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MicroROS V2 Robot Car📖
01.Introduction
  • 0.Unboxing Video
  • 1. Readme Learning Route
  • 2. Must Read Before Use
  • 3. Introduction to MicroROS V2 Control Board
  • 4. Introduction to LiDAR
  • 5. Introduction to ESP32 Camera Module
  • 6. FAQ
  • 7. About Charging
02.Assembly course
  • Assembly Video
03.Preparation
  • 1. Quickly Start
  • 2. Write Firmware
  • 3. Install VM and Start System
  • 4. Configure VM and Connect Robot
  • 5. Connect to MicroROS Agent
04.Quick start control course
  • 1. VM Keyboard Remote Control
  • 2. VM Handle Remote Control
  • 3. USB Handle Control
05.Robot control course
  • 1. Robot Information Release
  • 2. Robot Keyboard Control
  • 3. Robot Handle Control
  • 4. Robot State Estimation
  • 5. Linear Speed Calibration
  • 6. Angular Velocity Calibration
  • 7. Robot URDF Model
06.Lidar course
  • 1. Precautions for LiDAR Mapping and Navigation
  • 2. Lidar Avoid
  • 3. Lidar Following
  • 4. Lidar Guard
  • 5. Lidar Patrol
  • 6. Gmapping Mapping
  • 7. Cartographer Mapping
  • 8. Cartographer Quickly Repositioning Navigation
  • 9. Slam Toolx Mapping
  • 10. Navigation2 Single Point Navigation
  • 11. Navigation2 Multi Point Navigation
  • 12. Ros Robot App Mapping And Navigation
07.Road network planning
  • 1. Introduction to Road Network Planning and Quick Start
  • 2. Build a Pose Map
  • 3. Road Network Annotation
  • 4. Planning and Navigation Between Road Networks
  • 5. Road Network Navigation in Any Pose
  • 6. Path Replanning
08.Vision basic course
OpenCV Image Processing
  • 1. Introduction to OpenCV Source CV
  • 2. Open Source CV Geometric Transformation
  • 3. Open Source CV Image Processing and Drawing Text Line Segments
  • 4. Open Source CV Image Beautification
09.Visual General Course (For Standard&Superior version)
4. Yolov11&26 Visual Recognition
  • 1. YOLO Environment Setup
  • 2. CLI Usage
  • 3. Object Detection
  • 4. Instance Segmentation
  • 5. Pose Estimation
  • 6. Image Classification
  • 7. Oriented Bounding Boxes Object Detection
  • 8. Model Prediction
1. ESP32 Visual Recognition Module Configuration
  • 1. Serial Port Configuration WiFi Instructions
  • 2. Use ESP32 Camera Module in Robot Car
  • 3. Servo PTZ Calibration
2. OpenCV Visual Recognition
  • 1. Human Pose Estimation
  • 2. Object Detection
  • 3. AR Vision
  • 4. QR Code
  • 5. Color Recognition
  • 6. Facial Recognition
  • 7. AprilTag Machine Code Recognition
3. Mediapipe Visual Recognition
  • 1. Hand Detection
  • 2. Pose Detection
  • 3. Holistic Detection
  • 4. Facial Detection
  • 5. Facial Recognition
  • 6. Facial Special Effects
  • 7. 3D Object Recognition
  • 8. Brush
  • 9. Gesture Recognition
  • 10. Palm Target Localization
  • 11. Fingertip Trajectory Recognition
10.Visual Advanced Course (Just for Standard version)
1. Visual Control
  • 1. QR Code Recognition Control Action
  • 2. AprilTag Control Car Action Group
  • 3. Gesture Control Car Action Group
  • 4. Visual Line Tracking
2. Visual Tracking
  • 1. Color Tracking
  • 2. Facial Tracking
  • 3. QR Code Tracking
  • 4. Object Recognition And Tracking
  • 5. Mediapipe Palm Tracking
  • 6. Mediapipe Gesture Tracking
  • 7. Mediapipe Human Pose Tracking
  • 8. KCF Object Tracking
  • 9. AprilTag Machine Code Tracking
3. Visual Following
  • 1. Color Follow
  • 2. Face Follow
  • 3. QR Code Follow
  • 4. Mediapipe Palm Follow
  • 5. Mediapipe Gesture Follow
  • 6. Mediapipe Human Posture Follow
  • 7. AprilTag Machine Code Follow
  • 8. Object Follow
11.Visual Advanced Course (Just for Superior version)
1. Visual Control
  • 1. QR Code Recognition Control Action
  • 2. AprilTag Control Car Action Group
  • 3. Gesture Control Car Action Group
  • 4. Visual Line Tracking
2. Visual Tracking
  • 1. Color Tracking
  • 2. Facial Tracking
  • 3. QR Code Tracking
  • 4. YOLO Object Recognition and Tracking
  • 5. Mediapipe Palm Tracking
  • 6. Mediapipe Gesture Tracking
  • 7. Mediapipe Human Pose Tracking
  • 8. KCF Object Tracking
  • 9. AprilTag Machine Code Tracking
3. Visual Follow
  • 1. Color Follow
  • 2. Face Follow
  • 3. QR Code Follow
  • 4. Mediapipe Palm Follow
  • 5. Mediapipe Gesture Follow
  • 6. Mediapipe Human Pose Follow
  • 7. AprilTag Machine Code Follow
  • 8. Object Follow
12.AI Large Model Basic and Applications Course
1. AI Large Model Preparation
  • 1.AI Large Model Preparation
2. AI Large Model Basic Course
  • 1. AI Large Model Types and Principles
  • 2. RAG Retrieval Augmented and Training Samples
  • 3. Embodied Intelligent System Architecture
  • 4. Embodied Intelligence Core Source Code
3. AI Large Model Development
  • 1. Register Model Service Account
  • 2. Configure API KEY
  • 3. Dify Introduction
  • 4. Dify Basic Features
  • 5. RAG Knowledge Base Deployment
