Satellite navigation embedded intelligent car experimental teaching platform

Abstract

The utility model discloses a satellite navigation embedded intelligent trolley experimental teaching platform, including intelligent trolley (1), raspberry pi 4B (2), GPS & big dipper positioning module (3), IMU module (4), PCB drive board (5) and 12V power (6), the chassis of intelligent trolley (1) is mainly composed of two speed reduction drive wheels (7) and two GA370 motor (8), GPS & big dipper positioning module (3) and IMU module (4) with raspberry pi 4B (2) electricity is connected, 12V power (6) with raspberry pi 4B (2) and PCB drive board (5) electricity is connected, raspberry pi 4B (2) with PCB drive board (5) electricity is connected. The utility model can stimulate the subjective initiative and innovative spirit of student, can effectively realize teaching interaction simultaneously, promotes the ability of collaborative learning, self -directed learning and experience -based learning of student, and guides the cultivation and promotion of student comprehensive ability.

Description

Technical Field

[0001] This utility model relates to an experimental teaching platform for satellite navigation embedded intelligent vehicles, belonging to the field of Internet of Things technology. Background Technology

[0002] With the continuous advancement of mobile communication and satellite positioning technologies, navigation and positioning technology has gradually evolved into a global high-tech industry. This technology has permeated various fields, including but not limited to geographic information data collection, vehicle monitoring and dispatch management, various navigation services, navigation support in the aviation and maritime industries, military applications, mechanical control systems, research and development of autonomous driving technology, the development of smart agriculture, and various intelligent applications for mass consumers. Therefore, "Navigation and Positioning Technology," as an important professional course for Internet of Things (IoT) engineering and other related majors, faces many challenges. These challenges include emphasizing the cultivation of students' comprehensive practical abilities to adapt to the development needs of the IoT, and stressing the importance of "learning by doing" to reflect the profound contemporary relevance of the rapid development of IoT technology.

[0003] Currently, although many universities offer experimental courses in "Navigation and Positioning Technology," the focus is primarily on satellite signal analysis, especially for students majoring in Electronic Information Engineering. These courses cover seven core experimental topics, including user coordinate calculation, navigation data interpretation, satellite navigation and positioning signal characteristic analysis, signal acquisition technology, carrier-to-noise ratio calculation methods, signal search strategies, and signal tracking technology. On the other hand, some universities use virtual simulation technology for teaching, designing a series of virtual experiments centered on the BeiDou Navigation Satellite System. These experiments include understanding the basic principles of the BeiDou system, exploring its constellation structure, the satellite signal processing, methods for analyzing message information, and practical exercises using the BeiDou system for positioning calculations, aiming to provide an in-depth and intuitive learning experience through a simulation environment. However, these experimental teaching methods for Electronic Information Engineering majors have the following problems: 1. The teaching content is monotonous and cannot meet the needs of comprehensiveness and innovation; 2. The teaching methods are limited and lack targeted stimulation of students' learning interest. Utility Model Content

[0004] The technical problem to be solved by this utility model is to provide a satellite navigation embedded intelligent vehicle experimental teaching platform, which can effectively realize teaching interaction, improve students' collaborative learning, self-learning and experiential learning abilities, and guide the cultivation and improvement of students' comprehensive abilities.

[0005] To solve the above-mentioned technical problems, the technical solution of this utility model is as follows: An experimental teaching platform for a satellite navigation embedded intelligent vehicle includes an intelligent vehicle, a Raspberry Pi 4B, a GPS & BeiDou positioning module, an IMU module, a PCB driver board, and a 12V power supply. The chassis of the intelligent vehicle mainly consists of two reduction drive wheels and two GA370 motors. The GPS & BeiDou positioning module and the IMU module are electrically connected to the Raspberry Pi 4B. The 12V power supply is electrically connected to the Raspberry Pi 4B and the PCB driver board, and the Raspberry Pi 4B is electrically connected to the PCB driver board.

[0006] As a preferred option, the GPS & BeiDou positioning module is a high-performance BDS / GNSS positioning and navigation module based on ATGM336H-5N, which supports multiple satellite navigation systems, including all satellites of my country's BeiDou-2 and BeiDou-3, the US GPS, the Russian GLONASS, and the Japanese QZSS.

