Indoor unmanned aerial vehicle flight control navigation performance evaluation method based on autonomous positioning GNSS

Abstract

The invention provides an indoor unmanned aerial vehicle flight control navigation performance evaluation method based on a self-positioning GNSS. The method comprises the following steps that 1, an external unmanned aerial vehicle real-time state sensing device is installed; 2, indoor and external positioning devices are installed; step 3, starting a GNSS / RTK signal generator based on the autonomous positioning system; 4, testing the flight of the unmanned aerial vehicle; and 5, processing flight data of the unmanned aerial vehicle, and evaluating the performance. According to the invention, the unmanned aerial vehicle can still maintain efficient and safe flight performance in various operating environments and possible non-standard conditions. Through the comprehensive navigation performance evaluation, an operator and a manufacturer of the unmanned aerial vehicle can obtain detailed data about equipment performance, so that the design is optimized, and the reliability of a flight strategy is improved. The practical application efficiency of the unmanned aerial vehicle is improved, the application capability of the unmanned aerial vehicle in a complex environment is enhanced, and wide application of the unmanned aerial vehicle technology in the civil and commercial fields is promoted.

Description

Technical Field

[0001] The present invention belongs to the field of flight control and navigation performance evaluation of rotary-wing UAVs, which includes indoor autonomous positioning, GNSS / RTK signal generation, real-time state estimation, multi-sensor fusion, computer vision, etc., and is particularly capable of realizing real-time attitude detection and flight control and navigation performance evaluation of closed-source UAVs in indoor environments, and specifically relates to an indoor satellite positioning test method based on signal simulation and a UAV flight control and navigation performance evaluation system. Background Art

[0002] A multirotor aircraft is an aircraft that relies on multiple non-variable pitch propellers to provide control force and torque. It can easily complete basic flight maneuvers such as vertical take-off and landing, hovering, and turning. It is widely used in aerial photography, exploration and detection, agricultural plant protection, rescue and search and rescue, and it also has a certain market and development prospects in new fields such as short-distance freight logistics and manned short-distance transportation.

[0003] Compared with fixed-wing aircraft, the actuator of a rotorcraft is composed of motor propellers. When a failure occurs, it will directly affect the flight status of the drone, causing the drone to fail to fly according to the mission instructions or even crash. In outdoor scenes, it may cause economic losses or even casualties. Therefore, the safety and reliability of drone flight control and navigation performance have gradually become important assessment indicators for the use of drones in large venues. In order to verify this performance, it is usually necessary to regularly evaluate the flight control and navigation performance of drones. However, drone applications on the market usually choose closed-source drone equipment produced by mature companies such as DJI and Daotong. It is difficult to directly obtain drone status information, and it is impossible to perform GPS navigation flight indoors, and it is impossible to regularly evaluate the flight control and navigation performance of drones.

[0004] Therefore, the invention mainly aims at the current lack of flight control and navigation performance evaluation capabilities of UAVs, and proposes an indoor UAV flight control and navigation performance evaluation method based on GNSS / RTK signal generation of an autonomous positioning system, which can be deployed on UAVs with a high degree of closed source, reducing the difficulty of retrieving monitoring data; a UAV indoor positioning system based on a satellite positioning device is deployed indoors, which can realize satellite navigation indoors, and the flight control and navigation performance of the UAV can be evaluated in an indoor environment to verify the flight control and navigation performance of the UAV; a highly versatile flight control and navigation performance evaluation system for rotor UAVs is constructed, and the relevant mathematical definitions and descriptions, divisions and interfaces are unified using an open source system to improve the system's versatility. Summary of the invention

[0005] Since drone components may be affected by factors such as ambient temperature and humidity during flight, causing invisible damage, when a failure occurs, it will directly affect the flight status of the drone, causing the drone to fail to fly according to mission instructions or even crash, which may cause economic losses or even casualties in outdoor scenes. Therefore, the safety and reliability of drone flight control and navigation performance have gradually become important assessment indicators for the use of drones in large venues. In order to verify this performance, it is usually necessary to regularly evaluate the flight control and navigation performance of drones. However, drone applications on the market usually choose closed-source drone equipment produced by mature companies such as DJI and Daotong. It is difficult to directly obtain drone status information, and it is impossible to perform GPS navigation flight indoors, and it is impossible to evaluate the flight control and navigation performance of drones in indoor laboratory environments.

[0006] In order to realize the flight control and navigation performance evaluation of outdoor UAV (using GNSS / RTK signals for navigation) in an indoor laboratory environment, the present invention constructs an indoor UAV flight control and navigation performance evaluation method based on GNSS / RTK signal generation of an autonomous positioning system. The overall system consists of the following components: Figure 1 As shown. It includes the following steps:

[0007] Step 1: Install the external drone real-time status sensing device

[0008] Mount the external drone real-time status sensing device according to the mounting holes on the DJI Mavic 3E drone, and start the sensing device to start recording the current drone status data.

[0009] The external drone real-time status perception device is mainly used for aircraft with closed-source modules such as DJI and Docom, and obtains drone flight status information in real time by deploying multi-dimensional information sensors on the aircraft. The device contains 3 sets of accelerometers and gyroscopes of different models, and sends real-time data through the STM32 main processor.