  • 6. AI Application Development Chatbot
  • 7. RAG Knowledge Base and Chatbot
  • 8. AI Agent Workflow
4. AI Large Model (Text Version)
  • 1. Semantic Understanding and Instruction Following
  • 2. Multimodal Visual Understanding
  • 3. Multimodal Visual Understanding + Basic Control
  • 4. Multimodal Visual Understanding + Object Following
  • 5. Multimodal Visual Understanding + Visual Line Following
  • 6. Multimodal Visual Understanding + SLAM Navigation
  • 7. Multimodal Visual Understanding + SLAM Navigation + Visual Line Following
  • 8. Multimodal Visual Understanding + SLAM Navigation + Object Following
  • 9. Intent Inference + Multimodal Visual Understanding + SLAM Navigation
13.Openclaw Basics Course
  • 1. Openclaw Introduction
  • 2. Openclaw Core Concepts
  • 3. Openclaw Api-Key Configuration
  • 4. Openclaw Switching Models
  • 5. Openclaw System Prompt
  • 6. Openclaw Common CLI Command
14. Openclaw Interaction Course
  • 1. Openclaw Webchat Interaction
  • 2. Openclaw Tui Interaction
  • 3. Openclaw Whatsapp Interaction
15.Openclaw Development Course
  • 1. Openclaw Robot Mcp Service Integration
  • 2. Robot CLI Command Tools
  • 3. Robot-Control Plugin Tools
  • 4. Openclaw Skills Development
  • 5. Openclaw Scheduled Tasks
  • 6. Openclaw Multi Agent System
  • 7. Openclaw Proactive Interaction
  • 8. Openclaw Vector Memory
16.Openclaw Embodied Intelligence Course
  • 1. OpenClaw Natural Interaction + Basic Control
  • 2. OpenClaw Natural Interaction + Slam Navigation
  • 3. OpenClaw Natural Interaction + Multimodal Visual Understand
  • 4. OpenClaw Natural Interaction + Multimodal Visual Understand + Slam Navigation + Visual Follow
  • 5. OpenClaw Natural Interaction + Multimodal Visual Understand + Slam Navigation + Visual Track
17.Multi-machine Course
  • 1. Multi Machine Handle Control
  • 2. Multi Machine Keyboard Control
  • 3. Multi Machine Navigation
18.MicroROS-V2 Control Board Development Environment
  • 1. Set Up the ESP32-IDF Development Environment
  • 2. ESP32-IDF Configuration Tool
  • 3. ESP Flash Tool Flashing Firmware
  • 4. Install ESP32 Microros Components
  • 5. Install And Start Microros Agent
19.MicroROS-V2 Control Board ESP32 Basic Course
  • 1. Led Light Up
  • 2. Button Function
  • 3. Drive Buzzer
  • 4. Oled
  • 5. Usb Host Function
  • 6. Serial Communication
  • 7. Battery Voltage Detection
  • 8. Drive Pwm Servo
  • 9. Drive Motors
  • 10. Read Motor Encoder Data
  • 11. Pid Motor Speed Control
  • 12. Robot Kinematics Analysis Theory
  • 13. Read Imu Data
  • 14. Read Lidar Data
  • 15. Flash Data Storage
  • 16. Custom Partition Table
  • 17. Bluetooth Communication
  • 18. Wifi Connection
  • 19. ESP32 Visual Recognition Module
20.MicroROS-V2 Control Board Micro_ros Basic Course
  • 1. Publish Topics
  • 2. Subscribe To Topics
  • 3. Multi Topic Subscription And Publishing
  • 4. Subscribe To Buzzer Topic
  • 5. Subscribe To Pwm Servo Topic
  • 6. Subscribe To Speed Control Topic
  • 7. Publish Odometry Topic
  • 8. Publish Imu Data Topic
  • 9. Publish Lidar Data Topic
21. Linux System Course
  • 1.Introduction to Linux system
  • 2.Ubuntu file system
  • 3.Ubuntu common commands
  • 4.Ubuntu common editors
  • 5.Ubuntu software operation commands
  • 6.Virtual machine installation
  • 7.SSH remote control
  • 8.VNC remote control
  • 9.Transfer files remotely
  • 10.Driver library and communication
  • 11.Static IP and hotspot mode
  • 12.Bind device ID
  • 13.Capacity expansion and resource allocation
  • 14.Update system software sources
  • 15.Set root user password
  • 16.sudo free password
  • 17.Connect to WiFi network
  • 18.View system version
  • 19.Customized service management
  • 20.Back up system image
22.Docker Course
  • 1.Docker overview and docker installation
  • 2.Common commands for docker image containers
  • 3.Docker images deeply understand and publish images
23.ROS Basic Course
  • 1.Introduction to ROS2
  • 2.ROS2 install Humble
  • 3.ROS2 development environment
  • 4.ROS2 workspace
  • 5.ROS2 function package
  • 6.ROS2 node
  • 7.ROS2 topic communication
  • 8.ROS2 service communication
  • 9.ROS2 action communication
  • 10.ROS2 custom interface message
  • 11.ROS2 parameter service case
  • 12.ROS2 meta-function package
  • 13.ROS2 distributed communication
  • 14.ROS2 DDS
  • 15.ROS2 time related API
  • 16.ROS2 common command tools
  • 17.ROS2 rviz2 use
  • 18.ROS2 rqt toolbox
  • 19.ROS2 Launch startup file configuration
  • 20.ROS2 recording and playback tool
  • 21.ROS2 URDF model
  • 22.ROS2 Gazebo simulation platform
  • 23.ROS2 TF2 coordinate transformation
Download
  • Mapping_APP(iOS_Android)
  • Code
  • Software_Tools
  • VM_System_File
  • 3D_Model_File
  • ESP32_Firmware
  • Dify LLM Base
  • Instruction_Manual
  • VMware
Download ZIP