[0007] As a preferred embodiment, the IMU module mainly consists of three accelerometers corresponding to each single axis in three-dimensional space and three gyroscopes of the same single axis, used to sense the attitude, angle, speed, height and latitude and longitude of the intelligent vehicle, so that the intelligent vehicle can obtain the most accurate positioning information.

[0008] As a preferred embodiment, the PCB driver board includes a TB6612FNG driver chip, an STM32F103RCT6 microcontroller that controls the chip, a voltage conversion module, and an expansion interface.

[0009] Beneficial effects: Compared with the prior art, this utility model has the following characteristics: (1) An open hardware design is adopted. It includes a smart car, a Raspberry Pi 4B main controller, a PCB driver board, a GPS & Beidou positioning module, an IMU module, and other peripheral devices, which are selected by students according to the experimental content. The peripheral devices include a variety of hardware functional modules.

[0010] (2) Utilizing an open-source experimental platform. All hardware, operating systems, and software components of the Raspberry Pi are open-source and free, a feature that greatly promotes its widespread application and innovation. In particular, Python's hardware development libraries are extremely rich and comprehensive for the Raspberry Pi, with many libraries even compatible with the Arduino platform. Based on Python's inherent cross-platform advantage, numerous open-source library files can be easily accessed, enabling development and use on various operating systems and hardware platforms. In short, whether for hardware interface programming or building complex system applications, the combination of Raspberry Pi and Python provides a seamless and efficient development environment.

[0011] (3) Expanding the content of comprehensive practical activities. Based on the requirements of comprehensive practical objectives, human-computer interaction is conducted using displays, web interfaces, and cloud platforms. Sensors such as lidar and camera images are integrated, and different algorithms such as Dijkstra and A* are used to complete experiments including GPS / GNSS module integration and data analysis for intelligent vehicles, path planning, satellite signal simulation and anti-interference, real-time navigation and autonomous driving, high-precision positioning, network-enhanced positioning, and V2X communication. Experimental performance evaluation and optimization are then completed. Project-based experiments can also be conducted.

[0012] This invention utilizes the low-cost embedded system Raspberry Pi to develop a hardware-software integrated experimental teaching platform for satellite navigation embedded intelligent vehicles. It designs an experimental teaching content on "path planning using a Raspberry Pi intelligent vehicle," aiming to encourage students to fully utilize information and communication technologies and internet platforms, thereby stimulating their initiative and innovative spirit. Simultaneously, through experiments, interactive teaching can be effectively achieved, enhancing students' collaborative learning, self-directed learning, and experiential learning abilities, and guiding the cultivation and improvement of students' comprehensive abilities. Attached Figure Description

[0013] Figure 1 This is a schematic diagram of the hardware system composition of this utility model; Figure 2 This is an interface diagram of the GPS & Beidou positioning module of this utility model; Figure 3 This is the axial direction diagram of the IMU module of this utility model; Figure 4 This is a structural diagram of the PCB driver board of this utility model. Detailed Implementation

[0014] To further illustrate the technical means and effects adopted by this utility model in order to achieve the intended utility model purpose, the following detailed description of the specific implementation methods, structure, features and effects of this utility model is provided in conjunction with the accompanying drawings and preferred embodiments.

[0015] Example 1: The hardware system block diagram of the satellite navigation embedded intelligent car experimental teaching platform of this utility model is as follows: Figure 1 As shown, the device mainly consists of hardware devices such as a smart car 1, a Raspberry Pi 4B 2, a GPS & Beidou positioning module 3, an IMU module 4, a PCB driver board 5, and a 12V power supply 6. The GPS & Beidou positioning module 3 and the IMU module 4 are electrically connected to the Raspberry Pi 4B 2, respectively. The 12V power supply 6 is electrically connected to the Raspberry Pi 4B 2 and the PCB driver board 5, and the Raspberry Pi 4B 2 is electrically connected to the PCB driver board 5.

