetting, and enabling the unmanned aerial vehicle to be in a normal flight state immediately before a test fault according to a test requirement;
s2.3: fault injection, namely selecting an interested fault module according to test requirements, and sending fault parameter values of fault test cases to corresponding fault models;
s2.4: use case assessment: and judging flight test results after different fault parameter injection by collecting the motion state of the unmanned aerial vehicle after fault injection.
It is to be understood that the same or similar parts in the above embodiments may be referred to each other, and that in some embodiments, the same or similar parts in other embodiments may be referred to.
The following further describes the scheme of the invention by taking a fault test experiment of motor faults as an example.
The platform building and simulation process is carried out on a Win10 operating system computer with a main frequency of 2.9Ghz and a memory of 16G; the whole machine fault model adoptsA quad rotor comprising four motors; the weight of the machine body is 1.515kg, and the radius of the machine body is 0225m; PX4 is selected from the self-driving instrument.
The following experiment is a simulation test performed at a height of 30 meters where the unmanned aerial vehicle flies, and its flight task is a hover task. The fault test is motor fault.
The testing method specifically comprises the following steps:
step one: semi-physical simulation platform construction
According to S1.1 and S1.2, after a complete machine fault model and an environment fault model are built and a fault injection interface is designed, a dynamic link library is generated; and (3) building an unmanned aerial vehicle motion simulator, a three-dimensional environment simulator and a ground control station according to the step S1.3, and connecting with the flight control module to obtain a complete semi-physical simulation platform.
Step two: implementation of automated testing
Initializing: through compiling test script, initializing the communication connection of unmanned aerial vehicle motion simulator, three-dimensional environment simulator, ground control station and flight control module to judge its connection state through the connection information that unmanned aerial vehicle motion simulator fed back, in order to ensure that it is connected correctly. The test is motor fault, and the execution efficiency of one motor is respectively set to be 0.9, 0.6 and 0.1 for testing by changing the parameter value corresponding to the motor fault in the fault injection interface.
Resetting: before testing, a control instruction is sent through a testing program, so that the unmanned aerial vehicle flies to the high altitude at 30 meters and is in a hovering state, and the unmanned aerial vehicle reaches the flying state of an expected task before fault injection, so that a reset task is completed.
Fault injection: and sending the motor fault parameters written in advance to a complete machine fault model of the unmanned aerial vehicle motion simulator through UDP by a test program so as to trigger motor faults.
Test results: by observing the motion state of the unmanned aerial vehicle of the three-dimensional environment simulator, the test result is shown in the table one:
while embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the invention, and that variations, modifications, alternatives and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the invention.
Claims (6)
1. An unmanned aerial vehicle automatic test system based on semi-physical simulation is characterized by comprising a semi-physical simulation platform and a test module;
the semi-physical simulation platform comprises a flight control module, an unmanned aerial vehicle motion simulator, a three-dimensional environment simulator and a ground control station; the flight control module is in communication connection with the unmanned aerial vehicle motion simulator and the three-dimensional environment simulator, and the three-dimensional environment simulator and the ground control station are both in communication connection with the unmanned aerial vehicle motion simulator; the unmanned aerial vehicle motion simulator comprises a complete machine fault model, wherein the complete machine fault model is formed by combining a fault principle and designing fault parameters on the basis of the unmanned aerial vehicle motion model;
the test module is in communication connection with the flight control module, the unmanned plane motion simulator and the three-dimensional environment simulator and is used for injecting faults based on a fault test case library so as to perform fault tests and receive fault test results;
the complete machine fault model comprises a power system fault model, a load fault model and a sensor fault model;
the power system fault model is formed by superposing disturbance fault coefficients in the model of each module of the power system; the load fault model is formed by superimposing a change in moment of inertia due to a mass change occurring on the body in the moment of inertia model; the sensor fault model is formed by superposing zero offset drift amount, scale factor change, constant offset change and white noise in each sensor model;