[0010] It mainly includes the following three types of status information:

[0011] 1. UAV attitude information: The real-time angular velocity and acceleration information of the UAV is obtained by carrying an inertial sensor, and the estimated values ​​of the angle, angular velocity and angular acceleration of the pitch, roll and yaw directions of the UAV are obtained by using a Kalman filter;

[0012] 2. UAV position information: The UAV position information is obtained by the onboard position acquisition device (satellite positioning system), and is integrated with the airborne inertial sensor and altitude sensor information. The extended Kalman filter is used to achieve the fusion of multi-dimensional sensor information to obtain more accurate and smooth 3D position estimation information;

[0013] 3. UAV vibration information: Using inertial sensor perception information to generate UAV power spectrum density information and real-time vibration intensity information, the vibration state of the UAV during flight can be evaluated.

[0014] After denoising the data, the information is sent to the online UAV performance evaluation system on the ground through the remote communication module to carry out in-depth UAV performance evaluation.

[0015] Step 2: Indoor and outdoor positioning device installation

[0016] Use LiDAR as an indoor and outdoor positioning device to obtain the real-time position of the drone indoors, so that the GNSS / RTK signal generator can generate accurate indoor GNSS / RTK positioning signals. Connect the LiDAR to the DJI Mavic 3E drone through a proprietary connector, start the LiDAR positioning system to obtain the indoor coordinates of the drone, and send the indoor coordinates to the GNSS / RTK signal generator via wireless communication.

[0017] The indoor high-precision positioning system based on LiDAR is a cutting-edge technology that is mainly used to accurately track and locate objects or people in indoor environments. This system has a wide range of applications in many fields, including automatic navigation, robotics, security monitoring, and intelligent transportation systems. LiDAR systems usually use one or more laser transmitters and receivers to emit laser beams to the target from different angles and capture the laser reflected back to obtain the target's precise location information.

[0018] The indoor high-precision positioning system based on LiDAR uses SLAM (Simultaneous Localization and Mapping) technology, which is an advanced technology that can simultaneously perform self-localization and environmental map creation in unknown environments. It has a wide range of applications in many fields, including automatic navigation, robotics, security monitoring, and intelligent transportation systems. SLAM technology can update the position of the drone in real time through continuous LiDAR observations, while building a detailed three-dimensional map of the environment.

[0019] SLAM technology is mainly based on two core mathematical models: state estimation equation and observation equation:

[0020] State Estimation Equation (Motion Model)

[0021] x t =f(x t-1 ,u t ,w t )

[0022] Among them, x tis the state of the drone at time t (usually including position and direction). t-1 is the state at the previous moment. u is the control input (such as speed and direction change). t is process noise, which represents the uncertainty introduced by model imperfections or external influences.

[0023] The state estimation equation describes how the state of the UAV evolves from one time step to the next, depending on its control commands and inherent uncertainties.

[0024] Observation Model

[0025] z t =h(x t ,n t )

[0026] Among them, z t is the observation data obtained by sensors such as lidar at time t. t is the current estimated state of the robot. t is the observation noise, representing the measurement error.

[0027] The observation equation relates the robot’s predicted state to the data actually observed through sensors, helping to correct biases in the state estimate.

[0028] Based on this principle, we can calculate the precise position of the drone indoors through the lidar indoor positioning system, and then realize it through the GPS baseband signal data stream generation system and the GPS signal transmission system.

[0029] Step 3: Start the GNSS / RTK signal generator based on the autonomous positioning system

[0030] After obtaining the precise position of the drone indoors, we can start the GNSS / RTK signal generator based on the autonomous positioning system to generate precise GNSS / RTK positioning signals to realize GPS navigation of the drone, allowing the drone to rely on its own satellite positioning system to achieve position-controlled flight mode in indoor scenes.

[0031] GPS simulation signal data stream generation system based on GPS broadcast ephemeris file:

[0032] Step 3.1: Satellite data acquisition

[0033] Before generating a simulated GPS signal, you must first obtain the GPS ephemeris file of the target time. The ephemeris file mainly contains the precise orbital data of the satellite, which is crucial for the Global Positioning System (GPS), such as: 1. Satellite orbital parameters: These parameters describe the exact position and movement of the satellite in space. Including the satellite's ascending node right ascension, orbital inclination, perigee angular distance, eccentricity, mean anomaly and orbital period. 2. Clock data: the deviation between the satellite's atomic clock and the ground time, as well as the clock correction parameters. 3. Health status: Indicates whether the satellite is suitable for navigation and positioning services. 4. Atmospheric delay parameters: These parameters help the receiver correct the effects of ionospheric and tropospheric delays during signal propagation. These data are crucial for the generation of GPS baseband signal data streams. These parameters are then used to calculate the position of the satellite at any time using the Kepler equation:

[0034] E=M+esin E

[0035] Where, E is the eccentric anomaly; M is the mean anomaly, and the calculation formula is: M = n (tt 0 );n is the mean motion, which means the average angular velocity of the satellite orbiting the earth. t is the observation time. 0 is the reference time, usually the time when the satellite passes the perigee. e is the orbital eccentricity, which describes the flatness of the satellite orbit shape.

[0036] According to the anomaly angle, the position of the satellite in the orbital plane can be further calculated:

[0037] x′=a(cos Ee)

[0038]

[0039] Where x' and y' are the coordinates of the satellite in the orbital plane. a is the semi-major axis, which indicates the size of the satellite orbit.