Welcome to MicroROS V2 Robot Car repository

9. Publish Lidar Data Topic

Publishing LiDAR Data Topics

1. Experiment Objective

In this experiment, you will learn how to publish the ROS2 standard sensor_msgs/msg/LaserScan LiDAR scan data through micro-ROS. Through this example, you will master receiving and parsing UART (serial) LiDAR data, the use of a Ring Buffer, and the micro-ROS publishing configuration for large message types. This example publishes the topic /scan at 10 Hz, using the TminiPlus series LiDAR.

2. Experiment Principle

1. Keyword Explanation

ConceptDescription
LiDARLight Detection And Ranging, a sensor that calculates the distance to a target by emitting laser beams and measuring the reflection time. The TminiPlus is a small, low-cost single-line LiDAR commonly used for robot obstacle avoidance and SLAM
LaserScan messageThe ROS2 standard LiDAR scan message type, defined in sensor_msgs/msg/LaserScan, containing fields such as the angle range (angle_min/angle_max), the distance array (ranges) and the intensity array (intensities), describing the result of one 360° scan
ranges arrayThe core data field of the LaserScan message, storing distance measurements (in meters) in order of increasing angle. The array length is determined by (angle_max - angle_min) / angle_increment
Measurement frequencyThe number of complete scans completed by the LiDAR per second. The TminiPlus outputs 360° scan data at 10 Hz, with each scan containing multiple angle-distance data points, matching the micro-ROS publish frequency (10 Hz)
Ring BufferA fixed-size FIFO data structure that reuses storage space cyclically through head and tail pointers. This example uses a ring buffer to transfer LiDAR serial data between UART interrupt reception and main-loop processing, avoiding data loss
UART serial communicationUniversal Asynchronous Receiver/Transmitter. In this example, the TminiPlus LiDAR communicates with the ESP32 through the UART1 interface (TX/RX), requiring the correct baud rate and data format to parse the LiDAR protocol

2. Technical Architecture

This example receives the raw data packets of the TminiPlus LiDAR through the UART1 serial port, and after protocol parsing and ring buffer processing, publishes them as standard LaserScan messages at 10 Hz through micro-ROS.