the power system fault model includes:
a battery fault model, expressed as follows:
；
in the method, in the process of the invention,represents battery voltage, < >>Representing the effective discharge capacitance, +.>Indicating the voltage at which the battery is fully charged,fitting function for battery, +.>Representing a battery failure coefficient;
an electric tuning fault model, which is represented as follows:
；
in the method, in the process of the invention,representing the equivalent average voltage of the electric tone, +.>Throttle input representing electric throttle reception, +.>Which is indicative of the voltage of the battery,representing an electric regulation fault coefficient;
a motor fault model, expressed as follows:
；
in the method, in the process of the invention,indicating the actual instantaneous speed of the motor,/->For motor response speed failure coefficient, < >>Representing the maximum fault response coefficient,/->Representing the dynamic response speed of the motor, s representing the complex frequency,/->Representing motor execution efficiency failure coefficient,/->Is the desired steady state rotational speed;
a propeller failure model, expressed as follows:
；
wherein T represents the pulling force of the propeller, and M represents the torque of the propeller;represents the tension coefficient>Is the ratio of torque to pull, +.>Representing a tensile failure coefficient;
the load fault model is represented as follows:
；
；
where J represents the total moment of inertia,indicating normal rotational variables +.>Representing a varying moment of inertia, wherein->When the rotating mass center of the object rotates around the f axis, the moment of inertia of the object relative to the h axis is represented, and both f and h are x, y and z;
the additional moment generated by the mass offset is expressed as:
；
in the method, in the process of the invention,representing the additional moment generated by the mass shift, +.>Indicating mass changes occurring in the body, +.>Representation->Position in body coordinate system, +.>Representing a gesture rotation matrix +.>Representing gravitational acceleration;
the sensor fault model includes:
a gyroscope fault model, represented as follows:
；
；
in the method, in the process of the invention,representing the actual angular rate of the body; />Representing the measured angular rate; />All represent white noise; />Indicating zero offset drift amount of the gyroscope; />Representing a change in scale factor of the gyroscope; subscript->Representing the serial number of the gyroscope; />Representing a change in the bias of the gyroscope constant value; />The impact coefficient of the gyroscope is changed;
an accelerometer fault model, expressed as follows:
；
；
in the method, in the process of the invention,all represent white noise; />Representing the real acceleration of the machine body; />Representing the measured specific force;indicating the zero offset drift amount of the accelerometer; />Representing a change in the accelerometer scale factor; subscript->Representing accelerometer serial numbers; />Representing gravitational acceleration; />Representing a lever arm vector; />Representing a gesture rotation matrix; />Representing a unit vector along the z-axis;
a magnetometer fault model, represented as follows:
；
；
in the method, in the process of the invention,all represent white noise; />Representing magnetic field vectors under a geographic system; />Representing measured magnetic fieldsVector; />Representing a change in magnetometer scale factors; />Indicating the zero offset drift amount of the magnetometer; subscript->Representing magnetometer number; />Representing a change in magnetometer constant bias;
a barometer fault model, expressed as follows:
；
；
in the method, in the process of the invention,representing the measured height of the barometer; />Representing white noise; />Representing a change in the barometer scale factor; subscript->Indicating the barometer number; />Indicating a change in the constant bias of the barometer; />Indicating the zero offset drift amount of the barometer; />Representing the height of the machine body in geographic coordinates;
a GPS fault model, expressed as follows:
；
；
in the method, in the process of the invention,representing white noise; />Representing a change in the GPS scale factor; subscript r denotes GPS sequence number; />Representing a change in the GPS constant bias; />Indicating the zero offset drift amount of the GPS; />Representing a true position signal; />Representing the measured position signal.
2. The automated unmanned aerial vehicle test system of claim 1, further comprising an environmental fault model comprising a wind disturbance fault model and an obstacle model, the wind disturbance fault model being loaded in the unmanned aerial vehicle motion simulator or a three-dimensional environmental simulator, the obstacle model being loaded in the three-dimensional environmental simulator.