[0040] These two formulas are used to calculate the position of the satellite in its orbital plane, where x' is the direction from the center of the earth to the perigee, and y' is the direction perpendicular to x'. In fact, the orbit of the satellite is inclined relative to the equatorial plane of the earth, so the position of the orbital plane also needs to be converted to the geocentric coordinate system:

[0041] x=x′cos(Ω)-y′cos(i)sin(Ω)

[0042] y=x′sin(Ω)+y′cos(i)cos(Ω)

[0043] z=y′sin(i)

[0044] Where Ω is the right ascension of the ascending node and i is the orbital inclination.

[0045] Step 3.2: Signal Generation

[0046] After acquiring the satellite data, the line of sight direction and pseudorange from the satellite to the set receiver position are calculated using the indoor positioning information and base point coordinates obtained by the motion capture system, and then the corresponding GPS signal is calculated and generated from the satellite data, including the generation of pseudo-random noise code (PRN) and encoding of navigation messages.

[0047] Each GPS satellite is assigned a unique PRN code, which is a repeating binary sequence used in receivers to help identify and synchronize to a specific satellite signal.

[0048]

[0049] Where C(t) is the output code at time T, G i (t) is the state of the code generator at time t, D code is the data code, and Δt is the duration of the code element.

[0050] Navigation messages contain information such as the satellite's orbital parameters, clock correction parameters and health status, which are critical for a GPS receiver to calculate its position.

[0051]

[0052] Where D(t) is the navigation data word at time t, b k is the kth bit of binary data, and n is the length of the data word.

[0053] Step 3.3: Signal Modulation

[0054] The generated digital baseband signal needs to be converted into an analog signal through modulation so that it can be transmitted through the SDR hardware. BPSK (Binary Phase Shift Keying) is mainly used to modulate the navigation data and PRN code onto the carrier frequency to form a transmittable signal.

[0055] First, we need to integrate the baseband signal containing the navigation information and the PRN code. During the modulation process, the PRN code and the navigation message simultaneously affect the phase change of the carrier. The PRN code provides a unique identification code for each satellite, and the navigation message contains key information about the satellite's position and time. This integration ensures that the signal has a high degree of anti-interference ability and unique identification characteristics, allowing the receiver to accurately distinguish and lock a single satellite from multiple satellite signals.

[0056] φ(t)=π×(d(t)+c(t))mod2

[0057] Where d(t) is the navigation data bit and c(t) is the PRN code.

[0058] After acquiring the integrated signal, we encode the data onto the RF carrier using BPSK modulation, which represents a data bit (0 or 1) by changing the phase of the carrier.

[0059] s(t)=A·cos(2πf c t+φ(t))

[0060] Where s(t) is the modulated signal, A is the signal amplitude, and f c is the carrier frequency and φ(t) is the phase at time t, which depends on the data bit (usually 0 or π for GPS).

[0061] While BPSK modulation is in progress, we can extract the in-phase (I) and quadrature (Q) components from the modulated signal to form an IQ signal. This is done by parsing the modulated signal into its sine (quadrature component) and cosine (in-phase component) components, allowing the signal to carry more information and improve transmission efficiency.

[0062] I(t)=d(t)cos(2πf c t)

[0063] Q(t)=d(t)sin(2πf c t)

[0064] The generated analog signal needs to be sampled and converted into a format suitable for SDR hardware processing. The mathematical processing involved in the sampling process is mainly to discretize the analog signal, converting the continuous analog signal into a discrete signal for digital storage and processing. Each sample point calculates the phase and amplitude according to its corresponding time point to ensure that the integrity and accuracy of the signal are preserved.

[0065] s[n]=A·cos(2πf c nT+φ[n])

[0066] Where s[n] is the signal value at sampling time n, and T is the sampling interval.

[0067] Step 3.4: Use the GPS signal transmission system based on software radio to transmit GNSS / RTK positioning signals

[0068] In terms of the GPS signal transmission system, we selected HACKRF as the software defined radio (SDR) hardware, which provides an efficient and flexible signal transmission solution for GPS applications. This system can receive GPS signals generated by the GPS analog signal data stream generation system, which include navigation data and pseudo-random noise code (PRN). Then, using the high-performance digital-to-analog conversion function of HACKRF, these baseband signals are up-converted to the GPS operating frequency and sent through the RF interface.

[0069] First, the SDR device needs to receive the digital I / Q data generated from the GPS analog signal data stream generation system. These data are the baseband representation of the GPS signal, and then combine the in-phase and orthogonal components in the data stream into a complex signal for subsequent processing and modulation.

[0070] s[n]=I[n]+jQ[n]

[0071] Where s[n] is the complex baseband signal, I[n] and Q[n] are the in-phase and quadrature components at sampling point n, respectively.

[0072] After completing the signal recombination, we can convert the complex signal into an analog signal for subsequent signal frequency conversion and transmission. This step is achieved through a digital-to-analog converter, which mainly converts the processed digital signal into an analog signal to prepare for transmission. Indicates the mixing process of the signal and the carrier, converting the baseband signal to the RF band:

[0073]

[0074] Among them, v(t) is the analog signal, f c is the carrier frequency, and s(t) is the continuous-time complex signal obtained by interpolation and filtering from the digital baseband signal.