Parameter definition file: ~/esp/microROS_samples/lidar_publisher/main/main.c

Configuration ItemValueDescription
Node nametminiplus_publisherROS2 node name
Topic namescanTopic for publishing LiDAR scan data
Message typesensor_msgs/msg/LaserScanROS2 standard LiDAR message
Publish frequency10 HzTimer period 100 ms
QoS modebest_effortLarge messages use best-effort transmission to reduce latency
frame_idlaser_frameLiDAR coordinate frame identifier

Data flow: TminiPlus LiDAR → UART1 interrupt reception → ring buffer → LiDAR protocol parsing (tminiplus.c) → LaserScan message → micro-ROS timer callback (fills the timestamp and publishes).

The program also has a built-in Agent health check mechanism: when consecutive publish failures reach 10, it proactively pings the Agent to check the connection status.

3. Core Topic Quick Reference

Firmware Uplink (Sensor Data)

TopicMessage TypeQoSDescription
/scansensor_msgs/msg/LaserScanbest_effortLiDAR scan data (360° distance values), published at 10 Hz

Usage Example:

# View the LiDAR data (compressed array output)
ros2 topic echo /scan --no-arr

4. Middleware Configuration

micro-ROS uses a static memory model (RCUTILS_AVOID_DYNAMIC_ALLOCATION=ON), all entity memory is pre-allocated at compile time, and runtime dynamic creation is not supported.

Configuration file: ~/esp/extra_components/micro_ros_espidf_component/colcon.meta

| RMW_UXRCE_TRANSPORT | udp | UDP transport (Wi-Fi communication) | | RMW_UXRCE_MAX_PUBLISHERS | 6 | Maximum number of publishers | | RMW_UXRCE_MAX_SUBSCRIPTIONS | 6 | Maximum number of subscribers | | RMW_UXRCE_MAX_NODES | 1 | Maximum number of nodes | | RMW_UXRCE_MAX_SERVICES | 0 | Services not supported | | RMW_UXRCE_MAX_CLIENTS | 0 | Clients not supported | | RMW_UXRCE_MAX_HISTORY | 1 | History depth = 1, no message queuing | | UCLIENT_UDP_TRANSPORT_MTU | 4096 | Maximum UDP packet size 4 KB | | UCLIENT_MIN_HEARTBEAT_TIME_INTERVAL | 1 | Agent heartbeat interval 1 ms |

Key Constraints:

  • MAX_HISTORY=1 means there is no message buffer queue, so uplink topics (firmware → host) uniformly use best_effort QoS

  • MAX_SERVICES=0, MAX_CLIENTS=0 means only topic communication is supported, not Service/Client

  • After modifying colcon.meta, the micro-ROS library must be recompiled for the changes to take effect: run idf.py clean-microros in the project directory to clean and rebuild the micro-ROS library, then idf.py build to compile the firmware

3. Experiment Steps

1. Hardware Preparation

HardwareRequirement
MicroROS-V2 control board✅ Required
USB Type-C cable✅ Required
Wi-Fi network (2.4 GHz)✅ Required
TminiPlus LiDAR✅ Required (connected via UART1)

2. Prerequisites

  1. The ESP32-IDF development environment has been set up

  2. The MicroROS-V2 control board is connected to the computer via USB

  3. Configure the Wi-Fi SSID, password, Agent IP and port via idf.py menuconfig (the micro_ros menu item)

    xxxxxxxxxx
    cd ~/esp/microROS_samples/lidar_publisher
    source ~/esp/esp-idf/export.sh
    idf.py menuconfig

    image-20260723154206694

    1. Select example-app setting to set the domain ID to be consistent with the virtual machine

    image-20260811195117710

    After configuration, press "s" to save and press "q" to exit the configuration interface.