[0016] The intelligent vehicle 1 adopts a dual-drive structure, with its chassis mainly composed of two reduction drive wheels 7 and two GA370 motors 8. The output shafts of the two GA370 motors 8 are connected through a gear structure and coupling, allowing the two drive wheels to be arranged side-by-side on the same axis, effectively ensuring the stability of the intelligent vehicle's movement. The GA370 motor has an unloaded speed of up to 170 r / min, a reduction ratio of 35, a rated speed of 130 r / min, a rated current of less than 450 mA, and a maximum torque of 2.8 kg·cm, allowing the vehicle to better adapt to complex outdoor environments.

[0017] The Raspberry Pi 4B is a microcomputer motherboard based on the ARM architecture. It uses SD / MicroSD cards as storage media, is compact, and features 1 / 2 / 4 USB ports and a 10 / 100 / 1000Mbps Ethernet port (except for the Model A). These ports allow the Raspberry Pi to connect a keyboard, mouse, and network cable. More notably, the Raspberry Pi also has both analog video output for televisions and HDMI high-definition video output, all cleverly integrated into a motherboard only slightly larger than a credit card. Despite its small size, it possesses all the basic functions of a personal computer. In addition, the Raspberry Pi has many advantages, such as high portability, relatively simple operation, and low price, making it popular in education and among DIY enthusiasts.

[0018] The GPS & BeiDou positioning module described is a high-performance BDS / GNSS positioning and navigation module based on the ATGM336H-5N. This series of modules supports multiple satellite navigation systems, including all satellites of my country's BeiDou-2 and BeiDou-3, the US GPS, Russia's GLONASS, and Japan's QZSS. Its antenna network design employs a Π-type circuit with impedance matching (50Ω) and an antenna VSWR of less than 1.2, offering advantages such as high sensitivity, low power consumption, and low cost. Specific interface specifications for this module are as follows... Figure 2 As shown.

[0019] The IMU (Inertial Measurement Unit) module is an integrated device mainly composed of three accelerometers corresponding to each of the three-dimensional axes and three gyroscopes corresponding to each axis. The accelerometers detect the acceleration signals of an object along the three independent axes of its own coordinate system, while the gyroscopes measure the angular velocity changes of the vehicle relative to the navigation coordinate system. By collecting and processing the various signals acquired by the accelerometers and gyroscopes, appropriate calculations and analyses can be performed. This allows for the accurate determination of the intelligent vehicle's attitude and position information in space. In short, the IMU integrates and analyzes acceleration and angular velocity data to provide accurate motion information for the intelligent vehicle in three-dimensional space. It can be said that the IMU module compensates for the shortcomings of the GPS & BeiDou positioning modules; the two complement each other, forming the inertial navigation unit, which can accurately sense the intelligent vehicle's attitude, angle, speed, altitude, and latitude and longitude, enabling the intelligent vehicle to obtain the most accurate positioning information. The default IMU axis orientation is as follows: Figure 3 As shown, it should be noted that when installing on the smart car, it should be installed horizontally with the Y-axis facing forward.

[0020] The PCB driver board described is a custom expansion board based on the TB6612FNG driver chip and the STM32F103RCT6 microcontroller. Its physical form is as follows: Figure 4As shown, students can directly connect various peripheral devices via pin headers and sockets. The TB6612FNG chip contains several key pins with the following functions: AIN1 and AIN2, BIN1 and BIN2, PWMA and PWMB are ports used to input control signals; A01 and A02, B01 and B02 are output terminals used to drive the two motors; the STBY pin is used to control the chip's operating state, i.e., normal operation or standby. In terms of power supply, the VM pin accepts a voltage range of 4.5 to 15V for motor drive voltage input; while the VCC pin accepts a voltage range of 2.7 to 5.5V for logic level input. Each channel of this chip can provide a maximum continuous drive current of 1.2A, and in peak conditions, the current can reach 2A (continuous pulse) or 3.2A (single pulse). This allows it to support four motor control modes: forward, reverse, braking, and stop, sufficient to meet the various basic motion requirements of the intelligent car. Furthermore, the TB6612FNG chip supports high-frequency PWM, up to 100kHz, which makes the control of the smart car's movement speed more precise and delicate. Overall, this chip provides a powerful and flexible solution for the motion control of smart cars. The STM32F103RCT6 microcontroller is a 32-bit microcontroller with fast processing speed and relatively large memory, featuring a 72MHz main frequency and 64KB Flash storage. Its development environment is easy to set up, and its real-time performance is much faster than that of typical operating systems. From setting a pin high to measuring the actual output of an external pin with a multimeter, only nanoseconds are needed, enabling highly real-time control.