[0075] After completing the analog conversion of the signal, we will need to frequency convert the analog signal to the operating frequency of the GPS from the baseband frequency. This is an important step before the signal is transmitted to ensure that the signal is sent in the correct frequency band. The following formula describes the baseband signal through the analog mixer and the local oscillator (LO) frequency f RF Mixing process:

[0076] x(t)=v(t)cos(2πf RF t)-u(t)sin(2πf RF t)

[0077] Where x(t) is the up-converted RF signal, u(t) and v(t) are the I and Q components of the baseband signal, respectively, and f RF is the target radio frequency.

[0078] Finally, the signal power is boosted to a level sufficient to be transmitted through the antenna through the SDR device’s RF amplifier, ensuring that the signal can be effectively transmitted.

[0079] P out =Gain×P in

[0080] Among them, P out is the output power after amplification, Gain is the power input to the amplifier, P in is the gain of the amplifier.

[0081] In this way, the system can generate the customized GPS signals required by researchers, enable testing and performance evaluation of flight control systems and GPS receiving equipment, and provide researchers with a practical platform for experimenting and verifying new algorithms or technologies.

[0082] Step 4: Drone Flight Test

[0083] After completing the installation and startup of the real-time status perception device, indoor and outdoor positioning device, and GNSS / RTK signal generator, you can start the drone to begin indoor flight testing.

[0084] First, the UAV outdoor flight test device should be calibrated for accuracy. The UAV outdoor flight test device should be calibrated for reprojection errors before each use. The measuring equipment should be left stationary in place, and the measuring equipment should continuously read the position value within one minute, and the measurement accuracy convergence correction of the measuring equipment should be performed. After the calibration of the test device and the UAV itself is completed, the flight test can begin.

[0085] Step 4.1: Hovering endurance and positioning accuracy test

[0086] First, the hovering endurance and positioning accuracy tests were carried out. The drone took off, entered the hovering fixed-point mode, and landed after flying for 30 minutes.

[0087] Step 4.2: Hover test in wind resistance conditions

[0088] Deploy a wind farm in an indoor test scenario and test the drone's hovering state in the wind farm and landing after flying for 10 minutes

[0089] Step 4.3: Virtual Waypoint Flight Test

[0090] Plan waypoints according to the size of the site before takeoff, and start the route flight mode during flight, allowing the drone to complete the flight mission autonomously based on the waypoints.

[0091] Step 5: UAV flight data processing and performance evaluation

[0092] Step 5.1: Data acquisition

[0093] During the flight, the flight data of the drone is obtained in real time and saved in a log file.

[0094] The data acquisition module mainly obtains the real-time status information of the UAV and realizes the functions of real-time data collection, transmission, and storage. It is an important connection between the physical world and the UAV performance evaluation system. It needs to consider the classification and characteristics of the data, as well as various related issues of data acquisition behavior to ensure the accuracy of the acquired data.

[0095] Step 5.2: Data processing

[0096] After acquiring the flight data, the flight data is processed to facilitate the subsequent evaluation of the UAV's flight performance.

[0097] In order to use data information efficiently and filter out noise interference in the data, it is necessary to process the stored data, including data preprocessing and data feature extraction, as shown in the figure. Data preprocessing refers to various aspects of processing of raw data from the perspective of data science such as statistical analysis, mainly including outlier and abnormal point detection, sampling alignment and padding, and denoising. Feature extraction is the process of finding the features with the most information in the data and discarding some features with less information. By removing some unimportant features and redundant information to achieve data compression, the data volume can be effectively reduced and storage pressure can be alleviated. It mainly includes time series signal data feature extraction, feature extraction based on frequency domain analysis methods, feature extraction based on time domain analysis methods, etc. Figure 2 shown.

[0098] Step 5.3: UAV flight control performance evaluation

[0099] After obtaining the processed UAV flight data, we can use this data to evaluate the UAV flight control performance.

[0100] In the evaluation of the performance of the drone's navigation system, the core concern is to ensure the navigation accuracy and response speed of the drone in various environments and situations. The evaluation of the performance of the drone's navigation system involves multiple key indicators, including the drone's positioning accuracy, path tracking ability, adaptability to environmental changes, and stability under different operating conditions. These evaluation indicators are divided into several parts, as follows:

[0101] 1. Basic performance evaluation:

[0102] Flight stability: Assessing the drone’s control accuracy while maintaining a fixed altitude and speed, which is critical for performing precise aerial missions such as aerial photography or data collection.

[0103] Environmental adaptability: Test the flight performance of the drone under different meteorological conditions, including operational stability in strong winds, low or high temperatures, and its response speed to environmental changes.

[0104] 2. Emergency handling capabilities:

[0105] Emergency Obstacle Avoidance: Simulate emergencies, such as unexpected obstacles, to test the drone’s obstacle avoidance reaction time and effectiveness, and verify the sensitivity of its sensors and the effectiveness of its processing algorithms.

[0106] Autonomous landing: Whether the drone can land safely in the event of loss of control signal or insufficient power. During emergency landing, its positioning accuracy and stability of ground contact are tested.

[0107] 3. Accuracy test:

[0108] Position keeping capability: Through short-term fixed-point hovering tests, the position stability of the drone in windless and windy conditions is evaluated, which is critical for performing tasks that require high positioning accuracy.

[0109] Path accuracy: On the preset flight route, detect the deviation between the actual flight trajectory of the drone and the predetermined trajectory, and evaluate the accuracy and reliability of its flight control system.

[0110] 4. Endurance and stability test:

[0111] Continuous flight performance: Evaluate the performance degradation of the drone after long-term flight, including battery life and the durability of various parts of the aircraft.