3. The unmanned aerial vehicle automated test system based on semi-physical simulation of claim 2, wherein the wind disturbance fault model is represented as follows:
；
in the method, in the process of the invention,representing a total wind field value; />Representing an atmospheric turbulence wind field; />Representing a constant wind field; />Representing the tangential wind; />Indicating gusts;
wind disturbances can cause variations in the unmanned aerial vehicle force and moment, the force of the wind disturbance being described as follows:
；
；
in the method, in the process of the invention,representing the force generated by the wind disturbance; />The air resistance coefficient of the machine body is represented; />Is the speed of the air relative to the machine body; />Is a gesture rotation matrix; />Representing the speed of the unmanned aerial vehicle under the machine body coordinate system;
the moment generated by wind disturbance is described as follows:
；
；
in the method, in the process of the invention,representing the moment generated by wind disturbance; />Indicating the position of the point of action of the wind force in the body coordinate system +.>、/>、/>Respectively indicate that the positions are on-machineComponents on the x-axis, y-axis, z-axis in the volumetric coordinate system.
4. The unmanned aerial vehicle automatic test system based on semi-physical simulation according to claim 2, wherein the obstacle model is built by a three-dimensional environment simulator and is used for calling the obstacle model to simulate a specific task scene at a preset failure time during testing.
5. The automated unmanned aerial vehicle testing system based on semi-physical simulation of claim 1, wherein the testing module performs fault testing comprising:
initializing: the method comprises communication connection initialization, fault parameter initialization, fault test case initialization and three-dimensional scene initialization;
resetting: according to the test requirement, the unmanned aerial vehicle is in a normal flight state immediately before the test fault;
fault injection: selecting an interested fault module according to the test requirement, and sending a fault parameter value of a fault test case to a corresponding fault model;
use case assessment: and judging flight test results after different fault parameter injection by collecting the motion state of the unmanned aerial vehicle after fault injection.
6. An unmanned aerial vehicle automatic test method based on semi-physical simulation is characterized by comprising the following steps:
s1: building an unmanned aerial vehicle semi-physical simulation platform, which specifically comprises the following steps:
s1.1: building a complete machine fault model and an environment fault model; the complete machine fault model is formed by combining a fault principle on the basis of an unmanned plane motion model and designing fault parameters; the environment fault model comprises a wind disturbance fault model and an obstacle model; the complete machine fault model comprises a power system fault model, a load fault model and a sensor fault model; the power system fault model is formed by superposing disturbance fault coefficients in the model of each module of the power system; the load fault model is formed by superimposing a change in moment of inertia due to a mass change occurring on the body in the moment of inertia model; the sensor fault model is formed by superposing zero offset drift amount, scale factor change, constant offset change and white noise in each sensor model;
the power system fault model includes:
a battery fault model, expressed as follows:
；
in the method, in the process of the invention,represents battery voltage, < >>Representing the effective discharge capacitance, +.>Indicating the voltage at which the battery is fully charged,fitting function for battery, +.>Representing a battery failure coefficient;
an electric tuning fault model, which is represented as follows:
；
in the method, in the process of the invention,representing the equivalent average voltage of the electric tone, +.>Representation ofThrottle input received by electric control,/->Which is indicative of the voltage of the battery,representing an electric regulation fault coefficient;
a motor fault model, expressed as follows:
；
in the method, in the process of the invention,indicating the actual instantaneous speed of the motor,/->For motor response speed failure coefficient, < >>Representing the maximum fault response coefficient,/->Representing the dynamic response speed of the motor, s representing the complex frequency,/->Representing motor execution efficiency failure coefficient,/->Is the desired steady state rotational speed;
a propeller failure model, expressed as follows:
；
wherein T represents the pulling force of the propeller, and M represents the torque of the propeller;represents the tension coefficient>Is the ratio of torque to pull, +.>Representing a tensile failure coefficient;