[0021] Experimental environment setup MobaXterm is a comprehensive suite of remote computing tools that meets the diverse needs of various professional users in remote computing and network management. By simply downloading and running a single .exe file, users can easily access and operate all key remote network tools and technologies within their Windows desktop environment, including SSH, RDP, X11, VNC, SFTP, WSL, FTP, XDMCP, Serial, Telnet, and Rlogin. This design makes MobaXterm readily usable out of the box, requiring no complex configuration or installation processes; users can immediately begin efficient remote connection and management tasks.

[0022] In the course experiment, MobaXterm was used to remotely access a Raspberry Pi, primarily utilizing RDP and SFTP tools. RDP (Remote Desktop Protocol) is mainly used for remote access and control of Windows operating system hosts. To start a remote session, the "Session" option needs to be selected in the relevant application. A dialog box will then appear; in this dialog box, select "RDP" as the remote connection protocol. Next, the IP address of the remote host needs to be entered in the specified field; this is a unique numerical label identifying the target computer's location on the network. In addition to the IP address and username, the port number also needs to be entered. By default, RDP uses port 3389, but if the remote host is configured with a non-default port, the corresponding port number needs to be entered. Once all the necessary information is provided accurately, simply click the "OK" button. The system will begin establishing a connection with the remote host and, after successfully verifying your identity, allow you to log in and perform remote operations. SFTP (SSH File Transfer Protocol) functions mainly include file transfer, file management, and command-line interaction. It allows users to securely transfer files between local and remote servers, while providing convenient file management functions such as browsing, uploading, downloading, and deleting. Files can be uploaded and downloaded through a graphical interface.

[0023] The experimental program was written using the free and open-source Visual Studio (VS) Code editor. VS Code, a cross-platform code editor from Microsoft, facilitates the writing of C and Python programs and features intelligent plugin installation, syntax checking, intelligent code completion, and code comparison. In addition to installing the C compiler and Python interpreter, the C / C++ Extension Pack, Python, and Pylance plugins also need to be installed in VS Code to provide a better code editing and debugging experience, improving programming efficiency and quality. After the program is written, the corresponding files are uploaded to the appropriate folder on the Raspberry Pi via SFTP in MobaXterm for subsequent execution or use.

[0024] Experimental Design To familiarize students with the GPS / GNSS engineering environment, help them understand and master the wide range of applications of the Raspberry Pi, learn programming languages, and cultivate their multifaceted engineering practice abilities, a series of comprehensive experiments, from basic to advanced, were designed to enhance students' practical application capabilities in satellite navigation and positioning technology. Specific experimental teaching content includes: GPS / GNSS Module Integration and Data Analysis Experiment The GPS / GNSS module integration and data parsing experiment is a fundamental part of the entire satellite navigation embedded intelligent vehicle project, and it mainly involves the following steps: Experiment 1: Hardware Integration (1) GNSS module selection and connection: Students first need to select a suitable GNSS module (supporting GPS, Beidou and other multi-mode satellite systems), such as GPS & Beidou positioning module, and physically connect it to Raspberry Pi through UART or other communication interfaces (such as I2C or SPI).

[0025] (2) Power supply configuration: GNSS modules usually require an independent power supply. Students need to connect the power supply correctly and ensure that the voltage is stable within the operating range of the module.

[0026] (3) Start-up and initialization: Set the baud rate, data format and other relevant parameters according to the data manual of the selected GNSS module to ensure that the module can start up normally and begin to receive satellite signals.

[0027] Experiment 2: Software Configuration and Programming (1) Driver installation: If the module has dedicated drivers or library files, students need to learn how to install and configure these drivers on the Raspberry Pi so that the operating system can recognize and read the data output by the GNSS module.

[0028] (2) Reading NMEA data stream: NMEA-0183 is a standard data protocol widely used in GPS devices. Students will write Python or C language code to read the NMEA data stream output by the GNSS module through serial communication.

[0029] (3) NMEA data parsing: Parse the received NMEA messages and extract key information, such as latitude, longitude, altitude and time information in GPGGA messages, and ground speed and heading information in GPVTG messages.