[0112] Control stability: Whether the drone responds consistently during flight, especially when performing complex maneuvers or long-duration missions, and how stable and responsive its control system is.

[0113] Through these comprehensive performance evaluations, the efficiency and safety of drones can be ensured when performing missions. These tests not only help manufacturers improve drone design, but also provide operators with important information about machine performance, so that they can make better strategic decisions in actual operations. For example, endurance test results can guide operators on reasonable expectations of battery life, while the assessment of emergency handling capabilities directly affects mission planning and risk management strategies. This rigorous evaluation process ensures the continuous advancement of drone technology and significantly improves its application in civil and commercial fields. For key areas such as public safety and environmental monitoring, the optimization and reliability of this technology further expands the breadth and depth of its application, and promotes the innovation and standardization of drone technology on a global scale.

[0114] Step 5.4: UAV navigation performance evaluation

[0115] After obtaining the processed UAV flight data, we can use this data to evaluate the UAV navigation performance.

[0116] In the evaluation of the performance of the drone's navigation system, the core concern is to ensure the navigation accuracy and response speed of the drone in various environments and situations. The evaluation of the performance of the drone's navigation system involves multiple key indicators, including the drone's positioning accuracy, path tracking ability, adaptability to environmental changes, and stability under different operating conditions. These evaluation indicators are divided into several parts, as follows:

[0117] 1. Basic navigation skills:

[0118] In the basic navigation capability test of drones, we focus on the flight accuracy of drones on fixed routes. This includes two key aspects: first, the drone's ability to maintain position on the predetermined path, that is, its ability to maintain stability on the set flight route; second, the precise tracking of the preset route, which involves how the drone accurately adjusts the flight path to meet the new route requirements after receiving the route adjustment instruction. These tests help ensure that drones can maintain efficient and precise operations in complex routes and changing mission requirements.

[0119] 2. Height and speed control:

[0120] This assessment focuses on testing the stability of the drone in maintaining a specific flight altitude and speed. This includes not only the drone's ability to fly stably at a set altitude, but also its tolerance for altitude changes during flight, and how to quickly and accurately adjust the flight altitude to adapt to emergencies. At the same time, the drone's acceleration and deceleration capabilities are also tested, which is to evaluate its dynamic response capabilities when responding to emergencies or completing specific flight missions.

[0121] 3. Posture stability:

[0122] The attitude stability test of the drone focuses on evaluating the stability of the machine in various flight attitudes, especially when encountering challenges such as sudden changes in wind direction or sudden changes in operating instructions. The test will observe the drone's response speed and adjustment accuracy to ensure that the drone can quickly and accurately adjust its attitude during flight to maintain stable and safe flight.

[0123] 4. Sensor performance evaluation:

[0124] Sensor performance evaluation focuses on IMU (Inertial Measurement Unit) and GNSS (Global Navigation Satellite System). These sensors are key components of drone navigation systems, so the test aims to verify whether these devices can provide accurate and reliable position and orientation data in actual operation. Ensuring high sensor performance is the basis for achieving precise navigation and stable operation of drones.

[0125] 5. Emergency operation response:

[0126] In the evaluation of emergency operational response, attention is paid to the drone's ability to handle navigation errors or sudden changes in flight plans. This includes the drone's emergency stop and obstacle avoidance capabilities, testing how the drone can quickly and safely adjust its flight status to avoid accidents when facing potential collisions or flight restricted areas. These capabilities are critical to improving the drone's emergency response and overall safety.

[0127] The advantages and benefits of the present invention are that these tests ensure that the drone can maintain efficient and safe flight performance in various operating environments and non-standard situations that may be encountered. Through these comprehensive navigation performance evaluations, drone operators and manufacturers can obtain detailed data on equipment performance, which helps to optimize the design and improve the reliability of flight strategies. This not only improves the actual application efficiency of drones, but also strengthens their application capabilities in complex environments, promoting the widespread application of drone technology in civil and commercial fields. BRIEF DESCRIPTION OF THE DRAWINGS

[0128] Figure 1 It is a schematic diagram of the UAV flight control and navigation performance evaluation system.

[0129] Figure 2 It is a schematic diagram of the general process of data processing.

[0130] Figure 3 It is a schematic diagram of the connector of the real-time sensing device.

[0131] Figure 4 It is a 3D flight trajectory for flight control performance evaluation.

[0132] Figure 5 It is a schematic diagram of the X-coordinate data analysis results for flight control performance evaluation.

[0133] Figure 6 It is a schematic diagram of the Y-coordinate data analysis results of flight control performance evaluation.

[0134] Figure 7 It is a schematic diagram of the Z coordinate data analysis results of flight control performance evaluation.

[0135] Figure 8 It is a schematic diagram of the X-coordinate data analysis results of the UAV navigation performance evaluation.

[0136] Fig. 9 It is a schematic diagram of the Y-coordinate data analysis results of the UAV navigation performance evaluation.

[0137] Fig.10 It is a schematic diagram of the Z coordinate data analysis results of the UAV navigation performance evaluation. DETAILED DESCRIPTION

[0138] The present invention implements the flight control and navigation performance evaluation of the DJI Mavic 3E model, specifically:

[0139] Step 1: Install the external drone real-time status sensing device

[0140] Mount the external drone real-time status sensing device according to the mounting holes on the DJI Mavic 3E drone, and start the sensing device to record the current drone status data. Figure 3 As shown: our self-made real-time sensing device is installed in the middle part of the connecting piece, which is connected to the connecting piece through a shock-absorbing seat and screws. The bottom of the connecting piece is connected to the fixing hole on the DJI Mavic 3E drone to realize the mounting of the external drone real-time status sensing device.