  4. Connect the TminiPlus LiDAR to the UART1 interface of the control board via serial

3. Start micro_ros_agent (Terminal 1)

Start the micro-ROS Agent on the ROS2 host:

xxxxxxxxxx
cd ~/uros_ws
source install/local_setup.bash
ros2 run micro_ros_agent micro_ros_agent udp4 --port 8090 -v4

4. Build and Flash (Terminal 2)

​x

cd ~/esp/microROS_samples/lidar_publisher
source ~/esp/esp-idf/export.sh
idf.py build flash monitor

5. How to Stop

  1. Press Ctrl+] in Terminal 2 to exit the serial monitor (stop firmware execution)

  2. Press Ctrl+C in Terminal 1 to stop micro_ros_agent

4. Experiment Result

After the build and flash succeed, on the ROS2 host:

View the LiDAR scan data:

xxxxxxxxxx
ros2 topic echo /scan

Expected output (updated at 10 Hz):

xxxxxxxxxx
header:
  stamp:
    sec: xxx
    nanosec: xxx
  frame_id: laser_frame
angle_min: 0.0
angle_max: 6.283185  # 2π, 360 degrees
angle_increment: ...
range_min: 0.05
range_max: 12.0
ranges: [0.523, 0.518, 0.521, ...]
intensities: [...]

Visualize the LiDAR data in RViz:

xxxxxxxxxx
rviz2

In RViz:

  1. Add /scan via By topic → LaserScan display

  2. Set the Fixed Frame to laser_frame

  3. The point cloud image of the LiDAR scan can then be seen

  4. Change all topic quality settings to system default

image-20260811200825534

The control board serial port will output:

xxxxxxxxxx
I (xxx) MAIN: Connecting agent: 192.168.x.x:8888
I (xxx) MAIN: Connected agent: 192.168.x.x:8888
I (xxx) MAIN: time synced, offset=xxx ms
I (xxx) MAIN: Wi-Fi power save disabled

5. Program Analysis

Source code paths:

  • Firmware source code: ~/esp/microROS_samples/lidar_publisher/main/main.c

  • LiDAR driver: ~/esp/microROS_samples/lidar_publisher/components/lidar_tminiplus/lidar_tminiplus.h, tminiplus.c

  • UART driver: ~/esp/microROS_samples/lidar_publisher/components/uart/uart1.c

1. Main Program Logic

main/main.c is the program entry point, and the flow is as follows:

  1. In app_main():

    • Calls Uart1_Init() to initialize UART1 serial communication

    • Calls tminiplus_GPIO_Init() to initialize the LiDAR control GPIO (motor enable, etc.)

    • Sends the start command {0xA5, 0x60} to start the LiDAR scan

    • Delays 1 second to wait for the LiDAR to start

    • Initializes the Wi-Fi network and disables power saving mode

    • Sets the frame_id of the LaserScan message

    • Creates micro_ros_task

  2. In micro_ros_task:

    • Uses a loop-and-retry connection approach to initialize the support layer (slightly different from the separated ping-initialization in the previous examples)

    • Creates the node tminiplus_publisher

    • Publishes the topic scan in best_effort mode, with message type sensor_msgs/msg/LaserScan

    • Creates a 100 ms period timer (10 Hz) bound to the LiDAR data publishing callback

    • In the timer callback, fills the timestamp and then publishes

    • Triggers the Agent health check on continuous publish failures

    • Performs ROS time synchronization every 30 seconds

2. Key Components

components/lidar_tminiplus/lidar_tminiplus.h — LiDAR driver interface:

FunctionDescription
tminiplus_GPIO_Init()Initializes the LiDAR control GPIO (motor enable pin)
Lidar_tminiplus_Get_Msg()Gets the LaserScan message (value copy)
Lidar_tminiplus_Get_Msg_Ptr()Gets the LaserScan message pointer (zero copy)
Lidar_tminiplus_Set_Frame_Id(const char *frame_id)Sets the message frame_id
Lidar_tminiplus_stop_Scan(uint8_t on)Starts/stops the LiDAR scan
Lidar_tminiplus_Get_Distance(uint16_t angle)Gets the distance value at a specified angle

components/lidar_tminiplus/tminiplus.c — LiDAR serial protocol parsing:

Processes the serial data protocol of the TminiPlus LiDAR, receiving raw data packets through UART interrupts, parsing them into angle-distance pairs, and organizing them into a LaserScan message.

components/ring_buffer/ring_buffer.c — Ring buffer:

Provides an efficient FIFO data buffer for data transfer between UART interrupt reception and main-loop processing.

components/uart/uart1.c — UART1 driver:

FunctionDescription
Uart1_Init()Initializes the UART1 peripheral and configures parameters such as the baud rate
Uart1_Send_Data(uint8_t *data, size_t len)Sends data through UART1

LaserScan message structure:

The pointer returned by Lidar_tminiplus_Get_Msg_Ptr() points to a pre-allocated sensor_msgs__msg__LaserScan struct. In the timer callback, the timestamp is filled in and the message is published directly, avoiding the memory copy overhead of large messages.