[0030] Path planning experiment Experiment 3: Path planning by combining satellite navigation data with map APIs In this experiment, students will learn how to combine real-time satellite navigation positioning information with map service APIs (such as Google Maps or OpenStreetMap) to achieve optimal path planning from the current location to the target point.

[0031] (1) Real-time location update: Students first need to ensure that the car can obtain its own latitude and longitude coordinates in real time through the GPS / GNSS module and send this coordinate information to the server through the network.

[0032] (2) Map API call: Next, using the route planning function provided by the map API, the optimal route is requested based on the current location of the car and the set target location. This usually involves constructing HTTP / HTTPS requests and parsing the response data to obtain detailed route information, including turning instructions, travel distance, and estimated travel time.

[0033] (3) Path visualization and instruction generation: The planned path is visualized on the map, and the route is decomposed into a series of control instructions based on geographical location, such as going straight, turning left, turning right, etc., and factors such as traffic rules and road restrictions are taken into account.

[0034] (4) Command transmission and execution: Finally, these control commands are converted into a form that the intelligent vehicle can understand and execute, and sent to the vehicle's control system through serial port or other communication methods, so that it can drive autonomously according to the planned path.

[0035] Experiment 4: Custom Path Planning Algorithm Building upon Experiment 3, to enhance the adaptability and flexibility of path planning, a custom path planning algorithm suitable for the intelligent vehicle environment can be designed and implemented, such as Dijkstra's algorithm, A* search algorithm, or other optimization algorithms.

[0036] (1) Environment modeling: First, based on the actual application scenario, a map model that can represent the car's motion environment is established, including information such as obstacle locations and road network structure.

[0037] (2) Algorithm implementation: For Dijkstra's algorithm, students need to implement the process of calculating the shortest path for all reachable nodes starting from the starting point and selecting the path with the minimum cost when reaching the destination; For A* algorithm, in addition to considering the movement cost, a heuristic function needs to be introduced to evaluate the proximity of each node to the target node, so as to improve the search efficiency while ensuring that the optimal solution is found.

[0038] (3) Path conversion into control commands: After the algorithm obtains the optimal path, it also needs to be converted into specific control commands so that the car can navigate autonomously according to the commands.

[0039] Satellite signal simulation and anti-interference experiment Experiment 5: Satellite Signal Simulation and Anti-jamming Strategy Evaluation In this experiment, students will use specialized software tools to simulate various complex satellite signal environments in order to study and test the positioning performance of the intelligent vehicle under different adverse conditions, and explore and implement corresponding reliability improvement measures.

[0040] (1) Satellite signal status simulation: Students will first use simulation software to create and simulate various real-world satellite signal challenge situations, such as: Line of Sight (LOS): Simulates the occlusion effect of buildings, terrain, or other obstacles on satellite signals, and analyzes the positioning performance of the car in scenarios such as urban canyons, tunnels, and underground parking garages.

[0041] Multipath effect: This refers to the situation where a signal travels through multiple paths to reach the receiver, resulting in phase difference and attenuation, causing positioning errors, such as in environments with tall buildings or strong water reflections.

[0042] Noise interference: Simulates random noise from electronic devices, weather conditions, or other wireless power sources, affecting the signal-to-noise ratio and data demodulation quality of GPS / GNSS receivers.

[0043] (2) Positioning performance evaluation: The positioning system of the vehicle is run in different simulated environments, and its key performance indicators such as positioning accuracy, convergence speed, first positioning time and continuous positioning stability are recorded and analyzed. This helps to understand the limitations and weaknesses of the existing system in complex environments.

[0044] (3) Anti-interference strategy formulation and verification: In response to the identified problems and deficiencies, design and implement a series of anti-interference and optimization schemes, such as: Multi-constellation fusion: Combining signals from different satellite navigation systems to improve positioning redundancy and reliability.

[0045] Signal enhancement techniques: Employing more advanced receiver hardware or algorithms to increase resistance to multipath and noise, such as auxiliary enhancement systems like RAIM (Receiver Autonomous Integrity Monitoring) and SBAS (Satellite-Based Augmentation Systems).

[0046] Adaptive filtering algorithm: Uses Kalman filtering or other filtering algorithms to estimate and correct position errors in real time, thereby improving positioning performance.