[0141] Step 2: Indoor and outdoor positioning device installation

[0142] Use LiDAR as an indoor and outdoor positioning device to obtain the real-time position of the drone indoors, so that the GNSS / RTK signal generator can generate accurate indoor GNSS / RTK positioning signals. Connect the LiDAR to the DJI Mavic 3E drone through a proprietary connector, start the LiDAR positioning system to obtain the indoor coordinates of the drone, and send the indoor coordinates to the GNSS / RTK signal generator via wireless communication.

[0143] Step 3: Start the GNSS / RTK signal generator based on the autonomous positioning system

[0144] After obtaining the precise position of the drone indoors, we can start the GNSS / RTK signal generator based on the autonomous positioning system to generate precise GNSS / RTK positioning signals to realize GPS navigation of the drone. Through the four steps of satellite data acquisition, signal generation, signal modulation, and signal transmission, we can generate and transmit indoor GNSS / RTK positioning signals, allowing the drone to rely on its own satellite positioning system to achieve position control flight mode in indoor scenes.

[0145] Step 4: Drone Flight Test

[0146] After completing the installation and startup of the external drone real-time status perception device, indoor and outdoor positioning device, and GNSS / RTK signal generator, the drone can be put into flight testing.

[0147] Before the flight test, the UAV outdoor flight test device should be re-projected before each use, the measuring equipment should be left still, the measuring equipment continuously reads the position value within one minute, and the measuring accuracy convergence correction of the measuring equipment should be performed. After that, the hovering endurance and positioning accuracy test, the wind resistance hovering test, and the virtual waypoint flight test should be completed in sequence.

[0148] First, the hovering endurance and positioning accuracy tests were carried out. The drone took off, entered the hovering fixed-point mode, and landed after 30 minutes of flight. Then, a wind farm was deployed in an indoor test scene, and the drone was tested in the hovering state in the wind farm, landing after 10 minutes of flight. Finally, square waypoints were planned according to the size of the site before takeoff, and the route flight mode was started during the flight, allowing the drone to complete the flight mission autonomously based on the waypoints.

[0149] Step 5: UAV flight data processing and performance evaluation

[0150] During the flight, the flight data of the drone is obtained in real time through wireless communication and saved in a log file.

[0151] After obtaining the flight data, the flight data is processed to facilitate the subsequent evaluation of the flight performance of the drone. After obtaining the processed drone flight data, we can use this data to evaluate the flight control performance of the drone.

[0152] Evaluation of the UAV flight control performance, flight trajectory such as Figure 4 As shown in the figure, the drone takes off from a designated location indoors, enters the hovering fixed-point mode, and lands after hovering for 30 minutes. It can be seen from the figure that the drone basically remains near the take-off point within 30 minutes, with a good hovering fixed-point effect. As long as there is a deviation of about 0.5m when encountering gusts, it can be corrected in a short time. The analysis results of the drone's hovering flight are as follows Figures 5 to 7 As shown; Figure 5It is the following situation of the drone's X coordinate. The green line is our set value, and the red line is the drone's status value. As can be seen in the figure, the green line and the red line completely overlap on the X coordinate. The drone has excellent following and control capabilities on the X coordinate. Figure 6 It is the following situation of the Y coordinate of the drone. The green line is our set value, and the red line is the status value of the drone. As can be seen in the figure, the green line and the red line completely overlap on the Y coordinate. The drone has excellent following and control capabilities on the Y coordinate. Figure 7 It is the following situation of the drone in height. The green line is our set value, and the red line is the status value of the drone. As can be seen in the figure, the green line and the red line completely overlap in height. The drone has excellent following and control capabilities in height. It is concluded that the drone can achieve good self-control under the guidance of GNSS / RTK positioning signals and meet the qualified standards. For the performance evaluation of drone navigation, the drone flies according to the designated route based on the guidance of the GNSS / RTK navigation signal, and starts the route flight mode during flight, allowing the drone to complete the flight mission autonomously according to the waypoints. The results of the drone route flight analysis are as follows: Figures 8 to 10 As shown, Figure 8 It is the following situation of the drone's X coordinate. The green line is our set value, and the red line is the drone's status value. As can be seen in the figure, the green line and the red line completely overlap on the X coordinate. Under the navigation of the GNSS / RTK navigation signal, the drone can follow the route on the X coordinate and has a good route flight capability. Fig. 9 The following situation of the Y coordinate of the drone. The green line is our setting value, and the red line is the status value of the drone. As can be seen in the figure, the green line and the red line completely overlap on the Y coordinate. Under the navigation of the GNSS / RTK navigation signal, the drone can follow the route on the Y coordinate and has a good route flight capability. Fig.10 It is the following situation of the UAV's Z coordinate. The green line is our set value, and the red line is the status value of the UAV. As can be seen from the figure, the green line and the red line completely overlap on the Z coordinate. The UAV can follow the route on the Z coordinate under the navigation of the GNSS / RTK navigation signal, and has a good route flight capability. It is concluded that the UAV can achieve good following of the target position under the guidance of the GNSS / RTK positioning signal and meet the qualified standards.