the load fault model is represented as follows:
；
；
where J represents the total moment of inertia,indicating normal rotational variables +.>Representing a varying moment of inertia, wherein->When the rotating mass center of the object rotates around the f axis, the moment of inertia of the object relative to the h axis is represented, and both f and h are x, y and z;
the additional moment generated by the mass offset is expressed as:
；
in the method, in the process of the invention,representing the additional moment generated by the mass shift, +.>Indicating mass changes occurring in the body, +.>Representation->Position in body coordinate system, +.>Representing a gesture rotation matrix +.>Representing gravitational acceleration;
the sensor fault model includes:
a gyroscope fault model, represented as follows:
；
；
in the method, in the process of the invention,representing the actual angular rate of the body; />Representing the measured angular rate; />All represent white noise; />Indicating zero offset drift amount of the gyroscope; />Representing a change in scale factor of the gyroscope; subscript->Representing the serial number of the gyroscope; />Representing a change in the bias of the gyroscope constant value; />The impact coefficient of the gyroscope is changed;
an accelerometer fault model, expressed as follows:
；
；
in the method, in the process of the invention,all represent white noise; />Representing the real acceleration of the machine body; />Representing the measured specific force;indicating the zero offset drift amount of the accelerometer; />Representing a change in the accelerometer scale factor; subscript->Representing accelerometer serial numbers; />Representing gravitational acceleration; />Representing a lever arm vector; />Representing a gesture rotation matrix; />Representing a unit vector along the z-axis;
a magnetometer fault model, represented as follows:
；
；
in the method, in the process of the invention,all represent white noise; />Representing magnetic field vectors under a geographic system; />Representing the measured magnetic field vector; />Representing a change in magnetometer scale factors; />Indicating the zero offset drift amount of the magnetometer; subscript->Representing magnetometer number; />Representing a change in magnetometer constant bias;
a barometer fault model, expressed as follows:
；
；
in the method, in the process of the invention,representing the measured height of the barometer; />Representing white noise; />Representing a change in the barometer scale factor; subscript->Indicating the barometer number; />Indicating a change in the constant bias of the barometer; />Indicating the zero offset drift amount of the barometer; />Indicating that the body is in geographyHeight in coordinates;
a GPS fault model, expressed as follows:
；
；
in the method, in the process of the invention,representing white noise; />Representing a change in the GPS scale factor; subscript r denotes GPS sequence number; />Representing a change in the GPS constant bias; />Indicating the zero offset drift amount of the GPS; />Representing a true position signal; />Representing the measured position signal;
s1.2: generating a dynamic link library according to the built complete machine fault model and the environment fault model, and dynamically loading during simulation;
s1.3: building an unmanned aerial vehicle motion simulator, a three-dimensional environment simulator and a ground control station; the three-dimensional environment simulator and the ground control station are all in communication connection with the unmanned aerial vehicle motion simulator; the unmanned plane motion simulator and the three-dimensional environment simulator are all in communication connection with the flight control module to form a semi-physical simulation closed loop; during simulation, the complete machine fault model is used for being loaded to the unmanned plane motion simulator, the wind disturbance fault model is used for being loaded to the unmanned plane motion simulator or the three-dimensional environment simulator, and the obstacle model is used for being loaded to the three-dimensional environment simulator;
s2: the automatic test method specifically comprises the following steps:
s2.1: initializing, including communication connection initialization, fault parameter initialization, fault test case initialization and three-dimensional scene initialization;
s2.2: resetting, and enabling the unmanned aerial vehicle to be in a normal flight state immediately before a test fault according to a test requirement;
s2.3: fault injection, namely selecting an interested fault module according to test requirements, and sending fault parameter values of fault test cases to corresponding fault models;
s2.4: use case assessment: and judging flight test results after different fault parameter injection by collecting the motion state of the unmanned aerial vehicle after fault injection.