[0047] (4) Actual verification of the improvement measures: After applying the above improvement measures to the car positioning system, the test was carried out again under the same simulation conditions. The difference in positioning performance before and after the improvement was compared to verify the effectiveness of the proposed strategy.

[0048] Real-time navigation and autonomous driving experiments Experiment 6: Real-time Navigation and Autonomous Driving Function Implementation In this experiment, students will apply the optimal driving path previously obtained through satellite navigation data and path planning algorithms to the actual operation of the intelligent car, realizing the basic functions of autonomous driving based on satellite navigation.

[0049] (1) Path tracking control: Students need to write programs to convert the planned driving path into a series of control commands that the car can understand and execute, such as speed setting and steering angle. Using a PID (proportional-integral-derivative) controller or other advanced control strategies, ensure that the car can maintain good stability while following the preset path and can respond quickly to changes in the path.

[0050] (2) Motion control module integration: These control commands are transmitted to the motor drive system of the vehicle, and actions such as straight driving, turning and stopping are achieved by adjusting the speed or angle difference of the left and right wheels. The vehicle dynamics model is integrated to take into account the dynamic characteristics of the vehicle itself in order to optimize the control effect and ensure driving safety.

[0051] (3) Real-time feedback and correction: During actual driving, the current position information of the car is continuously obtained and compared with the planned path to implement online path correction, ensuring that it can still accurately drive along the predetermined trajectory even when affected by the environment (such as wind resistance, road conditions, etc.).

[0052] Experiment 7: GPS Navigation Combined with Multi-Sensor Obstacle Avoidance Technology Building upon Experiment 6, we further enhanced the vehicle's safety and environmental adaptability by integrating GPS navigation data with data from other sensors to design and implement obstacle avoidance functionality. (1) Multi-sensor data fusion: Install ultrasonic ranging sensors, infrared sensors, lidar and other equipment to collect real-time distance information of the surrounding environment. Process and fuse the received data from multiple sensors to eliminate noise interference and improve the accuracy of obstacle detection.

[0053] (2) Obstacle detection and avoidance strategy: When an obstacle is detected ahead and may affect the planned driving route, the obstacle avoidance algorithm is triggered to replan a new path around the obstacle. It implements a variety of obstacle avoidance strategies such as emergency braking, deceleration and avoidance, and steering to avoid the obstacle, and selects the most appropriate response based on factors such as the size, position and speed of the obstacle.

[0054] (3) Linkage between GPS navigation and local obstacle avoidance: While maintaining the global GPS navigation path, local obstacle avoidance strategies are combined to flexibly deal with unknown obstacles in complex road environments while following the overall navigation goal.

[0055] High-precision positioning technology exploration experiment Experiment 8: High-precision positioning technology and RTK applications In this experiment, students will conduct in-depth research and practice on the application of high-precision positioning technologies such as RTK (Real-Time Kinematic) in intelligent vehicles, so as to significantly improve the positioning accuracy of the vehicles to the centimeter level.

[0056] (1) Introduction to RTK system principle: Students first need to understand the composition and working principle of the RTK system, including the relationship between the base station, the rover station and the data link, and how dual-frequency or multi-frequency GPS receivers achieve high-precision positioning through carrier phase differential technology.

[0057] (2) RTK hardware integration: Purchase and install GNSS modules that support RTK function, and configure corresponding antennas, radios or network communication equipment (such as 4G / 5G / NTRIP) to establish a data connection with the base station.

[0058] (3) RTK software configuration and debugging: Learn to use RTK related software to set up the base station, initialize the rover station and adjust the parameters to ensure that continuous and stable differential corrections can be received.

[0059] (4) Centimeter-level positioning test: In an open and unobstructed environment, the RTK system was started and the real-time position information of the car was recorded during the driving process. The difference between the RTK positioning results and the conventional GPS positioning results was compared and analyzed to verify the positioning accuracy improvement effect brought by RTK technology.

[0060] (5) Environmental adaptability assessment: Test the performance of the RTK system in different scenarios, such as urban high-rise areas, mountain valleys, and forests, analyze the impact of multipath effect, signal blockage and other factors on RTK positioning accuracy, and explore improvement schemes.