LiDAR start control:

Source file: ~/esp/microROS_samples/lidar_publisher/main/main.c

xxxxxxxxxx
static const uint8_t s_start_cmd[2] = {0xA5, 0x60};

// Send the start command
Uart1_Send_Data((uint8_t *)s_start_cmd, sizeof(s_start_cmd));
vTaskDelay(pdMS_TO_TICKS(1000));  // Wait for the LiDAR to start

Timer callback timer_lidar_callback:

Source file: ~/esp/microROS_samples/lidar_publisher/main/main.c

xxxxxxxxxx
static void timer_lidar_callback(rcl_timer_t *timer, int64_t last_call_time)
{
    RCLC_UNUSED(last_call_time);

    if (timer == NULL) {
        return;
    }

    sensor_msgs__msg__LaserScan *msg_lidar = Lidar_tminiplus_Get_Msg_Ptr();
    if (msg_lidar == NULL) {
        return;
    }

    // Fill the timestamp
    struct timespec ts = get_timespec_now();
    msg_lidar->header.stamp.sec = (int32_t)ts.tv_sec;
    msg_lidar->header.stamp.nanosec = (uint32_t)ts.tv_nsec;

    // Publish (zero copy, avoiding the memory copy overhead of large messages)
    rcl_ret_t ret = rcl_publish(&publisher_lidar, msg_lidar, NULL);
    if (ret == RCL_RET_OK) {
        s_publish_fail_count = 0U;
        return;
    }

    s_publish_fail_count++;

    if ((s_publish_fail_count <= PUBLISH_FAIL_THRESHOLD) ||
        ((s_publish_fail_count % 10U) == 0U)) {
        printf("Failed: rcl_publish(&publisher_lidar, msg_lidar, NULL) | line=%d rc=%d\n",
               __LINE__, (int)ret);
    }

    // When consecutive failures reach the threshold, trigger the Agent health check
    if (s_publish_fail_count >= PUBLISH_FAIL_THRESHOLD) {
        s_need_agent_check = true;
    }
}

Agent health check mechanism (in the main loop):

Source file: ~/esp/microROS_samples/lidar_publisher/main/main.c

xxxxxxxxxx
if (s_need_agent_check) {
    rmw_ret_t ping_ret = rmw_uros_ping_agent(AGENT_PING_TIMEOUT_MS, AGENT_PING_ATTEMPTS);
    if (ping_ret == RMW_RET_OK) {
        ESP_LOGI(TAG, "agent ping ok");
    } else {
        ESP_LOGW(TAG, "agent ping failed");
    }
    s_need_agent_check = false;
}

time offset management:

Unlike the previous examples, this is the first example to use the s_have_valid_time_offset flag: if the time synchronization fails but has succeeded before, the previous offset is retained and continues to be used; if it has never succeeded, the local uptime is used as the timestamp.

6. Common Problems

ProblemCauseSolution
The LiDAR does not startThe start command was not sent or the power supply is insufficientCheck the UART1 connection and confirm the LiDAR power supply is normal (usually requires 5 V)
No data on the /scan topicSerial data parsing failure or baud rate mismatchConfirm the LiDAR baud rate matches the UART1 configuration, and check the serial parameters in tminiplus.c
A large number of zeros or abnormal values in the LiDAR dataThe LiDAR is blocked or out of the measurement rangeConfirm there are no obstacles blocking the LiDAR; the measurement range is 0.05 m ~ 12 m
ros2 topic echo /scan outputs too much dataThe LaserScan message contains a large number of distance valuesUse the --no-arr parameter to compress the array output: ros2 topic echo /scan --no-arr
Publish failure after the Agent connection dropsUDP connection interruptedThe code has a built-in Agent health check; it will proactively ping the Agent after 10 consecutive failures
Timestamp resets to zeroTime synchronization failed and no historical offsetEnsure the Agent is running normally at startup; the code uses the local uptime as a fallback
Publish Imu Data Topic
Introduction to Linux system

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