Claims

1. A method for evaluating indoor UAV flight control and navigation performance based on autonomous positioning GNSS, characterized by: The steps include: Step 1: Install the external drone real-time status sensing device An external drone real-time status perception device is mounted to obtain drone flight status information in real time by deploying multi-dimensional information sensors on the aircraft; the device contains three sets of accelerometers and gyroscopes of different models, and the real-time data is processed and sent through the STM32 main processor; Step 2: Indoor and outdoor positioning device installation Use laser radar as an indoor and outdoor positioning device to obtain the real-time position of the drone indoors, so that the GNSS / RTK signal generator can generate accurate indoor GNSS / RTK positioning signals; connect the laser radar to the drone through a connector, start the laser radar positioning system to obtain the indoor coordinates of the drone, and send the indoor coordinates to the GNSS / RTK signal generator through wireless communication; Step 3: Start the GNSS / RTK signal generator based on the autonomous positioning system After obtaining the precise indoor position of the drone, the GNSS / RTK signal generator based on the autonomous positioning system is started to generate precise GNSS / RTK positioning signals to realize GPS navigation of the drone, so that the drone can realize position control flight mode in indoor scenes by relying on its own satellite positioning system. Step 4: Drone Flight Test After completing the installation and startup of the real-time status perception device, indoor and outdoor positioning device, and GNSS / RTK signal generator, start the drone to begin indoor flight testing; Step 5: UAV flight data processing and performance evaluation It includes data acquisition, data processing, UAV flight control performance evaluation and UAV navigation performance evaluation.

2. The method for evaluating indoor UAV flight control and navigation performance based on autonomous positioning GNSS according to claim 1 is characterized in that: In step 1, the following three status information are included: Drone attitude information: The real-time angular velocity and acceleration information of the drone is obtained by carrying an inertial sensor, and the estimated values ​​of the angle, angular velocity and angular acceleration of the drone's pitch, roll, and yaw directions are obtained through the Kalman filter; UAV position information: The UAV position information is obtained by the onboard position acquisition device and integrated with the airborne inertial sensor and altitude sensor information. The extended Kalman filter is used to integrate the multi-dimensional sensor information to obtain more accurate and smooth three-dimensional position estimation information. UAV vibration information: Use inertial sensor perception information to generate UAV power spectrum density information and real-time vibration intensity information to evaluate the vibration status of the UAV during flight.

3. The method for evaluating indoor UAV flight control and navigation performance based on autonomous positioning GNSS according to claim 1 is characterized in that: In step 2, SLAM is based on two core mathematical models: the state estimation equation and the observation equation: State estimation equation: x t =f(x t-1 ,u t ,w t ) Among them, x t is the state of the drone at time t, including position and direction; x t-1 is the state at the previous moment; u t is the control input; w t is the process noise; The state estimation equation describes how the state of the UAV evolves from one time step to the next, depending on its control commands and inherent uncertainties; Observation equation: z t =h(x t ,n t ) Among them, z t is the observation data obtained by the lidar sensor at time t; x t is the current estimated state of the robot; n t is the observation noise, representing the measurement error.

4. The method for evaluating indoor UAV flight control and navigation performance based on autonomous positioning GNSS according to claim 1 is characterized in that: In step 3, it also includes: satellite data acquisition; Before generating a simulated GPS signal, you must first obtain the GPS ephemeris file of the target time. The ephemeris file contains the precise orbit data of the satellite. Use this data to calculate the position of the satellite at any time using the Kepler equation: E=M+esin E Among them, E is the eccentric anomaly; M is the mean anomaly, and the calculation formula is: M = n (t-t0); n is the average motion, which means the average angular velocity of the satellite around the earth; t is the observation time; t0 is the reference time, which is usually the time when the satellite passes the perigee; e is the orbital eccentricity, which describes the flattening of the satellite orbit shape; According to the anomaly angle, the position of the satellite in the orbital plane is further calculated: x′=a(cos Ee) Among them, x′ and y′ are the coordinates of the satellite in the orbital plane; a is the semi-major axis, which represents the size of the satellite orbit; These two formulas are used to calculate the position of the satellite in its orbital plane, where x′ is the direction from the center of the earth to the perigee, and y′ is the direction perpendicular to x′; in fact, the satellite's orbit is inclined relative to the earth's equatorial plane, so the position of the orbital plane also needs to be converted to the geocentric coordinate system: x=x′cos(Ω)-y′cos(i)sin(Ω) y=x′sin(Ω)+y′cos(i)cos(Ω) z=y′sin(i) Where Ω is the right ascension of the ascending node and i is the orbital inclination.

5. The method for evaluating indoor UAV flight control and navigation performance based on autonomous positioning GNSS according to claim 4 is characterized in that: In step 3, it also includes: signal generation; after acquiring satellite data, calculating the line of sight direction and pseudorange from the satellite to the set receiver position through the indoor positioning information and base point coordinates obtained by the motion capture system, and then calculating and generating the corresponding GPS signal from the satellite data, including generating a pseudo-random noise code PRN and a coding of the navigation message; Each GPS satellite is assigned a unique PRN code, which is a repeating binary sequence used in receivers to help identify and synchronize to a specific satellite signal; Where C(t) is the output code at time T, G i (t) is the state of the code generator at time t, D code is the data code, Δt is the duration of the code element; The navigation message contains the satellite's orbit parameters, clock correction parameters, and health status information; Where D(t) is the navigation data word at time t, b k is the kth bit of binary data, and n is the length of the data word.