[0061] (6) Error source analysis and compensation: Analyze various possible error sources in the RTK system, including satellite clock error, atmospheric delay, multipath effect, instrument deviation, etc., study and implement corresponding error models and correction methods to further improve positioning accuracy.

[0062] 3.6 Network-Enhanced Positioning and V2X Communication Experiment Experiment 9: Integration of Network-Enhanced Positioning and V2X Communication Technologies In this experiment, students will delve into how to improve the positioning performance and environmental awareness of smart cars by combining mobile communication networks (such as 4G / 5G) or Wi-Fi-assisted positioning technology with V2X (Vehicle-to-Everything) communication technology.

[0063] (1) Mobile Communication Network-Assisted Positioning: Students first need to understand and practice the basic principles of base station positioning using 4G / 5G networks, including location estimation based on methods such as CELLID, AOA (Angle of Arrival), and TDOA (Time Difference of Arrival). During the experiment, students will configure a smart car to connect to the 4G / 5G network and use the APIs or open-source libraries provided by the operators to obtain base station information and calculate the car's approximate location information. Then, the mobile communication network-assisted positioning results will be fused with satellite navigation positioning data to improve positioning accuracy in urban environments with severe signal obstruction or multipath interference.

[0064] (2) Wi-Fi Assisted Positioning: Wi-Fi fingerprint positioning technology is learned, and signal strength data of Wi-Fi APs (Access Points) within a specific area are collected to establish an indoor or outdoor Wi-Fi fingerprint database. In the experimental environment, the intelligent vehicle detects the signal strength of surrounding Wi-Fi APs in real time and compares it with the pre-built fingerprint database. The vehicle's location is determined through a matching algorithm. Similarly, the Wi-Fi assisted positioning results are combined with satellite navigation positioning to optimize the overall positioning performance.

[0065] (3) Application of V2X (Vehicle-to-Everything) communication technology: Explore the potential of V2X communication technology in positioning enhancement, such as V2I (Vehicle-to-Infrastructure) and V2V (Vehicle-to-Vehicle) communication, so that intelligent vehicles can interact with other vehicles and traffic infrastructure in real time; use V2X communication technology to receive location information from roadside units (RSU), other vehicles equipped with V2X devices or other positioning service nodes as positioning reference sources to further improve positioning accuracy and reliability; design and implement application scenarios, such as obtaining dynamic information such as the status of traffic lights at the intersection ahead and road congestion through V2X communication, and optimizing path planning and driving strategies in conjunction with high-precision positioning technology.

[0066] Performance evaluation and optimization experiments Experiment 10: Performance Evaluation and Optimization In this experiment, students will conduct detailed tests and comprehensive evaluations of key indicators such as positioning performance, response speed, and power consumption of the intelligent vehicle, and take corresponding optimization measures based on the test results to improve the overall performance of the entire satellite navigation embedded system.

[0067] (1) Positioning performance evaluation: Using actual road testing or simulated environment testing methods, compare the positioning accuracy, convergence speed, and stability in complex environments of different positioning technologies (such as GPS single-mode, multi-mode GNSS, RTK, network-assisted positioning, etc.). Develop a set of quantitative evaluation standards, including parameters such as root mean square error (RMSE), continuous positioning accuracy, and first positioning time, and record and analyze the experimental data.

[0068] (2) Response speed evaluation: The test system evaluates the time required from receiving external commands to executing corresponding actions (such as steering, acceleration, deceleration, stopping, etc.) to assess the real-time performance and responsiveness of the control system. Analyze the dynamic response characteristics of the system under conditions such as path planning updates, obstacle avoidance strategy adjustments, and positioning information updates.

[0069] (3) Power consumption assessment: Record and analyze the energy consumption of each module of the intelligent vehicle under various states (standby, driving, receiving satellite signals, processing data, communication, etc.), especially core components such as GNSS module, processor, sensors, and wireless communication module. Design and implement energy-saving strategies, such as sleep mode, low-power operation mode, and intelligent power management scheme, and verify their power consumption reduction effect through actual measurement.