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Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107168297A (en) * | 2017-07-03 | 2017-09-15 | 电子科技大学 | The reliability verification method and platform of a kind of flight-control computer |
| CN111856965A (en) * | 2020-06-22 | 2020-10-30 | 拓攻（南京）机器人有限公司 | Unmanned aerial vehicle visual semi-physical simulation system and simulation method thereof |
| CN113467248A (en) * | 2021-07-22 | 2021-10-01 | 南京大学 | Fault-tolerant control method for unmanned aerial vehicle sensor during fault based on reinforcement learning |
| WO2021244544A1 (en) * | 2020-06-03 | 2021-12-09 | 深圳市道通智能航空技术股份有限公司 | Unmanned aerial vehicle fault detection method, unmanned aerial vehicle, and unmanned aerial vehicle system |
| CN113917854A (en) * | 2021-10-18 | 2022-01-11 | 北京航空航天大学 | Semi-physical simulation platform of flight control system for large and medium-sized unmanned aerial vehicle |
| CN114167847A (en) * | 2022-02-14 | 2022-03-11 | 中航金城无人系统有限公司 | Test system and test method suitable for multi-rotor aircraft flight control system |
| CN115309069A (en) * | 2022-07-18 | 2022-11-08 | 芜湖职业技术学院 | Unmanned aerial vehicle semi-physical maintenance training system supporting fault injection |
| CN115982985A (en) * | 2022-12-23 | 2023-04-18 | 上海交通大学 | Modeling method for failure of flapping wings and actuating mechanism of bionic flapping wing aircraft |
| WO2023181650A1 (en) * | 2022-03-22 | 2023-09-28 | 株式会社豊田中央研究所 | Condition-estimating device, fault-determining device, and condition-estimating/fault-determining device |
-
2024
- 2024-01-03 CN CN202410006009.7A patent/CN117492383B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107168297A (en) * | 2017-07-03 | 2017-09-15 | 电子科技大学 | The reliability verification method and platform of a kind of flight-control computer |
| WO2021244544A1 (en) * | 2020-06-03 | 2021-12-09 | 深圳市道通智能航空技术股份有限公司 | Unmanned aerial vehicle fault detection method, unmanned aerial vehicle, and unmanned aerial vehicle system |
| CN111856965A (en) * | 2020-06-22 | 2020-10-30 | 拓攻（南京）机器人有限公司 | Unmanned aerial vehicle visual semi-physical simulation system and simulation method thereof |
| CN113467248A (en) * | 2021-07-22 | 2021-10-01 | 南京大学 | Fault-tolerant control method for unmanned aerial vehicle sensor during fault based on reinforcement learning |
| CN113917854A (en) * | 2021-10-18 | 2022-01-11 | 北京航空航天大学 | Semi-physical simulation platform of flight control system for large and medium-sized unmanned aerial vehicle |
| CN114167847A (en) * | 2022-02-14 | 2022-03-11 | 中航金城无人系统有限公司 | Test system and test method suitable for multi-rotor aircraft flight control system |
| WO2023181650A1 (en) * | 2022-03-22 | 2023-09-28 | 株式会社豊田中央研究所 | Condition-estimating device, fault-determining device, and condition-estimating/fault-determining device |
| CN115309069A (en) * | 2022-07-18 | 2022-11-08 | 芜湖职业技术学院 | Unmanned aerial vehicle semi-physical maintenance training system supporting fault injection |
| CN115982985A (en) * | 2022-12-23 | 2023-04-18 | 上海交通大学 | Modeling method for failure of flapping wings and actuating mechanism of bionic flapping wing aircraft |
Non-Patent Citations (4)
| Title |
|---|
| Safety_Assessment_Approach_to_UAVs_Based_on_Profust_Safety_Index_and_HIL_Simulation;戴训华等;IEEE;20231129;全文 * |
| 六旋翼无人机故障容错及精准定高策略研究;刘登攀;硕士电子期刊;20220315;全文 * |
| 基于半实物仿真的测试性验证系统设计与实现;吴喆;景博;余思奇;杨洲;邓森;;计算机测量与控制;20130925(第09期);全文 * |
| 转子系统碰摩故障的电流与振动信号分析方法研究;杨亚东;硕士电子期刊;20190815;全文 * |
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