6. The method for evaluating indoor UAV flight control and navigation performance based on autonomous positioning GNSS according to claim 5 is characterized in that: In step 3, it also includes: signal modulation; using BPSK to modulate the navigation data and the PRN code onto the carrier frequency to form a transmittable signal; φ(t)=π×(d(t)+c(t))mod2 Where, d(t) is the navigation data bit, c(t) is the PRN code; After acquiring the integrated signal, the data is encoded onto the RF carrier using BPSK modulation, which represents the data bits by changing the phase of the carrier. s(t)=A·cos(2πf c t+φ(t)) Where s(t) is the modulated signal, A is the signal amplitude, and f c is the carrier frequency, φ(t) is the phase at time t, which depends on the data bit; While BPSK modulation is in progress, the in-phase I and quadrature Q components are extracted from the modulated signal to form an IQ signal; this is accomplished by parsing the modulated signal into its sine and cosine components, allowing the signal to carry more information and improve transmission efficiency; I(t)=d(t)cos(2πf c t) Q(t)=d(t)sin(2πf c t) The generated analog signal needs to be sampled and converted into a format suitable for SDR hardware processing; the mathematical processing involved in the sampling process is to discretize the analog signal, converting the continuous analog signal into a discrete signal for digital storage and processing; each sampling point calculates the phase and amplitude according to its corresponding time point, ensuring that the integrity and accuracy of the signal are preserved; s[n]=A·cos(2πf c nT+φ[n]) Where s[n] is the signal value at sampling time n, and T is the sampling interval.

7. The method for evaluating indoor UAV flight control and navigation performance based on autonomous positioning GNSS according to claim 6 is characterized by: In step 3, it also includes: using a GPS signal transmission system based on software radio to transmit a GNSS / RTK positioning signal; First, the SDR device needs to receive the digital I / Q data generated from the GPS analog signal data stream generation system. These data are the baseband representation of the GPS signal, and then combine the in-phase and quadrature components in the data stream into a complex signal for subsequent processing and modulation. s[n]=I[n]+jQ[n] Where s[n] is the baseband signal in complex form, I[n] and Q[n] are the in-phase and quadrature components at sampling point n, respectively; After the signal is compounded, the complex signal is converted into an analog signal for subsequent signal frequency conversion and transmission; this step is achieved through a digital-to-analog converter, which converts the processed digital signal into an analog signal in preparation for transmission; the following formula Indicates the mixing process of the signal and the carrier, converting the baseband signal to the RF band: Among them, v(t) is the analog signal, f c is the carrier frequency, s(t) is the continuous-time complex signal obtained after interpolation and filtering from the digital baseband signal; After completing the analog conversion of the signal, the analog signal needs to be frequency-converted to convert the baseband frequency to the operating frequency of the GPS. This is an important step before the signal is transmitted to ensure that the signal is sent in the correct frequency band. The following formula describes the baseband signal through the analog mixer and the local oscillator (LO) frequency f RF Mixing process: x(t)=v(t)cos(2πf RF t)-u(t)sin(2πf RF t) Where x(t) is the up-converted RF signal, u(t) and v(t) are the I and Q components of the baseband signal, respectively, and f RF is the target radio frequency; Finally, the signal is boosted to a level sufficient to be transmitted through the antenna by the SDR device’s RF amplifier, ensuring effective signal transmission. P out =Gain×P in Among them, P out is the output power after amplification, Gain is the power input to the amplifier, P in is the gain of the amplifier.

8. The method for evaluating indoor UAV flight control and navigation performance based on autonomous positioning GNSS according to claim 1, characterized in that: In step 4, the UAV outdoor flight test device is calibrated for accuracy. The UAV outdoor flight test device should be calibrated for reprojection errors before each startup. The measuring device is left stationary in place, and the measuring device continuously reads the position value within one minute. The measurement accuracy convergence correction of the measuring device is performed, and the flight test is started after the calibration of the test device and the UAV itself is completed.

9. The method for evaluating indoor UAV flight control and navigation performance based on autonomous positioning GNSS according to claim 1, characterized in that: In step 5, the evaluation of the UAV flight control performance is divided into several parts, as follows: Basic performance evaluation, including: flight stability and environmental adaptability; Emergency handling capabilities, including: emergency obstacle avoidance and autonomous landing; Accuracy testing, including: position keeping capability and path accuracy; Endurance and stability tests, including: continuous flight performance and control stability.

10. The method for evaluating indoor UAV flight control and navigation performance based on autonomous positioning GNSS according to claim 1 or 9, characterized in that: In step 5, the evaluation of UAV navigation performance is divided into several parts, as follows: Basic navigation capability: In the basic navigation capability test of the drone, the flight accuracy of the drone on a fixed route is ensured; Altitude and speed control: ensuring the stability of the drone in maintaining a specific flight altitude and speed; Attitude stability: To ensure the attitude stability of the drone, the test focuses on evaluating the stability of the machine in various flight attitudes; Sensor performance evaluation: Sensor performance evaluation focuses on inertial measurement units (IMUs) and global navigation satellite systems (GNSSs); Emergency Operational Response: Ensures the drone’s ability to handle situations when it encounters a navigation error or needs to suddenly change its flight plan.

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