[0070] (4) System Optimization: Based on the above performance evaluation results, identify the bottlenecks and deficiencies in the system and propose targeted optimization measures. For example, improve the positioning algorithm, optimize hardware configuration, upgrade software protocols, enhance anti-interference capabilities, and rationally allocate computing resources. After implementing the optimization measures, conduct performance tests again and compare the performance differences before and after optimization to ensure the effectiveness of the improvement measures.

[0071] This invention not only enables students to fully grasp the basic principles and core technologies of satellite navigation and positioning, comprehensively improving their theoretical knowledge and practical skills, but also lays a solid foundation for their future development work in satellite navigation and positioning applications related to the Internet of Things, thereby better achieving the course objectives of "Navigation and Positioning Technology." Furthermore, it emphasizes cultivating their practical and innovative abilities in several key areas: (1) Embedded system development skills: Students will operate embedded platforms such as Raspberry Pi to configure hardware interfaces, write drivers and develop applications, thereby becoming familiar with the hardware and software working together of embedded systems and improving their ability to program efficiently under limited resources.

[0072] (2) Sensor fusion technology: By integrating GPS / GNSS modules with other types of sensors (such as ultrasonic, infrared, lidar, etc.), students can learn how to acquire and process multi-source data in real time, and use effective algorithms to fuse information in order to improve navigation and positioning accuracy and environmental perception capabilities.

[0073] (3) Application of path planning algorithms: Apply classic or modern path planning algorithms, such as Dijkstra and A*, in practice, and improve and optimize them in combination with actual application scenarios, so that students can understand and master how to formulate the optimal driving path based on real-time satellite navigation information and solve dynamic path planning problems.

[0074] (4) Solving practical engineering problems: During the experiment, various complex real-world scenarios are simulated, such as signal blockage, multipath effect, noise interference, etc., guiding students to design and implement corresponding solutions to these challenges, such as the application of RTK high-precision positioning technology, research on network-enhanced positioning technology, and design of power consumption control strategies, etc., to cultivate students' engineering thinking ability to analyze and solve problems and their hands-on practical ability.

[0075] (5) Project management and teamwork: The comprehensive project - remote control satellite navigation intelligent car "treasure hunt" competition requires students to form teams to complete the task together. This helps to exercise their project management skills, communication and coordination abilities and teamwork spirit, and also helps them to better understand and experience a complete technology research and development process.

[0076] The above description is merely a preferred embodiment of the present utility model and is not intended to limit the present utility model in any way. Although the present utility model has been disclosed above with reference to a preferred embodiment, it is not intended to limit the present utility model. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present utility model. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present utility model without departing from the scope of the present utility model shall still fall within the scope of the present utility model.

Claims

1. A satellite navigation embedded intelligent vehicle experimental teaching platform, characterized in that: The system includes a smart car (1), a Raspberry Pi 4B (2), a GPS & Beidou positioning module (3), an IMU module (4), a PCB driver board (5), and a 12V power supply (6). The chassis of the smart car (1) is mainly composed of two reduction drive wheels (7) and two GA370 motors (8). The GPS & Beidou positioning module (3) and the IMU module (4) are electrically connected to the Raspberry Pi 4B (2). The 12V power supply (6) is electrically connected to the Raspberry Pi 4B (2) and the PCB driver board (5). The Raspberry Pi 4B (2) is electrically connected to the PCB driver board (5).

2. The satellite navigation embedded intelligent vehicle experimental teaching platform according to claim 1, characterized in that: The GPS & Beidou positioning module (3) is a high-performance BDS / GNSS positioning and navigation module based on ATGM336H-5N, which supports multiple satellite navigation systems including all satellites of my country's Beidou-2 and Beidou-3, the US GPS, the Russian GLONASS and the Japanese QZSS.

3. The satellite navigation embedded intelligent vehicle experimental teaching platform according to claim 1, characterized in that: The IMU module (4) mainly consists of three accelerometers corresponding to each single axis in three-dimensional space and three gyroscopes of the same single axis, used to sense the attitude, angle, speed, height and latitude and longitude of the intelligent vehicle, so that the intelligent vehicle can obtain the most accurate positioning information.

4. The satellite navigation embedded intelligent vehicle experimental teaching platform according to claim 1, characterized in that: The PCB driver board (5) includes a TB6612FNG driver chip, an STM32F103RCT6 microcontroller that controls the chip, a voltage conversion module, and an expansion interface.

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